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. 2023 Mar 7;7(1):6.
doi: 10.1186/s41512-022-00139-5.

Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol

Simon Schwab  1 , Daniel Sidler  2 , Fadi Haidar  3 , Christian Kuhn  4 , Stefan Schaub  5 , Michael Koller  5 , Katell Mellac  5 , Ueli Stürzinger  6 , Bruno Tischhauser  6 , Isabelle Binet  4 , Déla Golshayan  7 , Thomas Müller  8 , Andreas Elmer  9 , Nicola Franscini  9 , Nathalie Krügel  9 , Thomas Fehr  10 , Franz Immer  9 ; Swisstransplant Kidney Working Group (STAN); Swiss Transplant Cohort Study
Collaborators, Affiliations

Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol

Simon Schwab et al. Diagn Progn Res. .

Abstract

Background: Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland.

Methods: The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis.

Discussion: Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration.

Study registration: Open Science Framework ID: z6mvj.

Keywords: Estimated glomerular filtration rate; Graft survival; Kidney transplantation; Patient-reported health status; Prediction model; Prognosis; Prognostic model; Quality of life; Risk calculator; Risk score; eGFR.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Schematic overview of the multivariable prediction models and the data sources involved. Three separate models will be developed to predict the three outcomes by a set of predictors. Predictors are based on SOAS data, while mid- and long-term outcomes were collected by a national multi-centre cohort study
See this image and copyright information in PMC

References

    1. Lv J-C, Zhang L-X. Prevalence and disease burden of chronic kidney disease. In: Liu B-C, Lan H-Y, Lv L-L, editors. Renal Fibrosis: Mechanisms and Therapies. Singapore: Springer Singapore; 2019. pp. 3–15.
    1. Annual Report . Swisstransplant. 2021.
    1. Wolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al. Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med. 1999;341:1725–1730. doi: 10.1056/NEJM199912023412303. - DOI - PubMed
    1. Schnuelle P, Lorenz D, Trede M, Van Der Woude FJ. Impact of renal cadaveric transplantation on survival in end-stage renal failure: evidence for reduced mortality risk compared with hemodialysis during long-term follow-up. J Am Soc Nephrol. 1998;9:2135–2141. doi: 10.1681/ASN.V9112135. - DOI - PubMed
    1. Koller MT, van Delden C, Müller NJ, Baumann P, Lovis C, Marti H-P, et al. Design and methodology of the Swiss Transplant Cohort Study (STCS): a comprehensive prospective nationwide long-term follow-up cohort. Eur J Epidemiol. 2013;28:347–355. doi: 10.1007/s10654-012-9754-y. - DOI - PMC - PubMed