Calculations of risk scores

Possum e derivatiEPASSVBHOMsurgical

apgarSapsApacheSofahellip

bull SAPSbull APACHE

Risk prediction on line

bull Risk prediction in surgery

[httpwwwriskpredictionorgukppindexphp] bull SFAR

bull This site has been developed to allow surgeons to estimate risk online for their patients undergoing surgery This service is provided for individual use to help surgeons more fully consent their patients by giving mortality and other surgical risk predictions based on relevant prognostic factors including age disease severity and co-morbidity Risk adjusted operative mortality can be used as an objective measure of outcome for monitoring performance within a centre or between centres

Surgical Risk scoresbull ACPGBI CRC Model - Association of Coloproctology of GB amp I Colorectal Cancer Model for

mortality in colorectal cancer bull ACPGBI MBO Model - Association of Coloproctology of GB amp I model for mortality

prediction in malignant bowel obstruction bull ACPGBI Lymph Node Harvesting Model - Association of Coloproctology of GB amp I model for

determining the number of nodes that should be found in each resection bull St Marks Lymph Node Positivity Model - calculates the probability of lymph node

metastases in patients undergoing local resection of rectal cancers and for patients whose nodal harvest was not sufficient to adequately stage the rectal cancer

bull CCF CLC Model - The Cleveland Clinic Colorectal Laparoscopic Conversion Model for prediction of conversion of lapararoscopic to open surgery in patients undergoing colonic or rectal surgery for benign or malignant disease

bull CCF IPF Model - The Cleveland Clinic Ileal Pouch Failure Model for prediction of ileal pouch failure in patients undergoing restorative proctocolectomy

bull CR-POSSUM - Used for predicting mortality in Colorectal Surgery (benign amp malignant) bull P-POSSUM - Used for predicting mortality (amp morbidity by POSSUM) in General Surgery bull O-POSSUM - Used for predicting mortality in Oesophagogastric Surgery bull Vascular-POSSUM - Used for predicting mortality in Vascular Surgery (all 4 models

availablebull MUST screening tool (malnutrition)

bull SAPSbull APACHE

Risk prediction on line

bull Risk prediction in surgery

[httpwwwriskpredictionorgukppindexphp] bull SFAR

bull This site has been developed to allow surgeons to estimate risk online for their patients undergoing surgery This service is provided for individual use to help surgeons more fully consent their patients by giving mortality and other surgical risk predictions based on relevant prognostic factors including age disease severity and co-morbidity Risk adjusted operative mortality can be used as an objective measure of outcome for monitoring performance within a centre or between centres

Surgical Risk scoresbull ACPGBI CRC Model - Association of Coloproctology of GB amp I Colorectal Cancer Model for

mortality in colorectal cancer bull ACPGBI MBO Model - Association of Coloproctology of GB amp I model for mortality

prediction in malignant bowel obstruction bull ACPGBI Lymph Node Harvesting Model - Association of Coloproctology of GB amp I model for

determining the number of nodes that should be found in each resection bull St Marks Lymph Node Positivity Model - calculates the probability of lymph node

metastases in patients undergoing local resection of rectal cancers and for patients whose nodal harvest was not sufficient to adequately stage the rectal cancer

bull CCF CLC Model - The Cleveland Clinic Colorectal Laparoscopic Conversion Model for prediction of conversion of lapararoscopic to open surgery in patients undergoing colonic or rectal surgery for benign or malignant disease

bull CCF IPF Model - The Cleveland Clinic Ileal Pouch Failure Model for prediction of ileal pouch failure in patients undergoing restorative proctocolectomy

bull CR-POSSUM - Used for predicting mortality in Colorectal Surgery (benign amp malignant) bull P-POSSUM - Used for predicting mortality (amp morbidity by POSSUM) in General Surgery bull O-POSSUM - Used for predicting mortality in Oesophagogastric Surgery bull Vascular-POSSUM - Used for predicting mortality in Vascular Surgery (all 4 models

availablebull MUST screening tool (malnutrition)

Risk prediction on line

bull Risk prediction in surgery

[httpwwwriskpredictionorgukppindexphp] bull SFAR

bull This site has been developed to allow surgeons to estimate risk online for their patients undergoing surgery This service is provided for individual use to help surgeons more fully consent their patients by giving mortality and other surgical risk predictions based on relevant prognostic factors including age disease severity and co-morbidity Risk adjusted operative mortality can be used as an objective measure of outcome for monitoring performance within a centre or between centres

Surgical Risk scoresbull ACPGBI CRC Model - Association of Coloproctology of GB amp I Colorectal Cancer Model for

mortality in colorectal cancer bull ACPGBI MBO Model - Association of Coloproctology of GB amp I model for mortality

prediction in malignant bowel obstruction bull ACPGBI Lymph Node Harvesting Model - Association of Coloproctology of GB amp I model for

determining the number of nodes that should be found in each resection bull St Marks Lymph Node Positivity Model - calculates the probability of lymph node

metastases in patients undergoing local resection of rectal cancers and for patients whose nodal harvest was not sufficient to adequately stage the rectal cancer

bull CCF CLC Model - The Cleveland Clinic Colorectal Laparoscopic Conversion Model for prediction of conversion of lapararoscopic to open surgery in patients undergoing colonic or rectal surgery for benign or malignant disease

bull CCF IPF Model - The Cleveland Clinic Ileal Pouch Failure Model for prediction of ileal pouch failure in patients undergoing restorative proctocolectomy

bull CR-POSSUM - Used for predicting mortality in Colorectal Surgery (benign amp malignant) bull P-POSSUM - Used for predicting mortality (amp morbidity by POSSUM) in General Surgery bull O-POSSUM - Used for predicting mortality in Oesophagogastric Surgery bull Vascular-POSSUM - Used for predicting mortality in Vascular Surgery (all 4 models

availablebull MUST screening tool (malnutrition)

bull This site has been developed to allow surgeons to estimate risk online for their patients undergoing surgery This service is provided for individual use to help surgeons more fully consent their patients by giving mortality and other surgical risk predictions based on relevant prognostic factors including age disease severity and co-morbidity Risk adjusted operative mortality can be used as an objective measure of outcome for monitoring performance within a centre or between centres

Surgical Risk scoresbull ACPGBI CRC Model - Association of Coloproctology of GB amp I Colorectal Cancer Model for

mortality in colorectal cancer bull ACPGBI MBO Model - Association of Coloproctology of GB amp I model for mortality

prediction in malignant bowel obstruction bull ACPGBI Lymph Node Harvesting Model - Association of Coloproctology of GB amp I model for

determining the number of nodes that should be found in each resection bull St Marks Lymph Node Positivity Model - calculates the probability of lymph node

metastases in patients undergoing local resection of rectal cancers and for patients whose nodal harvest was not sufficient to adequately stage the rectal cancer

bull CCF CLC Model - The Cleveland Clinic Colorectal Laparoscopic Conversion Model for prediction of conversion of lapararoscopic to open surgery in patients undergoing colonic or rectal surgery for benign or malignant disease

bull CCF IPF Model - The Cleveland Clinic Ileal Pouch Failure Model for prediction of ileal pouch failure in patients undergoing restorative proctocolectomy

bull CR-POSSUM - Used for predicting mortality in Colorectal Surgery (benign amp malignant) bull P-POSSUM - Used for predicting mortality (amp morbidity by POSSUM) in General Surgery bull O-POSSUM - Used for predicting mortality in Oesophagogastric Surgery bull Vascular-POSSUM - Used for predicting mortality in Vascular Surgery (all 4 models

availablebull MUST screening tool (malnutrition)

