Longitudinal trajectories of frailty are associated with short-term mortality in older people: a joint latent class models analysis using 2 UK primary care databases

Leena Elhussein; Danielle E Robinson; Antonella Delmestri; Andrew Clegg; Daniel Prieto-Alhambra; Alan Silman; Victoria Y Strauss

doi:10.1016/j.jclinepi.2024.111442

Longitudinal trajectories of frailty are associated with short-term mortality in older people: a joint latent class models analysis using 2 UK primary care databases

J Clin Epidemiol. 2024 Jun 26:173:111442. doi: 10.1016/j.jclinepi.2024.111442. Online ahead of print.

Authors

Leena Elhussein¹, Danielle E Robinson², Antonella Delmestri², Andrew Clegg³, Daniel Prieto-Alhambra², Alan Silman², Victoria Y Strauss²

Affiliations

¹ Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford, UK. Electronic address: Leena.elhussein@ndorms.ox.ac.uk.
² Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford, UK.
³ Academic Unit of Elderly Care and Rehabilitation, University of Leeds, Bradford, West Yorkshire, UK.

PMID: 38942178
DOI: 10.1016/j.jclinepi.2024.111442

Abstract

Objectives: Frailty is a dynamic health state that changes over time. Our hypothesis was that there are identifiable subgroups of the older population that have specific patterns of deterioration. The objective of this study was to evaluate the application of joint latent class model in identifying trajectories of frailty progression over time and their group-specific risk of death in older people.

Study design and setting: The primary care records of UK patients, aged over 65 as of January 1, 2010, included in the Clinical Practice Research Datalink: GOLD and AURUM databases, were analyzed and linked to mortality data. The electronic frailty index (eFI) scores were calculated at baseline and annually in subsequent years (2010-2013). Joint latent class model was used to divide the population into clusters with different trajectories and associated mortality hazard ratios. The model was built in GOLD and validated in AURUM.

Results: Five trajectory clusters were identified and characterized based on baseline and speed of progression: low-slow, low-moderate, low-rapid, high-slow, and high-rapid. The high-rapid cluster had the highest average starting eFI score; 7.9, while the low-rapid cluster had the steepest rate of eFI progression; 1.7. Taking the low-slow cluster as reference, low-rapid and high-rapid had the highest hazard ratios: 3.73 (95% CI 3.71, 3.76) and 3.63 (3.57-3.69), respectively. Good validation was found in the AURUM population.

Conclusion: Our research found that there are vulnerable subgroups of the older population who are currently frail or have rapid frailty progression. Such groups may be targeted for greater healthcare monitoring.

Keywords: Electronic frailty index; Frailty; Joint latent class models; Joint models; Longitudinal; Mortality; Older people.