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Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization
BACKGROUND: Comorbidities affect outcomes in heart failure (HF), but are not reflected in current HF classification. The aim of this study is to characterize HF groups that account for higher-order interactions between comorbidities and to investigate the association between comorbidity groups and o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812726/ https://www.ncbi.nlm.nih.gov/pubmed/33455580 http://dx.doi.org/10.1186/s12916-020-01881-7 |
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author | Gulea, Claudia Zakeri, Rosita Quint, Jennifer K. |
author_facet | Gulea, Claudia Zakeri, Rosita Quint, Jennifer K. |
author_sort | Gulea, Claudia |
collection | PubMed |
description | BACKGROUND: Comorbidities affect outcomes in heart failure (HF), but are not reflected in current HF classification. The aim of this study is to characterize HF groups that account for higher-order interactions between comorbidities and to investigate the association between comorbidity groups and outcomes. METHODS: Latent class analysis (LCA) was performed on 12 comorbidities from patients with HF identified from administrative claims data in the USA (OptumLabs Data Warehouse®) between 2008 and 2018. Associations with admission to hospital and mortality were assessed with Cox regression. Negative binomial regression was used to examine rates of healthcare use. RESULTS: In a population of 318,384 individuals, we identified five comorbidity clusters, named according to their dominant features: low-burden, metabolic-vascular, anemic, ischemic, and metabolic. Compared to the low-burden group (minimal comorbidities), patients in the metabolic-vascular group (exhibiting a pattern of diabetes, obesity, and vascular disease) had the worst prognosis for admission (HR 2.21, 95% CI 2.17–2.25) and death (HR 1.87, 95% CI 1.74–2.01), followed by the ischemic, anemic, and metabolic groups. The anemic group experienced an intermediate risk of admission (HR 1.49, 95% CI 1.44–1.54) and death (HR 1.46, 95% CI 1.30–1.64). Healthcare use also varied: the anemic group had the highest rate of outpatient visits, compared to the low-burden group (IRR 2.11, 95% CI 2.06–2.16); the metabolic-vascular and ischemic groups had the highest rate of admissions (IRR 2.11, 95% CI 2.08–2.15, and 2.11, 95% CI 2.07–2.15) and healthcare costs. CONCLUSIONS: These data demonstrate the feasibility of using LCA to classify HF based on comorbidities alone and should encourage investigation of multidimensional approaches in comorbidity management to reduce admission and mortality risk among patients with HF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-020-01881-7. |
format | Online Article Text |
id | pubmed-7812726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-78127262021-01-19 Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization Gulea, Claudia Zakeri, Rosita Quint, Jennifer K. BMC Med Research Article BACKGROUND: Comorbidities affect outcomes in heart failure (HF), but are not reflected in current HF classification. The aim of this study is to characterize HF groups that account for higher-order interactions between comorbidities and to investigate the association between comorbidity groups and outcomes. METHODS: Latent class analysis (LCA) was performed on 12 comorbidities from patients with HF identified from administrative claims data in the USA (OptumLabs Data Warehouse®) between 2008 and 2018. Associations with admission to hospital and mortality were assessed with Cox regression. Negative binomial regression was used to examine rates of healthcare use. RESULTS: In a population of 318,384 individuals, we identified five comorbidity clusters, named according to their dominant features: low-burden, metabolic-vascular, anemic, ischemic, and metabolic. Compared to the low-burden group (minimal comorbidities), patients in the metabolic-vascular group (exhibiting a pattern of diabetes, obesity, and vascular disease) had the worst prognosis for admission (HR 2.21, 95% CI 2.17–2.25) and death (HR 1.87, 95% CI 1.74–2.01), followed by the ischemic, anemic, and metabolic groups. The anemic group experienced an intermediate risk of admission (HR 1.49, 95% CI 1.44–1.54) and death (HR 1.46, 95% CI 1.30–1.64). Healthcare use also varied: the anemic group had the highest rate of outpatient visits, compared to the low-burden group (IRR 2.11, 95% CI 2.06–2.16); the metabolic-vascular and ischemic groups had the highest rate of admissions (IRR 2.11, 95% CI 2.08–2.15, and 2.11, 95% CI 2.07–2.15) and healthcare costs. CONCLUSIONS: These data demonstrate the feasibility of using LCA to classify HF based on comorbidities alone and should encourage investigation of multidimensional approaches in comorbidity management to reduce admission and mortality risk among patients with HF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-020-01881-7. BioMed Central 2021-01-18 /pmc/articles/PMC7812726/ /pubmed/33455580 http://dx.doi.org/10.1186/s12916-020-01881-7 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Gulea, Claudia Zakeri, Rosita Quint, Jennifer K. Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization |
title | Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization |
title_full | Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization |
title_fullStr | Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization |
title_full_unstemmed | Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization |
title_short | Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization |
title_sort | model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812726/ https://www.ncbi.nlm.nih.gov/pubmed/33455580 http://dx.doi.org/10.1186/s12916-020-01881-7 |
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