Number Needing Review: A Novel Metric to Assess Triage Efficiency of Large Vessel Occlusion Detection Systems

IF 2.1 Q3 CLINICAL NEUROLOGY
J. Catapano, Katriel E. Lee, S. Desai, India C. Rangel, H. Stonnington, K. Rumalla, C. Rutledge, V. Srinivasan, J. Baranoski, T. Cole, E. Winkler, A. Ducruet, F. Albuquerque, A. Jadhav
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引用次数: 1

Abstract

Endovascular thrombectomy is the gold‐standard treatment for large vessel occlusions (LVOs). A novel metric is introduced: the number needing review (NNR) to assess the triage efficiency of LVO detection systems. Patients with suspected ischemic stroke and images processed by RapidAI LVO detection software over 6 months were reviewed. Only patients with LVOs of the M1 segment were included. The NNR was calculated for an M1 occlusion. Of 559 patients, M1 occlusion was detected in 42 patients (7.5%). RapidAI LVO had a sensitivity of 71%, specificity of 94%, positive predictive value of 49%, and negative predictive value of 92% for M1 occlusion. When gaze deviation and hyperdense sign were combined with RapidAI LVO, the specificity and positive predictive value increased to 100% for an M1 occlusion. A negative RapidAI LVO result combined with a low (<15 mL, T max >6 seconds) or high (<50 mL, T max >6 seconds) T max threshold was found to have a specificity and positive predictive value of 100% for no occlusion. The combination of gaze deviation, hyperdense sign, positive RapidAI LVO, and negative RapidAI LVO with low T max threshold yielded an NNR of 24 per 100 cases. When combined with a negative RapidAI LVO and a high T max threshold, the NNR was 16 per 100 cases. Adding National Institutes of Health Stroke Scale score <7 decreased the NNR to 9 per 100 cases. Adding gaze deviation and hyperdense sign to the RapidAI LVO increases the specificity and positive predictive value for an M1 occlusion. When combined with a negative RapidAI LVO detection and either a low or high T max >6 seconds threshold, the NNR is significantly reduced.
需要回顾的数量:一种评估大血管闭塞检测系统分诊效率的新指标
血管内血栓切除术是治疗大血管闭塞(LVO)的金标准。介绍了一种新的指标:需要审查的数量(NNR),以评估LVO检测系统的分诊效率。对6个月以上疑似缺血性卒中患者和RapidAI LVO检测软件处理的图像进行了回顾。仅包括M1段LVO患者。计算M1闭塞的NNR。在559例患者中,42例(7.5%)患者检测到M1闭塞。RapidAI LVO对M1闭塞的敏感性为71%,特异性为94%,阳性预测值为49%,阴性预测值为92%。当视线偏差和高密度体征与RapidAI LVO相结合时,M1闭塞的特异性和阳性预测值增加到100%。RapidAI LVO阴性结果与低(6秒)或高(6秒。凝视偏差、高密度体征、阳性RapidAI LVO和阴性RapidAI LV奥与低T最大阈值的组合产生了每100例病例24例的NNR。当与阴性RapidAI LVO和高T最大阈值相结合时,NNR为16/100例。加上美国国立卫生研究院卒中量表评分6秒阈值,NNR显著降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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