Authors
Xiao Xu, Nihimbazwe Cesar, Epipode Ntawuyamara, Su-Hua Su
Published in
The Journal of international medical research. Volume 54. Issue 10. Pages 3000605261497564. Epub Oct 27, 2026.
Abstract
ObjectiveBased on retrospective longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) and Korean Longitudinal Study of Ageing (KLoSA) cohorts, we developed and externally validated an interpretable machine learning model to predict 4-year incident frailty in middle-aged and older adults with arthritis.MethodsModel development was performed using the CHARLS cohort (n = 2126) and included participants aged ≥45 years who had been diagnosed with arthritis and were nonfrail at their initial survey. New-onset frailty was tracked over a 4-year interval. Thirty-one candidate predictors were identified through a literature review, none of which overlapped with the deficits constituting the frailty index. Ten core features, including age, marital status, left- and right-hand grip strength, number of painful sites, knee pain, leg pain, back pain, number of hospitalizations, and antirheumatic medication use, were selected via combined least absolute shrinkage and selection operator-Boruta feature selection. Six algorithms were compared based on their area under the curves in the internal validation dataset, specificity, sensitivity, and other clinical applicability metrics. Tenfold cross-validation was employed. The selected model was frozen and transported to KLoSA without refitting. A prespecified recalibration-in-the-large was applied to account for the lower baseline frailty incidence in the Korean cohort. Model interpretability was achieved with SHapley Additive exPlanations global and local analyses, and an online calculator was developed.ResultsLogistic regression was selected as the final model because it achieved competitive discrimination with the least shrinkage between training and validation sets. Its discriminative ability, measured as the area under the receiver operating characteristic curve, was 0.764 (95% confidence interval: 0.707-0.816) internally and remained at 0.747 (95% confidence interval: 0.701-0.793) when the model was transported to KLoSA. Performance was stable across 10-fold cross-validation (mean, 0.772 ± 0.046). Discrimination was well preserved, although a prespecified recalibration-in-the-large was required to correct for the lower baseline frailty incidence in the Korean cohort (Brier score 0.205→0.065). In the SHapley Additive exPlanations ranking, the number of painful sites, antirheumatic medication use, leg pain, age, and grip strength contributed the most. Once a user enters the 10 easily collected variables, the calculator reports that person's probability of becoming frail within 4 years. In post hoc analyses, pain burden and low grip strength emerged as two independent risk dimensions. High pain burden remained associated with falls (adjusted odds ratio = 1.60) and hospitalization (1.66) even at a given level of frailty, whereas low grip strength and medication use were associated with an increased frailty risk without a corresponding excess of events.ConclusionsThis explainable machine learning model, developed using data from Chinese middle-aged and older patients with arthritis and externally validated in a KLoSA cohort, combines acceptable predictive accuracy with cross-population robustness. The tool estimates an individualized 4-year frailty risk probability using 10 routinely available indicators. Given the observational nature of both cohorts, this model should be viewed as a candidate risk stratification aid rather than a validated clinical tool. Formal impact evaluation through prospective validation is necessary before the model can be recommended for use in community settings.
PMID:
42947315
Bibliographic data and abstract were imported from PubMed on 28 Oct 2026.
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