Predictive accuracy of a perioperative hemodynamic indices-based prediction model for moderate-to-severe acute kidney injury after orthotopic heart transplantation.

BACKGROUND: Acute kidney injury is a common complication after orthotopic heart transplantation. Previous models have failed to consider the impact of multiple hemodynamic parameters on prediction performance. The objective of this study was aimed to develop machine-learning models incorporating multiple hemodynamic parameters to predict the risk of developing acute kidney injury after orthotopic heart transplantation. METHODS: We retrospectively analyzed 114 recipients of orthotopic heart transplantation, with postoperative stage 2-3 acute kidney injury as the prediction outcome. Preoperative characteristics, laboratory parameters, and intraoperative hemodynamics were evaluated. Potential predictors were first screened by univariate analysis, followed by least absolute shrinkage and selection operator regression for feature selection. Five machine-learning models were developed and evaluated via 5-fold cross-validation using multiple performance metrics. RESULTS: Postoperative stage 2-3 acute kidney injury incidence was 21.9% (25/114). Intraoperative hypotension and venous congestion were significantly associated with acute kidney injury. A total of 8 factors were ultimately filtered for constructing multiple machine-learning models, with the light gradient boosting machine model demonstrating the optimal performance (area under the receiver operating characteristic curve, 0.898; area under the precision-recall curve, 0.802; Brier score: 0.106). CONCLUSION: The machine-learning model based on incorporating perioperative hemodynamics (pulmonary artery systolic pressure, mean arterial pressure, central venous pressure) accurately predict stage 2-3 acute kidney injury postorthotopic heart transplantation, thereby optimizing hemodynamic management and improving outcomes.