PowerPoint PresentationUnits – Are We There Yet?
Ashish Khanna, MD, FCCP, FCCM The Cleveland Clinic Lerner College of Medicine
October 4, 2018 12:00 pm – 1:00 pm PDT
Moderator
2
Claire Manneh Director of Programs CHPSO, a Division of the Hospital Quality Institute
Housekeeping Items • All lines will be muted. Raise your hand if you wish
to be unmuted.
• A copy of the slides and recording will be available within 1-3 business days
• 1 CE unit will be provided to CHPSO/CHA member hospitals who attend the entire session (12:00 pm - 1:00 pm) • Complete the survey by October 11, 2018 • CE certs will be emailed within 3-5 business days 3
How to ask a question
4
5
Ashish Khanna, MD, FCCP, FCCM Cleveland Clinic Lerner College of Medicine – Staff Intensivist with the Center for
Critical Care – Assistant Professor of Anesthesiology
Prediction of Cardiorespiratory Compromise on Hospital General Care Units – Are We There Yet?
Ashish K. Khanna MD, FCCP, FCCM Staff Intensivist, Surgical ICU & Center for Critical Care
Staff Anesthesiologist, Department of General Anesthesiology Vice-chief for Research, Center for Critical Care
Assistant Professor of Anesthesiology CCLCM, Anesthesiology Institute & Department of Outcomes Research
Cleveland Clinic Foundation, Cleveland, OH,USA
@KhannaAshishCCF
Disclosures
Understand the challenges of predicting cardiorespiratory compromise, and how to address them1
Learn potential benefits of continuous monitoring on the general care floor to improve outcomes2
Identify who to monitor as well as what and how to monitor3
Translate expanded knowledge of cardiorespiratory compromise into improvements in practice and patient monitoring protocols – integrate better in-patient monitoring on the general care floors
4
Cardiorespiratory complications are the most common cause of 30-day postoperative mortality [4]
Unplanned ICU admissions may have respiratory indication in 17-47% of cases particularly postoperative pulmonary complications [5-8]
AHRQ rated postoperative respiratory failure as the fourth most common patient safety event [4]
Hypotension and hypoxemia on the general care floor (GCF) is common, profound, prolonged and cannot be reliably predicted [1,2]
49% of cardiac arrest on GCF had a respiratory cause – higher than ICU [3]
AHRQ Quality Indicators - 2015
Indicator Description Numerator Denominator Observed Rate per 1000
PSI #2 Death Rate on Low-Mortality DRG’s 1,822 5,636,509 0.32
PSI #4
PSI #11
PSI # 13
Death Rate among Surgical Inpatients with Serious Treatable Conditions
A: DVT B: Pneumonia C: Sepsis D: Cardiac Arrest; shock E: GI Bleed
Postoperative Respiratory Failure
22,402
4,822
185,587
2,228,418
501,689
118.62
10.05
9.61
Anesth Analg 2015 Sep;121(3):709-15. [9]
Desaturation on Surgical Wards Minutes hypoxemia per hour
Blinded ward monitoring
≈850 non-cardiac surgical patients [9]
Hypoxemia episodes of varying duration
90% of episodes during which saturation was <90% for ≥ 1 hour were missed!
[9]
Hypoxemia is common!
SpO2 Duration % of total <90% >10min/hr. 21% <90% >20min/hr. 8% <85% >5min/hr. 8% <90% 1 hour 37%
Hypoxemia goes undetected!
Post-operative Hypoxemia Common Culprits…
• Sleep Disordered Breathing/Obstructive Sleep Apnea
• V/Q mismatch and Shunt
• Age • Habitus • Type and Duration
of Anesthesia
E.g. surgery type/duration, type and duration of anesthesia, residual narcotics
E.g. Obesity, comorbidities, advanced-age, sleep disordered breathing/
obstructive sleep apnea
Risk Factors for Cardiorespiratory Compromise
Patient Factors
Treatment Factors
PERFECT STORM
Saunders R, Ajizian S, Mestek M. Chapter 11: Respiratory Compromise in Moderate and Deep Sedation. Moderate and Deep Sedation in Clinical Practice, Second Edition, ed. Richard D. Urman and Alan D. Kaye. Published by Cambridge University Press. © Cambridge University Press 2017.
