Prediction Model for PPCs in Patients Undergoing Lung Transplantation Using Machine Learning

Prediction Model for Postoperative Pulmonary Complications in Patients Undergoing Lung Transplantation Using Machine Learning: a Retrospective Cohort Study

Since the first human lung transplantation in 1963, significant advancements in immunosuppressive agents from the mid-1990s have greatly improved the quantity and quality of such procedures. In 2004, a total of 1,815 lung transplantations were globally reported. Patients undergoing this procedure are typically elderly and experience not only impaired lung function but also overall health instability. Despite successful outcomes, postoperative pulmonary complications (PPCs) can lead to serious consequences, including deterioration and fatality. PPCs resulting from lung transplantation may lead to prolonged hospitalization, increased complications, and the need for additional treatment. Various factors, such as age, smoking, pre-existing lung diseases, immunosuppressive drug use, diabetes, hypertension, infections, allergies, and immune disorders, are associated with the development of PPCs. 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 PPCs, with the ultimate goal of analyzing the incidence and risk factors of postoperative respiratory complications and developing a predictive model through machine learning.

Study Overview

• Status: Recruiting
• Conditions: Lung Transplantation
• Intervention / Treatment: Other: General anesthesia

Detailed Description

After the first report of lung transplantation in humans in 1963, rapid advancements in immunosuppressive agents since the mid-1990s have led to significant progress in both the quantity and quality of lung transplantation. In 2004, a total of 1,815 lung transplantations were reported worldwide. Patients undergoing lung transplantation are typically elderly, often experiencing not only impaired lung function but also overall instability in their health. Despite successful outcomes in lung transplantation, the occurrence of pulmonary complications after surgery can lead to deterioration or even fatal consequences.

Postoperative pulmonary complications (PPCs) can result in prolonged hospitalization, increased complications, and the need for additional treatment. Various factors are associated with the development of PPCs after lung transplantation, including age, smoking, pre-existing lung diseases (such as chronic obstructive pulmonary disease, pulmonary fibrosis, etc.), immunosuppressive drug use post-transplant, diabetes, hypertension, pulmonary hypertension, heart disease, infections, allergies, and immune disorders. The retrospective analysis of medical records of adult patients who underwent lung transplantation aims to investigate patient characteristics, anesthesia methods, intraoperative tests, and the occurrence of PPCs. The goal is to analyze the incidence and risk factors of postoperative respiratory complications and develop a predictive model through machine learning.

• Study Type: Observational
• Enrollment (Estimated): 214

Contacts and Locations

• Study Contact: Name: Hee Young Kim, MD, PhD; Phone Number: 82-10-7641-1774; Email: anekhy@gmail.com
• Study Contact Backup: Name: Hee Young Kim; Phone Number: 8255360219

Study Locations

• Korea, Republic of
  • Yangsan, Korea, Republic of
    • Recruiting
    • Pusan National University Yangsan Hospital
    • Contact: Hee Young Kim, MD, PhD; Phone Number: 82553602129; Email: yuvi1981@naver.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

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

Exclusion Criteria:

• None.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Postoperative pulmonary complications	Postoperative pulmonary complications such as pleural effusion, pneumothorax, hemothorax, chylothorax, atelectasis, pulmonary edema, acute respiratory distress syndrome, pneumonia, bronchial stenosis, pulmonary fibrosis and emphysema, postoperative tracheostomy, acute rejection occurring within the first year after lung transplantation, chronic rejection	Up to 1 year after lung transplantation

Collaborators and Investigators

• Sponsor: Pusan National University Yangsan Hospital
• Investigators: Principal Investigator: Hee Young Kim, MD, PhD, Department of Anesthesia and Pain Medicine, School of Medicine, Pusan National University

Study record dates

Study Major Dates

• Study Start (Actual): January 22, 2024
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): December 31, 2024

Study Registration Dates

• First Submitted: January 12, 2024
• First Submitted That Met QC Criteria: January 22, 2024
• First Posted (Actual): January 23, 2024

Study Record Updates

• Last Update Posted (Actual): January 25, 2024
• Last Update Submitted That Met QC Criteria: January 24, 2024
• Last Verified: January 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Physiological Effects of Drugs; Central Nervous System Depressants; Anesthetics
• Other Study ID Numbers: 55-2024-004

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No