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Background <p>The course of heart disease involves multiple endpoints, and how to characterize this course needs investigation. Multi-state models are commonly used to model the disease course, estimating state occupation probabilities (survival risks) in each state over time. These transition parameters are state- and time-specific, which makes the model difficult to use for characterization of the entire course and for comparative effectiveness of exposures.</p> Methods <p>A Discrete-time Split-state Framework has been proposed, which splits disease states into substates conditioned on past disease history. This framework is “memoryless” in that the newly created substates are independent of past history, thereby relaxing the Markov assumption. It is also “memorable” because the substates contain information about past history. In this paper, we leverage the “memoryless” and “memorable” features of the framework to synthesize the estimated transition parameters into two summary measures: Multimorbidity-Adjusted Life Year (MALY), and disease path. MALY takes the multimorbidity of each substate into consideration and estimates the adjusted life years in full health. Disease path describes the progression of disease and elucidates disease mechanisms.</p> Results <p>In the application, we characterized the course of heart disease using data from the Atherosclerosis Risk in Communities (ARIC) study. The disease course was modeled in five states: healthy, at metabolic risk, coronary heart disease (CHD), heart failure, and mortality. In this mid- to old-age population, the estimated MALY was 26.53 years (95% CI: 18.63, 34.77), and the corresponding multimorbidity-adjusted life expectancy was 80.92 years (95% CI: 79.30, 82.72). For healthy participants at baseline, the most likely paths were “Healthy → at metabolic risk → CVD mortality” (38%), “Healthy → non-CVD mortality” (23%), and “Healthy → at metabolic risk → heart failure → CVD mortality” (17%).</p> Conclusions <p>In summary, MALY and Disease Path characterize the course of heart disease and have potential use in precision prevention and prediction of heart disease.</p>

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Summary measures derived from a multi-state modeling framework to characterize the course of heart disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Ming Ding,
  • Feng-Chang Lin,
  • Michelle L. Meyer

摘要

Background

The course of heart disease involves multiple endpoints, and how to characterize this course needs investigation. Multi-state models are commonly used to model the disease course, estimating state occupation probabilities (survival risks) in each state over time. These transition parameters are state- and time-specific, which makes the model difficult to use for characterization of the entire course and for comparative effectiveness of exposures.

Methods

A Discrete-time Split-state Framework has been proposed, which splits disease states into substates conditioned on past disease history. This framework is “memoryless” in that the newly created substates are independent of past history, thereby relaxing the Markov assumption. It is also “memorable” because the substates contain information about past history. In this paper, we leverage the “memoryless” and “memorable” features of the framework to synthesize the estimated transition parameters into two summary measures: Multimorbidity-Adjusted Life Year (MALY), and disease path. MALY takes the multimorbidity of each substate into consideration and estimates the adjusted life years in full health. Disease path describes the progression of disease and elucidates disease mechanisms.

Results

In the application, we characterized the course of heart disease using data from the Atherosclerosis Risk in Communities (ARIC) study. The disease course was modeled in five states: healthy, at metabolic risk, coronary heart disease (CHD), heart failure, and mortality. In this mid- to old-age population, the estimated MALY was 26.53 years (95% CI: 18.63, 34.77), and the corresponding multimorbidity-adjusted life expectancy was 80.92 years (95% CI: 79.30, 82.72). For healthy participants at baseline, the most likely paths were “Healthy → at metabolic risk → CVD mortality” (38%), “Healthy → non-CVD mortality” (23%), and “Healthy → at metabolic risk → heart failure → CVD mortality” (17%).

Conclusions

In summary, MALY and Disease Path characterize the course of heart disease and have potential use in precision prevention and prediction of heart disease.