ArticleFrontiers in neurology2026
External validation of machine learning models for predicting prehospital delay in acute ischemic stroke: a retrospective cohort study.
Article in Frontiers in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background and purpose: Prehospital delay remains a major barrier to timely reperfusion therapy in acute ischemic stroke (AIS). We externally validated eight prediction algorithms and examined whether complex machine-learning models added value beyond logistic regression. Methods: This retrospective cohort study included consecutive patients with AIS from two hospitals in Kashgar, China (January 2019-December 2024). Hospital 1 ( Results: Delay occurred in 80.9 and 83.2% of the two cohorts. In the prespecified LASSO-plus-SMOTE analysis, XGBoost had the highest validation AUC (0.771; 95% CI, 0.734-0.807), but did not significantly outperform logistic regression (difference, 0.015; 95% CI, -0.003 to 0.034; Conclusion: Complex algorithms did not show a clear advantage over logistic regression. Because several predictors are available only after arrival, the framework is intended for retrospective health-system profiling and quality-improvement planning, not individual pre-arrival prediction. Universal interventions remain warranted given the high prevalence of delay.
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