Ignite Creation Date:
2024-05-06 @ 8:00 PM
Last Modification Date:
2024-10-26 @ 3:18 PM
Study NCT ID:
NCT06218758
Status:
RECRUITING
Last Update Posted:
2024-01-25
First Post:
2024-01-12
Brief Title:
Prediction Model for PPCs in Patients Undergoing Lung Transplantation Using Machine Learning
Sponsor:
Pusan National University Yangsan Hospital
Organization:
Pusan National University Yangsan Hospital
Study Overview
Official Title:
Prediction Model for Postoperative Pulmonary Complications in Patients Undergoing Lung Transplantation Using Machine Learning a Retrospective Cohort Study
Status:
RECRUITING
Status Verified Date:
2024-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 1815 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 1815 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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None