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Artificial intelligence algorithm for predicting mortality of patients with acute heart failure
AIMS: This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). METHODS AND RESULTS: 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patie...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613702/ https://www.ncbi.nlm.nih.gov/pubmed/31283783 http://dx.doi.org/10.1371/journal.pone.0219302 |
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author | Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Sang Eun Lee, Hae-Young Cho, Hyun-Jai Choi, Jin Oh Jeon, Eun-Seok Kim, Min-Seok Kim, Jae-Joong Hwang, Kyung-Kuk Chae, Shung Chull Baek, Sang Hong Kang, Seok-Min Choi, Dong-Ju Yoo, Byung-Su Kim, Kye Hun Park, Hyun-Young Cho, Myeong-Chan Oh, Byung-Hee |
author_facet | Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Sang Eun Lee, Hae-Young Cho, Hyun-Jai Choi, Jin Oh Jeon, Eun-Seok Kim, Min-Seok Kim, Jae-Joong Hwang, Kyung-Kuk Chae, Shung Chull Baek, Sang Hong Kang, Seok-Min Choi, Dong-Ju Yoo, Byung-Su Kim, Kye Hun Park, Hyun-Young Cho, Myeong-Chan Oh, Byung-Hee |
author_sort | Kwon, Joon-myoung |
collection | PubMed |
description | AIMS: This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). METHODS AND RESULTS: 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines–Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876–0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720–0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group(p<0.001). CONCLUSION: DAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models. |
format | Online Article Text |
id | pubmed-6613702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-66137022019-07-23 Artificial intelligence algorithm for predicting mortality of patients with acute heart failure Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Sang Eun Lee, Hae-Young Cho, Hyun-Jai Choi, Jin Oh Jeon, Eun-Seok Kim, Min-Seok Kim, Jae-Joong Hwang, Kyung-Kuk Chae, Shung Chull Baek, Sang Hong Kang, Seok-Min Choi, Dong-Ju Yoo, Byung-Su Kim, Kye Hun Park, Hyun-Young Cho, Myeong-Chan Oh, Byung-Hee PLoS One Research Article AIMS: This study aimed to develop and validate deep-learning-based artificial intelligence algorithm for predicting mortality of AHF (DAHF). METHODS AND RESULTS: 12,654 dataset from 2165 patients with AHF in two hospitals were used as train data for DAHF development, and 4759 dataset from 4759 patients with AHF in 10 hospitals enrolled to the Korean AHF registry were used as performance test data. The endpoints were in-hospital, 12-month, and 36-month mortality. We compared the DAHF performance with the Get with the Guidelines–Heart Failure (GWTG-HF) score, Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score, and other machine-learning models by using the test data. Area under the receiver operating characteristic curve of the DAHF were 0.880 (95% confidence interval, 0.876–0.884) for predicting in-hospital mortality; these results significantly outperformed those of the GWTG-HF (0.728 [0.720–0.737]) and other machine-learning models. For predicting 12- and 36-month endpoints, DAHF (0.782 and 0.813) significantly outperformed MAGGIC score (0.718 and 0.729). During the 36-month follow-up, the high-risk group, defined by the DAHF, had a significantly higher mortality rate than the low-risk group(p<0.001). CONCLUSION: DAHF predicted the in-hospital and long-term mortality of patients with AHF more accurately than the existing risk scores and other machine-learning models. Public Library of Science 2019-07-08 /pmc/articles/PMC6613702/ /pubmed/31283783 http://dx.doi.org/10.1371/journal.pone.0219302 Text en © 2019 Kwon et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kwon, Joon-myoung Kim, Kyung-Hee Jeon, Ki-Hyun Lee, Sang Eun Lee, Hae-Young Cho, Hyun-Jai Choi, Jin Oh Jeon, Eun-Seok Kim, Min-Seok Kim, Jae-Joong Hwang, Kyung-Kuk Chae, Shung Chull Baek, Sang Hong Kang, Seok-Min Choi, Dong-Ju Yoo, Byung-Su Kim, Kye Hun Park, Hyun-Young Cho, Myeong-Chan Oh, Byung-Hee Artificial intelligence algorithm for predicting mortality of patients with acute heart failure |
title | Artificial intelligence algorithm for predicting mortality of patients with acute heart failure |
title_full | Artificial intelligence algorithm for predicting mortality of patients with acute heart failure |
title_fullStr | Artificial intelligence algorithm for predicting mortality of patients with acute heart failure |
title_full_unstemmed | Artificial intelligence algorithm for predicting mortality of patients with acute heart failure |
title_short | Artificial intelligence algorithm for predicting mortality of patients with acute heart failure |
title_sort | artificial intelligence algorithm for predicting mortality of patients with acute heart failure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6613702/ https://www.ncbi.nlm.nih.gov/pubmed/31283783 http://dx.doi.org/10.1371/journal.pone.0219302 |
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