The eGFR-AI stacked machine-learning model predicted early postoperative kidney function category with an accuracy of 0.75 and a weighted ROC-AUC of 0.92, comparable to standard classifiers.
Observational (n=200)
No
Can a stacked machine-learning model accurately predict early postoperative eGFR category in surgical ICU patients?
A stacked machine-learning model can predict early postoperative kidney function categories using routine perioperative variables with high discrimination, though it did not outperform standard classifiers.
Background: Postoperative kidney dysfunction is a common and serious complication in surgical patients. Kidney function is typically assessed using the estimated glomerular filtration rate (eGFR), most often calculated with the CKD-EPI equation based on serum creatinine. While several machine learning models have been developed to predict acute kidney injury, few have focused on predicting postoperative eGFR category. This study aimed to develop a machine learning model capable of classifying surgical patients into eGFR categories G1, G2, or G3+ in the early postoperative period, based on preoperative and intraoperative data. Methods: We developed the two-layer “eGFR-AI” architecture. In the first layer, two XGBoost models compute the probability of eGFR being above 89 and 59 mL/min/1.73 m2, respectively, and their outputs are passed to a second-layer logistic regression model that produces the final classification. The dataset included 200 patients admitted postoperatively to the intensive care unit of a tertiary academic hospital during the first half of 2024. Input features comprised age, sex, body mass index, type and duration of surgery, ASA status, presence of sepsis or shock at admission, and history of arterial hypertension, diabetes mellitus, or chronic kidney disease. Model performance was evaluated using accuracy, F1 score, and area under the ROC curve (ROC-AUC) on a held-out testing set. Feature importance analysis and statistical testing of associations with acute kidney injury were also performed. Results: On a held-out test set, the final model achieved an accuracy of 0.75, a weighted F1 score of 0.75, and a weighted ROC-AUC of 0.92 (balanced accuracy 0.76; Matthews correlation coefficient 0.65; Cohen’s κ 0.62). The first-layer models reached ROC-AUC values of 0.85 (eGFR > 89) and 0.96 (eGFR > 59). In a head-to-head comparison on the same partition, the stacked model performed comparably to standard baseline classifiers (multinomial logistic regression, random forest, support-vector machine, single multiclass XGBoost, CatBoost, LightGBM) without demonstrating superiority. Chronic kidney disease and presence of sepsis or shock at admission emerged as the strongest predictors. In an exploratory analysis (n = 8 events), all patients diagnosed with acute kidney injury fell into the G3+ category. Conclusions: In this single-center pilot study, “eGFR-AI” shows that early postoperative kidney function category can be predicted from a small set of routinely available preoperative and intraoperative variables, with performance comparable to standard classifiers and the added benefit of calibrated, interpretable category-level probabilities. Given the limited unicentric cohort and reduced category granularity, these findings should be regarded as preliminary and hypothesis-generating: they support the feasibility of the approach and motivate external, multicenter validation before any clinical application.
Brenner et al. (Sun,) conducted a observational in Postoperative kidney dysfunction (n=200). eGFR-AI stacked machine-learning model vs. Standard baseline classifiers was evaluated on Classification into eGFR categories G1, G2, or G3+ in the early postoperative period. The eGFR-AI stacked machine-learning model predicted early postoperative kidney function category with an accuracy of 0.75 and a weighted ROC-AUC of 0.92, comparable to standard classifiers.