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NIHNATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASESR01

Understanding Long-term Outcomes of Living Kidney Donors using Real-World Evidence from National EHR Data

Active
Institution
NEW YORK UNIVERSITY SCHOOL OF MEDICINE, NY
Principal investigator
Michal A Mankowski
Funding
$861K
Duration
2025–2030
Open portfolio

Moonbase score

Still being scored

Abstract

Project Summary/Abstract We will conduct a detailed analysis of the long-term health outcomes of living kidney donors (LKDs) utilizing the comprehensive Cosmos dataset, which includes electronic health records (EHR) from over 245 million individuals, among them 90,000 LKDs. Our goal is to understand how reduced kidney function after donation impacts the health of LKDs compared to the general population. We aim to develop new clinical practices and care guidelines specifically for LKDs, addressing this critical gap in care. We will examine the natural history of kidney function, albuminuria, and systolic blood pressure (SBP) in LKDs, adjusting for variables such as age, sex, and medication use to accurately model these health indicators, thus providing an in-depth view of the health trajectory of LKDs following their donation. Furthermore, we will validate and possibly recalibrate the CKD-PC and PREVENT equations within our LKD cohort. The CKD-PC (Chronic Kidney Disease Prognosis Consortium) equations are used to predict renal outcomes based on factors such as age, sex, GFR, and albuminuria levels, originally validated in the general CKD population. The PREVENT equations are used to predict cardiovascular outcomes by integrating kidney function measures and have informed AHA guidelines on statin use for persons with CKD. We will adjust these well-established equations to more accurately reflect the unique post-donation physiological changes that LKDs experience, which will improve their utility in predicting risks such as cardiovascular diseases and end-stage kidney disease for this specific population. In addition, we plan to develop LKD-specific adaptations of the KFRE (Kidney Failure Risk Equation) predictive models. KFRE is used to estimate the risk of progression to kidney failure based on clinical inputs, including kidney function measures. We will integrate LKD status into the models, modifying parameters to cater specifically to the nuances of kidney donor profiles to enable more precise and individualized risk assessments for LKDs, which could improve post-donation care and monitoring. The culmination of our project will be a virtual consensus conference, where we will engage with key stakeholders from major nephrology and transplantation organizations. At this conference, we will present our research findings, discuss potential updates to clinical practice guidelines for LKDs, and strategize on the most effective methods to disseminate our research outcomes to healthcare providers and the broader LKD community. By leveraging the largest national EHR data, which includes detailed information on over 90,000 racially representative kidney donors, we strive to enhance medical care for LKDs. We aim to equip healthcare providers with tools and knowledge to manage LKDs' health trajectories effectively, ensuring better outcomes for those who've altruistically donated kidneys. We anticipate establishing a new standard of care for living kidney donors across their lifespan.

Research team

  • Michal A MankowskiPI
  • Allan B MassiePI
  • Dorry L. SegevPI

LOADING MOONBASE SCORE

R01NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASESKidney Endocrine and Digestive Disorders Study Section[KEDD]equationskidneydonationdonorsehrused predictkidney functionkidney failure

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