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Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis
BACKGROUND: Heart failure (HF) in patients undergoing maintenance hemodialysis (MHD) increases their hospitalization rates, mortality, and economic burden significantly. We aimed to develop and validate a predictive model utilizing contemporary deep phenotyping for individual risk assessment of all-...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515690/ https://www.ncbi.nlm.nih.gov/pubmed/37732398 http://dx.doi.org/10.1080/0886022X.2023.2255686 |
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author | Tang, Wenwu Zhang, Ying Wang, Zhixin Yuan, Xinzhu Chen, Xiaoxia Yang, Xiaohua Qi, Zhirui Zhang, Ju Li, Jie Xie, Xisheng |
author_facet | Tang, Wenwu Zhang, Ying Wang, Zhixin Yuan, Xinzhu Chen, Xiaoxia Yang, Xiaohua Qi, Zhirui Zhang, Ju Li, Jie Xie, Xisheng |
author_sort | Tang, Wenwu |
collection | PubMed |
description | BACKGROUND: Heart failure (HF) in patients undergoing maintenance hemodialysis (MHD) increases their hospitalization rates, mortality, and economic burden significantly. We aimed to develop and validate a predictive model utilizing contemporary deep phenotyping for individual risk assessment of all-cause mortality or HF hospitalization in patients on MHD. MATERIALS AND METHODS: A retrospective review was conducted from January 2017 to October 2022, including 348 patients receiving MHD from four centers. The variables were adjusted by Cox regression analysis, and the clinical prediction model was constructed and verified. RESULTS: The median follow-up durations were 14 months (interquartile range [IQR] 9–21) for the modeling set and 14 months (9–20) for the validation set. The composite outcome occurred in 72 (29.63%) of 243 patients in the modeling set and 39 (37.14%) of 105 patients in the validation set. The model predictors included age, albumin, history of cerebral hemorrhage, use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers/“sacubitril/valsartan”, left ventricular ejection fraction, urea reduction ratio, N-terminal prohormone of brain natriuretic peptide, and right atrial size. The C-index was 0.834 (95% CI 0.784–0.883) for the modeling set and 0.853 (0.798, 0.908) for the validation set. The model exhibited excellent calibration across the complete risk profile, and the decision curve analysis (DCA) suggested its ability to maximize patient benefits. CONCLUSION: The developed prediction model offered an accurate and personalized assessment of HF hospitalization risk and all-cause mortality in patients with MHD. It can be employed to identify high-risk patients and guide treatment and follow-up. |
format | Online Article Text |
id | pubmed-10515690 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-105156902023-09-23 Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis Tang, Wenwu Zhang, Ying Wang, Zhixin Yuan, Xinzhu Chen, Xiaoxia Yang, Xiaohua Qi, Zhirui Zhang, Ju Li, Jie Xie, Xisheng Ren Fail Research Article BACKGROUND: Heart failure (HF) in patients undergoing maintenance hemodialysis (MHD) increases their hospitalization rates, mortality, and economic burden significantly. We aimed to develop and validate a predictive model utilizing contemporary deep phenotyping for individual risk assessment of all-cause mortality or HF hospitalization in patients on MHD. MATERIALS AND METHODS: A retrospective review was conducted from January 2017 to October 2022, including 348 patients receiving MHD from four centers. The variables were adjusted by Cox regression analysis, and the clinical prediction model was constructed and verified. RESULTS: The median follow-up durations were 14 months (interquartile range [IQR] 9–21) for the modeling set and 14 months (9–20) for the validation set. The composite outcome occurred in 72 (29.63%) of 243 patients in the modeling set and 39 (37.14%) of 105 patients in the validation set. The model predictors included age, albumin, history of cerebral hemorrhage, use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers/“sacubitril/valsartan”, left ventricular ejection fraction, urea reduction ratio, N-terminal prohormone of brain natriuretic peptide, and right atrial size. The C-index was 0.834 (95% CI 0.784–0.883) for the modeling set and 0.853 (0.798, 0.908) for the validation set. The model exhibited excellent calibration across the complete risk profile, and the decision curve analysis (DCA) suggested its ability to maximize patient benefits. CONCLUSION: The developed prediction model offered an accurate and personalized assessment of HF hospitalization risk and all-cause mortality in patients with MHD. It can be employed to identify high-risk patients and guide treatment and follow-up. Taylor & Francis 2023-09-21 /pmc/articles/PMC10515690/ /pubmed/37732398 http://dx.doi.org/10.1080/0886022X.2023.2255686 Text en © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
spellingShingle | Research Article Tang, Wenwu Zhang, Ying Wang, Zhixin Yuan, Xinzhu Chen, Xiaoxia Yang, Xiaohua Qi, Zhirui Zhang, Ju Li, Jie Xie, Xisheng Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis |
title | Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis |
title_full | Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis |
title_fullStr | Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis |
title_full_unstemmed | Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis |
title_short | Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis |
title_sort | development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515690/ https://www.ncbi.nlm.nih.gov/pubmed/37732398 http://dx.doi.org/10.1080/0886022X.2023.2255686 |
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