Ignite Creation Date:
2024-10-26 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06531967
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
None
First Post:
2024-07-25
Brief Title:
Predicting Mortality in Kidney Transplant Recipients
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Development and Validation of a Prediction Model for Risk of Death in Kidney Transplant Recipients
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
mBox
Brief Summary:
Accurately predicting kidney recipient risk of death has a crucial interest because of the organ shortage the need to optimize allograft allocation by identifying high-risk patients who may not benefit from a transplant and improve the clinical decision-making after transplant to ensure that each patient survives as long as possible
However according to a literature review the investigators performed studies attempting to develop a kidney recipient death prediction model suffer from many shortcomings including the lack of key risk factors use of biased registry data small sample size lack of external validation in different countries and subpopulations and short follow-up
The present study thus aimed to address these limitations and develop a robust generalizable kidney recipient death prediction model
However according to a literature review the investigators performed studies attempting to develop a kidney recipient death prediction model suffer from many shortcomings including the lack of key risk factors use of biased registry data small sample size lack of external validation in different countries and subpopulations and short follow-up
The present study thus aimed to address these limitations and develop a robust generalizable kidney recipient death prediction model
Detailed Description:
The number of individuals suffering from end-stage chronic renal disease ESRD worldwide has increased over time exceeding seven million of patients in 2020 For individuals with ESRD kidney transplantation is the best treatment in terms of patient survival quality of life and from a cost-effective standpoint as compared with dialysis even in comorbid or elderly populations
Although the number of kidney transplantations performed each year has increased as well it follows a lower pace than the increase of individuals on the waiting-list resulting in an organ shortage There is therefore a need to optimize allograft allocation by identifying the high-risk patients who may not benefit from a transplant and improve the clinical decision-making after transplant to ensure that each patient survives as long as possible
In this context a kidney recipient death prediction model may improve transplant clinical practice allowing for the ability to evaluate the individual risk of post transplant mortality already before undergoing transplantation thereby guiding decision making However developing such a model is a very difficult task as death after kidney transplantation depends on many parameters such as donor age history or cause of death imaging parameters patients past medical history eg diabetes dialysis duration hypertension patients biological parameters as well as the function of the allograft which depends on patients immunological factors or allograft related parameters such as HLA mismatches or cold ischemia time
The goal of the present study was therefore to identify the determinants of death after kidney transplantation and to develop and validate a prediction model that would help optimize allograft allocation and post-transplant patient management using a large international highly phenotyped cohort of kidney recipients with extensive data collection and long-term follow-up
Although the number of kidney transplantations performed each year has increased as well it follows a lower pace than the increase of individuals on the waiting-list resulting in an organ shortage There is therefore a need to optimize allograft allocation by identifying the high-risk patients who may not benefit from a transplant and improve the clinical decision-making after transplant to ensure that each patient survives as long as possible
In this context a kidney recipient death prediction model may improve transplant clinical practice allowing for the ability to evaluate the individual risk of post transplant mortality already before undergoing transplantation thereby guiding decision making However developing such a model is a very difficult task as death after kidney transplantation depends on many parameters such as donor age history or cause of death imaging parameters patients past medical history eg diabetes dialysis duration hypertension patients biological parameters as well as the function of the allograft which depends on patients immunological factors or allograft related parameters such as HLA mismatches or cold ischemia time
The goal of the present study was therefore to identify the determinants of death after kidney transplantation and to develop and validate a prediction model that would help optimize allograft allocation and post-transplant patient management using a large international highly phenotyped cohort of kidney recipients with extensive data collection and long-term follow-up
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None