使用低表观扩散系数簇体积预测心搏骤停幸存者。

Prognostication of cardiac arrest survivors using low apparent diffusion coefficient cluster volume.

机构信息

Department of Emergency Medicine, Seoul National University Bundang Hospital, 166 Gumi-ro, Bundang-gu, Seongnam-si 463-707, Gyeonggi-do, Republic of Korea.

出版信息

Resuscitation. 2016 Mar;100:18-24. doi: 10.1016/j.resuscitation.2015.12.013. Epub 2016 Jan 13.

DOI:10.1016/j.resuscitation.2015.12.013

PMID:26774174

Abstract

INTRODUCTION

We developed a new neuroprognostication method for cardiac arrest (CA) using the relative volume of the most dominant cluster of low apparent diffusion coefficient (ADC) voxels and tested its performance in a multicenter setting.

METHODS

Adult (>15 years) out-of-hospital CA patients from three different facilities who underwent an MRI 12h after resuscitation were retrospectively analyzed. Patients with unknown long-term prognosis or poor baseline neurologic function were excluded. Average ADCs (mean and median), LADCV (relative volume of low-ADC voxels) and DC-LADCV (relative volume of most dominant cluster of low-ADC voxels) were extracted using different thresholds between 400 and 800 × 10(-6) mm(2) s(-1) at 10 × 10(-6) mm(2) s(-1) intervals. Area under the receiver operating characteristic curve (AUROC) and sensitivity for poor outcome (6-month cerebral performance category score >2) while maintaining 100% specificity were measured.

RESULTS

110 patients were analyzed. Average ADCs showed fair performance with an AUROC of 0.822 (95% confidence interval [CI], 0.744-0.900) for the mean and 0.799 (95% CI, 0.716-0.882) for the median. LADCV showed better performance with a higher AUROC (maximum, 0.925) in an ADC threshold range of 400 to 690 × 10(-6) mm(2) s(-1). DC-LADCV showed the best performance with a higher AUROC (maximum, 0.955) compared with LADCV in an ADC threshold range of 600 to 680 × 10(-6) mm(2) s(-1). DC-LADCV had a high sensitivity for poor outcomes (>80%) in a wide threshold range from 400 to 580 × 10(-6) mm(2) s(-1) with a maximum of 89.2%.

CONCLUSIONS

Quantitative analysis using DC-LADCV showed impressive performance in determining the prognosis of out-of-hospital CA patients in a multicenter setting.

摘要

简介

我们开发了一种新的心脏骤停（CA）神经预后方法，使用低表观扩散系数（ADC）体素最优势簇的相对体积，并在多中心环境中测试了其性能。

方法

回顾性分析了三家不同机构的成人（>15 岁）院外 CA 患者，他们在复苏后 12 小时进行了 MRI 检查。排除了长期预后未知或基线神经功能不良的患者。使用 400 至 800×10（-6）mm（2）s（-1）之间不同的阈值，在 10×10（-6）mm（2）s（-1）的间隔下提取平均 ADC（均值和中位数）、LADCV（低 ADC 体素的相对体积）和 DC-LADCV（最优势簇的低 ADC 体素的相对体积）。以保持 100%特异性为前提，测量不良预后（6 个月脑功能分类评分>2）的受试者工作特征曲线（AUROC）下面积和敏感性。

结果

共分析了 110 例患者。平均 ADC 表现出良好的性能，平均 AUROC 为 0.822（95%置信区间[CI]，0.744-0.900），中位数 AUROC 为 0.799（95%CI，0.716-0.882）。LADCV 在 ADC 阈值范围为 400 至 690×10（-6）mm（2）s（-1）时，表现出更好的性能，AUROC 更高（最大值为 0.925）。与 LADCV 相比，在 ADC 阈值范围为 600 至 680×10（-6）mm（2）s（-1）时，DC-LADCV 表现出最佳性能，AUROC 更高（最大值为 0.955）。在 ADC 阈值范围为 400 至 580×10（-6）mm（2）s（-1）时，DC-LADCV 具有高敏感性（>80%），最大敏感性为 89.2%。