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DOI 10.1093/esj/aakag085

Медицинская библиотека · European stroke journal · 2026

CATalyze-AI: accelerating referral for thrombectomy in acute stroke patients using an AI-based software. A prospective, multicentre, quasi-experimental cohort study.

Olive-Gadea Marta, Rizzo Federica, Cocho Dolores, Font Judit Roca, Rodrigo-Gisbert Marc, Muchada Marian, Del Fueyo Marta Rubiera, Rodriguez-Luna David, Simonetti Renato, Rodriguez-Villatoro Noelia, Pagola Jorge, Izquierdo Artur, Planells Gemma, Cortes Maria Jose, Font Maria Àngels, Brunelli Nicoletta, Bonura Adriano, Requena Manuel, Mayol Jordi, Murillo Ane, Jordà-Baleri Teresa, Cendrero Judith, Tomasello Alejandro, Molina Carlos, Urra Xabi, Garcia-Tornel Alvaro, Ribo Marc

СтатьяМногоцентровое исследованиеПеревод в очереди

Аннотация

INTRODUCTION

Timely transfer of patients with suspected LVO remains critical in acute stroke systems. Artificial intelligence (AI)-based imaging tools are increasingly implemented to support triage in non-thrombectomy centres. We assessed whether integrating an AI algorithm within established tele-stroke centres reduces time to transfer decision.

PATIENTS AND

METHODS

We conducted a prospective, multicentre, quasi-experimental study comparing consecutive cohorts in 2 tele-stroke centres referring to a single comprehensive stroke centre, before and after implementation of the Methinks Stroke Suite, an AI algorithm for LVO detection on non-contrast CT (NCCT) and CTA. Consecutive transferred patients with suspected acute ischaemic stroke were included. Remote vascular neurologists retained responsibility for final transfer decisions. Each phase spanned approximately 15 months. The primary outcome was time from arrival at the local centre to emergency medical services activation. Secondary outcomes included workflow intervals, imaging utilisation and algorithm performance.

RESULTS

We included 265 patients (136 in the post-implementation cohort; 129 in the pre-implementation cohort). Adjusted median time from arrival to transfer request did not differ (median difference - 2.40 min; 95% CI, -6.16 to 4.48). Post-implementation, time from imaging to transfer request (-7.16 min; 95% CI, -13.02 to -1.85) and arrival to groin puncture (-31.28 min; 95% CI, -60.89 to -13.74) decreased. Computed tomography angiography acquisition at referring centres increased (28%-81%), reducing repeat imaging at the comprehensive centre (79%-42%). Non-contrast CT-based AI prediction yielded a positive predictive value of 66% for endovascular treatment.

CONCLUSION

Artificial intelligence implementation was not associated with a shorter time to transfer decision. Fewer redundant imaging examinations were associated with a shorter time to reperfusion.

Для цитирования: Olive-Gadea Marta, Rizzo Federica, Cocho Dolores, Font Judit Roca, Rodrigo-Gisbert Marc, Muchada Marian, Del Fueyo Marta Rubiera, Rodriguez-Luna David, Simonetti Renato, Rodriguez-Villatoro Noelia, Pagola Jorge, Izquierdo Artur, Planells Gemma, Cortes Maria Jose, Font Maria Àngels, Brunelli Nicoletta, Bonura Adriano, Requena Manuel, Mayol Jordi, Murillo Ane, Jordà-Baleri Teresa, Cendrero Judith, Tomasello Alejandro, Molina Carlos, Urra Xabi, Garcia-Tornel Alvaro, Ribo Marc. CATalyze-AI: accelerating referral for thrombectomy in acute stroke patients using an AI-based software. A prospective, multicentre, quasi-experimental cohort study.. European stroke journal. 2026. DOI: 10.1093/esj/aakag085.

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