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Updated: Jun 9, 2026
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Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
Published on: April 12, 2021
A simple tool to predict outcomes after kidney transplant.
Bertram L Kasiske1, Ajay K Israni, Jon J Snyder
1Department of Medicine, Hennepin County Medical Center, University of Minnesota, Minneapolis, MN 55415, USA. kasis001@umn.edu
Summary
New kidney transplant prediction tools help assess graft loss risk. These user-friendly models simplify understanding of 5-year outcomes for patients and clinicians.
Area of Science:
- Nephrology
- Transplantation Medicine
- Biostatistics
Background:
- Limited tools exist for predicting kidney transplant outcomes.
- Accurate prediction is crucial for patient management and resource allocation.
Purpose of the Study:
- To develop and validate user-friendly prediction tools for kidney graft loss.
- To identify key predictors of graft loss at different time points post-transplant.
Main Methods:
- Retrospective cohort study using US Renal Data System (USRDS) data (2000-2006).
- Developed Cox proportional hazards models for graft loss prediction at pre-transplant, 7-day, and 1-year post-transplant assessment points.
- Created full and abbreviated prediction tools using USRDS, Organ Procurement and Transplantation Network, and Centers for Medicare & Medicaid Services data.
Main Results:
- Identified numerous variables predicting graft loss, but simplified models retained high discriminatory ability (98%) and explained variation (>80%).
- Abbreviated models required significantly fewer variables (11, 8, and 6 respectively) compared to full models (32, 29, and 18).
Conclusions:
- Developed practical, user-friendly risk-prediction tools for 5-year kidney graft loss.
- These tools aid clinicians and patients in understanding absolute and relative risks.
- Limitations include potential underreporting of comorbidities and modest individual risk discrimination.
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