Journal
DIAGNOSTICS
Volume 12, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/diagnostics12122947
Keywords
deep learning; heart failure; mortality; risk prediction; time-varying covariates
Categories
Funding
- Japan Society for the Promotion of Science Kakenhi Basic Research Fund [C 21K10287]
- Competitive Research Fund of The University of Aizu [2022-P-12]
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This study developed and validated a deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. The proposed model showed better prediction performance in terms of discrimination, calibration, and risk stratification compared to other deep learning and traditional statistical models, especially in identifying high-risk patients.
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.
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