Real-time prospective (shadow mode) validation of an AI-based clinical decision support system for predicting 3-month functional outcome in acute stroke: the VALIDATE study protocol.

Authors

Marta Rubiera, Martin Bendszus, Ronen R Leker, Adam Hilbert, Ingo Werren, Luis Miguel Lopez-Ramos, Mercedes Ayesta, Que Nguyen, Sussane Bonekamp, Victoria Sala, Hamza Jubran, Claudia Meza, Fatma Shalabi, Yoel Schwartzmann, David Cano, Malte von Tottleben, John Kelleher, Dietmar Frey, VALIDATE investigators

Published in

BMJ open. Volume 16. Issue 10. Pages e120511. Oct 07, 2026. Epub Oct 07, 2026.

Abstract

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.
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.
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.
NCT05622539.

PMID:
42843886
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.

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