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High-risk multimorbidity patterns on the road to cardiovascular mortality

BACKGROUND: Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current kno...

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Autores principales: Haug, Nina, Deischinger, Carola, Gyimesi, Michael, Kautzky-Willer, Alexandra, Thurner, Stefan, Klimek, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063814/
https://www.ncbi.nlm.nih.gov/pubmed/32151252
http://dx.doi.org/10.1186/s12916-020-1508-1
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author Haug, Nina
Deischinger, Carola
Gyimesi, Michael
Kautzky-Willer, Alexandra
Thurner, Stefan
Klimek, Peter
author_facet Haug, Nina
Deischinger, Carola
Gyimesi, Michael
Kautzky-Willer, Alexandra
Thurner, Stefan
Klimek, Peter
author_sort Haug, Nina
collection PubMed
description BACKGROUND: Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. METHODS: Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. RESULTS: In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2–0.3%), medium (0.3–1%) and high in-hospital mortality (2–11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50–59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. CONCLUSIONS: Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders.
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spelling pubmed-70638142020-03-13 High-risk multimorbidity patterns on the road to cardiovascular mortality Haug, Nina Deischinger, Carola Gyimesi, Michael Kautzky-Willer, Alexandra Thurner, Stefan Klimek, Peter BMC Med Research Article BACKGROUND: Multimorbidity, the co-occurrence of two or more diseases in one patient, is a frequent phenomenon. Understanding how different diseases condition each other over the lifetime of a patient could significantly contribute to personalised prevention efforts. However, most of our current knowledge on the long-term development of the health of patients (their disease trajectories) is either confined to narrow time spans or specific (sets of) diseases. Here, we aim to identify decisive events that potentially determine the future disease progression of patients. METHODS: Health states of patients are described by algorithmically identified multimorbidity patterns (groups of included or excluded diseases) in a population-wide analysis of 9,000,000 patient histories of hospital diagnoses observed over 17 years. Over time, patients might acquire new diagnoses that change their health state; they describe a disease trajectory. We measure the age- and sex-specific risks for patients that they will acquire certain sets of diseases in the future depending on their current health state. RESULTS: In the present analysis, the population is described by a set of 132 different multimorbidity patterns. For elderly patients, we find 3 groups of multimorbidity patterns associated with low (yearly in-hospital mortality of 0.2–0.3%), medium (0.3–1%) and high in-hospital mortality (2–11%). We identify combinations of diseases that significantly increase the risk to reach the high-mortality health states in later life. For instance, in men (women) aged 50–59 diagnosed with diabetes and hypertension, the risk for moving into the high-mortality region within 1 year is increased by the factor of 1.96 ± 0.11 (2.60 ± 0.18) compared with all patients of the same age and sex, respectively, and by the factor of 2.09 ± 0.12 (3.04 ± 0.18) if additionally diagnosed with metabolic disorders. CONCLUSIONS: Our approach can be used both to forecast future disease burdens, as well as to identify the critical events in the careers of patients which strongly determine their disease progression, therefore constituting targets for efficient prevention measures. We show that the risk for cardiovascular diseases increases significantly more in females than in males when diagnosed with diabetes, hypertension and metabolic disorders. BioMed Central 2020-03-10 /pmc/articles/PMC7063814/ /pubmed/32151252 http://dx.doi.org/10.1186/s12916-020-1508-1 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Haug, Nina
Deischinger, Carola
Gyimesi, Michael
Kautzky-Willer, Alexandra
Thurner, Stefan
Klimek, Peter
High-risk multimorbidity patterns on the road to cardiovascular mortality
title High-risk multimorbidity patterns on the road to cardiovascular mortality
title_full High-risk multimorbidity patterns on the road to cardiovascular mortality
title_fullStr High-risk multimorbidity patterns on the road to cardiovascular mortality
title_full_unstemmed High-risk multimorbidity patterns on the road to cardiovascular mortality
title_short High-risk multimorbidity patterns on the road to cardiovascular mortality
title_sort high-risk multimorbidity patterns on the road to cardiovascular mortality
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063814/
https://www.ncbi.nlm.nih.gov/pubmed/32151252
http://dx.doi.org/10.1186/s12916-020-1508-1
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