Survival analyses are commonly applied to study death or other events of interest. In such analyses, so-called competing risks may form an important problem. A competing risk is an event that either hinders the observation of the event of interest or modifies the chance that this event occurs. For example, when studying death on dialysis, receiving a kidney transplant is an event that competes with the event of interest. Conventional methods for survival analysis ignoring the competing event(s), such as the Kaplan-Meier method and standard Cox proportional hazards regression, may be inappropriate in the presence of competing risks, and alternative methods specifically designed for analysing competing risks data should then be applied. This problem deserves more attention in nephrology research and in the current article, we therefore explain the problem of competing risks in survival analysis and how using different techniques may affect study results.

Background. Patients on kidney replacement therapy comprise a vulnerable population and may be at increased risk of death from coronavirus disease 2019 (COVID-19). Currently, only limited data are available on outcomes in this patient population. Methods. We set up the ERACODA (European Renal Association COVID-19 Database) database, which is specifically designed to prospectively collect detailed data on kidney transplant and dialysis patients with COVID-19. For this analysis, patients were included who presented between 1 February and 1 May 2020 and had complete information available on the primary outcome parameter, 28-day mortality. Results. Of the 1073 patients enrolled, 305 (28%) were kidney transplant and 768 (72%) dialysis patients with a mean age of 60 ± 13 and 67 ± 14 years, respectively. The 28-day probability of death was 21.3% [95% confidence interval (95% CI) 14.3–30.2%] in kidney transplant and 25.0% (95% CI 20.2–30.0%) in dialysis patients. Mortality was primarily associated with advanced age in kidney transplant patients, and with age and frailty in dialysis patients. After adjusting for sex, age and frailty, in-hospital mortality did not significantly differ between transplant and dialysis patients [hazard ratio (HR) 0.81, 95% CI 0.59–1.10, P = 0.18]. In the subset of dialysis patients who were a candidate for transplantation (n = 148), 8 patients died within 28 days, as compared with 7 deaths in 23 patients who underwent a kidney transplantation <1 year before presentation (HR adjusted for sex, age and frailty 0.20, 95% CI 0.07–0.56, P < 0.01). Conclusions. The 28-day case-fatality rate is high in patients on kidney replacement therapy with COVID-19 and is primarily driven by the risk factors age and frailty. Furthermore, in the first year after kidney transplantation, patients may be at increased risk of COVID-19-related mortality as compared with dialysis patients on the waiting list for transplantation. This information is important in guiding clinical decision-making, and for informing the public and healthcare authorities on the COVID-19-related mortality risk in kidney transplant and dialysis patients.

High plasma phosphate is an independent risk factor for a more rapid decline in renal function and a higher mortality during the pre-dialysis phase. Plasma phosphate within the normal range is likely of vital importance in pre-dialysis patients.

Regular caffeine intake increases BP. When ingested through coffee, however, the blood pressure effect of caffeine is small.

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One of the most common requests that statisticians get from investigators are sample size calculations or sample size justifications. The sample size is the number of patients or other experimental units included in a study, and determining the sample size required to answer the research question is one of the first steps in designing a study. Although most statistical textbooks describe techniques for sample size calculation, it is often difficult for investigators to decide which method to use. There are many formulas available which can be applied for different types of data and study designs. However, all of these formulas should be used with caution since they are sensitive to errors, and small differences in selected parameters can lead to large differences in the sample size. In this paper, we discuss the basic principles of sample size calculations, the most common pitfalls and the reporting of these calculations.

BackgroundThis article summarizes the European Renal Association – European Dialysis and Transplant Association (ERA-EDTA) Registry’s 2015 Annual Report. It describes the epidemiology of renal replacement therapy (RRT) for end-stage renal disease (ESRD) in 2015 within 36 countries.MethodsIn 2016 and 2017, the ERA-EDTA Registry received data on patients who were undergoing RRT for ESRD in 2015, from 52 national or regional renal registries. Thirty-two registries provided individual patient-level data and 20 provided aggregated-level data. The incidence, prevalence and survival probabilities of these patients were determined.ResultsIn 2015, 81 373 individuals commenced RRT for ESRD, equating to an overall unadjusted incidence rate of 119 per million population (pmp). The incidence ranged by 10-fold, from 24 pmp in Ukraine to 232 pmp in the Czech Republic. Of the patients commencing RRT, almost two-thirds were men, over half were aged ≥65 years and a quarter had diabetes mellitus as their primary renal diagnosis. Treatment modality at the start of RRT was haemodialysis for 85% of the patients, peritoneal dialysis for 11% and a kidney transplant for 4%. By Day 91 of commencing RRT, 82% of patients were receiving haemodialysis, 13% peritoneal dialysis and 5% had a kidney transplant. On 31 December 2015, 546 783 individuals were receiving RRT for ESRD, corresponding to an unadjusted prevalence of 801 pmp. This ranged throughout Europe by more than 10-fold, from 178 pmp in Ukraine to 1824 pmp in Portugal. In 2015, 21 056 kidney transplantations were performed, equating to an overall unadjusted transplant rate of 31 pmp. This varied from 2 pmp in Ukraine to 94 pmp in the Spanish region of Cantabria. For patients commencing RRT during 2006–10, the 5-year unadjusted patient survival probabilities on all RRT modalities combined was 50.0% (95% confidence interval 49.9–50.1).

Diabetes, hypertension and cardiovascular disease have been listed as risk factors for severe coronavirus disease 2019 (COVID-19) since the first report of the disease in January 2020. However, this report did not mention chronic kidney disease (CKD) nor did it provide information on the relevance of estimated glomerular filtration rate (eGFR) or albuminuria. As the disease spread across the globe, information on larger populations with greater granularity on risk factors emerged. The recently published OpenSAFELY project analysed factors associated with COVID-19 death in 17 million patients. The picture that arose differs significantly from initial reports. For example, hypertension is not an independent risk factor for COVID-19 death [adjusted hazard ratio (aHR) 0.89], but renal disease very much is. Dialysis (aHR 3.69), organ transplantation (aHR 3.53) and CKD (aHR 2.52 for patients with eGFR <30 mL/min/1.73 m2) represent three of the four comorbidities associated with the highest mortality risk from COVID-19. The risk associated with CKD Stages 4 and 5 is higher than the risk associated with diabetes mellitus (aHR range 1.31–1.95, depending upon glycaemic control) or chronic heart disease (aHR 1.17). In another recent publication, the Global Burden of Disease collaboration identified that worldwide, CKD is the most prevalent risk factor for severe COVID-19. Moreover, the distribution of risk factors for COVID-19 mortality appears to be different in patients with CKD when compared with the general population. The high prevalence of CKD in combination with the elevated risk of mortality from COVID-19 in CKD necessitates urgent action for this group of patients. This article defines essential action points (summarized in Box 1), among which is advocating the inclusion of CKD patients in clinical trials testing the efficacy of drugs and vaccines to prevent severe COVID-19.