Surgical Risk scoresbull ACPGBI CRC Model - Association of Coloproctology of GB amp I Colorectal Cancer Model for

mortality in colorectal cancer bull ACPGBI MBO Model - Association of Coloproctology of GB amp I model for mortality

prediction in malignant bowel obstruction bull ACPGBI Lymph Node Harvesting Model - Association of Coloproctology of GB amp I model for

determining the number of nodes that should be found in each resection bull St Marks Lymph Node Positivity Model - calculates the probability of lymph node

metastases in patients undergoing local resection of rectal cancers and for patients whose nodal harvest was not sufficient to adequately stage the rectal cancer

bull CCF CLC Model - The Cleveland Clinic Colorectal Laparoscopic Conversion Model for prediction of conversion of lapararoscopic to open surgery in patients undergoing colonic or rectal surgery for benign or malignant disease

bull CCF IPF Model - The Cleveland Clinic Ileal Pouch Failure Model for prediction of ileal pouch failure in patients undergoing restorative proctocolectomy

bull CR-POSSUM - Used for predicting mortality in Colorectal Surgery (benign amp malignant) bull P-POSSUM - Used for predicting mortality (amp morbidity by POSSUM) in General Surgery bull O-POSSUM - Used for predicting mortality in Oesophagogastric Surgery bull Vascular-POSSUM - Used for predicting mortality in Vascular Surgery (all 4 models

availablebull MUST screening tool (malnutrition)

calculate a mortality risk online for patients using the

ACPGBI Colorectal Cancer Model

bull Calculate an ACP Scorebull Choose a value in each category that matches your patient from the drop down

lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for mortality It is important to say in this model by ticking the appropriate box whether or not the cancer was resected The reason for this is the value allocated to ASA status is dependent upon resection status

bull Parameters ndash Age ndash Cancer Resection Status cancer resected cancer NOT resected ndash ASA Status Cndash Cancer Staging Dukersquosndash Operative Urgency electiveurgentemergency

bullPOSSUM

bull physiological and operative severity scoring system for enumeration of morbidity and mortality

Calculate a CR-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a vsmall risk for mortality The more risky the procedure the more accurate is the predicted risk calculated below

CR POSSUMbull Physiological Parameters

ndash Agendash Cardiac No-mildmoderate Carcdiac failuresevere CFndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash Urea

bull If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if requiredndash Operative Parameters ndash Operation Type

bull Peritoneal Contamination bull Malignancy Status

bull CEPOD

Calculate a P-POSSUM Score

Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for morbidity and mortality This illustrates that even in the modified P-POSSUM formula used in this application still overestimates risk in low risk groups The more risky the procedure the more accurate is the predicted risk calculated below

P-POSSUM score

Physiological Parameters Age

Cardiac

Respiratory

ECG

Systolic BP

Pulse Rate

Haemoglobin

WBC

Urea

Sodium

Potassium

GCS I

f calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

Operative Parameters Operation Type Number of procedures Operative Blood Loss Peritoneal Contamination Malignancy Status CEPOD

Calculate an O-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below You must enter the patients actual age as well as selecting the age range otherwise an error will occur Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for mortality

O Possum score

bull Physiological Parameters ndash Age Range ndash BOTH FIELDS MUST BE COMPLETED ndash Actual Age BOTH FIELDS MUST BE COMPLETED ndash Cardiac ndash Respiratory ndash ECG ndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash WBC ndash Urea ndash Sodium ndash Potassium ndash GCS

ndash If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

bull Operative Parameters Operation Type Malignancy Status CEPOD

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

bullPOSSUM

bull physiological and operative severity scoring system for enumeration of morbidity and mortality

Calculate a CR-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a vsmall risk for mortality The more risky the procedure the more accurate is the predicted risk calculated below

CR POSSUMbull Physiological Parameters

ndash Agendash Cardiac No-mildmoderate Carcdiac failuresevere CFndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash Urea

bull If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if requiredndash Operative Parameters ndash Operation Type

bull Peritoneal Contamination bull Malignancy Status

bull CEPOD

Calculate a P-POSSUM Score

Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for morbidity and mortality This illustrates that even in the modified P-POSSUM formula used in this application still overestimates risk in low risk groups The more risky the procedure the more accurate is the predicted risk calculated below

P-POSSUM score

Physiological Parameters Age

Cardiac

Respiratory

ECG

Systolic BP

Pulse Rate

Haemoglobin

WBC

Urea

Sodium

Potassium

GCS I

f calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

Operative Parameters Operation Type Number of procedures Operative Blood Loss Peritoneal Contamination Malignancy Status CEPOD

Calculate an O-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below You must enter the patients actual age as well as selecting the age range otherwise an error will occur Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for mortality

O Possum score

bull Physiological Parameters ndash Age Range ndash BOTH FIELDS MUST BE COMPLETED ndash Actual Age BOTH FIELDS MUST BE COMPLETED ndash Cardiac ndash Respiratory ndash ECG ndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash WBC ndash Urea ndash Sodium ndash Potassium ndash GCS

ndash If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

bull Operative Parameters Operation Type Malignancy Status CEPOD

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

Calculate a CR-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a vsmall risk for mortality The more risky the procedure the more accurate is the predicted risk calculated below

CR POSSUMbull Physiological Parameters

ndash Agendash Cardiac No-mildmoderate Carcdiac failuresevere CFndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash Urea

bull If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if requiredndash Operative Parameters ndash Operation Type

bull Peritoneal Contamination bull Malignancy Status

bull CEPOD

Calculate a P-POSSUM Score

Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for morbidity and mortality This illustrates that even in the modified P-POSSUM formula used in this application still overestimates risk in low risk groups The more risky the procedure the more accurate is the predicted risk calculated below

P-POSSUM score

Physiological Parameters Age

Cardiac

Respiratory

ECG

Systolic BP

Pulse Rate

Haemoglobin

WBC

Urea

Sodium

Potassium

GCS I

f calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

Operative Parameters Operation Type Number of procedures Operative Blood Loss Peritoneal Contamination Malignancy Status CEPOD

Calculate an O-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below You must enter the patients actual age as well as selecting the age range otherwise an error will occur Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for mortality

O Possum score

bull Physiological Parameters ndash Age Range ndash BOTH FIELDS MUST BE COMPLETED ndash Actual Age BOTH FIELDS MUST BE COMPLETED ndash Cardiac ndash Respiratory ndash ECG ndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash WBC ndash Urea ndash Sodium ndash Potassium ndash GCS

ndash If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

bull Operative Parameters Operation Type Malignancy Status CEPOD

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

CR POSSUMbull Physiological Parameters

ndash Agendash Cardiac No-mildmoderate Carcdiac failuresevere CFndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash Urea

bull If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if requiredndash Operative Parameters ndash Operation Type

bull Peritoneal Contamination bull Malignancy Status

bull CEPOD

Calculate a P-POSSUM Score

Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for morbidity and mortality This illustrates that even in the modified P-POSSUM formula used in this application still overestimates risk in low risk groups The more risky the procedure the more accurate is the predicted risk calculated below

P-POSSUM score

Physiological Parameters Age

Cardiac

Respiratory

ECG

Systolic BP

Pulse Rate

Haemoglobin

WBC

Urea

Sodium

Potassium

GCS I

f calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

Operative Parameters Operation Type Number of procedures Operative Blood Loss Peritoneal Contamination Malignancy Status CEPOD

