401.5: Development of High-Performing and Multicenter-Validated Dynamic Prediction Models With Longitudinal Measurements of Serum Creatinine and Proteinuria for Death-Censored Kidney Graft Failure

Transplantation, vol. 106, pp. S376

Abstract

Introduction: Prediction models of graft survival in kidney transplantation allow risk stratification of kidney transplant recipients using big data and artificial intelligence. Many models require detailed information on allograft histology and presence of donor-specific antibodies which are not readily available in every transplant center. We developed a joint model using Bayesian estimation to correlate baseline transplant characteristics and longitudinal measurements of both serum creatinine (S-Cr) and urine protein-creatinine ratios (u-PCR) with death-censored graft failure (DCGF). Methods: 3596 kidney transplant recipients (KTRs) from four transplant centers in Europe were included with a total of 452.033 S-Cr and u-PCR measurements. Each KTR had a median of 112 (IQR: 52-163) consecutive measurements. The interval between measurements was 7 (IQR: 1-35) days. Median follow-up was 6 (IQR: 3-9) years. Model predictors included recipient and donor gender and age, total number of human leukocyte antigen-mismatches, donor type, pre-transplant DSAs, and dialysis vintage. Primary outcomes were DCGF six months after last S-Cr or u-PCR measurement and DCGF seven years post-transplantation. Two training and external validation methods have been used to assess best model performance: model 1; development in one center and validation in three remaining centers, and model 2; development on 80% of the total cohort and validation on the remaining 20% (Table 1). Results: A total of 549 (15%) out of 3596 KTRs experienced DCGF. Joint modeling revealed that recipient age, recipient gender, donor age, transplantation after dialysis initiation, and serial measurements of S-Cr and u-PCR were independent risk factors for DCGF. Our final joint models showed good calibration (prediction error: 0.01) and very high discrimination in the development and validation cohorts (6-months incident AUC 0.9 [95% CI: 0.87-0.93] and 7-years dynamic AUC 0.84 [95% CI: 0.81-0.87] for model 1, and 6-months incident AUC 0.98 [95% CI: 0.96-1.00] and 7-years dynamic AUC 0.85 [95% CI: 0.80-0.91] for model 2) (Table 1). Especially model 2 could be of interest as a personalized surveillance tool in clinical practice given its AUCs >0.95 from year 1 onwards.Conclusion: We developed a high performing and multicenter-validated, dynamic prediction model on death-censored allograft survival based on traditionally available baseline transplant characteristics and longitudinal measurements of S-Cr and u-PCR. This dynamic model can continuously be updated with new measurements and might enable personalized surveillance of kidney transplant recipients and objectify allograft prognosis (as illustrated in Figure 1). Of notice, our models can be extended with newly discovered dynamic biomarkers in the future if needed.

Authors 21

1. Soufian Meziyerh Aachen Leiden Transplant Center

   Leiden University Medical Center

   Affiliation as printed

   Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands

2. Anirudh Tomer

   Erasmus MC

   Affiliation as printed

   Dept. of Biostatistics, Erasmus University Medical Center, Rotterdam, Netherlands

3. Hessel Peters Sengers

   Amsterdam University Medical Centers

   Affiliation as printed

   Centre for Experimental and Molecular Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands

4. Danny van der Helm Aachen Leiden Transplant Center

   Leiden University Medical Center

   Affiliation as printed

   Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands

5. Maarten Coemans

   Universitair Ziekenhuis Leuven

   Affiliation as printed

   Dept. of Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium

6. Tobias T. Pieters

   University Medical Center Utrecht

   Affiliation as printed

   Dept. of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, Netherlands

7. Jan‐Hendrik Venhuizen

   University Medical Center Utrecht

   Affiliation as printed

   Utrecht Patient Oriented Database, University Medical Center Utrecht, Utrecht, Netherlands

8. Saskia Haitjema

   University Medical Center Utrecht

   Affiliation as printed

   Utrecht Patient Oriented Database, University Medical Center Utrecht, Utrecht, Netherlands

9. Dirk Jan A. R. Moes Aachen Department of Clinical Pharmacy and Toxicology

   Leiden University Medical Center

   Affiliation as printed

   Dept. of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, Netherlands

10. Wieneke Marleen Michels Aachen Leiden Transplant Center

    Leiden University Medical Center

    Affiliation as printed

    Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands

11. Paul van der Boog Aachen Leiden Transplant Center

    Leiden University Medical Center

    Affiliation as printed

    Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands

12. Tri Q. Nguyen

    University Medical Center Utrecht

    Affiliation as printed

    Dept. of Pathology, University Medical Center Utrecht, Utrecht, Netherlands

13. Sandrine Florquin

    Amsterdam University Medical Centers · University of Amsterdam

    Affiliation as printed

    Amsterdam Infection & Immunity Institute, University of Amsterdam, Amsterdam, Netherlands

    Dept. of Pathology, Amsterdam University Medical Centers, Amsterdam, Netherlands

14. Arjan van Zuilen

    University Medical Center Utrecht

    Affiliation as printed

    Dept. of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, Netherlands

15. Fréderike J. Bemelman

    Amsterdam University Medical Centers

    Affiliation as printed

    Renal Transplant Unit, Amsterdam University Medical Centers, Amsterdam, Netherlands

16. Azam S. Nurmohamed

    Amsterdam University Medical Centers

    Affiliation as printed

    Renal Transplant Unit, Amsterdam University Medical Centers, Amsterdam, Netherlands

17. Ewout Willem Steyerberg Aachen Department of Biomedical Data Sciences

    Leiden University Medical Center · Erasmus MC

    Affiliation as printed

    Dept. of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands

    Dept. of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands

18. Dimitris Rizopoulos

    Erasmus MC

    Affiliation as printed

    Dept. of Biostatistics, Erasmus University Medical Center, Rotterdam, Netherlands

19. Maarten Naesens

    Universitair Ziekenhuis Leuven · KU Leuven

    Affiliation as printed

    Dept. of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium

    Dept. of Nephrology and Renal Transplantation, University Hospitals Leuven, Leuven, Belgium

20. Aiko De Vries Aachen Leiden Transplant Center

    Leiden University Medical Center

    Affiliation as printed

    Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands

21. Jesper Kers Aachen Leiden Transplant Center

    Leiden University Medical Center · Amsterdam University Medical Centers · University of Amsterdam

    Affiliation as printed

    Dept. of Pathology, Amsterdam University Medical Centers, Amsterdam, Netherlands

    Leiden Transplant Center, Leiden University Medical Center, Leiden, Netherlands

    Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Netherlands