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

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.

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Key information

Conditions

Lung Transplantation

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Pusan National University Yangsan Hospital

Yangsan, South Korea

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

None.

Treatment and study plan

General anesthesia

Other

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

Primary outcomes

Postoperative acute kidney injury (AKI)

Time frame: Within 48 hours after lung transplantation

Diagnosis of postoperative AKI is based on the change in serum creatinine concentration within 48 hours after surgery.

Stage 1: An increase in serum creatinine of ≥ 0.3 mg/dL from baseline or a 1.5-2 times increase (≥ 1.5-2 times).

Stage 2: An increase in serum creatinine of > 2-3 times from baseline (> 2-3 times).

Stage 3: An increase in serum creatinine of ≥ 3 times from baseline or an increase to ≥ 4.0 mg/dL from baseline (≥ 4.0 mg/dL, only applicable if it increases by at least 0.5 mg/dL acutely), or initiation of renal replacement therapy.

Sponsors and collaborators

Lead sponsor

Pusan National University Yangsan Hospital

Other

Registry information

Official study title

Prediction Model for Postoperative Acute Kidney Injury in Patients Undergoing Lung Transplantation Using Machine Learning: a Retrospective Cohort Study

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2025
First posted: Jan 23, 2024
Registry last updated: Aug 1, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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