Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Predicting kidney graft failure using time-dependent renal function covariates.

Mattheus H J de Bruijne1, Yvo W J Sijpkens, Leendert C Paul

  • 1Department of Medical Statistics, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, The Netherlands.

Journal of Clinical Epidemiology
|June 19, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sparse Canonical Correlation Analysis for Multiple Measurements With Latent Trajectories.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.

Neuro-oncology advances·2025
Same author

Identifying Predictors for Heart Failure Outcomes in Phospholamban p.(Arg14del)-Positive Individuals.

JACC. Heart failure·2025
Same author

Application of image guided analyses to monitor fecal microbial composition and diversity in a human cohort.

Scientific reports·2025
Same author

Aortic Function in a Longitudinal 4D Flow MRI Study in Marfan Syndrome Patients Receiving Resveratrol.

Journal of magnetic resonance imaging : JMRI·2025
Same author

Cohort Profile Update: The Healthy Life in an Urban Setting (HELIUS) Study.

International journal of epidemiology·2025

Predicting kidney transplant failure is improved by a new model using time-dependent renal function. This approach enhances accuracy for identifying patients at risk of graft failure due to chronic rejection or recurrent disease.

Area of Science:

  • Nephrology
  • Transplantation Medicine
  • Biostatistics

Background:

  • Chronic rejection and recurrent disease are primary causes of late graft failure in renal transplantation.
  • Current outcome assessment often relies on Cox proportional hazard analysis with time-fixed covariates, potentially limiting predictive accuracy.

Purpose of the Study:

  • To develop and validate an improved model for predicting late graft failure in kidney transplantation.
  • To incorporate time-dependent renal function covariates to enhance prediction accuracy compared to traditional methods.

Main Methods:

  • A cohort of 692 kidney transplant recipients functioning for at least 6 months was studied.
  • Time-dependent covariates including reciprocal of serum creatinine (RC), ratio of RC to RC at 6 months (RC6), and time elapsed since last observation (TEL) were utilized.

Related Experiment Videos

  • Cox proportional hazard analysis was adapted to include these dynamic renal function parameters.

    • Main Results:

    • Cadaveric donor transplantation, lower RC, and a lower RC/RC6 ratio were independently associated with graft failure.
    • The predictive impact of the last recorded RC was influenced by its value, TEL, and time since transplantation.
    • Model validation demonstrated significantly higher failure predictions for patients who subsequently experienced graft failure.

    Conclusions:

    • Incorporating time-dependent renal function covariates significantly improves the prediction of late graft failure in kidney transplantation.
    • This enhanced predictive model offers a more accurate assessment of graft failure risk, aiding clinical management and patient outcomes.