Coronary artery bypass grafting vs. percutaneous coronary intervention in coronary artery disease patients with advanced chronic kidney disease: A Chinese single-center study

ObjectivesAims to compare the contemporary and long-term outcomes of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) in coronary artery disease (CAD) patients with advanced chronic kidney disease (CKD).Methods823 CAD patients with advanced CKD (eGFR < 30 ml/min/1.73 m2) were collected, including 247 patients who underwent CABG and 576 patients received PCI from January 2014 to February 2021. The primary endpoint was all-cause death. The secondary endpoints included major adverse cardiac and cerebrovascular events (MACCEs), myocardial infarction (MI), stroke and revascularization.ResultsMultivariable Cox regression models were used and propensity score matching (PSM) was also performed. After PSM, the 30-day mortality rate in the CABG group was higher than that in the PCI group but without statistically significant (6.6% vs. 2.4%, p = 0.24). During the first year, patients referred for CABG had a hazard ratio (HR) of 1.42 [95% confidence interval (CI), 0.41–3.01] for mortality compared with PCI. At the end of the 5-year follow-up, CABG group had a HR of 0.58 (95%CI, 0.38–0.86) for repeat revascularization, a HR of 0.77 (95%CI, 0.52–1.14) for survival rate and a HR of 0.88(95%CI, 0.56–1.18) for MACCEs as compared to PCI.ConclusionsAmong patients with CAD and advanced CKD who underwent CABG or PCI, the all-cause mortality and MACCEs were comparable between the two groups in 30 days, 1-year and 5 years. However, CABG was only associated with a significantly lower risk for repeat revascularization compared with PCI at 5 years follow-up

Prediction of Acute Kidney Injury Following Isolated Coronary Artery Bypass Grafting in Heart Failure Patients with Preserved Ejection Fraction Using Machine Leaning with a Novel Nomogram

Background: The incidence of postoperative acute kidney injury (AKI) is high due to insufficient perfusion in patients with heart failure. Heart failure patients with preserved ejection fraction (HFpEF) have strong heterogeneity, which can obtain more accurate results. There are few studies for predicting AKI after coronary artery bypass grafting (CABG) in HFpEF patients especially using machine learning methodology. Methods: Patients were recruited in this study from 2018 to 2022. AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. The machine learning methods adopted included logistic regression, random forest (RF), extreme gradient boosting (XGBoost), gaussian naive bayes (GNB), and light gradient boosting machine (LGBM). We used the receiver operating characteristic curve (ROC) to evaluate the performance of these models. The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were utilized to compare the prediction model. Results: In our study, 417 (23.6%) patients developed AKI. Among the five models, random forest was the best predictor of AKI. The area under curve (AUC) value was 0.834 (95% confidence interval (CI) 0.80–0.86). The IDI and NRI was also better than the other models. Ejection fraction (EF), estimated glomerular filtration rate (eGFR), age, albumin (Alb), uric acid (UA), lactate dehydrogenase (LDH) were also significant risk factors in the random forest model. Conclusions: EF, eGFR, age, Alb, UA, LDH are independent risk factors for AKI in HFpEF patients after CABG using the random forest model. EF, eGFR, and Alb positively correlated with age; UA and LDH had a negative correlation. The application of machine learning can better predict the occurrence of AKI after CABG and may help to improve the prognosis of HFpEF patients