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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

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 prediction models are developed with data from a national multi-centre cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is kidney graft survival (censored for death of recipient); the secondary outcomes are 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 Cox model and linear mixed-effects models for the primary and the two secondary out-comes, 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 existing risk scores for 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 need 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. State-of-the-art methodology and best practice in prediction modelling are applied to data from a nationwide prospective multi-centre 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. Keywords: Prediction model, prognostic model, prognosis, risk calculator, risk score, kidney transplantation, graft survival, quality of life, glomerular filtration rate

Systemic thinking may be traced hack to several roots. Some of them can he found in Taoism, the basic concepts of which are the achievement of cosmic harmony and a well-balanced social order. Others can be found in Greek philosophy. Similarly, modern physics in its most advanced branches is now recognizing basic aspects of these same roots in a scientific guise. The more the process of research and theory building advances, the more phenomena are recognized as complex and interdependent with other phenomena. Interdisciplinary research and the constitution of new disciplines are contributing to a scientific approximation of integral reality, which is becoming more and more like the one everyone knows as prescientific. The transcendence of the narrow boundaries of positivist sciences seems to be becoming a necessity for scientific evolution. The ecological crisis of the twentieth century may itself lead to increased systemic thinking, and it is in full awareness of the fact that there are no simple solutions that the systemic evaluator tries to cope with the problems of the dynamics of social and political interventions in the Third World as a means of development co-operation..