Development, validation, and clinical utility of a risk prediction model to identify older women with dementia for proactive palliative care

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BACKGROUND: Accurately estimating one-year mortality risk in older women with dementia can inform clinical decision-making, facilitate timely advanced care planning, and optimise palliative care delivery. This study aimed to develop, validate, and assess the clinical utility of a prediction model for one-year all-cause mortality in this population using a nationally representative Australian cohort. METHODS: This prognostic study utilised data from the 1921-26 cohort of the nationally representative, population-based Australian Longitudinal Study on Women's Health (ALSWH) and linked national and state-based administrative health records. Candidate predictors were identified through a systematic review and expert consultation, then refined using a data-driven statistical approach. A multivariable binary logistic regression model was developed and validated to predict one-year all-cause mortality. RESULTS: The analysis included 1576 older women with dementia (mean age, 72.6 ± 1.5 years). The model demonstrated good discrimination (AUC: 75.1 %, 95 % CI: 72.7 %-77.5 %) and excellent calibration (slope = 1.00, 95 % CI: 0.87-1.13; intercept = 0.00, 95 % CI: 0.11 - 0.11). Model validation using both 10-fold cross-validation and 1000 bootstrap iterations showed minimal optimism in its predictive performance, with AUC optimism of 0.0047 and 0.0042, respectively. Decision curve analysis indicated a net benefit across probability thresholds from 0.24 to 0.88, supporting the model's clinical utility for guiding palliative care decisions. CONCLUSION: This prediction model, incorporating readily available predictors, demonstrated compelling performance and clinical utility for identifying older women with dementia at high risk of one-year mortality. The model has the potential to facilitate timely palliative care interventions and is publicly accessible via a web-based calculator. Further external validation in diverse populations and healthcare settings is warranted to confirm its generalisability.

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