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Post-stroke sequelae risk score (PSSRS): a 16-year retrospective study developing an internally validated multi-algorithm model for post-stroke sequelae

Year: 2026

Type: article

Abstract: Introduction: Post-stroke sequelae (PSS) encompass persistent functional impairments after stroke and may benefit from early risk stratification. We developed and internally validated a time-to-event prediction model based on routinely available clinical and biochemical variables. Method: = 628) sets before preprocessing, feature selection, tuning, and model comparison. Univariate Cox, multivariable Cox, least absolute shrinkage and selection operator (LASSO), and random survival forest (RSF) analyses identified eight intersection variables. A total of 101 survival-model pipelines were compared using training-set resampling; the selected model was then evaluated in the internal test set. Result: The StepCox (forward) + RSF pipeline showed the highest prespecified training-resampling performance. Its Harrell C-index was 0.87 in the training set and 0.85 in the internal test set. Test-set time-dependent AUROCs at 1, 3, and 5 years were 0.800 (95% CI, 0.760-0.840), 0.830 (0.790-0.870), and 0.820 (0.780-0.860), respectively; corresponding AUPRCs were 0.748, 0.772, and 0.761, and Brier scores were 0.140, 0.170, and 0.180. Calibration slopes were 0.96, 0.93, and 0.90. LDH, CK-MB, and DBIL were among the leading contributors in the model-interpretation analyses. Conclusion: PSSRS provides an internally validated framework for estimating individualized risk of post-stroke sequelae. External temporal and multicenter validation is required before clinical implementation.

Source: Frontiers in Neurology

DOI: 10.3389/fneur.2026.1833445