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TRUVACE RECORD VERSION
record: TRV-2026-1755
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-17T22:03:03.824364Z
status: published
lens: g_space
sector: health
headline: 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
dek: 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…
gain_title: An AI decision support tool could give clinicians individualised 3-month outcome estimates for several stroke treatment options during hyperacute care, where such predictions are rarely available.
problem_title: (none)
trace_subject: (none)
gain_reading: An AI decision support tool could give clinicians individualised 3-month outcome estimates for several stroke treatment options during hyperacute care, where such predictions are rarely available.
gain_evidence: AI-based clinical decision support systems (CDSS) could provide real-time prognostic estimates | hyperacute treatment decisions remain complex and are rarely informed by individualised outcome predictions
problem_reading: (none)
problem_evidence: (none)
quick_read: The VALIDATE protocol describes a prospective, multicentre observational study at three tertiary stroke centres. Consecutive patients with acute ischaemic stroke will be enrolled while treating physicians continue standard practice.

A researcher will enter patient data into the VALIDATE-CDSS app in real time, but the system will not influence clinical decisions. The study matters because evidence on AI prediction tools in routine stroke workflows is scarce. As a protocol, it reports no performance findings, so whether the tool is usable or accurate is still unknown.
limitation: 
tag: Evidence-backed gain
key_points: The system predicts 3-month functional outcome on the modified Rankin Scale for four strategies: intravenous thrombolysis, endovascular thrombectomy, combined therapy or no reperfusion. | Predictions are generated at three time points: baseline clinical data, non-contrast CT and CT angiography. | The primary outcome is real-world feasibility and usability within the hyperacute stroke workflow. | Secondary outcomes include predictive performance, agreement between suggested and actual treatments, incremental value as data accumulate, and bias across predefined subgroups.
rundown: The study design is observational and run in shadow mode. A dedicated researcher collects data and enters it through a mobile application, so clinical decisions are not influenced by the AI output.

Ethics approvals came from committees at Vall d'Hebron Institut de Recerca, Heidelberg Medical Faculty and Hadassah Medical Center-Ein Kerem, and the trial is registered as NCT05622539.

Results will be published in open-access journals and conference presentations. Anonymised data and metadata will be deposited in the Zenodo repository once the study is complete.
sources:
- peer_reviewed | BMJ Open | https://doi.org/10.1136/bmjopen-2026-120511 | 2026-10-07
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