[10]
[2]
Incidence Of Patients With An Average Number Of Minutes Per Hour Of Hypoxemia > X During Monitoring
A. SpO2<95% B. SpO2<90% C. SpO2<85%
[2]
[2]
[11]
• At 95% threshold, at least 10 min/hour of hypoxemia in 79% of patients • At 90% threshold, at least 10 min/hour of hypoxemia in 19% of patients • At 85% threshold, at least 10 min/hour of hypoxemia in 1% of patients
[11]
If We Cannot Predict It, Can We Monitor It?
•Better monitoring? •Who to monitor? •What to monitor? •How to monitor?
Anesthesiology 2015;122:659-65
• 9% STOP-BANG
response
42% - 2hr of last check
[12]
Association of Opioids and Sedatives with Increased Risk of In-Hospital Cardiopulmonary Arrest.
Overdyk FJ, Dowling O, Marino J, et. al. PLOS ONE 11.2 (2016): e0150214.
Opioids/Sedative Use 2007-1012
Odds Ratio* 95% CI
Opioid only 28.0% 31.4% 1.81 (1.77, 1.85)
Sedative only 13.8% 14.3% 1.82 (1.78, 1.87)
Neither Opioid Nor Sedative 17.2% 32.6% Ref.
“Low Acuity” Patients Cardiac Arrest
General Care Floor 21,564
Manually recorded data were, on an average 6.5% higher than those recorded via automated systems!
Anesth Analg 2014;118;326-31
If we cannot predict it, can we monitor it ??
• Better monitoring? YES • Who to monitor? EVERYONE • What to monitor? • How to monitor? CONTINUOUS (AUTOMATED)
[13]
Pulse Oximetry… • 22,992 participants • No decrease in-hospital mortality • No decrease in-hospital stay • No decrease in transfer to ICU • No decrease in overall mortality
• Potential increased vigilance and decreased pulmonary complications
Can Continuous Monitoring Help?
ICU transfers
Future Directions??
WHAT TO MONITOR? CARDIORESPIRATORY
compromise is common, goes undetected and is largely
unpredictable
HOW TO MONITOR? CONTINUOUS (AUTOMATED)
Continuous monitoring of all patients on the regular floor appears to be the answer to detecting cardiorespiratory
compromise early
all patients receiving opioid therapy in the postoperative
period, especially when supplementary oxygen is
administered
Continuous monitoring of capnography and oximetry can provide clinicians with
early identification of evolving Respiratory
Compromise, allowing for early intervention that may
prevent tragic consequences
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Figure 5a.
Fig 5. Monitor Trend data: a. Opioid induced respiratory depression b. Aspiration peumonia
Figure 5a.
[16]
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SaO2 %
Figure 5a.
Fig 5. Monitor Trend data: a. Opioid induced respiratory depression b. Aspiration peumonia
Figure 5a.
PRODIGY • Increasing concerns over unmonitored mortality and morbidity in patients during opioid therapy for
acute pain (postop/medical)1,2
• Need a simple tool to assess risk of developing respiratory compromise • Study Purpose - Derive and validate an RC risk assessment tool to guide use of Microstream™
monitoring technology (capnography/pulse ox) • Methodology:
• Consenting, adult patients receiving parenteral opioid therapy (postop or medical) pain on the hospital ward.
• Monitored for 24-48 hours using blinded, non-alarming Microstream™ technology • Endpoints – Monitor-confirmed RC or Opioid-Related ADE
• 1,650 patients at 16 sites in US (9), EU (4), and Asia (3) • Assessing 12 high risk variables for prediction rule
• Collecting data from EMR and Monitors
1Cashman, J. N. (2004). British Journal of Anaesthesia, 93(2), 212–223.
2Herzig SJ et al. (2014). Journal of Hospital Medicine, 9(2), 73–81.
