Predicting mortality risk after a fall in older adults using health care spending patterns: a population-based cohort study.

Autor:	Katsiferis A; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.; Statistics Denmark, Copenhagen, Denmark., Mortensen LH; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.; Statistics Denmark, Copenhagen, Denmark., Khurana MP; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark., Mishra S; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore., Jensen MK; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.; Statistics Denmark, Copenhagen, Denmark., Bhatt S; Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.; Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
Jazyk:	angličtina
Zdroj:	Age and ageing [Age Ageing] 2023 Aug 01; Vol. 52 (8).
DOI:	10.1093/ageing/afad159
Abstrakt:	Objective: To develop a prognostic model of 1-year mortality for individuals aged 65+ presenting at the emergency department (ED) with a fall based on health care spending patterns to guide clinical decision-making. Design: Population-based cohort study (n = 35,997) included with a fall in 2013 and followed 1 year. Methods: Health care spending indicators (dynamical indicators of resilience, DIORs) 2 years before admission were evaluated as potential predictors, along with age, sex and other clinical and sociodemographic covariates. Multivariable logistic regression models were developed and internally validated (10-fold cross-validation). Performance was assessed via discrimination (area under the receiver operating characteristic curve, AUC), Brier scores, calibration and decision curve analysis. Results: The AUC of age and sex for mortality was 72.5% [95% confidence interval 71.8 to 73.2]. The best model included age, sex, number of medications and health care spending DIORs. It exhibited high discrimination (AUC: 81.1 [80.5 to 81.6]), good calibration and potential clinical benefit for various threshold probabilities. Overall, health care spending patterns improved predictive accuracy the most while also exhibiting superior performance and clinical benefit. Conclusions: Patterns of health care spending have the potential to significantly improve assessments on who is at high risk of dying following admission to the ED with a fall. The proposed methodology can assist in predicting the prognosis of fallers, emphasising the added predictive value of longitudinal health-related information next to clinical and sociodemographic predictors. (© The Author(s) 2023. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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