in hospital predictive models - take heart...
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In Hospital Predictive Models
Dana P. Edelson, MD MS, FAHA, FHMExecutive Medical Director for Inpatient Quality and Safety
Take Heart America– October 20, 2016
Disclosures
Employment:
• The University of ChicagoResearch support:
• National Heart Lung Blood Institute of NIH
• Philips Healthcare
Ownership interests:
• Founder & CEO, QuantHC
• Patent pending, ARCD.P0535US.P2
Other:
• Immediate Past Chair, Systems of Care Subcommittee,
American Heart Association
• Co-Chair, GWTG-R Adult Research Task Force
• Member, CDC Ward Sepsis Working Group
In-hospital vs out-of hospital cardiac arrest
Predicting IHCA | 3
Out-of-hospital cardiac arrest
VF
In-hospital cardiac arrest
Asystole
PEA
Etiology of in-hospital cardiac arrest
Predicting IHCA | 4
Respiratory failure
Hypotension
Cardiac ischemia
Arrhythmia Metabolic
Other/Unknown
Sandroni, Resus, 2004
Etiology of in-hospital cardiac arrest
Predicting IHCA | 5
Respiratory failure
Hypotension
Cardiac ischemia
Arrhythmia Metabolic
Other/Unknown
Sandroni, Resus, 2004
Delays in recognition are costly
• 1 hr delay in ICU transfer
3% increased odds of
hospital death
• Transfer within 6 hrs saves 2
days in the ICU
8
Sabermetrics: rethinking baseball statistics
11
Wins above replacement (WAR)
Additional wins a player’s team has achieved over expected without
that player
What’s our WAR score equivalent?
Modified Early Warning Score (MEWS)
14
Score 3 2 1 0 1 2 3
Respiratory rate (RPM) — ≤ 8 — 9-14 15-20 21-29 ≥ 30
Heart rate (BPM) — ≤ 40 41-50 51-100 101-110 111-129 ≥ 130
Systolic BP ≤ 70 71-80 81-100 101-199 ≥ 200
Temperature (°C) — ≤35 — 35.0-38.4 — >38.5 —
AVPU — — — AlertReact to Voice
React to Pain
Unresp
Subbe, QJM, 2001
Cardiac arrest AUC: 0.76
eCART 2TM – cubic spline logistic regression
n=269,999 admission from five hospitals
Churpek, AJRCCM 2014
The strongest predictors of deterioration
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40
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48
51
63
66
77
100
0 20 40 60 80 100
Glucose
White blood cell count
Blood urea nitrogen
Temperature
Pulse pressure index
Diastolic blood pressure
Systolic blood pressure
Age
Heart rate
Respiratory rate
Age
20
40
60
80
100
Respiratory rate 20
40
60
Ris
k o
f Event
1.4
1.6
1.8
Predictors interact with one another
0% 20% 40% 60% 80% 100%
Percent of Events Captured
RRT Called
ICU
Transfer
[n=383]
eCART
Cardiac
Arrest
[n=10] High risk eCART threshold met 31 hours prior to RRT
(High/Mod Risk)
Silent phase eCART implementation in three med-surg units
Kang, CCM, 2016
Predicting IHCA | 38
Don’t ditch your early warning scores for
SIRS or qSOFA!
Am J Respir Crit Care Med. 2016 Sep 20. [Epub ahead of print]
From: A Prospective Study of Nighttime Vital Sign Monitoring Frequency and Risk of Clinical Deterioration
JAMA Intern Med. 2013;():-. doi:10.1001/jamainternmed.2013.7791
Nighttime vital sign monitoring
Predicting IHCA | 40
Risk Stratified Clinical Decision Support Matrix
High
• Automatic RRT
• Continuous monitoring
Moderate
• Bedside Rounding between RN/MD
• Proactive rounding by RRT
Normal• Standard care
Low• No night-time vital signs
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Summary:
Cardiac arrest in the hospital is often
predictable and preventable
Real-time, data-driven decision support
to is possible today
Stop driving your Lamborghini in traffic!