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BACKGROUND: Frailty is common after stroke and linked to poor outcomes, but many measures are clinician-rated, time-consuming, and not suited to patient-reported data. To address these issues, we developed and validated a frailty score from the Swedish Stroke Register (Riksstroke) three-month follow-up questionnaire.

METHODS: We analyzed responses from 19,470 stroke survivors to nine patient-reported items covering function, mood, fatigue, pain and general health, in the 2021-2022 Riksstroke questionnaire. Dimensionality was assessed with Mokken Scale Analysis and exploratory factor analysis. Item response theory (IRT) was used for score computation. Competing graded response IRT models (unidimensional, correlated-factor, bifactor) were compared, and measurement fairness was examined using differential item functioning (DIF) across age, sex, and education. Prognostic validity was tested with Kaplan-Meier curves and Cox regression for all-cause mortality.

RESULTS: From the Mokken Scale Analysis, all items met scalability criteria. Factor analysis suggested two correlated interpretable facets (Physical Functioning; Well-being/Mental Health). A bifactor IRT model provided the best fit to the data, comprising a general frailty dimension while addressing the strong correlation between the facets. DIF was minimal for sex and education, with modest age-related effects. Higher frailty scores were associated with increased mortality in adjusted Cox models and Kaplan-Meier curves. Tools for computing frailty scores are available at https://github.com/joakimwallmark/frailty-irt-scores.

CONCLUSIONS: A robust, fair, and prognostically meaningful frailty score can be derived from patient-reported items in Riksstroke. More broadly, the study demonstrates how routinely collected patient-reported outcome measures can be leveraged to build scalable frailty scores, offering efficient cost-effective tools for monitoring outcome and guiding quality improvement in stroke care.