Clinical trial NCT06394596: Predicting Prognostic Factors in Kidney Transplantation Using A Machine Learning
Official title: Predicting Prognostic Factors in Kidney Transplantation: A Machine Learning Approach to Enhance Outcome Prediction
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Kidney Transplant Failure and Rejection
Keywords: deep learning
kidney transplant
prognosis
survival
Lead sponsor: Asan Medical Center
Sponsor class: OTHER
Collaborators: name: Asan Institute for Life Sciences
name: Korea Health Industry Development Institute
First posted: 2024-05-01
Enrollment: 4077
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 4077 patients who underwent kidney transplantation at Asan Medical Center from June 1990 to May 2015

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 graft's 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.

Eligibility criteria: Inclusion Criteria:

* Patients who have received kidney transplantation (including multiple times of transplantation) at this hospital.
* Patients who have listened to and understood a detailed explanation of this study, and have voluntarily decided to participate and provided written consent.

Exclusion Criteria:

* Patients who are receiving a multi-organ transplantation (e.g. simultaneous pancreas and kidney transplantation, simultaneous heart and kidney transplantation)

Primary outcomes:
measure: 5-year graft survival
timeFrame: 5 years
Secondary outcomes:
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Asan Medical Center
Study officials:
References: