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Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning

Authors :
Keijiro Nakamura
Xue Zhou
Naohiko Sahara
Yasutake Toyoda
Yoshinari Enomoto
Hidehiko Hara
Mahito Noro
Kaoru Sugi
Ming Huang
Masao Moroi
Masato Nakamura
Xin Zhu
Source :
Diagnostics, Vol 12, Iss 12, p 2947 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We also compared the performance of the proposed model with those of classical deep learning- and traditional statistical-based models. The present study enrolled 730 patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2020. A recurrent neural network-based model (RNNSurv) involving time-varying covariates was developed and validated. The proposed RNNSurv showed better prediction performance than those of a deep feed-forward neural network-based model (referred as “DeepSurv”) and a multivariate Cox proportional hazard model in view of discrimination (C-index: 0.839 vs. 0.755 vs. 0.762, respectively), calibration (better fit with a 45-degree line), and ability of risk stratification, especially identifying patients with high risk of mortality. The proposed RNNSurv demonstrated an improved prediction performance in consideration of temporal information from time-varying covariates that could assist clinical decision-making. Additionally, this study found that significant risk and protective factors of mortality were specific to risk levels, highlighting the demand for an individual-specific clinical strategy instead of a uniform one for all patients.

Details

Language :
English
ISSN :
12122947 and 20754418
Volume :
12
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.1181fc7a716a41e5bb6bddedf8e872ed
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics12122947