Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset
Affiliation
Univ Arizona, Coll EngnUniv Arizona, Dept Syst & Ind Engn, Dept Biomed Engn
Issue Date
2020-08Keywords
critical careheart failure
intensive care units
machine learning
time series
heart
cardiology
prediction
chronic disease
ICU
intensive care unit
Metadata
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JMIR PUBLICATIONS, INCCitation
Essay, P., Balkan, B., & Subbian, V. (2020). Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset. JMIR Medical Informatics, 8(8), e19892.Journal
JMIR MEDICAL INFORMATICSRights
© Patrick Essay, Baran Balkan, Vignesh Subbian. Originally published in JMIR Medical Informatics (http://medinform.jmir.org),07.08.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Background: Heart failure is a leading cause of mortality and morbidity worldwide. Acute heart failure, broadly defined as rapid onset of new or worsening signs and symptoms of heart failure, often requires hospitalization and admission to the intensive care unit (ICU). This acute condition is highly heterogeneous and less well-understood as compared to chronic heart failure. The ICU, through detailed and continuously monitored patient data, provides an opportunity to retrospectively analyze decompensation and heart failure to evaluate physiological states and patient outcomes. Objective: The goal of this study is to examine the prevalence of cardiovascular risk factors among those admitted to ICUs and to evaluate combinations of clinical features that are predictive of decompensation events, such as the onset of acute heart failure, using machine learning techniques. To accomplish this objective, we leveraged tele-ICU data from over 200 hospitals across the United States. Methods: We evaluated the feasibility of predicting decompensation soon after ICU admission for 26,534 patients admitted without a history of heart failure with specific heart failure risk factors (ie, coronary artery disease, hypertension, and myocardial infarction) and 96,350 patients admitted without risk factors using remotely monitored laboratory, vital signs, and discrete physiological measurements. Multivariate logistic regression and random forest models were applied to predict decompensation and highlight important features from combinations of model inputs from dissimilar data. Results: The most prevalent risk factor in our data set was hypertension, although most patients diagnosed with heart failure were admitted to the ICU without a risk factor. The highest heart failure prediction accuracy was 0.951, and the highest area under the receiver operating characteristic curve was 0.9503 with random forest and combined vital signs, laboratory values, and discrete physiological measurements. Random forest feature importance also highlighted combinations of several discrete physiological features and laboratory measures as most indicative of decompensation. Timeline analysis of aggregate vital signs revealed a point of diminishing returns where additional vital signs data did not continue to improve results. Conclusions: Heart failure risk factors are common in tele-ICU data, although most patients that are diagnosed with heart failure later in an ICU stay presented without risk factors making a prediction of decompensation critical. Decompensation was predicted with reasonable accuracy using tele-ICU data, and optimal data extraction for time series vital signs data was identified near a 200-minute window size. Overall, results suggest combinations of laboratory measurements and vital signs are viable for early and continuous prediction of patient decompensation.Note
Open access journalISSN
2291-9694EISSN
2291-9694PubMed ID
32663162DOI
10.2196/19892Version
Final published versionae974a485f413a2113503eed53cd6c53
10.2196/19892
Scopus Count
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Except where otherwise noted, this item's license is described as © Patrick Essay, Baran Balkan, Vignesh Subbian. Originally published in JMIR Medical Informatics (http://medinform.jmir.org),07.08.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
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