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Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities

AIMS: Chronic heart failure (CHF) has an increasing burden of comorbidities, which affect clinical outcomes. Few studies have focused on the clustering and hierarchical management of patients with CHF based on comorbidity. This study aimed to explore the cluster model of CHF patients based on comorb...

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Autores principales: Zheng, Chu, Han, Linai, Tian, Jing, Li, Jing, He, Hangzhi, Han, Gangfei, Wang, Ke, Yang, Hong, Yan, Jingjing, Meng, Bingxia, Han, Qinghua, Zhang, Yanbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8788137/
https://www.ncbi.nlm.nih.gov/pubmed/34779142
http://dx.doi.org/10.1002/ehf2.13708
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author Zheng, Chu
Han, Linai
Tian, Jing
Li, Jing
He, Hangzhi
Han, Gangfei
Wang, Ke
Yang, Hong
Yan, Jingjing
Meng, Bingxia
Han, Qinghua
Zhang, Yanbo
author_facet Zheng, Chu
Han, Linai
Tian, Jing
Li, Jing
He, Hangzhi
Han, Gangfei
Wang, Ke
Yang, Hong
Yan, Jingjing
Meng, Bingxia
Han, Qinghua
Zhang, Yanbo
author_sort Zheng, Chu
collection PubMed
description AIMS: Chronic heart failure (CHF) has an increasing burden of comorbidities, which affect clinical outcomes. Few studies have focused on the clustering and hierarchical management of patients with CHF based on comorbidity. This study aimed to explore the cluster model of CHF patients based on comorbidities and to verify their relationship with clinical outcomes. METHODS AND RESULTS: Electronic health records of patients hospitalized with CHF from January 2014 to April 2019 were collected, and 12 common comorbidities were included in the latent class analysis. The Fruchterman–Reingold layout was used to draw the comorbidity network, and analysis of variance was used to compare the weighted degrees among them. The incidence of clinical outcomes among different clusters was presented on Kaplan–Meier curves and compared using the log‐rank test, and the hazard ratio was calculated using the Cox proportional risk model. Sensitivity analysis was performed according to the left ventricular ejection fraction. Four different clinical clusters from 4063 total patients were identified: metabolic, ischaemic, high comorbidity burden, and elderly‐atrial fibrillation. Compared with the metabolic cluster, patients in the high comorbidity burden cluster had the highest adjusted risk of combined outcome and all‐cause mortality {1.67 [95% confidence interval (CI), 1.40–1.99] and 2.87 [95% CI, 2.17–3.81], respectively}, followed by the elderly‐atrial fibrillation and ischaemic clusters. The adjusted readmission risk of patients with ischaemic, high comorbidity burden, and elderly‐atrial fibrillation clusters were 1.35 (95% CI, 1.08–1.68), 1.39 (95% CI, 1.13–1.72), and 1.42 (95% CI, 1.14–1.77), respectively. The comorbidity network analysis found that patients in the high comorbidity burden cluster had more and higher comorbidity correlations than those in other clusters. Sensitivity analysis revealed that patients in the high comorbidity burden cluster had the highest risk of combined outcome and all‐cause mortality (P < 0.05). CONCLUSIONS: The difference in adverse outcomes among clusters confirmed the heterogeneity of CHF and the importance of hierarchical management. This study can provide a basis for personalized treatment and management of patients with CHF, and provide a new perspective for clinical decision making.
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spelling pubmed-87881372022-02-01 Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities Zheng, Chu Han, Linai Tian, Jing Li, Jing He, Hangzhi Han, Gangfei Wang, Ke Yang, Hong Yan, Jingjing Meng, Bingxia Han, Qinghua Zhang, Yanbo ESC Heart Fail Original Articles AIMS: Chronic heart failure (CHF) has an increasing burden of comorbidities, which affect clinical outcomes. Few studies have focused on the clustering and hierarchical management of patients with CHF based on comorbidity. This study aimed to explore the cluster model of CHF patients based on comorbidities and to verify their relationship with clinical outcomes. METHODS AND RESULTS: Electronic health records of patients hospitalized with CHF from January 2014 to April 2019 were collected, and 12 common comorbidities were included in the latent class analysis. The Fruchterman–Reingold layout was used to draw the comorbidity network, and analysis of variance was used to compare the weighted degrees among them. The incidence of clinical outcomes among different clusters was presented on Kaplan–Meier curves and compared using the log‐rank test, and the hazard ratio was calculated using the Cox proportional risk model. Sensitivity analysis was performed according to the left ventricular ejection fraction. Four different clinical clusters from 4063 total patients were identified: metabolic, ischaemic, high comorbidity burden, and elderly‐atrial fibrillation. Compared with the metabolic cluster, patients in the high comorbidity burden cluster had the highest adjusted risk of combined outcome and all‐cause mortality {1.67 [95% confidence interval (CI), 1.40–1.99] and 2.87 [95% CI, 2.17–3.81], respectively}, followed by the elderly‐atrial fibrillation and ischaemic clusters. The adjusted readmission risk of patients with ischaemic, high comorbidity burden, and elderly‐atrial fibrillation clusters were 1.35 (95% CI, 1.08–1.68), 1.39 (95% CI, 1.13–1.72), and 1.42 (95% CI, 1.14–1.77), respectively. The comorbidity network analysis found that patients in the high comorbidity burden cluster had more and higher comorbidity correlations than those in other clusters. Sensitivity analysis revealed that patients in the high comorbidity burden cluster had the highest risk of combined outcome and all‐cause mortality (P < 0.05). CONCLUSIONS: The difference in adverse outcomes among clusters confirmed the heterogeneity of CHF and the importance of hierarchical management. This study can provide a basis for personalized treatment and management of patients with CHF, and provide a new perspective for clinical decision making. John Wiley and Sons Inc. 2021-11-14 /pmc/articles/PMC8788137/ /pubmed/34779142 http://dx.doi.org/10.1002/ehf2.13708 Text en © 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Zheng, Chu
Han, Linai
Tian, Jing
Li, Jing
He, Hangzhi
Han, Gangfei
Wang, Ke
Yang, Hong
Yan, Jingjing
Meng, Bingxia
Han, Qinghua
Zhang, Yanbo
Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities
title Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities
title_full Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities
title_fullStr Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities
title_full_unstemmed Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities
title_short Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities
title_sort hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8788137/
https://www.ncbi.nlm.nih.gov/pubmed/34779142
http://dx.doi.org/10.1002/ehf2.13708
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