Ignite Creation Date:
2024-05-06 @ 8:28 PM
Last Modification Date:
2024-10-26 @ 3:28 PM
Study NCT ID:
NCT06394596
Status:
COMPLETED
Last Update Posted:
2024-05-01
First Post:
2024-04-28
Brief Title:
Predicting Prognostic Factors in Kidney Transplantation Using A Machine Learning
Sponsor:
Sung Shin
Organization:
Asan Medical Center
Study Overview
Official Title:
Predicting Prognostic Factors in Kidney Transplantation A Machine Learning Approach to Enhance Outcome Prediction
Status:
COMPLETED
Status Verified Date:
2024-04
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:
Kidney transplantation KT is the most effective treatment for end-stage renal disease offering improved quality of life and long-term survival However predicting transplant survival and assessing prognostic factors is complex due to the multifaceted nature of patient variables and individualized treatments Traditional methods have fallen short in their predictive accuracy This study aims to develop machine learning algorithms capable of parsing extensive clinical data to identify key prognostic indicators that can potentially forecast survival rates for KT recipients By incorporating baseline characteristics of donors and recipients the model strives to unearth patterns linking donor and recipient profiles thereby offering insights into modifiable factors that could influence postoperative outcomes The goal is to provide a tool that aids clinicians in improving the prognosis and quality of life for KT recipients
Detailed Description:
Kidney transplantation KT is the most effective treatment modality for end-stage renal disease ESRD offering patients the opportunity to ahieve improved quality of life and long-term survival Advances in surgical techniques and immunosuppressive regimens have substantially decreased immediate postoperative complications and acute rejection episodes
Considering that KT is the most frequently performed organ transplantation improving the longevity of transplant survival could benefit many individuals The efficacy of KT is often gauged by graft function which is a critical determinant of the grafts long-term survival and a key metric in evaluating transplant success While post-transplant graft function is influenced by a spectrum of variables-from the characteristics of donors and recipients to immunosuppressive strategies-this complexity presents challenges in forecasting outcomes particularly over the long term Traditional methods such as the kidney donor risk index KDRI and Cox regression analyses have fallen short in their predictive accuracy
The prediction of transplant survival and the assessment of prognostic factors are complex due to the multifaceted nature of patient variables and the individualization of perioperative treatments Yet with the rise of machine learning and advanced computational analytics researchers are now poised to decode the intricacies of data with clinical significance potentially transforming patient care post-transplantation The integration of deep learning algorithms into clinical practice in the field of transplantation is a relatively nascent area but is rapidly gaining traction
This study aims to develop machine learning algorithms capable of parsing extensive clinical data to pinpoint key prognostic indicators which can potentially forecast survival rates for KT recipients By incorporating baseline characteristics of both donors and recipients the present model strives to unearth patterns linking donor and recipient profiles thereby offering insights into modifiable factors that could influence postoperative outcomes Through this we seek to provide a tool that aids clinicians in improving the prognosis and quality of life for KT recipients
Considering that KT is the most frequently performed organ transplantation improving the longevity of transplant survival could benefit many individuals The efficacy of KT is often gauged by graft function which is a critical determinant of the grafts long-term survival and a key metric in evaluating transplant success While post-transplant graft function is influenced by a spectrum of variables-from the characteristics of donors and recipients to immunosuppressive strategies-this complexity presents challenges in forecasting outcomes particularly over the long term Traditional methods such as the kidney donor risk index KDRI and Cox regression analyses have fallen short in their predictive accuracy
The prediction of transplant survival and the assessment of prognostic factors are complex due to the multifaceted nature of patient variables and the individualization of perioperative treatments Yet with the rise of machine learning and advanced computational analytics researchers are now poised to decode the intricacies of data with clinical significance potentially transforming patient care post-transplantation The integration of deep learning algorithms into clinical practice in the field of transplantation is a relatively nascent area but is rapidly gaining traction
This study aims to develop machine learning algorithms capable of parsing extensive clinical data to pinpoint key prognostic indicators which can potentially forecast survival rates for KT recipients By incorporating baseline characteristics of both donors and recipients the present model strives to unearth patterns linking donor and recipient profiles thereby offering insights into modifiable factors that could influence postoperative outcomes Through this we seek to provide a tool that aids clinicians in improving the prognosis and quality of life for KT recipients
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