Heterogeneity in mortality risk prediction: a study of vulnerable adults in the Canadian longitudinal study on aging

Abstract

Background

Mortality prediction models are essential for clinical decision-making, but their performance may vary across patient subgroups. This study aimed to evaluate how a general mortality prediction model performs across subgroups defined by vulnerability factors and to test whether model improvements could improve prediction accuracy.

Methods

We analyzed data from 49,266 participants in the Canadian Longitudinal Study on Aging. A general mortality prediction model (Model A) was developed using Cox proportional hazard regression with LASSO, incorporating variables spanning sociodemographic factors, lifestyle habits, comorbidities, and physical/cognitive function measures. Performance was evaluated across subgroups defined by age, frailty, multimorbidity, cognitive function, and functional impairment using discrimination (c-index), calibration, and Brier scores. We tested two additional strategies: incorporating subgroup-specific variables (Model B) and developing tailored models for different mortality risk categories (Models C1, C2, C3).

Results

Over a median 6-year follow-up, 7.5% (3672) participants died. The general model performed well overall (c-index: 0.82, 95% CI 0.80–0.84; Brier: 0.036, 95% CI 0.032–0.040), but performance varied across subgroups. It was lower in frail individuals (c-index: 0.73, 95% CI 0.71–0.75; Brier: 0.12, 95% CI 0.11–0.13) and those with multiple chronic conditions (c-index: 0.76, 95% CI 0.75–0.78; Brier: 0.08, 95% CI 0.07–0.08), with risk underestimated in these groups. Neither incorporating subgroup variables nor developing risk-stratified models significantly improved performance.

Conclusion

Important variability in performance, particularly in vulnerable groups, highlights the limitations of a one-size-fits-all and underscores the need for more granular predictive models that account for subpopulation-specific characteristics to enhance mortality risk prediction.