Background

Some patients resuscitated from out-of-hospital cardiac arrest (OHCA) progress to death by neurological criteria (DNC). We hypothesized that initial brain imaging, electroencephalography (EEG), and arrest characteristics predict progression to DNC.

Methods

We identified comatose OHCA patients from January 2010 to February 2020 treated at a single quaternary care facility in Western Pennsylvania. We abstracted demographics and arrest characteristics; Pittsburgh Cardiac Arrest Category, initial motor exam and pupillary light reflex; initial brain CT grey-to-white ratio (GWR), sulcal or basal cistern effacement; initial EEG background and suppression ratio. We used two modeling approaches: fast and frugal tree (FFT) analysis to create an interpretable clinical risk stratification tool and ridge regression for comparison. We used bootstrapping to randomly partition cases into 80% training and 20% test sets and evaluated test set sensitivity and specificity.

Results

We included 1,569 patients, of whom 147 (9%) had diagnosed DNC. Across bootstrap samples, >99% of FFTs included three predictors: sulcal effacement, and in cases without sulcal effacement, the combination of EEG background suppression and GWR ≤ 1.23. This tree had mean sensitivity and specificity of 87% and 81%. Ridge regression with all available predictors had mean sensitivity 91% and mean specificity 83%. Subjects falsely predicted as likely to progress to DNC generally died of rearrest or withdrawal of life sustaining therapies due to poor neurological prognosis. Two of these cases awakened from coma during the index hospitalization.

Conclusions

Sulcal effacement on presenting brain CT or EEG suppression with GWR ≤ 1.23 predict progression to DNC after OHCA.

中文翻译：

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根据院外心脏骤停后神经学标准对进展至死亡的早期风险分层

背景

根据神经学标准（DNC），一些从院外心脏骤停（OHCA）复苏的患者进展至死亡。我们假设最初的脑成像、脑电图 (EEG) 和逮捕特征可以预测 DNC 的进展。

方法

我们确定了 2010 年 1 月至 2020 年 2 月期间在宾夕法尼亚州西部的一家四级护理机构接受治疗的昏迷 OHCA 患者。我们提取了人口统计数据和逮捕特征；匹兹堡心脏骤停类别、初始运动检查和瞳孔对光反射；初始脑 CT 灰白比 (GWR)、脑沟或基底池消失；初始脑电图背景和抑制比。我们使用了两种建模方法：快速节俭树 (FFT) 分析来创建可解释的临床风险分层工具，并使用岭回归进行比较。我们使用 bootstrapping 将案例随机划分为 80% 的训练集和 20% 的测试集，并评估测试集的敏感性和特异性。

结果

我们纳入了 1,569 名患者，其中 147 名 (9%) 被诊断为 DNC。在 bootstrap 样本中，>99% 的 FFT 包括三个预测因子：脑沟消失，在没有脑沟消失的情况下，EEG 背景抑制和 GWR ≤ 1.23 的组合。该树的平均敏感性和特异性分别为 87% 和 81%。使用所有可用预测因子进行岭回归的平均敏感性为 91%，平均特异性为 83%。错误预测可能进展为 DNC 的受试者通常因神经学预后不良而再次停止或停止维持生命的治疗而死亡。其中两名病例在住院期间从昏迷中醒来。

结论

脑 CT 或 EEG 抑制且 GWR ≤ 1.23 时脑沟消失可预测 OHCA 后进展为 DNC。