Clinical trial NCT06218758: Prediction Model for PPCs in Patients Undergoing Lung Transplantation Using Machine Learning
Official title: Prediction Model for Postoperative Pulmonary Complications in Patients Undergoing Lung Transplantation Using Machine Learning: a Retrospective Cohort Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Lung Transplantation
Lead sponsor: Pusan National University Yangsan Hospital
Sponsor class: OTHER
First posted: 2024-01-23
Enrollment: 214
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients 18 years of age or older who underwent lung transplantation for end-stage lung disease

Brief summary: 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.

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* None.

Primary outcomes:
measure: Postoperative pulmonary complications
timeFrame: Up to 1 year after lung transplantation
Secondary outcomes:
Other outcomes:
Locations:
city: Yangsan
country: Korea, Republic of
facility: Pusan National University Yangsan Hospital
Study officials:
affiliation: Department of Anesthesia and Pain Medicine, School of Medicine, Pusan National University
name: Hee Young Kim, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: