Format

Send to

Choose Destination
JACC Cardiovasc Imaging. 2019 Apr;12(4):681-689. doi: 10.1016/j.jcmg.2018.04.026. Epub 2018 Jun 13.

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.

Author information

1
Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania.
2
Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky.
3
Department of Cardiology, Geisinger, Danville, Pennsylvania.
4
Department of Epidemiology and Health Services Research, Geisinger, Danville, Pennsylvania.
5
Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania; Department of Biomedical Engineering, University of Kentucky, Lexington, Kentucky; Department of Radiology, Geisinger, Danville, Pennsylvania. Electronic address: bkf@gatech.edu.

Abstract

OBJECTIVES:

The goal of this study was to use machine learning to more accurately predict survival after echocardiography.

BACKGROUND:

Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, there may be significant predictive information within additional echocardiography-derived measurements combined with clinical electronic health record data.

METHODS:

Mortality was studied in 171,510 unselected patients who underwent 331,317 echocardiograms in a large regional health system. The authors investigated the predictive performance of nonlinear machine learning models compared with that of linear logistic regression models using 3 different inputs: 1) clinical variables, including 90 cardiovascular-relevant International Classification of Diseases, Tenth Revision, codes, and age, sex, height, weight, heart rate, blood pressures, low-density lipoprotein, high-density lipoprotein, and smoking; 2) clinical variables plus physician-reported EF; and 3) clinical variables and EF, plus 57 additional echocardiographic measurements. Missing data were imputed with a multivariate imputation by using a chained equations algorithm (MICE). The authors compared models versus each other and baseline clinical scoring systems by using a mean area under the curve (AUC) over 10 cross-validation folds and across 10 survival durations (6 to 60 months).

RESULTS:

Machine learning models achieved significantly higher prediction accuracy (all AUC >0.82) over common clinical risk scores (AUC = 0.61 to 0.79), with the nonlinear random forest models outperforming logistic regression (p < 0.01). The random forest model including all echocardiographic measurements yielded the highest prediction accuracy (p < 0.01 across all models and survival durations). Only 10 variables were needed to achieve 96% of the maximum prediction accuracy, with 6 of these variables being derived from echocardiography. Tricuspid regurgitation velocity was more predictive of survival than LVEF. In a subset of studies with complete data for the top 10 variables, multivariate imputation by chained equations yielded slightly reduced predictive accuracies (difference in AUC of 0.003) compared with the original data.

CONCLUSIONS:

Machine learning can fully utilize large combinations of disparate input variables to predict survival after echocardiography with superior accuracy.

KEYWORDS:

echocardiography; electronic health records; machine learning; mortality

PMID:
29909114
PMCID:
PMC6286869
[Available on 2020-04-01]
DOI:
10.1016/j.jcmg.2018.04.026

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center