A Machine Learning Prediction Model for Postoperative Acute Kidney Injury in Non-Cardiac Surgery Patients
NCT ID: NCT07030166
Last Updated: 2025-09-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
2500 participants
OBSERVATIONAL
2025-07-01
2026-12-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
OTHER
Study Groups
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Development group
The development group is used for fitting the model and optimizing the model. We used preoperative demographic characteristics (gender, age, BMI, marital status, and occupation, etc.), laboratory indicators (blood and urine routine, liver and kidney function, coagulation function and other blood test indicators), preoperative comorbidities and surgical information (surgical department, surgical grade, ASA grade, operation time, anesthesia method, intraoperative position, intake and output volume, vital signs, and intraoperative medication, etc.) Variables such as logistic regression, extreme gradient boosting, decision tree, random forest and Bayesian are used for screening, and multiple methods such as machine learning are employed for modeling.
No intervention measures were used.
The exposure factors were the perioperative related operations experienced by the patients and their individual conditions
Testing group
The testing set is used for the initial performance evaluation of the model. We use indicators such as discrimination and calibration for model comparison and optimization to select the best model.
No intervention measures were used.
The exposure factors were the perioperative related operations experienced by the patients and their individual conditions
External (time) validation group
The external (time) validation group is used for future generalization ability assessment. We prospectively collected patient-related data. In addition to the same variables as those in the development group and the testing group, we also evaluated and collected the frailty status of patients before the operation, and recorded prognostic indicators such as the incidence of in-hospital complications, in-hospital mortality, length of hospital stay and hospitalization cost of patients. We used the data from the external (time) validation group to validate the model performance, incorporated the frailty assessment as a new predictor into the model, calculated the incremental values and evaluated the performance of the updated model.
No intervention measures were used.
The exposure factors were the perioperative related operations experienced by the patients and their individual conditions
Interventions
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No intervention measures were used.
The exposure factors were the perioperative related operations experienced by the patients and their individual conditions
Eligibility Criteria
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Inclusion Criteria
* Undergo non-cardiac surgery
Exclusion Criteria
* 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
18 Years
ALL
No
Sponsors
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Lanyue Zhu
OTHER
Responsible Party
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Lanyue Zhu
Attending Physician
Locations
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Zhongda Hospital Southeast University
Nanjing, , China
Countries
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Central Contacts
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Other Identifiers
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2025ZDSYLL200-P01
Identifier Type: -
Identifier Source: org_study_id
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