Predicting STA-MCA Anastomosis Patency Using FLOW800

Brain and Spine(2023)

引用 0|浏览8
暂无评分
摘要
INTRODUCTION: STA-MCA bypass is the “work horse” for cerebral flow augmentation that yields high rates of anastomosis patency. However, the persistence of no-flow due to insufficient local demand presents a challenge. Here, we assessed intraoperative real-time hemodynamics measured by ICG-based FLOW800 to characterize risk and predict patency prior to anastomosis, aiding surgeons in choosing the optimal recipient and anastomosis site. METHODS: A retrospective and exploratory data analysis of 39 patients were conducted using FLOW800 software to assess 4 regions of interest (proximal/distal recipient and adjacent/remote gyrus) for 4 hemodynamic parameters: speed, delay, rise-time, and time-to-peak. Medical records were used to classify patients into flow and no-flow groups based on immediate or long-term anastomosis patency. Pre-anastomosis hemodynamics were compared between the two groups via univariate and multivariate analysis. Principal Component Analysis (PCA) was used to identify “high-risk” and “low-risk” groups for no-flow. RESULTS: 35 patients demonstrated immediate flow post-revascularization and were compared to 4 patients with immediate or long-term no-flow post-revascularization. No-flow groups had significantly greater Proximal Recipient Speed (238.3 ± 120.8) and Distal Recipient Speed (241.0 ± 117.0) than the flow group (138.5 ± 93.6; 142.1 ± 103.8, p < 0.05). Based on PCA, a “high-risk” group (n = 10) including no-flow patients was characterized by high flow speed, and a “low-risk” (n = 6) for opposite parameters. Patients with follow-ups in the “low-risk” group maintained robust flow (4/4), whereas patients with follow-up in the “high-risk" group demonstrated reduced or absent flow (5/7). CONCLUSIONS: Our results suggest that high recipient speed measured intraoperatively elevates the risk of no patency or reduced flow, potentially due to reduced local demand. Continued FLOW800-based ROI metric analysis could guide intraoperative decision making of specific recipient and anastomosis site selection.
更多
查看译文
关键词
sta-mca
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要