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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 The rapid course of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic calls for fast implementation of clinical trials to assess the effects of new treatment and prophylactic interventions. Building trial platforms embedded in existing data infrastructures is an ideal way to address such questions within well-defined subpopulations. METHODS We developed a trial platform building on the infrastructure of two established national cohort studies: the Swiss human immunodeficiency virus (HIV) Cohort Study (SHCS) and Swiss Transplant Cohort Study (STCS). In a pilot trial, termed Corona VaccinE tRiAL pLatform (COVERALL), we assessed the vaccine efficacy of the first two licensed SARS-CoV-2 vaccines in Switzerland and the functionality of the trial platform. RESULTS Using Research Electronic Data Capture (REDCap), we developed a trial platform integrating the infrastructure of the SHCS and STCS. An algorithm identifying eligible patients, as well as baseline data transfer ensured a fast inclusion procedure for eligible patients. We implemented convenient re-directions between the different data entry systems to ensure intuitive data entry for the participating study personnel. The trial platform, including a randomization algorithm ensuring balance among different subgroups, was continuously adapted to changing guidelines concerning vaccination policies. We were able to randomize and vaccinate the first trial participant the same day we received ethics approval. Time to enroll and randomize our target sample size of 380 patients was 22 days. CONCLUSION Taking the best of each system, we were able to flag eligible patients, transfer patient information automatically, randomize and enroll the patients in an easy workflow, decreasing the administrative burden usually associated with a trial of this size.

Background The burden of antimicrobial resistance is high in solid organ transplant (SOT) recipients. Among Swiss SOT recipients, we assessed temporal trends of ESBL-producing Enterobacterales (ESBL-E), identified risk factors for ESBL-E, and assessed the impact of resistance on patient outcome. Methods Data from the Swiss Transplant Cohort Study (STCS), a nationwide prospective cohort of SOT-recipients, were analysed. Temporal trends were described for ESBL-detection among Escherichia coli and non-Escherichia coli. In a nested case–control study, cases with ESBL-E infection were 1:1 matched (by time since transplantation, organ transplant, pathogen) to controls infected with non-ESBL-E. Factors associated with resistance and with unfavourable 30-day outcome (death, infection relapse, graft loss) were assessed. Results From 2012 to 2018, we identified 1′212 infection episodes caused by Enterobacterales in 1′074 patients, thereof 11.4% (138/1′212) caused by ESBL-E. The proportion of ESBL-production among Escherichia coli remained stable over time (p = 0.93) but increased for non-E. coli (p = 0.02) Enterobacterales. In the case–control study (n = 102), antibiotic pre-treatment was independently associated with ESBL-production (aOR = 2.6, 95%-CI: 1.0–6.8, p = 0.046). Unfavourable outcome occurred in 24/51 (47%) cases and 9/51 (18%) controls (p = 0.003). Appropriate empiric antibiotic therapy was the only modifiable factor associated with unfavourable outcome. Conclusions In Swiss SOT-recipients, proportion of infections with ESBL-producing non-E. coli Enterobacterales increased in recent years. Antibiotic pre-treatment represents a risk factor for ESBL-E. Improving appropriateness of empiric antibiotic treatment might be an important measure to reduce unfavourable outcome, which was observed in almost half of SOT-recipients with ESBL-E infections.