Calculate an O-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below You must enter the patients actual age as well as selecting the age range otherwise an error will occur Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for mortality

O Possum score

bull Physiological Parameters ndash Age Range ndash BOTH FIELDS MUST BE COMPLETED ndash Actual Age BOTH FIELDS MUST BE COMPLETED ndash Cardiac ndash Respiratory ndash ECG ndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash WBC ndash Urea ndash Sodium ndash Potassium ndash GCS

ndash If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

bull Operative Parameters Operation Type Malignancy Status CEPOD

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

Calculate a P-POSSUM Score

Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for morbidity and mortality This illustrates that even in the modified P-POSSUM formula used in this application still overestimates risk in low risk groups The more risky the procedure the more accurate is the predicted risk calculated below

P-POSSUM score

Physiological Parameters Age

Cardiac

Respiratory

ECG

Systolic BP

Pulse Rate

Haemoglobin

WBC

Urea

Sodium

Potassium

GCS I

f calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

Operative Parameters Operation Type Number of procedures Operative Blood Loss Peritoneal Contamination Malignancy Status CEPOD

Calculate an O-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below You must enter the patients actual age as well as selecting the age range otherwise an error will occur Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for mortality

O Possum score

bull Physiological Parameters ndash Age Range ndash BOTH FIELDS MUST BE COMPLETED ndash Actual Age BOTH FIELDS MUST BE COMPLETED ndash Cardiac ndash Respiratory ndash ECG ndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash WBC ndash Urea ndash Sodium ndash Potassium ndash GCS

ndash If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

bull Operative Parameters Operation Type Malignancy Status CEPOD

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

P-POSSUM score

Physiological Parameters Age

Cardiac

Respiratory

ECG

Systolic BP

Pulse Rate

Haemoglobin

WBC

Urea

Sodium

Potassium

GCS I

f calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

Operative Parameters Operation Type Number of procedures Operative Blood Loss Peritoneal Contamination Malignancy Status CEPOD

Calculate an O-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below You must enter the patients actual age as well as selecting the age range otherwise an error will occur Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for mortality

O Possum score

bull Physiological Parameters ndash Age Range ndash BOTH FIELDS MUST BE COMPLETED ndash Actual Age BOTH FIELDS MUST BE COMPLETED ndash Cardiac ndash Respiratory ndash ECG ndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash WBC ndash Urea ndash Sodium ndash Potassium ndash GCS

ndash If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

bull Operative Parameters Operation Type Malignancy Status CEPOD

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

Calculate an O-POSSUM Score

bull Choose a value in each category that matches your patient from the drop down lists in both the physiological and operative parameters tables below You must enter the patients actual age as well as selecting the age range otherwise an error will occur Default values (the lowest score) are shown for each category Simply submitting the form as it is without changing the values (ie a young fit patient having a minor operation) still gives a risk for mortality

O Possum score

bull Physiological Parameters ndash Age Range ndash BOTH FIELDS MUST BE COMPLETED ndash Actual Age BOTH FIELDS MUST BE COMPLETED ndash Cardiac ndash Respiratory ndash ECG ndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash WBC ndash Urea ndash Sodium ndash Potassium ndash GCS

ndash If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

bull Operative Parameters Operation Type Malignancy Status CEPOD

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

O Possum score

bull Physiological Parameters ndash Age Range ndash BOTH FIELDS MUST BE COMPLETED ndash Actual Age BOTH FIELDS MUST BE COMPLETED ndash Cardiac ndash Respiratory ndash ECG ndash Systolic BP ndash Pulse Rate ndash Haemoglobin ndash WBC ndash Urea ndash Sodium ndash Potassium ndash GCS

ndash If calculating risk in a preoperative patient you will need to estimate the parameters below You can return and modify the parameters post-operatively if required

bull Operative Parameters Operation Type Malignancy Status CEPOD

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

Equazione di Possum(da Copeland)bull R1 rischio di mortalitagravebull R2rischio di morbilitagrave bull Una volta ottenuti i punteggi

bull - Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave operatoria)

bull - Ln (R21 - R2) = -591 + (016 x punteggio fisiologico) + (019 x punteggio di gravitagrave operatoria)

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

Equazioni di POSSUM

bull R1Rischio di mortalitagrave bull R2Rischio di morbilitagravebull Una volta ottenuti i punteggi

bull R1=- Ln (R11 - R1) = -704 + (013 x punteggio fisiologico) + (016 x punteggio di gravitagrave op)

bull R2=- Ln (R21 - R2) = -591 + (016 x punteggio fisioloacutegico) + (019 x punteggio di gravitagrave op)

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

World J Surg Oncol 2008 Apr 9639 Application of Portsmouth modification of physiological and operative severity scoring system

for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery Tamijmarane A Bhati CS Mirza DF Bramhall SR

Mayer DA Wigmore SJ Buckels JAbull BACKGROUND Pancreatoduodenectomy (PD) is associated with high incidence of

morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications METHOD A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality RESULTS 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 +- 330 Mean operative score was 1367 +- 342 Mean operative score rose to 2028 +- 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP (observed to Predicted) morbidity ratio was 13-14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model CONCLUSION P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

Stratification of morbidity according to physiology score Horizontal lines within boxes boxes and error bars represent median interquartile range and

range respectively P lt 0001 (Kruskal Wallis Test)

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

bull AIM To establish a scoring system for predicting the incidence of postoperative complications and mortality in general surgery based on the physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) and to evaluate its effi cacy

bull METHODS Eighty-four patients with postoperative complications or death and 172 patients without postoperative complications who underwent surgery in our department during the previous 2 years were retrospectively analyzed by logistic regression Fifteen indexes were investigated including age cardiovascular function respiratory function blood test results endocrine function central nervous system function hepatic function renal function nutritional status extent of operative trauma and course of anesthesia Modifi ed POSSUM (M-POSSUM) was developed using significant risk factors with its effi cacy evaluated

bull RESULTS The significant risk factors were found to be age cardiovascular function respiratory function hepatic function renal function blood test results endocrine function nutritional status duration of operation intraoperative blood loss and course of anesthesia These factors were all included in the scoring system There were signifi cant differences in the scores between the patients with and without postoperative complications between the patients died and survived with complications and between the patients died and survived without complications The receiver operating characteristic curves showed that the M-POSSUM could accurately predict postoperative complications and mortality

bull CONCLUSION M-POSSUM correlates well with postoperative complications and mortality and is more accurate than POSSUM

physiological and operative severityscore for the enumeration of mortality and morbidity

(POSSUM)

bull POSSUM is limited by its somewhat subjective nature and incomplete evaluation of cardiac signs We propose the modified POSSUM (M-POSSUM)as a reasonable practical and objective scoring system that can be used across a broad disease spectrum in general surgery

M-POSSUMindice 0 1 2 3 4etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea Sindrintestino cortotrapianto di intestino

ematopoietico normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

Stato nutrizionale normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score 15 12-14 9-11 = o lt8

Ferita operatoria Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico Aritmiabassa PA lt 30 min PA sempre bassarianimazione cardiopolmonare

