The incidence of acute kidney injury (AKI) after coronary artery bypass grafting (CABG) is high and has been reported to range from 6.7% to 39% [1, 2, 3]. AKI has been associated with increased morbidity and mortality after CABG [3, 4, 5], which further increases in the more severe stages of AKI; and is associated with increased short-term and long-term mortality [4, 6, 7, 8, 9]. AKI after cardiac surgery also increases intensive care unit (ICU) length of stay and resource utilization [2, 10].

The development of AKI involves a variety of mechanisms, including ischemic reperfusion injury, renal toxin release, hemolysis, oxidative stress and cytokine secretion, which can cause a systemic inflammatory response, endothelial damage and renal tubular cell damage [1, 11, 12, 13]. Previous studies have shown that older age, low ejection fraction, a previous history of kidney disease, and increased time on cardiopulmonary bypass are important predictors of the development of AKI [6, 14, 15, 16].

While patients with heart failure and reduced ejection fraction are more likely to develop AKI after CABG, patients with preserved ejection fraction may also develop AKI after surgery which will also seriously affect the prognosis of those patients. The research methods for disease risk prediction models are constantly being updated. The introduction of machine learning methods now offers another technique to predict the occurrence of adverse events following surgery [17]. Traditional logistic analysis generally deals with data with appropriate size, single types of data, structured data and simple parameter models that meet certain assumptions. For large and complex data, machine learning methods can be used to obtain more accurate risk prediction. In this study, we sought to predict AKI in patients with preserved ejection fraction after isolated CABG using machine learning methodology.

Our study used machine learning methods, whereas previous studies included a small population and mixed surgical types. Our results suggest that the EF, eGFR, age, Alb, UA, and LDH are independent risk factors for AKI in HFpEF patients after CABG using the random forest model. The incidence of AKI was 23.6% in our study which is similar to previous studies [1, 2, 3].

While there are many studies on acute renal injury after cardiac surgery, only a few used the machine learning method to predict AKI after cardiac surgery [20, 21, 22]. However, there are only a few studies on patients with HFpEF undergoing isolated CABG by machine learning. In this study, we used a new machine learning method, to make a risk prediction model for this group of patients to better predict the occurrence of AKI following CABG surgery to decrease morbidity and mortality in these patients.

The eGFR, EF and age are important risk factors for predicting postoperative AKI in this study, which is consistent with many previous studies [1, 2, 16]. The eGFR is an index to reflect the basic function of the kidney. An abnormal eGFR prior to surgery indicates poor renal function and a group of patients who will be more prone to acute kidney injury after surgery. Increased age is a risk factor for AKI as the renal function of the human body gradually declines with age. EF is an important indicator of cardiac function, and a low EF leads to low renal perfusion, which can lead to oliguria and is more prone to acute renal injury [19, 23, 24, 25].

Although LDH is not specifically produced by kidney, it can predict the occurrence of renal injury, as noted in previous studies [22]. Previous studies have not found that preoperative albumin is a risk factor for predicting AKI after cardiac surgery. However, previous studies [26] have suggested that albumin infusion before CABG can reduce the occurrence of acute renal injury after surgery. In addition, studies [27] have shown that the increase of albumin absorption by renal tubules can reduce the occurrence of AKI. Albumin, a risk factor found in our study, can be used to predict AKI after bypass surgery. It may further improve the prediction of acute kidney injury and identify patients with potential risks at an early stage. In addition, our results also show that uric acid is an independent risk factor for AKI. Tang H et al. [28] showed that when uric acid is increased before cardiac surgery, there is an increased risk of AKI after cardiac surgery. Previous studies have also found that increased preoperative uric acid levels is an independent risk factor for AKI after cardiac surgery [29].

There are limitations of this study. Our study was a single-center, retrospective study, with some selection bias. However, all the included patients were HFpEF, and were not compared with other heart failure patients. In the future, we will try to increase these variables to further improve the prediction model. In this study, the diagnosis of AKI was based on KDIGO criteria. Since diuretics are used in many patients after surgery, urine volume was not used as one of the diagnostic criteria of AKI.

Ejection fraction, estimated glomerular filtration rate, age, albumin, uric acid, and lactate dehydrogenase are independent risk factors for acute kidney injury in heart failure preserved ejection fraction patients after coronary artery bypass grafting by the random forest model. The application of machine learning can better predict clinical events.

CABG, coronary artery bypass grafting; AKI, acute kidney injury; KDIGO, Kidney Disease Improving Global Outcomes; ROC, receiver operating characteristic; eGFR, estimated glomerular filtration rate; IABP, intra-aortic balloon pump; CI, confidence interval; ICU, intensive care unit; BMI, body mass index; PCI, percutaneous coronary intervention; LVEF, left ventricular ejection fraction; IQR, interquartile range; OR, odds ratio.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

XJH, TSL and JBZ were responsible for the design, supervision of the study, and revision of the manuscript. XJH drafted the manuscript. KZ and SJX designed a statistical plan. YL and RD participated in the revision of the manuscript and the coordination of the study. XJH participated in data acquisition. All authors contributed to editorial changes in the manuscript. All authors read and agreed to the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

The studies involving human participants were approved by Ethics Committee of Beijing Anzhen Hospital (2021094X). All consents were obtained from the study participants oral.

We thank all the patients who participated in the trial.

This work was supported by the National Natural Science Foundation of China (grant nos. 81570373 and PX2023024).

The authors declare no conflict of interest.