High Risk Predictor Variables
• High Risk Variables for Prediction Rule • Age > 65 y • Known or suspected sleep-disordered breathing (OSA, snoring, etc.) • High risk surgery during first 24 hours post-operatively • PCA or epidural therapy • Obese (BMI >30) • Multiple opioid or concurrent CNS/sedating medication • High opioid dosage (>30mg oral morphine per day or equivalent) • Major organ failure • Diabetes • Chronic heart failure or other significant cardiac disease • Smoke (> 20 packs per year) • COPD or other significant pulmonary disease (including respiratory events
before ward admittance)
PRODIGY: Primary Objective
• Derive and validate a risk assessment tool to identify subjects at risk of having respiratory depression* (RD) while undergoing opioid therapy on the hospital ward.
• Respiratory Depression Episode • etCO2 ≤ 15 or ≥ 60 mmHg for ≥ 3
minutes, or
Primary Objective Endpoint
*Respiratory depression is a clinical diagnosis made after reviewing monitoring data in conjunction with the clinical data and consistent with accepted pathophysiologic mechanisms.
PRODIGY: Secondary Objectives
1. To compare subjects that will develop versus not develop RD.
• RD risk versus no-risk subjects compared in terms of:
• Incidence of respiratory AE’s. • Healthcare resource utilization • Subject mortality at 30 days.
2. To characterize the predictive values of etCO2, SpO2, RR and evaluate the value of the Integrated Pulmonary Index (IPI) in predicting RD and ORADE.
• The predictive value (sensitivity, specificity, etc.) of etCO2, SpO2, RR, PR and IPI value’s variations will be correlated with the occurrence of RD and ORADE.
Secondary Objectives Endpoint
3. To measure healthcare utilization costs during the study period.
• Cost associated with events and actions taken will be estimated retrospectively using standard cost data from different countries.
1-month follow up
• 1-Month Follow Up • Visit at 1-Month post enrollment (30 days ±10 days) • May be performed by phone (if subject has been discharged)
• 1 Month Follow Up eCRF data collection: • Adverse Events • Healthcare resource utilization (including hospital length of stay,
readmission rate and related primary diagnosis)
Adverse events • Adverse Events (AE) collected:
• All AEs with an underlying respiratory cause • All adverse device effects (ADE) • All Serious Adverse Events (SAE) (including sepsis events or related to opioid
therapy)
• Diagnosis and description (including confirmation of respiratory nature) • Actions taken / treatment (including vital signs, and date and time of rescue
related actions when applicable) • Assessment of seriousness
• Relatedness to the event (including opioid therapy or device) • Outcome or resolution and date of the resolution
Clinical event committee
• CEC Composition • Independent CEC consisting of a minimum of three (3) non-Medtronic, non-study
investigator clinicians • Experienced in evaluating respiratory compromise, respiratory depression, and
interpreting continuous monitor data.
• CEC Members • Dr. Luca Brazzi • Dr. Albert Dahan • Dr. Leif Saager • Dr. Toby Weingarten
• Responsibilities • Review AE’s to classify the event as respiratory or non-respiratory. • Review AE’s to determine the relatedness (including device or opioid therapy). • Review RD episodes and classify based on the RD / primary endpoint definition
Prediction Of Cardiorespiratory Compromise On
Hospital General Care Units – Are We There Yet?
• Cardiorespiratory decompensation on the GCF remains common and hard to predict
• Continuous, smarter monitoring and early proactive intervention may be preventative
• Who and what to monitor remains a challenge • Trials such as PRODIGY will aim to answer this
critical question
References [1]Sun Z, Sessler DI, Dalton JE, Devereaux P, Shahinyan A, Naylor A, et al. Postoperative hypoxemia is common and persistent: a prospective blinded observational study. Anesth Analg 2015;121(3):709–15.
[2]Khanna AK, Sessler DI, Sun Z, Naylor AJ, You J, Hesler BD, et al. Using the stop-bang questionnaire to predict hypoxaemia in patients recovering from noncardiac surgery: a prospective cohort analysis. Br J Anaesth 2016;116(5):632–40.
[3]Perman SM, Stanton E, Soar J, Berg RA, Donnino MW, Mikkelsen ME, et al. Location of in-hospital cardiac arrest in the United States - variability in event rate and outcomes. J Am Heart Assoc 2016;5(10) pii: e003638.
[4]2015 National Healthcare Quality and Disparities Report and 5th Anniversary Update on the National Quality Strategy Content Last Reviewed May 2016. Rockville, MD: Agency for Healthcare Research and Quality; April 2016.
