Extracorporeal cardiopulmonary resuscitation (ECPR) has been shown to improve neurologically favorable survival in patients with refractory out-of-hospital cardiac arrest (OHCA) caused by shockable rhythms. Further refinement of patient selection is needed to focus this resource-intensive therapy on those patients likely to benefit. This study sought to create a selection model using machine learning (ML) tools for refractory cardiac arrest patients undergoing ECPR.

中文翻译：

---

机器学习确定了体外心肺复苏中更高的生存率。


体外心肺复苏 (ECPR) 已被证明可以改善由可电击节律引起的难治性院外心脏骤停 (OHCA) 患者的神经系统生存率。需要进一步细化患者选择，将这种资源密集型治疗集中于那些可能受益的患者。本研究试图使用机器学习 (ML) 工具为接受 ECPR 的难治性心脏骤停患者创建一个选择模型。