ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2026
Characterizing heterogeneity in health-related quality of life trajectories in coronary artery disease: a longitudinal latent Markov analysis.
Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis study used latent Markov modelling (LMM) to identify distinct HRQOL states and predictors of transitions in a large CAD cohort.
methodData were from 6,030 patients in the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) registry who underwent coronary angiography between 2004 and 2014. HRQOL was assessed using the 7-item Seattle Angina Questionnaire (SAQ-7) at 2 weeks, 1 year, and 3 years post-angiography. LLM identified distinct subgroups and transition probabilities between states. Covariates were incorporated via multinomial logistic regressions for state membership and transitions.
resultThe mean age was 65.5 years (SD 10.6); 79.3% of the participants were male. Four states were identified: Poor, Moderate, Optimal, and Good. At week 2, Poor, Moderate, and Optimal comprised 28.2%, 39.0%, and 32.8% of patients, respectively. The Good state emerged at year 1, becoming predominant by year 3 (70.0%). The Good state showed high stability (85% probability of persistence). Among patients starting in Poor, 53% transitioned to Good and 15% to Optimal by year 3. Younger age (Odds Ratio [OR] = 0.97 per year, 95% confidence interval [CI]: 0.95–0.99), male sex (OR = 2.18, 95% CI: 1.35–3.54), and coronary artery bypass grafting (OR = 1.98, 95% CI: 1.04–3.74) were associated with transitions from Poor to Optimal state.
conclusionHRQOL trajectories after CAD diagnosis are dynamic and heterogeneous, with most patients experiencing improvement within one year. Person-centred latent state modelling offers insight into long-term health status and may guide tailored recovery strategies.
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