Cardiovascular Center, National Taiwan University Hospital Yunlin Branch, Douliu City, Yunlin County, Taiwan.
Department of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
Eur J Med Res. 2023 Sep 15;28(1):347. doi: 10.1186/s40001-023-01294-1.
It is common to support cardiovascular function in critically ill patients with extracorporeal membrane oxygenation (ECMO). The purpose of this study was to identify patients receiving ECMO with a considerable risk of dying in hospital using machine learning algorithms.
A total of 1342 adult patients on ECMO support were randomly assigned to the training and test groups. The discriminatory power (DP) for predicting in-hospital mortality was tested using both random forest (RF) and logistic regression (LR) algorithms.
Urine output on the first day of ECMO implantation was found to be one of the most predictive features that were related to in-hospital death in both RF and LR models. For those with oliguria, the hazard ratio for 1 year mortality was 1.445 (p < 0.001, 95% CI 1.265-1.650).
Oliguria within the first 24 h was deemed especially significant in differentiating in-hospital death and 1 year mortality.
在危重病患者中使用体外膜肺氧合（ECMO）支持心血管功能很常见。本研究的目的是使用机器学习算法识别在医院死亡风险较高的接受 ECMO 治疗的患者。
共有 1342 名接受 ECMO 支持的成年患者被随机分配到训练组和测试组。使用随机森林（RF）和逻辑回归（LR）算法测试预测住院死亡率的判别能力（DP）。
在 ECMO 植入后的第一天的尿量被发现是 RF 和 LR 模型中与住院死亡相关的最具预测性特征之一。对于少尿患者，1 年死亡率的风险比为 1.445（p<0.001，95%CI 1.265-1.650）。
在最初 24 小时内出现少尿被认为是区分住院死亡和 1 年死亡率的重要指标。
