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Predicting Prognostic Factors in Kidney Transplantation Using A Machine Learning

NCT06394596 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 4077

Last updated 2024-05-01

No results posted yet for this study

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

Interventions

OTHER

Prognostic factors affecting 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

Sponsors & Collaborators

  • Asan Institute for Life Sciences

    collaborator UNKNOWN

  • Korea Health Industry Development Institute

    collaborator OTHER_GOV

  • Sung Shin

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-01-01
Primary Completion
2024-01-01
Completion
2024-02-01

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06394596 on ClinicalTrials.gov