Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery

BACKGROUND Acute kidney injury (AKI) is a common and serious complication of major abdominal surgery. However, predictive models specific to pancreatic surgery remain scarce. AIM To develop and validate an interpretable machine learning model for early prediction of postoperative AKI following pancreatic surgery. METHODS Adults undergoing pancreaticoduodenectomy or distal or total pancreatectomy from 2014 to 2024 were retrospectively analyzed. AKI was defined by the kidney disease Improving glob