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

Recruiting now

Conditions studied: Kidney Injury, Acute

In brief

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.

Key facts

Study ID

NCT07030166

Run by

Lanyue Zhu

People needed

10000

Starts

2025-07-01

Expected to finish

2026-12-31

Last updated by the study team

2026-04-02

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

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

You may not qualify if…

• 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

Where it is running

• Zhongda Hospital Southeast University — Nanjing, China (enrolling)

Full record on ClinicalTrials.gov

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