This study aims to develop and externally validate machine learning prediction models (FORECAST-PC) to determine 90-day functional outcomes for patients suffering from acute ischemic stroke in the posterior circulation who are selected for endovascular thrombectomy (EVT)
Outcome prediction after endovascular treatment (EVT) for posterior circulation (PC) acute ischemic stroke remains challenging, as current prognostic tools are often limited strictly to basilar artery occlusions or rely heavily on anterior circulation data. The FORECAST-PC study is an investigator-initiated, retrospective, international multicenter cohort study designed to develop and validate comprehensive PC outcome prediction models across key stages of the clinical pathway: Baseline, Pre-EVT, Immediately Post-EVT, and 24-hours Post-EVT. The models are derived from patients across three European centers and externally validated on an unseen cohort from 11 international centers to ensure robustness against geographic domain shifts and variations in clinical workflows. The clinical utility of these models is assessed using Decision Curve Analysis to demonstrate the net benefit of model-guided prognostication over standard clinical assumptions.
Study Type
OBSERVATIONAL
Enrollment
732
Application of machine learning gradient-boosted decision tree models (Full-Feature and Light versions) at admission and 24-hours to predict 90-day functional outcome.
Centre Hospitalier Universitaire Vaudois
Lausanne, Switzerland
Unfavorable Functional Outcome at 90 Days
Defined as a dichotomized 90-day modified Rankin Scale (mRS) score of 3 to 6 (where 0-2 is favorable and 3-6 is unfavorable).
Time frame: 90 days
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.