Development and Validation of a Novel Nomogram for Preoperative Prediction of In-Hospital Mortality After Coronary Artery Bypass Grafting Surgery in Heart Failure With Reduced Ejection Fraction.
Development and Validation of a Novel Nomogram for Preoperative Prediction of In-Hospital Mortality After Coronary Artery Bypass Grafting Surgery in Heart Failure With Reduced Ejection Fraction.
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开发和验证一种新型列线图，用于术前预测射血分数降低的心力衰竭患者进行冠状动脉搭桥手术后的院内死亡率。
DOI：
10.3389/fcvm.2021.709190
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发表时间：
2021
影响因子：
3.6
通讯作者：
Dong R
中科院分区：
文献类型：
--
作者：
Yan P;Liu T;Zhang K;Cao J;Dang H;Song Y;Zheng J;Zhao H;Wu L;Liu D;Huang Q;Dong R
背景和目标：射血分数降低的心力衰竭（HFrEF）患者是接受冠状动脉旁路移植术（CABG）的最具挑战性的患者之一。几种手术风险评分通常用于预测接受CABG的患者的风险。然而，这些风险评分并不专门针对HFrEF患者。我们的目的是开发和验证一种新的诺模图评分来预测CABG后HFrEF患者的院内死亡风险。方法：回顾性研究489例HFrEF并接受CABG的患者。结局为术后院内死亡。约70%（n = 342）的患者随机组成一个训练队列，其余（n = 147）的验证队列。从训练队列中推导出多变量逻辑回归模型，并以列线图形式显示，以预测HFrEF患者的术后死亡率。模型的性能进行了评估的歧视和校准。此外，我们比较了该模型与EuroSCORE-2的歧视和校准。结果如下：培训队列342例患者中有26例（7.6%）发生术后死亡，验证队列147例患者中有10例（6.8%）发生术后死亡。8个术前因素与术后死亡相关，包括年龄、危重状态、近期心肌梗死、卒中、左心室射血分数（LVEF）≤ 35%、LV扩张、血清肌酐升高和联合手术。列线图在预测训练和验证队列中CABG后死亡风险方面具有良好的区分度，C指数分别为0.889（95%CI，0.839-0.938）和0.899（95%CI，0.835-0.963），并且在预测死亡概率低于40%的患者中显示出拟合良好的校准曲线。与EuroSCORE-2相比，诺模图在训练队列（0.889 vs. 0.762，p = 0.005）以及验证队列（0.899 vs. 0.816，p = 0.039）中具有显著更高的C指数。此外，诺模图在训练和验证队列中的校准和重新分类效果均优于EuroSCORE-2。EuroSCORE-2低估了术后死亡风险，尤其是高危患者。结论：该列线图提供了HFrEF患者CABG后死亡风险的最佳术前估计，并有可能有助于识别院内死亡风险高的HFrEF患者。
Background and Aims: Patients with heart failure with reduced ejection fraction (HFrEF) are among the most challenging patients undergoing coronary artery bypass grafting surgery (CABG). Several surgical risk scores are commonly used to predict the risk in patients undergoing CABG. However, these risk scores do not specifically target HFrEF patients. We aim to develop and validate a new nomogram score to predict the risk of in-hospital mortality among HFrEF patients after CABG. Methods: The study retrospectively enrolled 489 patients who had HFrEF and underwent CABG. The outcome was postoperative in-hospital death. About 70% (n = 342) of the patients were randomly constituted a training cohort and the rest (n = 147) made a validation cohort. A multivariable logistic regression model was derived from the training cohort and presented as a nomogram to predict postoperative mortality in patients with HFrEF. The model performance was assessed in terms of discrimination and calibration. Besides, we compared the model with EuroSCORE-2 in terms of discrimination and calibration. Results: Postoperative death occurred in 26 (7.6%) out of 342 patients in the training cohort, and in 10 (6.8%) out of 147 patients in the validation cohort. Eight preoperative factors were associated with postoperative death, including age, critical state, recent myocardial infarction, stroke, left ventricular ejection fraction (LVEF) ≤35%, LV dilatation, increased serum creatinine, and combined surgery. The nomogram achieved good discrimination with C-indexes of 0.889 (95%CI, 0.839–0.938) and 0.899 (95%CI, 0.835–0.963) in predicting the risk of mortality after CABG in the training and validation cohorts, respectively, and showed well-fitted calibration curves in the patients whose predicted mortality probabilities were below 40%. Compared with EuroSCORE-2, the nomogram had significantly higher C-indexes in the training cohort (0.889 vs. 0.762, p = 0.005) as well as the validation cohort (0.899 vs. 0.816, p = 0.039). Besides, the nomogram had better calibration and reclassification than EuroSCORE-2 both in the training and validation cohort. The EuroSCORE-2 underestimated postoperative mortality risk, especially in high-risk patients. Conclusions: The nomogram provides an optimal preoperative estimation of mortality risk after CABG in patients with HFrEF and has the potential to facilitate identifying HFrEF patients at high risk of in-hospital mortality.
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影响因子：
3.4
作者：
Chalmers, John;Pullan, Mark;Poullis, Michael
通讯作者：
Poullis, Michael
影响因子：
39.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
3.4
作者：
Nashef, SAM;Rogues, F;Salamon, R
通讯作者：
Salamon, R
影响因子：
4.6
作者：
Fukunaga, Naoto;Ribeiro, Roberto Vanin Pinto;Rao, Vivek
通讯作者：
Rao, Vivek
影响因子：
9.7
作者：
Bouabdallaoui, Nadia;Stevens, Susanna R.;Rouleau, Jean L.
通讯作者：
Rouleau, Jean L.