High-risk multimorbidity patterns on the road to cardiovascular mortality

Area of Science:

• Computational epidemiology
• Health informatics
• Longitudinal health studies

Background:

• Multimorbidity, the presence of multiple diseases in one patient, is common.
• Understanding disease interactions over a patient's lifetime is crucial for personalized prevention.
• Current knowledge on disease trajectories is limited in scope and duration.

Purpose of the Study:

• To identify critical events that influence future disease progression in patients.
• To analyze long-term disease trajectories and associated mortality risks.
• To inform personalized prevention strategies for multimorbidity.

Main Methods:

• Population-wide analysis of 9 million patient histories over 17 years.
• Algorithmic identification of 132 multimorbidity patterns.
• Calculation of age- and sex-specific risks for acquiring future disease sets.

Main Results:

• Identified 3 groups of multimorbidity patterns associated with low, medium, and high in-hospital mortality.
• Specific disease combinations significantly increase the risk of reaching high-mortality states.
• Diabetes and hypertension, especially with metabolic disorders, elevate mortality risk in men and women.

Conclusions:

• The approach forecasts disease burdens and identifies critical events for targeted prevention.
• Disease progression and mortality risks are strongly influenced by specific multimorbidity patterns.
• Cardiovascular disease risk increases more in females than males with diabetes, hypertension, and metabolic disorders.