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

Recruiting
Conditions
Kidney Injury, Acute
Registration Number
NCT07030166
Lead Sponsor
Lanyue Zhu
Brief 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.

Detailed Description

The data of this study are divided into two parts: retrospective and prospective. The retrospective data were from the electronic medical records of adult patients who underwent non-cardiac surgery during hospitalization from July 2015 to June 2025. The ratio of the training set, the internal validation set and the test set was 7:1:2. The prospective data is an external (temporal) validation set. Data collection began in July 2025 and is expected to end in February 2026

Recruitment & Eligibility

Status
RECRUITING
Sex
All
Target Recruitment
2500
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 operation time is less than 2 hours

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Acute kidney injuryWithin 7 days after the operation
Secondary Outcome Measures
NameTimeMethod
Postoperative mortalityPerioperative period
Hospitalization costsPerioperative period
Hospital staysPerioperative period
Postoperative complicationsPerioperative period

Trial Locations

Locations (1)

Zhongda Hospital Southeast University

🇨🇳

Nanjing, China

Zhongda Hospital Southeast University
🇨🇳Nanjing, China

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