Journal Article

High-risk multimorbidity patterns on the road to cardiovascular mortality

Thurner, Stefan, Gyimesi, Michael, Haug, Nina, Klimek, Peter, Kautzky-Willer, Alexandra, Deischinger, Carola

2020

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Aged / Beyond Big Data to new Biomedical and Health Data Science: moving to next century precision health / Biomedical data / Biomedicine / Cardiovascular disease / Cardiovascular diseases / Cardiovascular Diseases - epidemiology / Cardiovascular Diseases - mortality / Chronic illnesses / Codes / Comorbidities / Comorbidity / Datasets / Diabetes / Diabetes mellitus / Disease trajectories / Diseases / Elderly / Elderly patients / Female / Health / Health risks / Hospitals / Humans / Hypertension / Machine learning / Male / Medicine / Medicine & Public Health / Metabolic disorders / Metabolic syndrome / Metabolism / Middle Aged / Mortality / Multimorbidity / Multimorbidity - trends / Occupations / Patient outcomes / Prevention / Research Article / Statistics / Survival Rate / Time / Womens health