Postoperative acute kidney injury (AKI) is a major cause of morbidity and mortality in patients who undergo intraabdominal surgery procedures. AKI leads to increased risks of adverse outcomes such as hospital readmission and progression to chronic kidney disease, to reduced short- and long-term survival, and to greater costs to both patients and hospital systems. There are no effective treatments to prevent postoperative AKI, and as such, appropriate perioperative management is critical for mitigating its risk. However, there is an incomplete understanding of the ways in which patient and perioperative management factors interact to influence the risk of postoperative AKI. Traditional methods only identify generic, global risk factors and only quantify the average effects of each factor on AKI risk. Using an institutional cohort of intraabdominal surgery patients containing detailed preoperative (e.g., patient demographics, risk factors, comorbidities) and intraoperative data (e.g., hemodynamic variables, medications, fluid management), we will utilize a novel interpretable machine learning framework (SHAP [Shapley Additive exPlanations]) to model postoperative AKI risk. Machine learning provides the ability to model complex relationships among predictor variables, and SHAP quantifies the contribution to AKI risk for each variable in individual patients. We will compare this approach to traditional methods, comparing both predictive performance and the clinical understanding provided by the models. The knowledge gained will support the development and validation of novel, interpretable models in a large, multi-institution cohort and analyses to identify modifiable risk factors at all phases of the perioperative period so that personalized perioperative management strategies can be developed to reduce the risk of postoperative AKI and long-term renal outcomes. SPECIFIC AIMS Postoperative acute kidney injury (AKI) is a common and major cause of morbidity and mortality in surgical patients. ADDIN EN.CITE 1 Postoperative AKI is associated with a 4-fold increased risk of mortality ADDIN EN.CITE 2 and increased costs (ranging from $12,100 to $47,700 per patient). ADDIN EN.CITE 3 AKI patients face greater long-term risks of developing chronic kidney disease (CKD), end stage renal disease (ESRD), and associated morbidity and mortality. ADDIN EN.CITE 4 Despite the severe ramifications of postoperative AKI, there are no effective strategies to identify individuals with an elevated risk or inform efforts to prevent its occurrence.5 Optimization of perioperative management may provide the greatest opportunity to fine-tune strategies to minimize AKI risk, as it is a volatile period with an abundance of potential opportunities for intervention. ADDIN EN.CITE 6,7 However, the complex relationships between perioperative factors and the risk of adverse renal outcomes have made it difficult to develop effective tools to guide perioperative patient management towar...