BMJ Open. 2026 Oct 7;16(10):e120511. doi: 10.1136/bmjopen-2026-120511.
ABSTRACT
INTRODUCTION: Reperfusion therapies improve outcomes in acute ischaemic stroke, but hyperacute treatment decisions remain complex and are rarely informed by individualised outcome predictions. Artificial intelligence (AI)-based clinical decision support systems (CDSS) could provide real-time prognostic estimates, yet prospective evidence of their feasibility and performance within routine workflows is scarce. We aim to prospectively evaluate the real-time feasibility, usability and predictive performance of an AI-based CDSS (VALIDATE-CDSS) for individualised outcome prediction in acute stroke care.
METHODS AND ANALYSIS: Prospective, multicentre observational study enrolling consecutive patients with acute ischaemic stroke at three tertiary stroke centres. Clinical management will follow standard practice at the discretion of treating physicians. In parallel, a dedicated researcher will collect patient data in real time and enter them into VALIDATE-CDSS via a mobile application, operating in shadow mode without influencing clinical decisions. The system will generate individualised predictions of 3-month functional outcome (modified Rankin Scale) for four treatment strategies (intravenous thrombolysis, endovascular thrombectomy, combined therapy or no reperfusion) at three sequential time points: baseline clinical data, non-contrast CT and CT angiography. The primary outcome is the real-world feasibility and usability of VALIDATE-CDSS within the hyperacute stroke workflow. Secondary outcomes include predictive performance, agreement between model-suggested and actual treatments, incremental value with increasing data availability and potential bias across predefined subgroups.
ETHICS AND DISSEMINATION: Enrolment began after approval by the ethics committees of all participating centres (Vall d'Hebron Institut de Recerca, PR(AG)432-2023; Ethikkommission der Medizinischen Fakultät Heidelberg, S-687/2023; Helsinki Committee, Hadassah Medical Center-Ein Kerem, HMO-0529-22). Results will be disseminated through peer-reviewed open-access journals and conference presentations. Following open science principles, anonymised data and metadata will be deposited in the Zenodo repository on study completion.
TRIAL REGISTRATION NUMBER: NCT05622539.
PMID:42843886 | DOI:10.1136/bmjopen-2026-120511