Elsevier

JACC: Heart Failure

Volume 10, Issue 9, September 2022, Pages 637-647
JACC: Heart Failure

Clinical Research
Electronic Health Record-Based Deep Learning Prediction of Death or Severe Decompensation in Heart Failure Patients

https://doi.org/10.1016/j.jchf.2022.05.010Get rights and content
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Abstract

Background

Surgical mechanical ventricular assistance and cardiac replacement therapies, although life-saving in many heart failure (HF) patients, remain high-risk. Despite this, the difficulty in timely identification of medical therapy nonresponders and the dire consequences of nonresponse have fueled early, less selective surgical referral. Patients who would have ultimately responded to medical therapy are therefore subjected to the risk and life disruption of surgical therapy.

Objectives

The purpose of this study was to develop deep learning models based upon commonly-available electronic health record (EHR) variables to assist clinicians in the timely and accurate identification of HF medical therapy nonresponders.

Methods

The study cohort consisted of all patients (age 18 to 90 years) admitted to a single tertiary care institution from January 2009 through December 2018, with International Classification of Disease HF diagnostic coding. Ensemble deep learning models employing time-series and densely-connected networks were developed from standard EHR data. The positive class included all observations resulting in severe progression (death from any cause or referral for HF surgical intervention) within 1 year.

Results

A total of 79,850 distinct admissions from 52,265 HF patients met observation criteria and contributed >350 million EHR datapoints for model training, validation, and testing. A total of 20% of model observations fit positive class criteria. The model C-statistic was 0.91.

Conclusions

The demonstrated accuracy of EHR-based deep learning model prediction of 1-year all-cause death or referral for HF surgical therapy supports clinical relevance. EHR-based deep learning models have considerable potential to assist HF clinicians in improving the application of advanced HF surgical therapy in medical therapy nonresponders.

Key Words

heart failure (HF)
machine learning
mechanical circulatory support
ventricular assist device (VAD)

Abbreviations and Acronyms

AUC
area under the curve
BNP
brain natriuretic peptide
BP
blood pressure
ECMO
extracorporeal membrane oxygenation
EF
ejection fraction
EHR
electronic health record
HF
heart failure
LOS
length of stay
LSTM
long short-term memory
PR
precision recall
VAD
ventricular assist device

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