There is limited evidence on prognostic models that incorporate clinical guideline recommendations during the acute management of ischemic stroke. This study aimed to develop and validate clinical guideline-based prognostic models of functional disability following a first-ever acute ischemic stroke. A multicenter retrospective cohort study was conducted using data from 899 adult patients (aged 18 years or older) with a first-ever acute ischemic stroke, and enrolled in the Malaysian National Stroke Registry (NSR) between January 2009 and December 2019. The primary outcome was functional disability at three months post-discharge, defined as a modified Rankin Scale (mRS) score of 3 to 5. A multivariable logistic regression model was developed using random sampling data splitted into two cohorts: the development (n = 674; 75%) and the internal validation (n = 225; 25%). Model performance was evaluated by discrimination area under the curve (AUC) and calibration Hosmer–Lemeshow (HL) test. The final prognostic model showed factors associated with increased risk of functional disability include age adjusted odds ratio (aOR) 1.02, 95% CI 1.00–1.04, p = 0.024, female gender aOR 1.54, 95% CI 1.06–2.23, p = 0.024, and diabetes aOR 1.66, 95% CI 1.15–2.40, p = 0.007. While those factors associated with decreased risk of disability include posterior circulation infarct (POCI) aOR 0.38, 95% CI 0.18–0.78, p = 0.009, and adherence to key performance indicators (KPIs); antiplatelet therapy aOR 0.48, 95% CI 0.29–0.80, p = 0.005, lipid-lowering therapy aOR 0.56, 95% CI 0.37–0.83, p = 0.004, stroke education aOR 0.09, 95% CI 0.05–0.16, p < 0.001, and rehabilitation aOR 0.43, 95% CI 0.29–0.64, p < 0.001. The fitted model demonstrated good discrimination (AUC: 0.803; 95% CI, 0.78–0.83 for development, and 0.777; 95% CI, 0.75–0.81 for validation) and calibration (HL test: p = 0.473 for development, p = 0.967 for validation). A clinical guideline-guided prognostic model of functional disability following a first-ever acute ischemic stroke was successfully developed and validated, demonstrating strong performance and potential clinical relevance. This model can help clinicians predict stroke outcomes, guide informed clinical decision-making, and support the development of personalized stroke care.
Mohammed et al. (2026) studied this question.