Heart failure (HF) is a global health issue, and coronary artery bypass graft (CABG) is one of the most effective surgical treatments for HF with coronary artery disease. Unfortunately, the incidence of postoperative acute kidney injury (AKI) is high in HF patients following CABG, and there are few tools to predict AKI after CABG surgery for such patients. The aim of this study is to establish a nomogram to predict the incidence of AKI after CABG in patients with impaired left ventricular ejection fraction (LVEF).
From 2012 to 2017, Clinical information of 1208 consecutive patients who had LVEF< 50% and underwent isolated CABG was collected to establish a derivation cohort. A novel nomogram was developed using the logistic regression model to predict postoperative AKI among these patients. According to the same inclusion criteria and the same period, we extracted the data of patients from 6 other large cardiac centers in China ( n = 540) from the China Heart Failure Surgery Registry (China-HFSR) database for external validation of the new model. The nomogram was compared with 3 other available models predicting renal failure after cardiac surgery in terms of calibration, discrimination and net benefit.
In the derivation cohort ( n = 1208), 90 (7.45%) patients were diagnosed with postoperative AKI. The nomogram included 7 independent risk factors: female, increased preoperative creatinine(> 2 mg/dL), LVEF< 35%, previous myocardial infarction (MI), hypertension, cardiopulmonary bypass(CPB) used and perioperative blood transfusion. The area under the receiver operating characteristic curve (AUC) was 0.738, higher than the other 3 models. By comparing calibration curves and decision curve analyses (DCA) with other models, the novel nomogram showed better calibration and greater net benefit. Among the 540 patients in the validation cohort, 104 (19.3%) had postoperative AKI, and the novel nomogram performed better with respect to calibration, discrimination and net benefit.