ArticleDiagnostic and prognostic research2023
Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol.
Article in Diagnostic and prognostic research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Enhancing post-kidney transplant prognostication: an interpretable machine learning approach for longitudinal outcome prediction.NPJ digital medicine · 2025Article
- Association of Kidney Graft Long-term Outcome With Recipient Cystathionine Gamma-lyase Polymorphisms and Hydrogen Sulfide Levels: A Cohort Study.Transplantation direct · 2025Article
- Prediction of Renal Graft Function 1 Year After Adult Deceased-Donor Kidney Transplantation Using Variables Available Prior to Transplantation.Annals of transplantation · 2024Article
- Preoperative Risk Assessment of Early Kidney Graft Loss.Transplantation direct · 2024Article
- Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol.Diagnostic and prognostic research · 2023Article
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19 authors.
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Abstract
backgroundMany 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.
methodsThe 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.
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