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
Lanyue Zhu
10,000 participants
Jul 1, 2025
OBSERVATIONAL
Conditions
Summary
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.
Eligibility
Inclusion Criteria2
- years old or above
- Undergo non-cardiac surgery
Exclusion Criteria6
- 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
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Interventions
The exposure factors were the perioperative related operations experienced by the patients and their individual conditions
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT07030166