Predicting Prognostic Factors in Kidney Transplantation Using A Machine Learning

NCT06394596 | Completed DMC

• 1 other identifier: 2022-1276
• Study Type: observational
• Target: 4,077
• Locations: 1 country
• Sites: 1

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.

Conditions

Kidney Transplant Failure and Rejection

Keywords

deep learning; kidney transplant; prognosis; survival

Outcome Measures

Primary Outcomes (1)

• 5-year graft survival

  The primary outcome measured was a 5-year graft survival, defined as the absence of any need for dialysis or re-transplantation five years following the initial transplantation

  5 years

Study Arms (1)

Kidney transplant patients

Patients who underwent kidney transplantation at a single center

Other: Prognostic factors affecting graft survival

Interventions

Prognostic factors affecting graft survival OTHER

The primary outcome measured was a 5-year graft survival, defined as the absence of any need for dialysis or re-transplantation five years following the initial transplantation

Kidney transplant patients

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

4077 patients who underwent kidney transplantation at Asan Medical Center from June 1990 to May 2015

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Sung Shin: lead
• Asan Institute for Life Sciences: collaborator
• Korea Health Industry Development Institute: collaborator

Study Sites (1)

Asan Medical Center

Seoul, 05505, South Korea

Location

Related Publications (1)

• Kim JM, Jung H, Kwon HE, Ko Y, Jung JH, Kwon H, Kim YH, Jun TJ, Hwang SH, Shin S. Predicting prognostic factors in kidney transplantation using a machine learning approach to enhance outcome predictions: a retrospective cohort study. Int J Surg. 2024 Nov 1;110(11):7159-7168. doi: 10.1097/JS9.0000000000002028.

  PMID: 39116448 DERIVED

MeSH Terms

Conditions

Rejection, Psychology

Condition Hierarchy (Ancestors)

Social Behavior; Behavior

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: April 28, 2024
• First Posted: May 1, 2024
• Study Start: January 1, 2023
• Primary Completion: January 1, 2024
• Study Completion: February 1, 2024
• Last Updated: May 1, 2024

Record last verified: 2024-04

Locations

KR (1)