Evidence map›Paper›PMID 26771782›Full record

Observational studyCritical care medicine2016

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

Matthew M Churpek, Trevor C Yuen, Christopher Winslow, David O Meltzer, Michael W Kattan, Dana P Edelson

Abstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Critical care medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 316 papers, 19 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
316citing papers in PubMed, 19 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

316 citing papers in PubMed, 19 syntheses or guidelines pooled it.

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256 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Matthew M Churpek1Department of Medicine, University of Chicago, Chicago, IL. 2Department of Medicine, NorthShore University HealthSystem, Evanston, IL. 3Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH.
Trevor C Yuen
Christopher Winslow
David O Meltzer
Michael W Kattan
Dana P Edelson

Funding

TRANSLATIONAL RESEARCH UNIVERSITY OF CHICAGO: CLINICAL TRIALSUL1RR024999 · NCRR · UNIVERSITY OF CHICAGO · PI SOLWAY, JULIAN · 2007 to 2011
$25.3M
Re-Engineering Translational Research at the University of ChicagoUL1TR000430 · NCATS · UNIVERSITY OF CHICAGO · PI SOLWAY, JULIAN · 2012 to 2016
$20.2M
Research and Mentoring in Cost-effectiveness and Hospital-based Elder CareK24AG031326 · NIA · UNIVERSITY OF CHICAGO · PI MELTZER, DAVID O · 2008 to 2012
$906k
Predicting In-hospital Cardiac Arrest Using Electronic Health Record DataK08HL121080 · NHLBI · UNIVERSITY OF CHICAGO · PI CHURPEK, MATTHEW MICHAEL · 2014 to 2018
$751k
NCATS NIH HHS UL1 TR000430NCRR NIH HHS UL1 RR024999NHLBI NIH HHS K08 HL121080NIA NIH HHS K24 AG031326
6 · The paper itself

Abstract

objectiveMachine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database.

designObservational cohort study.

settingFive hospitals, from November 2008 until January 2013. PATIENTS: Hospitalized ward patients

interventionsNone MEASUREMENTS AND MAIN

resultsDemographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]).

conclusionsIn this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.

Indexed as

Models, StatisticalAge FactorsCohort StudiesDiagnostic Techniques and ProceduresHeart ArrestHumansIntensive Care UnitsLogistic ModelsMachine LearningNeural Networks, ComputerRisk AssessmentROC CurveSocioeconomic FactorsSupport Vector MachineSurvival AnalysisTime Factors

Identifiers

PMID26771782
PMCPMC4736499

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.