[Impact of artificial intelligence on therapeutic metrics of cerebrovascular attack during the COVID-19 pandemic]. / Impacto de la inteligencia artificial en las métricas terapéuticas del ataque cerebrovascular durante la pandemia COVID-19.

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.

Introducción: El inicio de la pandemia COVID-19, obligó a implementar cambios en el sistema de atención de los servicios de emergencia. Coincidentemente, en nuestra institución, implementamos el software de inteligencia artificial (IA), RAPID.AI, para el análisis de imágenes en el ataque cerebrovascular isquémico (ACVi). Nuestro objetivo fue evaluar el impacto del uso de la IA junto a los cambios en el triage durante la pandemia por COVID-19 en pacientes con ACVi por oclusión de gran vaso cerebral (OGVC). Métodos: Se crearon 2 grupos de pacientes con ACVi por OGVC tratados con terapia de reperfusión endovenosa más endovascular o terapia endovascular directa. Grupo 1: pacientes de enero 2019 a junio 2020; Grupo 2: pacientes de julio 2020 a diciembre de 2021, estudiados con RAPID.AI. Se analizaron datos clínicos, y métricas temporales. Se compararon según hora de arribo de 08:00 a 20:00 h (diurno) vs. 20:01 a 7:59 h (nocturno). Resultados: El grupo 1 comprendió 153 pacientes y el grupo 2 133. En el grupo 2 la métrica puerta-imagen y adquisición de la imagen fueron menores, con menor tiempo puerta-inicio de imagen y puerta-recanalización; los pacientes en horario nocturno presentaron mayor NIHSS y tiempos inicio-ingreso con menor proporción de independencia funcional a 90 días. Conclusiones: El uso de la IA para el análisis de imágenes junto a un menor tiempo puerta-fin de imagen, permitió acortar el intervalo hasta la punción inguinal. En el análisis por horarios durante la pandemia, los pacientes ingresados en horario diurno presentaron métricas puerta-imagen, tiempo de imagen y puertarecanalización significativamente menores.

Impacto de la inteligencia artificial en las métricas terapéuticas del ataque cerebrovascular durante la pandemia COVID-19 / Impact of artificial intelligence on therapeutic metrics of cerebrovascular attack during the COVID-19 pandemic

Resumen Introducción : El inicio de la pandemia COVID-19, obligó a implementar cambios en el sistema de aten ción de los servicios de emergencia. Coincidentemente, en nuestra institución, implementamos el software de inteligencia artificial (IA), RAPID.AI, para el análisis de imágenes en el ataque cerebrovascular isquémico (ACVi). Nuestro objetivo fue evaluar el impacto del uso de la IA junto a los cambios en el triage durante la pandemia por COVID-19 en pacientes con ACVi por oclusión de gran vaso cerebral (OGVC). Métodos : Se crearon 2 grupos de pacientes con ACVi por OGVC tratados con terapia de reperfusión endovenosa más endovascular o terapia endovascu lar directa. Grupo 1: pacientes de enero 2019 a junio 2020; Grupo 2: pacientes de julio 2020 a diciembre de 2021, estudiados con RAPID.AI. Se analizaron datos clínicos, y métricas temporales. Se compararon según hora de arribo de 08:00 a 20:00 h (diurno) vs. 20:01 a 7:59 h (nocturno). Resultados : El grupo 1 comprendió 153 pacientes y el grupo 2 133. En el grupo 2 la métrica puerta-imagen y adquisición de la imagen fueron menores, con menor tiempo puerta-inicio de imagen y puerta-recanalización; los pacientes en horario nocturno presentaron mayor NIHSS y tiempos inicio-ingreso con menor proporción de independencia funcional a 90 días. Conclusiones : El uso de la IA para el análisis de imá genes junto a un menor tiempo puerta-fin de imagen, permitió acortar el intervalo hasta la punción inguinal. En el análisis por horarios durante la pandemia, los pacientes ingresados en horario diurno presentaron métricas puerta-imagen, tiempo de imagen y puerta-recanalización significativamente menores.

Abstract 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 en dovascular 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. Clini cal 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 propor tion 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.

In situ tissue engineering: endothelial growth patterns as a function of flow diverter design.

BACKGROUND: Vascular remodeling in response to implantation of a tissue engineering scaffold such as a flow diverter (FD) leads to the cure of intracranial aneurysms. We hypothesize that the vascular response is dependent on FD design, and CD34+ progenitor cells play an important role in the endothelialization of the implant. METHODS: Sixteen rabbit aneurysms were randomly treated with two different single-layer braided FDs made of cobalt-chrome alloys. The FD-48 and FD-72 devices had 48 and 72 wires, respectively. Aneurysm occlusion rate was assessed during the final digital subtraction angiogram at 10, 20, 30, and 60 days (n=2 per device per time point). Implanted vessels were analyzed with scanning electron microscopy for tissue coverage, endothelialization, and immuno-gold labeling for CD34+ cells. RESULTS: Complete aneurysm occlusion rates were similar between the devices; however, complete or near complete occlusion was more frequently observed in aneurysms with neck ≤4.2 mm (p=0.008). Total tissue coverage at 10 days over the surface of the FD-48 and FD-72 devices was 56.4±11.6% and 76.6±3.6%, respectively. Endothelial cell growth over the surface was time-dependent for the FD-72 device (Spearman's r=0.86, p=0.013) but not for the FD-48 device (Spearman's r=-0.59, p=0.094). The endothelialization score was marginally correlated with the distance from the aneurysm neck for the FD-48 device (Spearman's r=1, p=0.083) but not for the FD-72 device (Spearman's r=0.8, p=0.33). CD34+ cells were present along the entirety of both devices at all time points. CONCLUSIONS: This study gives preliminary evidence that temporal and spatial endothelialization is dependent on FD design. Circulating CD34+ progenitor cells contribute to endothelialization throughout the healing process.