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Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning
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 classic...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777280/ https://www.ncbi.nlm.nih.gov/pubmed/36552953 http://dx.doi.org/10.3390/diagnostics12122947 |
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author | Nakamura, Keijiro Zhou, Xue Sahara, Naohiko Toyoda, Yasutake Enomoto, Yoshinari Hara, Hidehiko Noro, Mahito Sugi, Kaoru Huang, Ming Moroi, Masao Nakamura, Masato Zhu, Xin |
author_facet | Nakamura, Keijiro Zhou, Xue Sahara, Naohiko Toyoda, Yasutake Enomoto, Yoshinari Hara, Hidehiko Noro, Mahito Sugi, Kaoru Huang, Ming Moroi, Masao Nakamura, Masato Zhu, Xin |
author_sort | Nakamura, Keijiro |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9777280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97772802022-12-23 Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning Nakamura, Keijiro Zhou, Xue Sahara, Naohiko Toyoda, Yasutake Enomoto, Yoshinari Hara, Hidehiko Noro, Mahito Sugi, Kaoru Huang, Ming Moroi, Masao Nakamura, Masato Zhu, Xin Diagnostics (Basel) Article 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. MDPI 2022-11-25 /pmc/articles/PMC9777280/ /pubmed/36552953 http://dx.doi.org/10.3390/diagnostics12122947 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nakamura, Keijiro Zhou, Xue Sahara, Naohiko Toyoda, Yasutake Enomoto, Yoshinari Hara, Hidehiko Noro, Mahito Sugi, Kaoru Huang, Ming Moroi, Masao Nakamura, Masato Zhu, Xin Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning |
title | Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning |
title_full | Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning |
title_fullStr | Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning |
title_full_unstemmed | Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning |
title_short | Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning |
title_sort | risk of mortality prediction involving time-varying covariates for patients with heart failure using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777280/ https://www.ncbi.nlm.nih.gov/pubmed/36552953 http://dx.doi.org/10.3390/diagnostics12122947 |
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