Kidney Transplant Failure and RejectionNCT06394596
Predicting Prognostic Factors in Kidney Transplantation Using A Machine Learning
An observational study of Kidney Transplant Failure and Rejection, run by Sung Shin at 1 site in South Korea.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Kidney Transplant Failure and Rejection
- What is being tested?
- Prognostic factors affecting graft survival
- How many people
- 4,077 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Jan 2023to 1 Feb 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: 4077 patients who underwent kidney transplantation at Asan Medical Center from June 1990 to May 2015
Who may be able to join
- 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.
Who cannot join
- Patients who are receiving a multi-organ transplantation (e.g. simultaneous pancreas and kidney transplantation, simultaneous heart and kidney transplantation)
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Predicting Prognostic Factors in Kidney Transplantation: A Machine Learning Approach to Enhance Outcome Prediction
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.
Read the 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.
- Conditions
- Kidney Transplant Failure and Rejection
- Being tested
- Prognostic factors affecting graft survival
- Keywords
- deep learning, kidney transplant, prognosis, survival
- Medical subject headings
- Rejection, Psychology
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Prognostic factors affecting graft survivalOther
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
| Study group | Role | What they receive |
|---|---|---|
| Kidney transplant patients | Patients who underwent kidney transplantation at a single center Receives: Other: Prognostic factors affecting graft survival |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 May 2024
- Study start1 Jan 2023
- Primary completion1 Jan 2024
- Study completion1 Feb 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Kidney Transplant Failure and RejectionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 4,077 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sung Shin
- Sponsor type
- University, hospital or other
- Collaborators
- Asan Institute for Life Sciences, Korea Health Industry Development Institute
- Responsible party
- Sponsor-investigator, Sung Shin, Asan Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06394596
- Sponsor's protocol ID
- 2022-1276
- Status last verified
- Apr 2024
- First submitted
- 28 Apr 2024
- Last updated
- 1 May 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: The authors will provide the raw data supporting the findings of this article upon request, without any unwarranted delays or restrictions.
Publications
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.
PubMed 39116448
Study at a glance
From the registry record, last updated by the sponsor on 1 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Prognostic Factors in Kidney Transplantation: A Machine Learning Approach to Enhance Outcome Prediction
- ClinicalTrials.gov ID
- NCT06394596
- Acronym / sponsor ID
- 2022-1276
- Conditions
- Kidney Transplant Failure and Rejection
- Keywords
- deep learning, kidney transplant, prognosis, survival
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 4,077 participants (actual)
- Lead sponsor
- Sung ShinUniversity, hospital or other
- Collaborators
- Asan Institute for Life Sciences, Korea Health Industry Development Institute
- Study start
- 1 Jan 2023 (actual)
- Primary completion
- 1 Jan 2024 (actual)
- Study completion
- 1 Feb 2024 (actual)
- First posted
- 1 May 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Kidney transplant patients | – | Other: Prognostic factors affecting graft survivalPatients who underwent kidney transplantation at a single center |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- 4077 patients who underwent kidney transplantation at Asan Medical Center from June 1990 to May 2015
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (1 listed)
- Patients who are receiving a multi-organ transplantation (e.g. simultaneous pancreas and kidney transplantation, simultaneous heart and kidney transplantation)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Prognostic factors affecting graft survivalThe 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 | Other | Kidney transplant patients |
Primary outcome
5-year graft survival
Time frame: 5 years
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 May 2024.
- Derived from this study: PubMed 39116448Kim 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.
- Individual participant dataSharing planned: Undecided. The authors will provide the raw data supporting the findings of this article upon request, without any unwarranted delays or restrictions.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Kidney Transplant Failure and Rejection, run by Sung Shin at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 May 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
5-year graft survival (time frame: 5 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06394596, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 May 2024.