Abstract 4140120: Artificial intelligence-driven morbidity prediction in acute kidney injury after acute type A aortic dissection surgery

Background: Acute kidney injury (AKI) often complicates acute type A aortic dissection (ATAAD), with elevated comorbidity rates and a significant tie to in-hospital mortality. Identifying risk factors early can mitigate AKI severity. Research Questions: This research endeavors to develop and corroborate predictive models leveraging Machine Learning (ML) techniques from Artificial Intelligence to forecast AKI occurrences in ATAAD-afflicted individuals. Methods: The study employed various machine learning (ML) algorithms including Gradient Boosting Machine (GBM), LightGBM, Random Forest (RF), K-Nearest Neighbors (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Naive Bayes (NB), Logistic Regression (LR), and ensemble methods (combining LR&LightGBM), employing tenfold cross-validation. Model performance was evaluated using SHapley Additive exPlanations (SHAP). A web-based tool for predicting AKI incidence was developed using Streamlit, based on the most effective model. The analysis involved 1350 ATAAD patients, among whom 586 (43.4%) developed post-operative AKI. Patients were divided into two cohorts: 85% for training and 15% for testing, with 126 features included in the predictive model. Results: Incorporating top 10 features, LightGBM (AUROC=0.886, 95% CI 0.841-0.930) excelled in predictive accuracy, calibration, and clinical utility, identifying key factors such as ventilation time in ICU, hourly urine output post-surgery, diuretic use, Scr, heart rate, urea, administration of recombinant human brain natriuretic peptide and ebrantil, MCHC, and blood glucose as associated with ATAAD-AKI. Conclusion(s): These ML models are robust tools for predicting AKI in ATAAD patients, with LightGBM's superior predictive ability standing out. They offer valuable support for clinical decision-making in ATAAD management, helping optimize postoperative strategies to minimize AKI occurrence after surgery.