M Possum quadro 1indice 0 1 2 3 4

etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG

Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

M Possum quadro 20 1 2 3 4

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale

normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea

Sindrintestino cortotrapianto di intestino

M Possum quadro 30 1 2 3 4

ematopoietico

normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

M Possum (quadro 4)0 1 2 3 4

Stato nutrizionale

normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score

15 12-14 9-11 = o lt8

Ferita operatoria

Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico

Aritmiabassa PA lt 30 min

PA sempre bassarianimazione cardiopolmonare

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

bull AIM To establish a scoring system for predicting the incidence of postoperative complications and mortality in general surgery based on the physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) and to evaluate its effi cacy

bull METHODS Eighty-four patients with postoperative complications or death and 172 patients without postoperative complications who underwent surgery in our department during the previous 2 years were retrospectively analyzed by logistic regression Fifteen indexes were investigated including age cardiovascular function respiratory function blood test results endocrine function central nervous system function hepatic function renal function nutritional status extent of operative trauma and course of anesthesia Modifi ed POSSUM (M-POSSUM) was developed using significant risk factors with its effi cacy evaluated

bull RESULTS The significant risk factors were found to be age cardiovascular function respiratory function hepatic function renal function blood test results endocrine function nutritional status duration of operation intraoperative blood loss and course of anesthesia These factors were all included in the scoring system There were signifi cant differences in the scores between the patients with and without postoperative complications between the patients died and survived with complications and between the patients died and survived without complications The receiver operating characteristic curves showed that the M-POSSUM could accurately predict postoperative complications and mortality

bull CONCLUSION M-POSSUM correlates well with postoperative complications and mortality and is more accurate than POSSUM

physiological and operative severityscore for the enumeration of mortality and morbidity

(POSSUM)

bull POSSUM is limited by its somewhat subjective nature and incomplete evaluation of cardiac signs We propose the modified POSSUM (M-POSSUM)as a reasonable practical and objective scoring system that can be used across a broad disease spectrum in general surgery

M-POSSUMindice 0 1 2 3 4etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea Sindrintestino cortotrapianto di intestino

ematopoietico normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

Stato nutrizionale normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score 15 12-14 9-11 = o lt8

Ferita operatoria Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico Aritmiabassa PA lt 30 min PA sempre bassarianimazione cardiopolmonare

M Possum quadro 1indice 0 1 2 3 4

etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG

Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

M Possum quadro 20 1 2 3 4

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale

normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea

Sindrintestino cortotrapianto di intestino

M Possum quadro 30 1 2 3 4

ematopoietico

normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

M Possum (quadro 4)0 1 2 3 4

Stato nutrizionale

normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score

15 12-14 9-11 = o lt8

Ferita operatoria

Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico

Aritmiabassa PA lt 30 min

PA sempre bassarianimazione cardiopolmonare

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

physiological and operative severityscore for the enumeration of mortality and morbidity

(POSSUM)

bull POSSUM is limited by its somewhat subjective nature and incomplete evaluation of cardiac signs We propose the modified POSSUM (M-POSSUM)as a reasonable practical and objective scoring system that can be used across a broad disease spectrum in general surgery

M-POSSUMindice 0 1 2 3 4etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea Sindrintestino cortotrapianto di intestino

ematopoietico normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

Stato nutrizionale normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score 15 12-14 9-11 = o lt8

Ferita operatoria Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico Aritmiabassa PA lt 30 min PA sempre bassarianimazione cardiopolmonare

M Possum quadro 1indice 0 1 2 3 4

etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG

Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

M Possum quadro 20 1 2 3 4

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale

normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea

Sindrintestino cortotrapianto di intestino

M Possum quadro 30 1 2 3 4

ematopoietico

normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

M Possum (quadro 4)0 1 2 3 4

Stato nutrizionale

normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score

15 12-14 9-11 = o lt8

Ferita operatoria

Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico

Aritmiabassa PA lt 30 min

PA sempre bassarianimazione cardiopolmonare

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

M-POSSUMindice 0 1 2 3 4etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea Sindrintestino cortotrapianto di intestino

ematopoietico normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

Stato nutrizionale normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score 15 12-14 9-11 = o lt8

Ferita operatoria Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico Aritmiabassa PA lt 30 min PA sempre bassarianimazione cardiopolmonare

M Possum quadro 1indice 0 1 2 3 4

etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG

Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

M Possum quadro 20 1 2 3 4

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale

normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea

Sindrintestino cortotrapianto di intestino

M Possum quadro 30 1 2 3 4

ematopoietico

normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

M Possum (quadro 4)0 1 2 3 4

Stato nutrizionale

normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score

15 12-14 9-11 = o lt8

Ferita operatoria

Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico

Aritmiabassa PA lt 30 min

PA sempre bassarianimazione cardiopolmonare

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

M Possum quadro 1indice 0 1 2 3 4

etagrave lt60 60-69 70-79 gt80

Sist circolatorio NormalefunzcardiacaPAECG

Funz cardiaca grado 1lieve ipertens e anormalitagrave ECGbradicardia o tachicsinoatrialebasso voltaggio derivaz arti infBBB

Funz cardiaca grado 2moderata ipertens controllata da terapiaextrasistoli atriale occasionali

Funzcardiaca grado 3IMlt3 mesiipertens moderata in terapiaritmi ectopicimodificaz ST-Tfibrillazatriale

Insufficienza cardiaca seriainscardiaca acutamalattia ipertensiva

Sistrespiratorio normale Fumo di vecchia databronchite cronicaasmainfezioni respiratoriestrie polmonari ispessimenti

Modesta COPDmodesta alterazfunzrespmodesto enfisema

Moderata COPDfunz resp da moderata a seriamente anormale

Insufficienza respiratoria

M Possum quadro 20 1 2 3 4

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale

normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea

Sindrintestino cortotrapianto di intestino

M Possum quadro 30 1 2 3 4

ematopoietico

normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

M Possum (quadro 4)0 1 2 3 4

Stato nutrizionale

normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score

15 12-14 9-11 = o lt8

Ferita operatoria

Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico

Aritmiabassa PA lt 30 min

PA sempre bassarianimazione cardiopolmonare

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

M Possum quadro 20 1 2 3 4

Funzepatica normale Storia di epatitecirrosibilirub totlt342 mmoll

bilirub tot342-513 mmoll

bilirub totgt513 mmoll

Funzrenale normale BUNlt101 mmolltCreatininlt170 mmoll

BUN 101-15 mmolltcreat 170-300 mmoll

Insuff renale in dialisi

Tratto gastrointestinale

normale Storia di gastroenterite cronicaulcera peptica ben controllata

Malattia gastrointest attivaemorragiaperforaz di ulceramdi Crohn attiva

Fistola intestpercutanea

Sindrintestino cortotrapianto di intestino

M Possum quadro 30 1 2 3 4

ematopoietico

normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

M Possum (quadro 4)0 1 2 3 4

Stato nutrizionale

normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score

15 12-14 9-11 = o lt8

Ferita operatoria

Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico

Aritmiabassa PA lt 30 min

PA sempre bassarianimazione cardiopolmonare

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

M Possum quadro 30 1 2 3 4

ematopoietico

normale Piastrine GB lievemente diminuitiHB gt85 grl

Malattiacome leucemia stabileGBgt145 109l

Malaplasticasindr da ipersplenismoleucemiaecc

endocrino normale Glicemia lievemente aumentataglucosio urinario assenteipertiroidismo e ipotiroidsmo trattatiacromegaliagottamal reumatoide

Glicemia lievemente aumentataglucosio urinario presentediabete controll da terapia oraleormonoterpiagotta attiva

