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

NCT ID: NCT06218745

Last Updated: 2025-08-01

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 214 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-01-22
• Study Completion Date: 2025-06-30

Brief 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.

Detailed Description

Since the first report of lung transplantation in humans in 1963, there has been rapid progress in both the quantity and quality of lung transplantation, driven by the significant advancements in immunosuppressive agents since the mid-1990s. In 2004, a total of 1,815 lung transplantations were reported worldwide. Patients undergoing lung transplantation are often elderly and face not only impaired lung function but also overall health instability, leading to the potential occurrence of respiratory complications post-surgery, even with successful lung transplantation outcomes.

Postoperative acute renal injury (AKI) can result in temporary or even chronic renal dysfunction. AKI following surgery can lead to an increase in hospitalization duration, complications, and the need for additional treatment. Various factors are associated with postoperative renal dysfunction after lung transplantation, including sustained hypoperfusion, hypoperfusion related to intraoperative and postoperative bleeding, heart failure, acute myocardial infarction, pulmonary embolism, sepsis, and more. Medications related to renal dysfunction include those associated with thrombosis or embolism, such as aminoglycosides, amphotericin B, non-steroidal anti-inflammatory drugs (NSAIDs), proton-pump inhibitors, contrast agents, and others. Additionally, graft-versus-host disease is known to be related to renal dysfunction.

The retrospective analysis of medical records from adult patients who underwent lung transplantation aims to investigate patient characteristics, anesthesia methods, intraoperative tests, and the occurrence of postoperative acute renal dysfunction. The goal is to analyze the incidence and risk factors of postoperative renal dysfunction and develop a predictive model through machine learning.

Conditions

Lung Transplantation

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

General anesthesia

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

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Adult patients 18 years of age or older who underwent lung transplantation for end-stage lung disease

Exclusion Criteria

• None.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Pusan National University Yangsan Hospital

OTHER

Sponsor Role: lead

Responsible Party

Kim Hee Young

Assistant professor for fund

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Hee Young Kim

Role: PRINCIPAL_INVESTIGATOR

Department of Anesthesia and Pain Medicine, School of Medicine, Pusan National University

Locations

Pusan National University Yangsan Hospital

Yangsan, , South Korea

Countries

South Korea

Other Identifiers

55-2024-003

Identifier Type: -

Identifier Source: org_study_id