IMPORTANCE During the past decades, improvements in the prevention and management of myocardial infarction, stroke, and pulmonary embolism have led to a decline in cardiovascular mortality in the... Show moreIMPORTANCE During the past decades, improvements in the prevention and management of myocardial infarction, stroke, and pulmonary embolism have led to a decline in cardiovascular mortality in the general population. However, it is unknown whether patients receiving dialysis have also benefited from these improvements.OBJECTIVE To assess the mortality rates for myocardial infarction, stroke, and pulmonary embolism in a large cohort of European patients receiving dialysis compared with the general population.DESIGN, SETTING, AND PARTICIPANTS In this cohort study, adult patients who started dialysis between 1998 and 2015 from 11 European countries providing data to the European Renal Association Registry were and followed up for 3 years. Data were analyzed from September 2020 to February 2022.EXPOSURES Start of dialysis.MAIN OUTCOMES AND MEASURES The age- and sex-standardized mortality rate ratios (SMRs) with 95% CIs were calculated by dividing the mortality rates in patients receiving dialysis by the mortality rates in the general population for 3 equal periods (1998-2003, 2004-2009, and 2010-2015).RESULTS In total, 220 467 patients receiving dialysis were included in the study. Their median (IQR) age was 68.2 (56.5-76.4) years, and 82 068 patients (37.2%) were female. During follow-up, 83 912 patients died, of whom 7662 (9.1%) died because of myocardial infarction, 5030 (6.0%) died because of stroke, and 435 (0.5%) died because of pulmonary embolism. Between the periods 1998 to 2003 and 2010 to 2015, the SMR of myocardial infarction decreased from 8.1 (95% CI, 7.8-8.3) to 6.8 (95% CI, 6.5-7.1), the SMR of stroke decreased from 7.3 (95% CI, 7.0-7.6) to 5.8 (95% CI, 5.5-6.2), and the SMR of pulmonary embolism decreased from 8.7 (95% CI, 7.6-10.1) to 5.5 (95% CI, 4.5-6.6).CONCLUSIONS AND RELEVANCE In this cohort study of patients receiving dialysis, mortality rates for myocardial infarction, stroke, and pulmonary embolism decreased more over time than in the general population. Show less
Background In the general population with coronavirus disease 2019 (COVID-19), obesity is associated with an increased risk of mortality. Given the typically observed obesity paradox among patients... Show moreBackground In the general population with coronavirus disease 2019 (COVID-19), obesity is associated with an increased risk of mortality. Given the typically observed obesity paradox among patients on kidney function replacement therapy (KFRT), especially dialysis patients, we examined the association of obesity with mortality among dialysis patients or living with a kidney transplant with COVID-19. Methods Data from the European Renal Association COVID-19 Database (ERACODA) were used. KFRT patients diagnosed with COVID-19 between 1 February 2020 and 31 January 2021 were included. The association of Quetelet's body mass index (BMI) (kg/m(2)), divided into: <18.5 (lean), 18.5-24.9 (normal weight), 25-29.9 (overweight), 30-34.9 (obese I) and >= 35 (obese II/III), with 3-month mortality was investigated using Cox proportional-hazards regression analyses. Results In 3160 patients on KFRT (mean age: 65 years, male: 61%), 99 patients were lean, 1151 normal weight (reference), 1160 overweight, 525 obese I and 225 obese II/III. During follow-up of 3 months, 28, 20, 21, 23 and 27% of patients died in these categories, respectively. In the fully adjusted model, the hazard ratios (HRs) for 3-month mortality were 1.65 [95% confidence interval (CI): 1.10, 2.47], 1 (ref.), 1.07 (95% CI: 0.89, 1.28), 1.17 (95% CI: 0.93, 1.46) and 1.71 (95% CI: 1.27, 2.30), respectively. Results were similar among dialysis patients (N = 2343) and among those living with a kidney transplant (N = 817) (P-interaction = 0.99), but differed by sex (P-interaction = 0.019). In males, the HRs for the association of aforementioned BMI categories with 3-month mortality were 2.07 (95% CI: 1.22, 3.52), 1 (ref.), 0.97 (95% CI: 0.78. 1.21), 0.99 (95% CI: 0.74, 1.33) and 1.22 (95% CI: 0.78, 1.91), respectively, and in females corresponding HRs were 1.34 (95% CI: 0.70, 2.57), 1 (ref.), 1.31 (95% CI: 0.94, 1.85), 1.54 (95% CI: 1.05, 2.26) and 2.49 (95% CI: 1.62, 3.84), respectively. Conclusion In KFRT patients with COVID-19, on dialysis or a kidney transplant, obesity is associated with an increased risk of mortality at 3 months. This is in contrast to the obesity paradox generally observed in dialysis patients. Additional studies are required to corroborate the sex difference in the association of obesity with mortality. Show less
Noordzij, M.; Diepen, M. van; Caskey, F.C.; Jager, K.J. 2022
To describe how often a disease or another health event occurs in a population, different measures of disease frequency can be used. The prevalence reflects the number of existing cases of a... Show moreTo describe how often a disease or another health event occurs in a population, different measures of disease frequency can be used. The prevalence reflects the number of existing cases of a disease. In contrast to the prevalence, the incidence reflects the number of new cases of disease and can be reported as a risk or as an incidence rate. Prevalence and incidence are used for different purposes and to answer different research questions. In this article, we discuss the different measures of disease frequency and we explain when to apply which measure. Copyright (c) 2010 S. Karger AG, Basel Show less
Wal, W.M. van der; Noordzij, M.; Dekker, F.W.; Boeschoten, E.W.; Krediet, R.T.; Korevaar, J.C.; Geskus, R.B. 2010
When comparing the causal effect of peritoneal dialysis (PD) and hemodialysis (HD) treatment on lowering mortality in renal patients, using observational data, it is necessary to adjust for... Show moreWhen comparing the causal effect of peritoneal dialysis (PD) and hemodialysis (HD) treatment on lowering mortality in renal patients, using observational data, it is necessary to adjust for different forms of confounding and informative censoring. Both the type of dialysis treatment that is started with and mortality are affected by baseline covariates. Longitudinal and baseline variables can affect both the probability of switching from one type of dialysis to the other, and mortality. Longitudinal and baseline variables can also affect the probability of receiving a kidney transplant, possibly causing informative censoring. Adjusting for longitudinal variables by including them as covariates in a regression model potentially causes bias, for instance by losing a possible indirect effect of dialysis on mortality via these longitudinal variables. Instead, we fitted a marginal structural model (MSM) to estimate the causal effect of dialysis type, adjusted for confounding and informative censoring. We used the MSM to compare the hazard of death as well as cumulative survival between the potential treatment trajectories "always PD" and "always HD" over time, conditional on age and diabetes mellitus status. We used inverse probability weighting (IPW) to fit the MSM. Show less
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... Show moreAlthough 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. Show less
