Rahadian Rayhan Erlangga, Okada Yohei, Shahidah Nur, Hong Dehan, Ng Yih Yng, Chia Michael Y C, Gan Han Nee, Leong Benjamin S H, Mao Desmond R, Ng Wei Ming, Doctor Nausheen Edwin, Ong Marcus Eng Hock
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
Resusc Plus. 2024 Mar 18;18:100606. doi: 10.1016/j.resplu.2024.100606. eCollection 2024 Jun.
Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refractory VF/VT in OHCA patients.
This was a retrospective study examining adult non-traumatic OHCA patients brought into the emergency department by Singapore EMS from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. Data from April 2010 to March 2020 were extracted for this study. Refractory VF/VT was defined as VF/VT persisting or recurring after at least one shock. Features were selected based on expert clinical opinion and availability to dispatch prior to arrival at scene. Multivariable logistic regression (MVR), LASSO and random forest (RF) models were investigated. Model performance was evaluated using receiver operator characteristic (ROC) area under curve (AUC) analysis and calibration plots.
20,713 patients were included in this study, of which 860 (4.1%) fulfilled the criteria for refractory VF/VT. All models performed comparably and were moderately well-calibrated. ROC-AUC were 0.732 (95% CI, 0.695 - 0.769) for MVR, 0.738 (95% CI, 0.701 - 0.774) for LASSO, and 0.731 (95% CI, 0.690 - 0.773) for RF. The shared important predictors across all models included male gender and public location.
The machine learning models developed have potential clinical utility to improve outcomes in cases of refractory VF/VT OHCA. Prediction of refractory VF/VT prior to arrival at patient's side may allow for increased options for intervention both by EMS and tertiary care centres.
难治性室颤（VF）或室性心动过速（VT）是院外心脏骤停（OHCA）治疗中的一项挑战。本研究旨在开发并验证可由紧急医疗服务（EMS）实施的机器学习模型，以预测OHCA患者的难治性VF/VT。
这是一项回顾性研究，纳入了新加坡EMS从泛亚复苏结局研究（PAROS）登记处送往急诊科的成年非创伤性OHCA患者。提取了2010年4月至2020年3月的数据用于本研究。难治性VF/VT定义为至少一次电击后仍持续或复发的VF/VT。根据专家临床意见和到达现场前调度时的可用性选择特征。研究了多变量逻辑回归（MVR）、套索回归（LASSO）和随机森林（RF）模型。使用受试者操作特征（ROC）曲线下面积（AUC）分析和校准图评估模型性能。
本研究纳入了20713例患者，其中860例（4.1%）符合难治性VF/VT标准。所有模型表现相当，校准适度良好。MVR的ROC-AUC为0.732（95%CI，0.695 - 0.769），LASSO为0.738（95%CI，0.701 - 0.774），RF为0.731（95%CI，0.690 - 0.773）。所有模型共有的重要预测因素包括男性性别和公共场所。
所开发的机器学习模型在改善难治性VF/VT OHCA病例的结局方面具有潜在的临床应用价值。在到达患者身边之前预测难治性VF/VT可能会增加EMS和三级护理中心的干预选择。