[5] Piercy M, Lau S, Loh E, Reid D, Santamaria J, Mackay P. Unplanned admission to the intensive care unit in postoperative patients–an indicator of quality of anaesthetic care? Anaesth Intensive Care 2006;34(5):592–8.
[6] Quinn TD, Gabriel RA, Dutton RP, Urman RD. Analysis of unplanned postoperative admissions to the intensive care unit. J Intensive Care Med 2017;32(7):436–43.
[7] Bhat SASV, Chaudhari LS. Audit of intensive care unit admissions from the operating room. Indian J Anaesth 2006;50(3):193–200.
[8] Sabate S,Mazo V, Canet J. Predicting postoperative pulmonary complications: implications for outcomes and costs. Curr Opin Anaesthesiol 2014;27(2):201–9.
[9] Zhuo Sun, MD, Daniel I. Sessler, MD, Jarrod E. Dalton, PhD, et al. Postoperative hypoxemia is common and persistent: a prospective blinded observational study. Anesth Analy 2015 Sep;121(3):709-715.
[10]Saunders R, Ajizian S, Mestek M. Chapter 11: Respiratory Compromise in Moderate and Deep Sedation. Moderate and Deep Sedation in Clinical Practice, Second Edition, ed. Richard D. Urman and Alan D. Kaye. Published by Cambridge University Press. © Cambridge University Press 2017.
[11]Allan W. Belcher, DO, Ashish K. Khanna, MD, Steve Leung, MD, et al. Long-acting patient-controlled opioids are not associated with more postoperative hypoxemia than short-acting patient-controlled opioids after noncardiac surgery: a cohort analysis. Anesth Analg. 2016 Dec;123(6):1471-1479.
[12] Lee LA, Caplan RA, Stephens LS, Posner KL, Terman GW, Voepel-Lewis T, et al. Postoperative opioid-induced respiratory depression: a closed claims analysis. Anesthesiology 2015;122(3):659–65.
[13]Andreas H. Taenzer, MS, MD, Joshua Pyke, BE, PhD, Michael D. Herrick, MD, Thomas M. Dodds, MD, Susan P. McGrath, PhD. A comparison of oxygen saturation data in patients with low oxygen saturation using automated continuous monitoring and intermittent manual data charting. Anesth Analg 2014;118:326-331.
[14]Lam, T., Nagappa, M., Wong, J., Singh, M., Wong, D., & Chung, F. (2017). Continuous Pulse Oximetry and Capnography Monitoring for Postoperative Respiratory Depression and Adverse Events: A Systematic Review and Meta-analysis. Anesthesia & Analgesia, 125(6), 2019-2029.
[15]Stoelting RK, and Overdyk FJ. Conclusions and Recommendations from June 08, 2011 Conference on Electronic Monitoring Strategies Essential Monitoring Strategies to Detect Clinically Significant Drug- Induced Respiratory Depression in the Postoperative Period. Anesthesia Patient Safety Foundation Initiatives.
[16]Lynn LA, Curry JP. Patterns of unexpected in-hospital deaths: a root cause analysis. Patient Saf Surg. 2011, 5:3.
Questions?
64
Featured Faculty
Allison Massari – Patient experience and compassionate care advocate
Jim Bagian and Yvonne Cagle – former NASA astronauts
Arjun Srinivasan, MD – Associate Director for Healthcare- Associated Infection Prevention Programs at the CDC
Elliott Main, MD – CMQCC, Leading the QI Maternity Workshop with Kim Werkmeister, HQI
More details: www.hqinstitute.org
or other resources at info@chpso.org – We will de-identify your hospital and provider
names • CE Information • Slides and Recording
67
Prediction of Cardiorespiratory Compromise on Hospital General Care Units – Are We There Yet?Ashish Khanna, MD, FCCP, FCCMThe Cleveland Clinic Lerner College of MedicineOctober 4, 201812:00 pm – 1:00 pm PDT
Moderator
Today’s Speaker
Prediction of Cardiorespiratory Compromise on Hospital General Care Units – Are We There Yet?
Disclosures
Objectives
AHRQ Quality Indicators - 2015
Post-operative Hypoxemia Common Culprits…
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The RNF – Real time monitoring!
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Follow-up Email