A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients

A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients: Development, Validation, and the Incremental Value of Frailty Assessment

Primary objectives of this study is to develop and validate a predictive model for acute kidney injury after non-cardiac surgery based on machine learning. Secondary objectives of this study is to incorporate frailty assessment as a new predictor into the model and measure its incremental value was measured.

The data in this study are divided into two parts: retrospective and prospective. The retrospective data served as the development set, sourced from the electronic medical records of adult patients who underwent non-cardiac surgery during hospitalization between July 2015 and June 2025. The prospective data constituted an external (temporal) validation set, with data collection commencing in July 2025 and expected to conclude in February 2026.

Inclusion Criteria:

• 18 years old or above
• Undergo non-cardiac surgery

Exclusion Criteria:

• At least one measurement of serum creatinine (SCr) was not conducted before and after the operation
• End-stage renal disease (ESRD) that has received dialysis within the past year
• Baseline SCr ≥ 4.5 mg/dl (because the clinical criteria for AKI based on elevated SCr may not be applicable to these patients)
• Acute kidney injury occurred within 7 days before the operation
• The surgical procedure is renal surgery
• The operation time is less than 2 hours