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C U R T I N D , O ’ D O N N E L L D , D O Y L E D , G A L L A G H E R P , O ’ M A H O N Y D .
U N I V E R S I T Y C O L L E G E C O R K , I R E L A N D
E U G M S , S E P T E M B E R 2 1 , 2 0 1 7
HOMR model accurately predicts 1-year mortality in older
hospitalized patients
Background
George
Estimating prognosis
doctors are inaccurate and overly optimistic
1. Parkes CM. Accuracy of predictions of survival in later stages of cancer. BMJ 1972;ii: 29-31. [PMC free article][PubMed] 2. Christakis N, Lamont E. Extent and determinants of error in doctors' prognoses. BMJ 2000;320: 469-73. [PMC free article][PubMed] 3. Vigano A, Dorgan M, Buckingham J, Bruera E, Suarez-Alzamor ME. Survival prediction in terminal cancer patients: a systematic review of the
medical literature. Palliat Med 2000;14: 363-74. [PubMed] 4. Christakis NA, Lamont ER. Extent and determinants of error in physicians' prognoses in terminally ill patients. West J Med. 2000 May; 172(5):
310–313.
Prediction Models
Prediction Models
Assessing Performance of Prediction models
Discrimination (C Statistic)
Calibration
Transportability
Assessing Performance of Prediction models
Discrimination (C Statistic)
Calibration
Transportability
Discrimination (C Statistic)
C statistic
≥0.9 Excellent
0.8 -0.89 Very good
0.7-0.79 Good
0.6-0.69 Fair
0.5-0.59 Poor
Discrimination (C Statistic)
C statistic
≥0.9 Excellent
0.8 -0.89 Very good
0.7-0.79 Good
0.6-0.69 Fair
0.5-0.59 Poor
Discrimination (C Statistic)
C statistic
≥0.9 Excellent
0.8 -0.89 Very good
0.7-0.79 Good
0.6-0.69 Fair
0.5-0.59 Poor
>
Assessing Performance of Prognostic models
Discrimination (C Statistic)
Calibration
Transportability
Calibration
Agreement between observed and predicted outcomes
<10% difference indicates good calibration
Assessing Performance of Prognostic models
Discrimination (C Statistic)
Calibration
Transportability
Transportability
Different population
Different location
Different investigators
Systematic Review of Prognostic Models
“testing of transportability was limited”
“insufficient evidence at this time to recommend the widespread use..”
JAMA, 2012
The Hospital-patient One-year Mortality Risk (HOMR) Model (2014)
HOMR model
Predicts 1 year mortality after hospitalization
Cohort >3 million; Adults of all ages
C statistic 0.9
<1% difference between observed and expected mortality
HOMR model
HOMR model
HOMR score
Predicted risk
47 70%
46 63%
45 58%
44 53%
43 50%
42 46%
41 43%
40 37%
39 32%
Assessment of the performance of the HOMR model in an older Irish cohort
Methods
Adult inpatients ≥65 under care of geriatric medicine service
January 2013 –March 2015
Primary outcome: death within 1 year after discharge from hospital
Results
Characteristic Male 43%
Mean age 82
Emergency admission 94%
Independent 67%
Home care 21.3%
Nursing home 7.7%
1409 patients
1150 alive
259 Dead
(18.4%)
Baseline
...........................................................................
1 year
Results
C statistic 0.79
(95% CI 0.75 -0.82)
Results
Calibration:
Deaths: 259 (18.4%)
Results
Calibration:
Deaths: 259 (18.4%)
Predicted deaths 403 (28.8%)
Results
Calibration:
Deaths: 259 (18.4%)
Predicted deaths 403 (28.8%)
Odds ratio for death: Irish population = North American population
Calibration
0
10
20
30
40
50
60
70
80
90
100
25 27 29 31 33 35 37 39 41 43 45 47 49 51
Predicted Observed
% dead at one year
HOMR score
Calibration
0
10
20
30
40
50
60
70
80
90
100
25 27 29 31 33 35 37 39 41 43 45 47 49 51
Predicted Observed
Medium risk
% dead at one year
HOMR score
Low risk
High risk
Re-calibration
0
10
20
30
40
50
60
70
80
90
100
25 27 29 31 33 35 37 39 41 43 45 47 49 51
Predicted Observed
% dead at one year
HOMR score
Discussion
Discussion
Model Descrip-on C-‐Sta-s-c: Deriva-on Valida-on Independent valida-on
HELP, 2000 Pa$ents ≥80 years, emergency admissions
C= 0.73 (N=1266)
C=0.74 (N=150)
-‐
Walter et al, 2001 Pa$ents ≥70 years, discharged from general medicine service
C=0.75 (N=1495)
C=0.79 (N=1427)
C=0.72 (N=122; pa$ents ≥75; 5 year mortality predic$on )
BISEP, 2003 Pa$ents ≥70 years, admiJed under general medicine service
C=0.83 (N=525)
C=0.77 (N=1246)
C=0.73 (N=122; pa$ents ≥75; 5 year mortality predic$on )
Levine et al, 2007 Pa$ents ≥65 years discharged from general medicine service
C=0.67 (N=2739)
C=0.65 (N=3643) -‐
MPI, 2008
Pa$ents ≥65 years admiJed to geriatric unit
C=0.75 C=0.75 -‐
Silver Code, 2010 Pa$ents ≥75 admiJed through the emergency department
C=0.66 (N=5457)
C=0.64 (N=5456)
-‐
HOMR, 2014 Adult pa$ents admiJed under non-‐psychiatric hospital services
C=0.92 (N=319 531)
C=0.89 -‐0.92 (N= 2 862 996)
C=0.79 (N=1409; pa$ents ≥65 years discharged from geriatric service)
Discussion
Model Descrip-on C-‐Sta-s-c: Deriva-on Valida-on Independent valida-on
HELP, 2000 Pa$ents ≥80 years, emergency admissions
C= 0.73 (N=1266)
C=0.74 (N=150)
-‐
Walter et al, 2001 Pa$ents ≥70 years, discharged from general medicine service
C=0.75 (N=1495)
C=0.79 (N=1427)
C=0.72 (N=122; pa$ents ≥75; 5 year mortality predic$on )
BISEP, 2003 Pa$ents ≥70 years, admiJed under general medicine service
C=0.83 (N=525)
C=0.77 (N=1246)
C=0.73 (N=122; pa$ents ≥75; 5 year mortality predic$on )
Levine et al, 2007 Pa$ents ≥65 years discharged from general medicine service
C=0.67 (N=2739)
C=0.65 (N=3643) -‐
MPI, 2008
Pa$ents ≥65 years admiJed to geriatric unit
C=0.75 C=0.75 -‐
Silver Code, 2010 Pa$ents ≥75 admiJed through the emergency department
C=0.66 (N=5457)
C=0.64 (N=5456)
-‐
HOMR, 2014 Adult pa$ents admiJed under non-‐psychiatric hospital services
C=0.92 (N=319 531)
C=0.89 -‐0.92 (N= 2 862 996)
C=0.79 (N=1409; pa$ents ≥65 years discharged from geriatric service)
Discussion
Model C statistic
HOMR 0.79
CHA2DS2-VASc 0.68
HAS-BLED 0.69
Discussion
Can the model be used to predict 1-year mortality in older hospitalized patients?
Conclusion
Conclusion
Prognostic models are important
HOMR model is robust
Compares favourably to other prognostic models
Re-calibrated model needs to be tested
Thank you