Diabete instabile con terapia orale

Nefropatia diabetica

M Possum (quadro 4)0 1 2 3 4

Stato nutrizionale

normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score

15 12-14 9-11 = o lt8

Ferita operatoria

Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico

Aritmiabassa PA lt 30 min

PA sempre bassarianimazione cardiopolmonare

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

M Possum (quadro 4)0 1 2 3 4

Stato nutrizionale

normale Malnutrizione lievealbumina 30-35 grldecremento di peso lt25 kgm

Malnutrizione moderataalbumina lt30 grldecremento di peso lt25 -5 kgm

cachessia

Glasgow Coma Score

15 12-14 9-11 = o lt8

Ferita operatoria

Minoretempo operatorio lt 2 hemorragialt300 ml

Moderatatempo operatorio 2-4 hemorragia300-500 ml

Maggiore tempo operatorio gt4 hemorragiagt500 mlescissionne di tumore maligno

Maggioreescissione gt 3 organimalattia tumorale non resecabile l

Decorso anestetico

Aritmiabassa PA lt 30 min

PA sempre bassarianimazione cardiopolmonare

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

Ding LA Sun LQ Chen SX Qu LL Xie DF Modifiedphysiological and operative score for the enumeration ofmortality and morbidity risk assessment model in generalsurgery World J Gastroenterol 2007 13(38) 5090-5095

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

Predictive formula of M POssum

bull Logistic regression analysis yielded statistically signifi cant equations for both morbidity and mortality

bull The morbidity equation was

lnR1-R = -7287 + 0765M-POSSUM (P =0000)bull the mortality equation waslnR1-R = -10000 + 0681M-POSSUM (P = 0000) The predictive accuracy of morbidity equation and

mortality equation was 836 and 941 respectively

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

O-POSSUM Score

observed 30-day morbidity rate 58Obs Mortality 12

observed 1-year mortality 38 for males (mean age 79 years) and 29 for females (mean age 84 years)

N Z Med J 2006 May 19119(1234)U1986 Comment in N Z Med J 2006119(1234)U1981 Audit of morbidity and mortality following neck of femur fracture using the POSSUM scoring systemYoung W Seigne R Bright S Gardner M

Malnutrition Universal Screening Tool (MUST)

lsquoMUSTrsquo is a five-step screening tool to identify adults who are malnourished at risk of malnutrition or obese It also includes management guidelines which can be used to develop a care plan The tool is being used both in hospitals and in the community It is easy to use and can be used by all care workers

bull Full details of this tool can be found at the followingbull httpwwwbapenorgukthe-musthtmbull Calculate Riskbull Use the form below to estimate the risk of malnutrition Please note that the

figures entered for weight must be in kilograms and the figure entered for height must be in centimetres Conversion charts for Imperial units can be found here (opens in a new window)

bull Parameters Current weight (Kg) Current height (cms) Previous healthy weight Is the patient acutely ill and there has been or is likely to be no nutritional intake for gt5 days

bull This is the patients weight when they were healthy or the weight prior to any unplanned weight loss in the last 3-6 months

Application of Portsmouth modification of physiological and operative severity scoring system for enumeration of morbidity and mortality (P-POSSUM) in pancreatic surgery

Appou Tamijmarane Chandra S Bhati Darius F Mirza Simon R BramhallDavid A Mayer Stephen J Wigmore and John AC BuckelsWorld Journal of Surgical

Oncology 2008 639 doi1011861477-7819-6-39bull Abstract Background Pancreatoduodenectomy (PD) is associated with high incidence of morbidity and mortality We have applied P-POSSUM in predicting the incidence of outcome after PD to identify those who are at the highest risk of developing complications

bull Method A prospective database of 241 consecutive patients who had PD from January 2002 to September 2005 was retrospectively updated and analysed P-POSSUM score was calculated for each patient and correlated with observed morbidity and mortality

bull Results 30 days mortality was 78 and morbidity was 448 Mean physiological score was 1607 plusmn 330 Mean operative score was 1367 plusmn 342 Mean operative score rose to 2028 plusmn 252 for the complex major operation (p lt 0001) with 2 fold increase in morbidity and 35 fold increase in mortality For groups of patients with a physiological score of (less than or equal to) 18 the OP

bull (observed to Predicted) morbidity ratio was 13ndash14 and with a physiological score of gt18 the OP ratio was nearer to 1 Physiological score and white cell count were significant in a multivariate model

bull Conclusion P-POSSUM underestimated the mortality rate While P-POSSUM analysis gave a truer prediction of morbidity underestimation of morbidity and potential for systematic inaccuracy in prediction of complications at lower risk levels is a significant issue for pancreatic surgery

E-PASS scoring

Estimation of physiologic ability and surgical stress

bull Estimation of Physiologic Ability and Surgical Stress (E-PASS) scoring system

bull E-PASS=a pre-operative risk score (PRS) a surgical stress score (SSS) and a comprehensive risk score (CRS) which is calculated from the PRS and SSS

bull CRS=PRS+SSSbull E-PASS=KCRS

equations of the E-PASS scoring system

bull The equations of the E-PASS scoring system are as follows (data from Haga et al1)

(1) Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

equations of the E-PASS scoring system are as follows (data from Haga et al1)

bull (1) PRS = -00686 + 000345X1 +0323X2 +0205X3

+0153X4 +0148X5 +00666X6 where X1 is age X2 the presence (1) or absence (0) of severe heart disease X3 the presence (1) or absence (0) of severe pulmonary disease X4 the presence (1) or absence (0) of diabetes mellitus X5 the performance status index (range 0-4) and X6 the American Society of Anesthesiologists physiological status classification (range 1-5)

E-PASS scoring

Estimation of physiologic ability and surgical stress

bull Estimation of Physiologic Ability and Surgical Stress (E-PASS) scoring system

bull E-PASS=a pre-operative risk score (PRS) a surgical stress score (SSS) and a comprehensive risk score (CRS) which is calculated from the PRS and SSS

bull CRS=PRS+SSSbull E-PASS=KCRS

equations of the E-PASS scoring system

bull The equations of the E-PASS scoring system are as follows (data from Haga et al1)

(1) Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

equations of the E-PASS scoring system are as follows (data from Haga et al1)

bull (1) PRS = -00686 + 000345X1 +0323X2 +0205X3

+0153X4 +0148X5 +00666X6 where X1 is age X2 the presence (1) or absence (0) of severe heart disease X3 the presence (1) or absence (0) of severe pulmonary disease X4 the presence (1) or absence (0) of diabetes mellitus X5 the performance status index (range 0-4) and X6 the American Society of Anesthesiologists physiological status classification (range 1-5)

bull Estimation of Physiologic Ability and Surgical Stress (E-PASS) scoring system

bull E-PASS=a pre-operative risk score (PRS) a surgical stress score (SSS) and a comprehensive risk score (CRS) which is calculated from the PRS and SSS

bull CRS=PRS+SSSbull E-PASS=KCRS

equations of the E-PASS scoring system

bull The equations of the E-PASS scoring system are as follows (data from Haga et al1)

(1) Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

equations of the E-PASS scoring system are as follows (data from Haga et al1)

bull (1) PRS = -00686 + 000345X1 +0323X2 +0205X3

+0153X4 +0148X5 +00666X6 where X1 is age X2 the presence (1) or absence (0) of severe heart disease X3 the presence (1) or absence (0) of severe pulmonary disease X4 the presence (1) or absence (0) of diabetes mellitus X5 the performance status index (range 0-4) and X6 the American Society of Anesthesiologists physiological status classification (range 1-5)

equations of the E-PASS scoring system

bull The equations of the E-PASS scoring system are as follows (data from Haga et al1)

