Development and Validation of a Web-based Prediction Model for Acute Kidney Injury after surgery

Background: Acute kidney injury after surgery is associated with high mortality and morbidity. The purpose of this study is to develop and validate a risk prediction tool for the occurrence of postoperative acute kidney injury requiring renal replacement therapy (AKI-Dialysis). Methods: This retrospective cohort study had 2,299,502 surgical patients over 2015-2017 from the American College of Surgeons National Surgical Quality Improvement Program Database (ACS-NSQIP). Eleven predictors were selected for the predictive model: age, history of congestive heart failure, diabetes, ascites, emergency surgery, hypertension requiring medication, preoperative serum creatinine, hematocrit, sodium, preoperative sepsis, and surgery type. The predictive model was trained using 2015-2016 data (n=1,487,724) and further tested using 2017 data (n=811,778). A risk model was developed using multivariable logistic regression. Results: AKI-Dialysis occurred in 0.3% (n=6,853) of patients. The unadjusted 30-day postoperative mortality rate associated with AKI-Dialysis was 37.5%. The AKI risk prediction model had high AUC (area under the receiver operating characteristic curve, training cohort: 0.89, test cohort: 0.90) for postoperative AKI-Dialysis. Conclusions: This model provides a clinically useful bedside predictive tool for postoperative acute kidney injury requiring dialysis.