High-risk multimorbidity patterns on the road to cardiovascular mortality
Abstract 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 cur...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-03-01
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| Series: | BMC Medicine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12916-020-1508-1 |
| _version_ | 1811341361377443840 |
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| author | Nils Haug Carola Deischinger Michael Gyimesi Alexandra Kautzky-Willer Stefan Thurner Peter Klimek |
| author_facet | Nils Haug Carola Deischinger Michael Gyimesi Alexandra Kautzky-Willer Stefan Thurner Peter Klimek |
| author_sort | Nils Haug |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T18:54:26Z |
| format | Article |
| id | doaj.art-3ad63ea1047a4fe18b50ce66f52bf385 |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-04-13T18:54:26Z |
| publishDate | 2020-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-3ad63ea1047a4fe18b50ce66f52bf3852022-12-22T02:34:18ZengBMCBMC Medicine1741-70152020-03-0118111210.1186/s12916-020-1508-1High-risk multimorbidity patterns on the road to cardiovascular mortalityNils Haug0Carola Deischinger1Michael Gyimesi2Alexandra Kautzky-Willer3Stefan Thurner4Peter Klimek5Section for Science of Complex Systems, CeMSIIS, Medical University of ViennaGender Medicine Unit, Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of ViennaGesundheit Österreich GmbHGender Medicine Unit, Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of ViennaSection for Science of Complex Systems, CeMSIIS, Medical University of ViennaSection for Science of Complex Systems, CeMSIIS, Medical University of ViennaAbstract 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.http://link.springer.com/article/10.1186/s12916-020-1508-1Disease trajectoriesComorbiditiesMetabolic syndromeCardiovascular diseaseMachine learning |
| spellingShingle | Nils Haug Carola Deischinger Michael Gyimesi Alexandra Kautzky-Willer Stefan Thurner Peter Klimek High-risk multimorbidity patterns on the road to cardiovascular mortality BMC Medicine Disease trajectories Comorbidities Metabolic syndrome Cardiovascular disease Machine learning |
| 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 | Disease trajectories Comorbidities Metabolic syndrome Cardiovascular disease Machine learning |
| url | http://link.springer.com/article/10.1186/s12916-020-1508-1 |
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