Key result
A random forest machine learning model effectively predicted acute kidney injury after coronary artery bypass grafting in patients with heart failure with preserved ejection fraction, achieving an AUC of 0.834.
Why the study?
Few studies have predicted postoperative AKI after CABG specifically in HFpEF patients, particularly using machine learning methodology.
Can machine learning models accurately predict acute kidney injury following isolated CABG in patients with HFpEF?
Cohort (n=1,767)
No
Can machine learning models accurately predict acute kidney injury following isolated CABG in patients with HFpEF?
Effect estimate: AUC 0.834 (95% CI 0.80-0.86)
Machine learning, particularly random forest modeling, can effectively predict postoperative acute kidney injury in HFpEF patients undergoing CABG using routine clinical variables.
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.
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Hou et al. (2024) conducted a cohort in Heart failure with preserved ejection fraction (HFpEF) undergoing isolated CABG (n=1,767). Random forest machine learning model vs. Other machine learning models (LGBM, XGBoost, GNB, LR) was evaluated on Area under the curve (AUC) for predicting acute kidney injury (AUC 0.834, 95% CI 0.80-0.86). A random forest machine learning model effectively predicted acute kidney injury after coronary artery bypass grafting in patients with heart failure with preserved ejection fraction, achieving an AUC of 0.834.
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