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Prediction Model for Postoperative AKI in Patients Undergoing Lung Transplantation Using Machine Learning

NCT06218745 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 214

Last updated 2025-08-01

No results posted yet for this study

Summary

Since 1963, lung transplantation progress has surged due to immunosuppressive agent advancements. In 2004, 1,815 global lung transplantations were reported. Elderly recipients face impaired lung function and health instability, leading to potential respiratory complications post-surgery.

Postoperative acute renal injury (AKI) can cause temporary or chronic dysfunction, increasing hospitalization, complications, and additional treatment needs. Various factors contribute to postoperative renal dysfunction after lung transplantation, including sustained hypoperfusion, bleeding, heart failure, acute myocardial infarction, pulmonary embolism, sepsis, and medications. Retrospective analysis of adult lung transplant patients' records aims to explore characteristics, anesthesia methods, intraoperative tests, and postoperative acute renal dysfunction, analyzing incidence and risk factors to develop a machine learning predictive model.

Conditions

  • Lung Transplantation

Interventions

OTHER

General anesthesia

General anesthesia using 2% propofol, and remifentanil for lung transplantation

Sponsors & Collaborators

  • Pusan National University Yangsan Hospital

    lead OTHER

Principal Investigators

  • Hee Young Kim · Department of Anesthesia and Pain Medicine, School of Medicine, Pusan National University

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-01-22
Primary Completion
2025-06-30
Completion
2025-06-30

Countries

  • South Korea

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 NCT06218745 on ClinicalTrials.gov