(1) Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

equations of the E-PASS scoring system are as follows (data from Haga et al1)

bull (1) PRS = -00686 + 000345X1 +0323X2 +0205X3

+0153X4 +0148X5 +00666X6 where X1 is age X2 the presence (1) or absence (0) of severe heart disease X3 the presence (1) or absence (0) of severe pulmonary disease X4 the presence (1) or absence (0) of diabetes mellitus X5 the performance status index (range 0-4) and X6 the American Society of Anesthesiologists physiological status classification (range 1-5)

equations of the E-PASS scoring system are as follows (data from Haga et al1)

bull (1) PRS = -00686 + 000345X1 +0323X2 +0205X3

+0153X4 +0148X5 +00666X6 where X1 is age X2 the presence (1) or absence (0) of severe heart disease X3 the presence (1) or absence (0) of severe pulmonary disease X4 the presence (1) or absence (0) of diabetes mellitus X5 the performance status index (range 0-4) and X6 the American Society of Anesthesiologists physiological status classification (range 1-5)

bull (1) PRS = -00686 + 000345X1 +0323X2 +0205X3

+0153X4 +0148X5 +00666X6 dove X1 egrave etograve X2la presenza (1) o assenza (0) di malattia cardiaca severa X3

la presenza (1) o assenza (0)di malattia polmonare severa X4 la presenza (1) o assenza (0) di diabete mellitus X5 il performance status index (range 0-4) X6 la classificazione di stato fisico della American

Society of Anesthesiologists (ASA Ps) (range 1-5)

bull Severe heart disease is defined as heart failure of New York Heart Association class III or IV or severe arrhythmia requiring mechanical support

bull Severe pulmonary disease is defined as any condition with a percentage vital capacity of less than 60 andor a percentage forced expiratory volume in 1 second of less than 50

bull Diabetes mellitus is defined according to the World Health Organization criteria

bull Performance status index is defined by the Japanese Society for Cancer Therapy

SSSsurgical stress core

bull (2) SSS = -0342 + 00139X1 +00392X2 +0352X3 where X1 is blood loss (in grams) divided by body weight (in kilograms) X2 the operating time (in hours) and X3 the extent of the skin incision (0 indicates a minor incision for laparoscopic or thoracoscopic surgery including laparoscopic- or thoracoscopic-assisted surgery 1 laparotomy or thoracotomy alone and 2 laparotomy and thoracotomy)

(

bull 2) SSS = -0342 + 00139X1 +00392X2 +0352X3 dove X1 egrave la perdita ematica (in grammi) diviso per il peso corporeo (in kg) X2 tempo operatorio ( h) X3 lrsquoestensione della incisione cutanea (0 indica una incisione minore laparoscopica o toracoscopica 1 laparotomia o toracototomia da sole 2 laparotomia e toracotomia

comprehensive risk score (CRS)

bull 3) CRS = -0328 + (0936 x PRS) + (0976 x SSS)

Esempio di di EPassbull 70 annibull Copdbull Ipertesobull Gastrect 5 hperdite 800 ml stimatehelliphellip

bull PRS = -00686 + 00034570+03230 +02051 +0153X4

+0148X5 +006663assumiamo X5=1hellip

bull PRS=349bull SSS =04345bull CRS = -0328 + (326) + (04240)=335 cioegrave mortalitagrave

0-5morbilitagrave 44

Incidence of mortality and morbidity accordingto CRS The graph appears to demonstrate that patients in the ge10 categoryare at particularly high risk

of mortality and in the 5 to lt10 and ge10categories at particularly high risk of morbidity Bars show 95 confidence intervals Estimation of physiologic ability and

surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

bull Severe heart disease is defined as heart failure of New York Heart Association class III or IV or severe arrhythmia requiring mechanical support

bull Severe pulmonary disease is defined as any condition with a percentage vital capacity of less than 60 andor a percentage forced expiratory volume in 1 second of less than 50

bull Diabetes mellitus is defined according to the World Health Organization criteria

bull Performance status index is defined by the Japanese Society for Cancer Therapy

SSSsurgical stress core

bull (2) SSS = -0342 + 00139X1 +00392X2 +0352X3 where X1 is blood loss (in grams) divided by body weight (in kilograms) X2 the operating time (in hours) and X3 the extent of the skin incision (0 indicates a minor incision for laparoscopic or thoracoscopic surgery including laparoscopic- or thoracoscopic-assisted surgery 1 laparotomy or thoracotomy alone and 2 laparotomy and thoracotomy)

(

bull 2) SSS = -0342 + 00139X1 +00392X2 +0352X3 dove X1 egrave la perdita ematica (in grammi) diviso per il peso corporeo (in kg) X2 tempo operatorio ( h) X3 lrsquoestensione della incisione cutanea (0 indica una incisione minore laparoscopica o toracoscopica 1 laparotomia o toracototomia da sole 2 laparotomia e toracotomia

comprehensive risk score (CRS)

bull 3) CRS = -0328 + (0936 x PRS) + (0976 x SSS)

Esempio di di EPassbull 70 annibull Copdbull Ipertesobull Gastrect 5 hperdite 800 ml stimatehelliphellip

bull PRS = -00686 + 00034570+03230 +02051 +0153X4

+0148X5 +006663assumiamo X5=1hellip

bull PRS=349bull SSS =04345bull CRS = -0328 + (326) + (04240)=335 cioegrave mortalitagrave

0-5morbilitagrave 44

Incidence of mortality and morbidity accordingto CRS The graph appears to demonstrate that patients in the ge10 categoryare at particularly high risk

of mortality and in the 5 to lt10 and ge10categories at particularly high risk of morbidity Bars show 95 confidence intervals Estimation of physiologic ability and

surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

SSSsurgical stress core

bull (2) SSS = -0342 + 00139X1 +00392X2 +0352X3 where X1 is blood loss (in grams) divided by body weight (in kilograms) X2 the operating time (in hours) and X3 the extent of the skin incision (0 indicates a minor incision for laparoscopic or thoracoscopic surgery including laparoscopic- or thoracoscopic-assisted surgery 1 laparotomy or thoracotomy alone and 2 laparotomy and thoracotomy)

(

bull 2) SSS = -0342 + 00139X1 +00392X2 +0352X3 dove X1 egrave la perdita ematica (in grammi) diviso per il peso corporeo (in kg) X2 tempo operatorio ( h) X3 lrsquoestensione della incisione cutanea (0 indica una incisione minore laparoscopica o toracoscopica 1 laparotomia o toracototomia da sole 2 laparotomia e toracotomia

comprehensive risk score (CRS)

bull 3) CRS = -0328 + (0936 x PRS) + (0976 x SSS)

Esempio di di EPassbull 70 annibull Copdbull Ipertesobull Gastrect 5 hperdite 800 ml stimatehelliphellip

bull PRS = -00686 + 00034570+03230 +02051 +0153X4

+0148X5 +006663assumiamo X5=1hellip

bull PRS=349bull SSS =04345bull CRS = -0328 + (326) + (04240)=335 cioegrave mortalitagrave

