A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients
ConditionKidney Injury, Acute
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorLanyue Zhu
About this trial
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 criteria
Qualifiers
18 years old or above
Undergo non-cardiac surgery
Disqualifiers
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
Trial design
Treatments tested in this trial
- No intervention measures were used.
Treatment groups
10,000 Participants
are divided into 2 treatment groupsSponsors and collaborators
Lanyue Zhu
Lead sponsor
Zhongda Hospital
Sponsor institution
Source ClinicalTrials.gov