Ignite Creation Date:
2024-10-26 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06534840
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-07-30
Brief Title:
Evaluation of Pulmonary Complications in Liver Transplantation Patients Based on Machine Learning
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Establishment and Evaluation of Moderate-severe Prediction Model of Pulmonary Complications in Liver Transplantation Patients Based on Machine Learning Algorithm
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The main objective of this study is to develop a machine learning model that predicts moderate-severe prediction model of pulmonary complications in liver transplantation patients within 14 postoperative day using a real-world local preoperative and intraoperative electronic health records not administrative codes
Detailed Description:
Postoperative pulmonary complications can increase the length of hospital stay and medical costs In particular moderate to severe pulmonary complications which often require clinical intervention once occur will lead to significantly prolonged postoperative hospitalization or even cause permanent damage or death in severe cases A number of risk-stratified cation models have been developed to identify patients at increased risk of postoperative pulmonary complications However these models were built by using the traditional regression analysis However the traditional prediction methods have the disadvantages of limited processing power of nonlinear models and outlier and relatively single selection variables The obtained models have poor accuracy and the quantification degree is not enough so it is difficult to popularize clinical application Artificial machine learning can use it by analyzing a large number of specific features in the rich data set to identify and learn to accurately predict the diagnosis and prognosis of diseases and surpass traditional prediction models in dealing with classification problems The algorithms are flexible and it is more and more widely used in clinical practice research However there are few reports on machine learning models predicting prognostic models related to postoperative pulmonary complications in liver transplantation patients Therefore we aimed to build predictive models using artificial machine learning methods to screen for their risk factors in order to provide early intervention and individualized treatment for high-risk patients
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None