[Impact of artificial intelligence on therapeutic metrics of cerebrovascular attack during the COVID-19 pandemic].

Autor:	Cirio JJ; Unidad de ACV, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina. E-mail: jjcirio@gmail.com., Diluca P; Neurorradiología, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina., Ciardi C; Unidad de ACV, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina., Scrivano E; Neurorradiologia Intervencionista, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina., Lundquist J; Neurorradiologia Intervencionista, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina., Lylyk IR; Neurorradiologia Intervencionista, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina., Pérez N; Neurorradiologia Intervencionista, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina., Lylyk PN; Neurocirugia Clínica, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina., Bleise C; Neurorradiologia Intervencionista, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina., Lylyk P; Neurorradiologia Intervencionista, La Sagrada Familia, Instituto Médico ENERI, Buenos Aires, Argentina.
Jazyk:	Spanish; Castilian
Zdroj:	Medicina [Medicina (B Aires)] 2023; Vol. 83 (5), pp. 705-718.
Abstrakt:	Introduction: The start of the COVID-19 pandemic forced the implementation of changes in the emergency services care system. Concomitantly, at our institution, we implemented the artificial intelligence (AI) software, RAPID.AI, for image analysis in ischemic stroke (IS). Our objective was to evaluate the impact of the use of AI together with the changes in the triage during the COVID-19 pandemic in patients with stroke due to large vessel occlusion (LVO). Methods: We included patients with IS due to LVO treated with intravenous reperfusion therapy plus endovascular or direct endovascular therapy. Results: Two groups were created. Group 1: patients from January 2019 to June 2020; Group 2: patients from July 2020 to December 2021, studied with RAPID.AI. Clinical data and temporal metrics were analyzed. They were compared according to arrival time from 08:00 to 20:00 (daytime) vs 20:01 to 7:59 (night). Results: We included 286 patients, 153 in group 1 and 133 in group 2. In group 2, door-image metric and image duration were lower, with shorter door-image onset and door-recanalization times; patients who arrived at night had higher NIHSS and longer time from onset-to-door with lower proportion of functional independence at 90 days (mRS = 2). Conclusions: The use of AI for image analysis along with a shorter door to end of image time allowed to reduce the interval to groin puncture. In the analysis by hours during the pandemic, patients admitted in daytime hours had significantly lower door to image, image time acquisition, and door to recanalization metrics.
Databáze:	MEDLINE
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