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

NCT07030166 | Recruiting

• 1 other identifier: 2025ZDSYLL200-P01
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Kidney Injury, Acute

Outcome Measures

Primary Outcomes (1)

• Acute kidney injury

  Within 7 days after the operation

Secondary Outcomes (4)

• Postoperative complications

  Perioperative period

• Postoperative mortality

  Perioperative period

• Hospitalization costs

  Perioperative period

• Hospital stays

  Perioperative period

Study Arms (2)

Development group

The development group was used for model development, five-fold cross-validation, and model optimization. The investigators collected a comprehensive set of variables for feature selection, including preoperative demographic characteristics (sex, age, body mass index, marital status, occupation, etc.), laboratory indicators (routine blood and urine tests, liver and kidney function, coagulation function, etc.), preoperative comorbidities, and surgical information (surgical department, surgical classification, American Society of Anesthesiologists physical status classification, intraoperative position, fluid intake and output, vital signs, intraoperative medication use, etc.). Subsequently, multiple machine learning methods, including logistic regression, extreme gradient boosting, decision tree, random forest, and Bayesian approaches, were employed for model building and optimization.

Other: No intervention measures were used.

External (time) validation group

The external (time) validation group is used for future generalization ability assessment. The investigators prospectively collected patient-related data. In addition to the same variables as those in the development group and the testing group, the investigators 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. The investigators 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.

Other: No intervention measures were used.

Interventions

No intervention measures were used. OTHER

The exposure factors were the perioperative related operations experienced by the patients and their individual conditions

Development group; External (time) validation group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients who are scheduled to undergo surgery at Zhongda Hospital Southeast University.

You may qualify if:

• 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

Sponsors & Collaborators

• Lanyue Zhu: lead

Study Sites (1)

Zhongda Hospital Southeast University

Nanjing, China

RECRUITING

MeSH Terms

Conditions

Acute Kidney Injury

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Central Study Contacts

Yue Lan Zhu

CONTACT

+8618795969178; zhulanyue1993@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Attending Physician

Study Record Dates

• First Submitted: June 7, 2025
• First Posted: June 22, 2025
• Study Start: July 1, 2025
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: April 2, 2026

Record last verified: 2026-03

Locations

CN (1)