The Second School of Clinical Medicine, Southern Medical University, Department of Critical Care Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Department of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Postgrad Med. 2022 Jan;134(1):37-46. doi: 10.1080/00325481.2021.1925562. Epub 2021 May 20.
: 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.
本研究旨在开发一种列线图模型,以预测接受静脉-动脉体外膜肺氧合(VA-ECMO)治疗的难治性心源性休克(RCS)患者的生存情况。
2018 年 1 月至 2019 年 12 月,广东省人民医院共有 235 例 RCS 患者接受 VA-ECMO 支持,2020 年 1 月至 12 月,4 家三级甲等医院共有 209 例患者接受 VA-ECMO 支持,分别作为开发队列(DC)和验证队列(VC)。最终,DC 和 VC 分别纳入 137 例和 98 例患者。采用多变量逻辑回归分析识别变量,仅将这些独立风险因素纳入列线图模型。采用受试者工作特征曲线(ROC)、校准图、决策曲线和临床影响曲线评估列线图的区分能力、预测准确性和临床应用价值。
列线图纳入了 ECMO 前心搏骤停(pre-ECA)、乳酸(Lac)、正性肌力评分(IS)和改良重症患者营养风险评分(mNUTRIC 评分)。该模型在 DC 中具有良好的区分能力,ROC 曲线下面积(AUROC)及其 95%置信区间(CI)为 0.959(0.911-0.986)。VC 的 AUROC(95%CI)为 0.928(0.858-0.971)。DC 和 VC 的校准图均呈现出良好的校准结果。列线图的决策曲线和临床影响曲线为 RCS 患者提供了更高的获益。
本研究建立了一个由 pre-ECA、Lac、IS 和 mNUTRIC 评分组成的预测列线图,可帮助临床医生快速准确地预测接受 VA-ECMO 治疗的 RCS 患者出院时的生存概率。