Communications Engineering Department, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain.
Communications Engineering Department, University of the Basque Country UPV/EHU, 48013 Bilbao, Spain.
Resuscitation. 2019 Sep;142:153-161. doi: 10.1016/j.resuscitation.2019.03.048. Epub 2019 Apr 18.
Automated detection of return of spontaneous circulation (ROSC) is still an unsolved problem during cardiac arrest. Current guidelines recommend the use of capnography, but most automatic methods are based on the analysis of the ECG and thoracic impedance (TI) signals. This study analysed the added value of EtCO for discriminating pulsed (PR) and pulseless (PEA) rhythms and its potential to detect ROSC.
A total of 426 out-of-hospital cardiac arrest cases, 117 with ROSC and 309 without ROSC, were analysed. First, EtCO values were compared for ROSC and no ROSC cases. Second, 5098 artefact free 3-s long segments were automatically extracted and labelled as PR (3639) or PEA (1459) using the instant of ROSC annotated by the clinician on scene as gold standard. Machine learning classifiers were designed using features obtained from the ECG, TI and the EtCO value. Third, the cases were retrospectively analysed using the classifier to discriminate cases with and without ROSC.
EtCO values increased significantly from 41 mmHg 3-min before ROSC to 57 mmHg 1-min after ROSC, and EtCO was significantly larger for PR than for PEA, 46 mmHg/20 mmHg (p < 0.05). Adding EtCO to the machine learning models increased their area under the curve (AUC) by over 2 percentage points. The combination of ECG, TI and EtCO had an AUC for the detection of pulse of 0.92. Finally, the retrospective analysis showed a sensitivity and specificity of 96.6% and 94.5% for the detection of ROSC and no-ROSC cases, respectively.
Adding EtCO improves the performance of automatic algorithms for pulse detection based on ECG and TI. These algorithms can be used to identify pulse on site, and to retrospectively identify cases with ROSC.
在心脏骤停期间，自动检测自主循环恢复（ROSC）仍然是一个未解决的问题。目前的指南建议使用二氧化碳描记法，但大多数自动方法都是基于心电图（ECG）和胸阻抗（TI）信号的分析。本研究分析了呼气末二氧化碳（EtCO）在区分有脉搏（PR）和无脉搏（PEA）节律方面的额外价值及其检测 ROSC 的潜力。
共分析了 426 例院外心脏骤停病例，其中 117 例有 ROSC，309 例无 ROSC。首先，比较了有 ROSC 和无 ROSC 病例的 EtCO 值。其次，自动提取了 5098 个无干扰的 3 秒长片段，并使用现场临床医生标注的 ROSC 即时作为金标准，将其标记为 PR（3639）或 PEA（1459）。使用从 ECG、TI 和 EtCO 值获得的特征设计了机器学习分类器。第三，使用分类器对有和无 ROSC 的病例进行回顾性分析。
EtCO 值从 ROSC 前 3 分钟的 41mmHg 显著增加到 ROSC 后 1 分钟的 57mmHg，PR 的 EtCO 值明显大于 PEA，分别为 46mmHg/20mmHg（p<0.05）。将 EtCO 添加到机器学习模型中，使曲线下面积（AUC）增加了 2 个百分点以上。ECG、TI 和 EtCO 的组合检测脉搏的 AUC 为 0.92。最后，回顾性分析显示，检测 ROSC 和非 ROSC 病例的敏感性和特异性分别为 96.6%和 94.5%。
添加 EtCO 可提高基于 ECG 和 TI 的自动脉搏检测算法的性能。这些算法可用于现场识别脉搏，并回顾性识别 ROSC 病例。