0-5morbilitagrave 44

Incidence of mortality and morbidity accordingto CRS The graph appears to demonstrate that patients in the ge10 categoryare at particularly high risk

of mortality and in the 5 to lt10 and ge10categories at particularly high risk of morbidity Bars show 95 confidence intervals Estimation of physiologic ability and

surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

bull 2) SSS = -0342 + 00139X1 +00392X2 +0352X3 dove X1 egrave la perdita ematica (in grammi) diviso per il peso corporeo (in kg) X2 tempo operatorio ( h) X3 lrsquoestensione della incisione cutanea (0 indica una incisione minore laparoscopica o toracoscopica 1 laparotomia o toracototomia da sole 2 laparotomia e toracotomia

comprehensive risk score (CRS)

bull 3) CRS = -0328 + (0936 x PRS) + (0976 x SSS)

Esempio di di EPassbull 70 annibull Copdbull Ipertesobull Gastrect 5 hperdite 800 ml stimatehelliphellip

bull PRS = -00686 + 00034570+03230 +02051 +0153X4

+0148X5 +006663assumiamo X5=1hellip

bull PRS=349bull SSS =04345bull CRS = -0328 + (326) + (04240)=335 cioegrave mortalitagrave

0-5morbilitagrave 44

Incidence of mortality and morbidity accordingto CRS The graph appears to demonstrate that patients in the ge10 categoryare at particularly high risk

of mortality and in the 5 to lt10 and ge10categories at particularly high risk of morbidity Bars show 95 confidence intervals Estimation of physiologic ability and

surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

comprehensive risk score (CRS)

bull 3) CRS = -0328 + (0936 x PRS) + (0976 x SSS)

Esempio di di EPassbull 70 annibull Copdbull Ipertesobull Gastrect 5 hperdite 800 ml stimatehelliphellip

bull PRS = -00686 + 00034570+03230 +02051 +0153X4

+0148X5 +006663assumiamo X5=1hellip

bull PRS=349bull SSS =04345bull CRS = -0328 + (326) + (04240)=335 cioegrave mortalitagrave

0-5morbilitagrave 44

Incidence of mortality and morbidity accordingto CRS The graph appears to demonstrate that patients in the ge10 categoryare at particularly high risk

of mortality and in the 5 to lt10 and ge10categories at particularly high risk of morbidity Bars show 95 confidence intervals Estimation of physiologic ability and

surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

Esempio di di EPassbull 70 annibull Copdbull Ipertesobull Gastrect 5 hperdite 800 ml stimatehelliphellip

bull PRS = -00686 + 00034570+03230 +02051 +0153X4

+0148X5 +006663assumiamo X5=1hellip

bull PRS=349bull SSS =04345bull CRS = -0328 + (326) + (04240)=335 cioegrave mortalitagrave

0-5morbilitagrave 44

Incidence of mortality and morbidity accordingto CRS The graph appears to demonstrate that patients in the ge10 categoryare at particularly high risk

of mortality and in the 5 to lt10 and ge10categories at particularly high risk of morbidity Bars show 95 confidence intervals Estimation of physiologic ability and

surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

Incidence of mortality and morbidity accordingto CRS The graph appears to demonstrate that patients in the ge10 categoryare at particularly high risk

of mortality and in the 5 to lt10 and ge10categories at particularly high risk of morbidity Bars show 95 confidence intervals Estimation of physiologic ability and

surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

Estimation of physiologic ability and surgical stress (E-PASS) as a predictor of immediate outcome after elective abdominal aortic aneurysm surgery

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

Br J Surg 2007 Jun94(6)717-21 Comment in Br J Surg 2007 Oct94(10)1308 author reply 1308-9 VBHOM a data economic

model for predicting the outcome after open abdominal aortic aneurysm surgeryTang T Walsh SR Prytherch DR Lees T Varty K Boyle JR

Audit Research Committee of the Vascular Society of Great Britain and Ireland

bull BACKGROUND Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair METHODS A binary logistic regression model of risk of in-hospital mortality was built from the 2002-2004 submission to the UK National Vascular Database (NVD) (2718 patients) The subset of NVD data items used comprised serum levels of urea sodium and potassium haemoglobin white cell count sex age and mode of admission The model was applied prospectively using Hosmer-Lemeshow methodology to a test data set from the Cambridge Vascular Unit RESULTS The validation set contained 327 patients of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA Outcome following elective and non-elective AAA repair could be described accurately using the same model The overall mean predicted risk of death was 1413 per cent and 48 deaths were predicted The actual number of deaths was 53 (chi(2) = 840 10 df P = 0590 no evidence of lack of fit) The model also demonstrated good discrimination (c-index = 0852) CONCLUSION The VBHOM approach has the advantage of using simple objective clinical data that are easy to collect routinely The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery (c) 2007 British Journal of

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

Vascular Biochemistry and Haematology Outcome Models (VBHOM)

bull serum levels ofndash urea ndashsodium and potassium ndashhaemoglobin ndashwhite cell count ndashsex ndashage ndash mode of admission

Eur J Vasc Endovasc Surg 2007 Nov34(5)505-13 Epub 2007 Sep 14 LinksComment in Eur J Vasc Endovasc Surg 2007 Nov34(5)497-8 Comparison of risk-scoring

methods in predicting the immediate outcome after elective open abdominal aortic aneurysm surgery

Tang TY Walsh SR Fanshawe TR Seppi V Sadat U Hayes PD Varty K Gaunt ME Boyle JR

bull BACKGROUND amp OBJECTIVES The aim of this study was to apply three simple risk - scoring systems to prospectively collected data on all elective open Abdominal Aortic Aneurysm (AAA) operations in the Cambridge Academic Vascular Unit over a 6 - year period (January 1998 to January 2004) to compare their predictive values and to evaluate their validity with respect to prediction of mortality and post-operative complications METHODS 204 patients underwent elective open infra-renal AAA repair Data were prospectively collected and risk assessment scores were calculated for mortality and morbidity according to the Glasgow Aneurysm Score (GAS) VBHOM (Vascular Biochemistry and Haematology Outcome Models) and Estimation of Physiologic Ability and Surgical Stress (E-PASS) RESULTS The mortality rate was 63 (13204) and 59 (121204) experienced a post-operative complication (30-day outcome) For GAS VBHOM and E-PASS the receiver operating characteristics (ROC) curve analysis for prediction of in-hospital mortality showed area under the curve (AUC) of 084 (95 confidence interval [CI] 076 to 092 plt00001) 082 (95 CI 068 to 095 p=00001) and 092 (95 CI 087 to 097 plt00001) respectively There were also significant correlations between post-operative complications and length of hospital stay and each of the three scores but the correlation was substantially higher in the case of E-PASS CONCLUSIONS All three scoring systems accurately predicted the risk of mortality and morbidity in patients undergoing elective open AAA repair Among these E-PASS seemed to be the most accurate predictor in this patient population

Eur J Vasc Endovasc Surg 2008 Oct 13 [Epub ahead of print] LinksPredicting Risk in Elective Abdominal Aortic Aneurysm Repair A

Systematic Review of Current EvidencePatterson BO Holt PJ Hinchliffe R Loftus IM Thompson MM

bull OBJECTIVE To examine and compare existing pre-operative risk prediction methods for elective abdominal aortic aneurysm (AAA) repair DESIGN Systematic review METHODS Medline EMBASE and the Cochrane library were searched for articles that related to risk prediction models used for elective AAA repair RESULTS 680 abstracts were reviewed and after exclusions 28 articles encompassing 10 risk models were identified The most frequently studied of these were the Glasgow Aneurysm Score (GAS) the Physiological and Operative Severity Score for enUmeration of Mortality (POSSUM) predictor equation and the Vascular Biochemistry and Haematology Outcome Model (VBHOM) All models had strengths and weaknesses and some had unique features which were identified and discussed CONCLUSION The GAS appeared to be the most useful and consistently validated score at present for open repair Other systems were either not validated fully or were not consistently accurate Some had significant drawbacks which appeared to severely limit their clinical application Recent work has shown that no scores consistently predicted the risk associated with endovascular aneurysm repair (EVAR) Pre-operative risk stratification is a vital component of modern surgical practice and we propose the need for a comprehensive new risk scoring method for AAA repair incorporating anatomical and physiological data

