Predicting kidney injury after lung transplantation using machine learning
Prediction Model for Postoperative Acute Kidney Injury in Patients Undergoing Lung Transplantation Using Machine Learning: a Retrospective Cohort Study
This study is trying to use machine learning to see if it can predict which adult patients are at risk of kidney injury after having lung transplants.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 214 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Pusan National University Yangsan Hospital Academic / other |
| Locations | 1 site (Yangsan) |
| Trial ID | NCT06218745 on ClinicalTrials.gov |
What this trial studies
This observational study aims to develop a machine learning predictive model for postoperative acute renal injury (AKI) in adult patients undergoing lung transplantation. By conducting a retrospective analysis of medical records, the study will explore various factors such as anesthesia methods, intraoperative tests, and patient characteristics that contribute to the risk of AKI. The goal is to identify patterns and risk factors that can help predict which patients are at higher risk for renal dysfunction following surgery. This approach leverages advancements in machine learning to enhance patient outcomes in lung transplantation.
Who should consider this trial
Good fit: Ideal candidates for this study are adult patients aged 18 years or older who are undergoing lung transplantation for end-stage lung disease.
Not a fit: Patients who are not undergoing lung transplantation or those under 18 years of age may not receive any benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to improved prediction and management of postoperative kidney injury, potentially reducing complications and hospital stays for lung transplant patients.
How similar studies have performed: While the use of machine learning in predicting postoperative complications is gaining traction, this specific application in lung transplantation and AKI prediction is relatively novel and has not been extensively tested.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Adult patients 18 years of age or older who underwent lung transplantation for end-stage lung disease Exclusion Criteria: * None.
Where this trial is running
Yangsan
- Pusan National University Yangsan Hospital — Yangsan, Korea, Republic of (Recruiting)
Study contacts
- Principal investigator: Hee Young Kim — Department of Anesthesia and Pain Medicine, School of Medicine, Pusan National University
- Study coordinator: Hee Young Kim, MD, PhD
- Email: anekhy@gmail.com
- Phone: 82-10-7641-1774
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.