Nomogram to predict survival outcome of patients with veno-arterial extracorporeal membrane oxygenation after refractory cardiogenic shock

列线图 医学 心源性休克 体外膜肺氧合 接收机工作特性 置信区间 曲线下面积 逻辑回归 队列 体外 内科学 外科 心脏病学 心肌梗塞
作者
Huifang Wang,Chunbo Chen,Binfei Li,Zhou Cheng,Zeng Wang,Huang Xiang-wei,Minghai Xian,Jian Zhuang,Jimei Chen,Chengbin Zhou,Yiyu Deng
出处
期刊:Postgraduate Medicine [Informa]
卷期号:134 (1): 37-46 被引量:4
标识
DOI:10.1080/00325481.2021.1925562
摘要

: This study aims to develop a nomogram model to predict the survival of refractory cardiogenic shock (RCS) patients that received veno-arterial extracorporeal membrane oxygenation (VA-ECMO).A total of 235 and 209 RCS patients were supported with VA-ECMO from January 2018 to December 2019 in Guangdong Provincial People's Hospital, and from January 2020 to December 2020 in four third-grade and class-A hospitals were a development cohort (DC) and validation cohort (VC), respectively. Finally, 137 and 98 patients were included in the DC and VC. Multivariate logistic regression analysis was used to identify variables, and only these independent risk factors were used to establish the nomogram model. The receiver operating characteristic curve (ROC), calibration plot, decision curve, and clinical impact curves were used to evaluate the nomogram's discriminative ability, predictive accuracy, and clinical application value.Pre-ECMO cardiogenic arrest (pre-ECA), lactate (Lac), inotropic score (IS), and modified nutrition risk in the critically ill score (mNUTRIC score) were incorporated into the nomogram. This showed good discrimination in the DC, with an area under ROC (AUROC) and a 95% confidence interval (CI) of 0.959 (0.911-0.986). The AUROC (95% CI) of the VC was 0.928 (0.858-0.971). The calibration plots of the DC and VC presented good calibration results. The decision curve and clinical impact curve of the nomogram provided improved benefits for RCS patients.This study established a prediction nomogram composed of pre-ECA, Lac, IS, and mNUTRIC scores that could help clinicians to predict the survival probability at hospital discharge precisely and rapidly for RCS patients that received VA-ECMO.
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