Automated evaluation of collateral circulation for outcome prediction in acute ischemic stroke

医学 侧支循环 冲程(发动机) 抵押品 内科学 回顾性队列研究 多元分析 心脏病学 财务 机械工程 工程类 经济
作者
Valéria Cristina Scavasine,Gabriel Abrahão Stoliar,Bernardo Corrêa de Almeida Teixeira,Viviane Flumignan Zétola,Marcos Christiano Lange
出处
期刊:Journal of stroke and cerebrovascular diseases [Elsevier BV]
卷期号:33 (4): 107584-107584 被引量:6
标识
DOI:10.1016/j.jstrokecerebrovasdis.2024.107584
摘要

Abstract

Introduction

The assessment of collateral circulation in acute ischemic stroke management is essential. Modern tools, such as Brainomix's e-CTA, powered by artificial intelligence, provide detailed insights into collateral assessment. This retrospective study aims to identify factors contributing to favorable collateral status and compare outcomes between patients with good collaterals (grade 3) and fair collaterals (grade 0-2).

Method

This retrospective study included 97 patients admitted to the Stroke Unit at the Hospital de Clínicas of the Federal University of Paraná, Brazil, from September 2021 to January 2023. Comparative analyses involved demographic factors, cardiovascular risk factors, and the combined outcome of mortality and moderate to severe disability at discharge, 30-day, and 90-day follow-ups.

Results

Among the 97 cases, 58.8% showed 'good collaterals' with a grade 3 status. Variables affecting collateral status included age (p=0.042), neutrophil-lymphocyte ratio (p=0.005), and initial NIHSS scores (p<0.001). The presence of good collaterals according to e-CTA reduced the odds of death and moderate-severe disability at discharge (p=0.003; OR 0.27) and at 30 days (p=0.015; OR 0.33), although this effect diminished at the 90-day mark after multivariate analysis.

Discussion

Automated collateral assessment through e-CTA is a valuable tool in acute ischemic stroke evaluation. Good e-CTA collateral score serve as a promising imaging biomarker, guiding informed clinical decisions during Stroke Unit hospitalizations. This study highlights the relationship between collaterals and stroke outcomes and underscores the potential for AI-driven tools to enhance stroke care management.
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