Surgical Apgar score

Surgical Apgar Score Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008 bull lowest heart ratebull lowest mean arterial pressurebull estimated blood lossbull A score built from these 3 predictors has proved

strongly predictive of the risk of major postoperative complications and death in general and vascular surgery

bull The score was thus developed using these 3 variables and their beta coefficients were used to weight the points allocated to each variable in a 10-point score (Table 1)

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

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80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Eur J Vasc Endovasc Surg 2008 Oct 13 [Epub ahead of print] LinksPredicting Risk in Elective Abdominal Aortic Aneurysm Repair A

Systematic Review of Current EvidencePatterson BO Holt PJ Hinchliffe R Loftus IM Thompson MM

bull OBJECTIVE To examine and compare existing pre-operative risk prediction methods for elective abdominal aortic aneurysm (AAA) repair DESIGN Systematic review METHODS Medline EMBASE and the Cochrane library were searched for articles that related to risk prediction models used for elective AAA repair RESULTS 680 abstracts were reviewed and after exclusions 28 articles encompassing 10 risk models were identified The most frequently studied of these were the Glasgow Aneurysm Score (GAS) the Physiological and Operative Severity Score for enUmeration of Mortality (POSSUM) predictor equation and the Vascular Biochemistry and Haematology Outcome Model (VBHOM) All models had strengths and weaknesses and some had unique features which were identified and discussed CONCLUSION The GAS appeared to be the most useful and consistently validated score at present for open repair Other systems were either not validated fully or were not consistently accurate Some had significant drawbacks which appeared to severely limit their clinical application Recent work has shown that no scores consistently predicted the risk associated with endovascular aneurysm repair (EVAR) Pre-operative risk stratification is a vital component of modern surgical practice and we propose the need for a comprehensive new risk scoring method for AAA repair incorporating anatomical and physiological data

Surgical Apgar score

Surgical Apgar Score Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008 bull lowest heart ratebull lowest mean arterial pressurebull estimated blood lossbull A score built from these 3 predictors has proved

strongly predictive of the risk of major postoperative complications and death in general and vascular surgery

bull The score was thus developed using these 3 variables and their beta coefficients were used to weight the points allocated to each variable in a 10-point score (Table 1)

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

10

20

30

40

50

60

70

80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Surgical Apgar score

Surgical Apgar Score Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008 bull lowest heart ratebull lowest mean arterial pressurebull estimated blood lossbull A score built from these 3 predictors has proved

strongly predictive of the risk of major postoperative complications and death in general and vascular surgery

bull The score was thus developed using these 3 variables and their beta coefficients were used to weight the points allocated to each variable in a 10-point score (Table 1)

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

10

20

30

40

50

60

70

80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Surgical Apgar Score Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008 bull lowest heart ratebull lowest mean arterial pressurebull estimated blood lossbull A score built from these 3 predictors has proved

strongly predictive of the risk of major postoperative complications and death in general and vascular surgery

bull The score was thus developed using these 3 variables and their beta coefficients were used to weight the points allocated to each variable in a 10-point score (Table 1)

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

10

20

30

40

50

60

70

80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

10

20

30

40

50

60

70

80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

10

20

30

40

50

60

70

80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

10

20

30

40

50

60

70

80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul

A MD MPH ++ Does the Surgical Apgar Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

10

20

30

40

50

60

70

80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Frequenza delle complicanze a seconda del Surgical Apgar Score

Regenbogen Scott E MD MPH + Lancaster R Todd MD + Lipsitz Stuart R ScD ++ Greenberg Caprice C MD MPH ++ Hutter Matthew M MD MPH + Gawande Atul A MD MPH ++ Does the Surgical Apgar

Score Measure Intraoperative Performance Annals of Surgery 248(2)320-328 August 2008

0

10

20

30

40

50

60

70

80

Ko maggiori

0-2

3-4

5-6

7-8

9-10

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Apache IIIbull the APACHE III is used to produce an equation predicting hospital mortality

after the first day of ICU treatment There are 4 components age major disease category (reason for ICU admission) acute (current) physiology and prior site of healthcare (eg hospital floor emergency room etc) The physiologic variables require scoring of the following vital sign and laboratory abnormalities pulse rate mean blood pressure temperature respiratory rate PaO2P (A-a) O2 hematocrit white blood cell count creatinine urine output blood urea nitrogen serum sodium albumin bilirubin glucose acid-base status and neurologic status

bull The last 2 physiologic parameters are hybrid values specific to APACHE III [3] It is important to note that an additional scoring variable must be used to update the APACHE III score based on changes in the patients physiologic status

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

references

bull Knaus WA Draper EA Wagner DP it al APACHE II A severity of disease classification system Crit Care Med 198513818-829

bull Le Gall Jr Lemeshow S Saulnier F A new Simplified Acute Physiology Score (SAPS II) based on a EuropeanNorth American multicenter study JAMA 19932702957-2963

bull Knaus WA Wagner DP Draper EA et al The APACHE III Prognostic System Chest 19911001619-1636

bull Beck DH Taylor BL Millar B et al Prediction of outcome from intensive care A prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit Crit Care Med 1997259-15

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Apache II score

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Saps II score

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Sofa score

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Comparison of the performance of SAPS II SAPS 3 APACHE II and their customized prognostic models in a surgical intensive care unit

Y Sakr1 C Krauss1 A C K B Amaral2 A Reacutea-Neto2 M Specht1 K Reinhart1 and G Marx1

bull We compared the performance of SAPS 3 with SAPS II and the Acute Physiology and Chronic Health Evaluation (APACHE) II score in surgical ICU patients

bull Methods Prospectively collected data from all patients admitted to a German university hospital postoperative ICU between August 2004 and December 2005 were analysed The probability of ICU mortality was calculated for SAPS II APACHE II adjusted APACHE II (adj-APACHE II) SAPS 3 and SAPS 3 customized for Europe [C-SAPS3 (Eu)] using standard formulas To improve calibration of the prognostic models a first-level customization was performed using logistic regression on the original scores and the corresponding probability of ICU death was calculated for the customized scores (C-SAPS II C-SAPS 3 and C-APACHE II)

bull Results The study included 1851 patients Hospital mortality was 9 Hosmer and Lemeshow statistics showed poor calibration for SAPS II APACHE II adj-APACHE II SAPS 3 and C-SAPS 3 (Eu) but good calibration for C-SAPS II C-APACHE II and C-SAPS 3 Discrimination was generally good for all models [area under the receiver operating characteristic curve ranged from 078 (C-APACHE II) to 089 (C-SAPS 3)] The C-SAPS 3 score appeared to have the best calibration curve on visual inspection

bull Conclusions In this group of surgical ICU patients the performance of SAPS 3 was similar to that of APACHE II and SAPS II Customization improved the calibration of all prognostic models

Comparison of Apache IISaps and Sofa scores

Comparison of Apache IISaps and Sofa scores