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Machine Learning Models for Prediction of Acute Kidney Injury After Noncardiac Surgery

NCT06146829 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 88367

Last updated 2024-04-10

No results posted yet for this study

Summary

Acute kidney injury (AKI) is a common surgical complication characterized by a rapid decline in renal function. Patients with AKI are at an increased risk of developing chronic kidney disease and end-stage renal disease, which has been associated with an increased risk of morbidity, mortality and financial burdens. Identifying high-risk patients for postoperative AKI early can facilitate the development of preventive and therapeutic management strategies, and prediction models can be helpful in this regard.

The goal of this retrospective study is to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms, and to simplify the models by including only preoperative variables or only important predictors.

Conditions

  • Postoperative Acute Kidney Injury

Interventions

OTHER

no intervention

no intervention

Sponsors & Collaborators

  • Rao Sun

    lead OTHER

Principal Investigators

  • Rao Sun · Tongji Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-11-27
Primary Completion
2023-12-15
Completion
2023-12-15

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06146829 on ClinicalTrials.gov