MedFusion-gP-AKI: development and multicenter validation of a machine learning fusion model for early prediction of KDIGO stage 3 acute kidney injury in critically ill traumatic cervicothoracic spinal cord injury patients

KDIGO stage-3 acute kidney injury (AKI), a life-threatening complication in critically ill patients with traumatic cervicothoracic spinal cord injury (TCTSCI), was associated with a 49.3% 60-day mortality and a median survival of 20 days in a combined MIMIC-IV/eICU analysis, underscoring its severe clinical consequences and the need for early identification and prediction. To address this need, MedFusion-GP-AKI was developed as a multimodal deep learning framework trained on the MIMIC-IV/eICU cohort and externally validated in 188 patients from four tertiary Chinese centers. Missing data were imputed with a GAN-based method, and key predictors were derived from the original dataset using NOTEARS, variational bottleneck, and adversarial analysis, yielding eleven variables led by lactate, mean arterial pressure, temperature, potassium, and TCTSCI level, with the dataset subsequently balanced using an SMOTified-GAN. Fifteen baseline models were benchmarked under uniform protocols, and the best-performing architectures were integrated into an ensemble that achieved AUCs of 0.938, 0.909, 0.969, 0.945, and 0.921 with APs of 0.841, 0.884, 0.992, 0.927, and 0.878 across pre- and post-SMOTE training, validation, and external cohorts, demonstrating reliable discrimination and calibration, stable clinical net benefit across thresholds, and balanced overall classification performance with strong generalizability across independent institutions. SHAP analysis confirmed that model attributions aligned with known clinical and physiological patterns, and a web-based calculator was developed for practical use. Overall, this study connects artificial intelligence, nephrology, and critical care by using multimodal deep learning and causal inference to predict severe AKI occurrence from early clinical data in critically ill TCTSCI patients. Acute kidney injury (AKI) is a serious complication after spinal cord injury, particularly among critically ill patients with injuries to the neck and upper back. These patients often develop low blood pressure and poor organ blood flow, making the kidneys vulnerable to damage. In this study, we built and tested MedFusion-GP-AKI, a deep learning model that predicts the risk of severe (KDIGO stage 3) AKI using routine clinical data collected within the first 24 h after admission to the intensive care unit. The model was trained on two large U.S. databases and tested in four hospitals in China. It accurately identified patients at high risk of developing AKI and provided interpretable results that explained which clinical factors, such as lactate, blood pressure, temperature, and potassium levels, most strongly influenced the predictions. A free web-based calculator allows clinicians to estimate AKI risk early and plan timely preventive strategies. This tool could help improve outcomes and guide individualized kidney care in patients with severe spinal cord injury.