Department of Emergency Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan.
Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan; Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
Am J Emerg Med. 2023 Sep;71:86-94. doi: 10.1016/j.ajem.2023.06.022. Epub 2023 Jun 17.
Most prediction models, like return of spontaneous circulation (ROSC) after cardiac arrest (RACA) or Utstein-based (UB)-ROSC score, were developed for prehospital settings to predict the probability of ROSC in patients with out-of-hospital cardiac arrest (OHCA). A prediction model has been lacking for the probability of ROSC in patients with OHCA at emergency departments (EDs).
In the present study, a point-of-care (POC) testing-based model, POC-ED-ROSC, was developed and validated for predicting ROSC of OHCA at EDs.
DESIGN, SETTINGS AND PARTICIPANTS: Prospectively collected data for adult OHCA patients between 2015 and 2020 were analysed. POC blood gas analysis obtained within 5 min of ED arrival was used.
The primary outcome was ROSC. In the derivation cohort, multivariable logistic regression was used to develop the POC-ED-ROSC model. In the temporally split validation cohort, the discriminative performance of the POC-ED-ROSC model was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with RACA or UB-ROSC score using DeLong test.
The study included 606 and 270 patients in the derivation and validation cohorts, respectively. In the total cohort, 471 patients achieved ROSC. Age, initial cardiac rhythm at ED, pre-hospital resuscitation duration, and POC testing-measured blood levels of lactate, potassium and glucose were significant predictors included in the POC-ED-ROSC model. The model was validated with fair discriminative performance (AUC: 0.75, 95% confidence interval [CI]: 0.69-0.81) with no significant differences from RACA (AUC: 0.68, 95% CI: 0.62-0.74) or UB-ROSC score (AUC: 0.74, 95% CI: 0.68-0.79).
Using only six easily accessible variables, the POC-ED-ROSC model can predict ROSC for OHCA resuscitated at ED with fair accuracy.
大多数预测模型，如心脏骤停后自主循环恢复（ROSC）或基于乌斯坦（UB）的ROSC 评分，都是为院前环境开发的，用于预测院外心脏骤停（OHCA）患者 ROSC 的概率。目前尚缺乏急诊室（ED）OHCA 患者 ROSC 概率的预测模型。
本研究开发并验证了一种基于即时检测（POC）的模型，即 POC-ED-ROSC，用于预测 ED 接受 OHCA 患者的 ROSC。
设计、地点和参与者：分析了 2015 年至 2020 年期间成年 OHCA 患者的前瞻性采集数据。使用 ED 到达后 5 分钟内获得的即时血气分析。
主要结局是 ROSC。在推导队列中，使用多变量逻辑回归开发 POC-ED-ROSC 模型。在时间分割验证队列中，使用接受者操作特征（ROC）曲线下面积（AUC）评估 POC-ED-ROSC 模型的判别性能，并使用 DeLong 检验与 RACA 或 UB-ROSC 评分进行比较。
研究纳入了推导队列和验证队列的 606 名和 270 名患者。在总队列中，471 名患者达到 ROSC。年龄、ED 初始心搏节律、院前复苏时间以及 POC 检测测量的血乳酸、钾和葡萄糖水平是纳入 POC-ED-ROSC 模型的显著预测因子。该模型的判别性能良好（AUC：0.75，95%置信区间[CI]：0.69-0.81），与 RACA（AUC：0.68，95%CI：0.62-0.74）或 UB-ROSC 评分（AUC：0.74，95%CI：0.68-0.79）无显著差异。
仅使用六个易于获取的变量，POC-ED-ROSC 模型可以准确预测 ED 复苏的 OHCA 患者的 ROSC。
