Kitty J. Jager

Cardiovascular and noncardiovascular mortality among patients starting dialysis

CONTEXT Cardiovascular mortality is considered the main cause of death in patients receiving dialysis and is 10 to 20 times higher in such patients than in the general population. OBJECTIVE To evaluate if high overall mortality in patients starting dialysis is a consequence of increased cardiovascular mortality risk only or whether noncardiovascular mortality is equally increased. DESIGN, SETTING, AND PATIENTS Using data from between January 1, 1994, and January 1, 2007, age-stratified mortality in a European cohort of adults starting dialysis and receiving follow-up for a mean of 1.8 (SD, 1.1) years (European Renal Association-European Dialysis and Transplant Association [ERA-EDTA] Registry [N = 123,407]) was compared with the European general population (Eurostat). MAIN OUTCOME MEASURES Cause of death was recorded by ERA-EDTA codes in patients and matching International Statistical Classification of Diseases, 10th Revision codes in the general population. Standardized cardiovascular and noncardiovascular mortality rates, their ratio, difference, and relative excess of cardiovascular over noncardiovascular mortality were calculated. RESULTS Overall all-cause mortality rates in patients and the general population were 192 per 1000 person-years (95% confidence interval [CI], 190-193) and 12.055 per 1000 person-years (95% CI, 12.05-12.06), respectively. Cause of death was known for 90% of the patients and 99% of the general population. In patients, 16,654 deaths (39%) were cardiovascular and 21,654 (51%) were noncardiovascular. In the general population, 7,041,747 deaths (40%) were cardiovascular and 10,183,322 (58%) were noncardiovascular. Cardiovascular and noncardiovascular mortality rates in patients were respectively 38.1 per 1000 person-years (95% CI, 37.2-39.0) and 50.1 per 1000 person-years (95% CI, 48.9-51.2) higher than in the general population. On a relative scale, standardized cardiovascular and noncardiovascular mortality were respectively 8.8 (95% CI, 8.6-9.0) and 8.1 (95% CI, 7.9-8.3) times higher than in the general population. The ratio of these rates, ie, relative excess of cardiovascular over noncardiovascular mortality in patients starting dialysis compared with the general population, was 1.09 (95% CI, 1.06-1.12). Relative excess in a sensitivity analysis in which unknown/missing causes of death were regarded either as noncardiovascular or cardiovascular varied between 0.90 (95% CI, 0.88-0.93) and 1.39 (95% CI, 1.35-1.43). CONCLUSION Patients starting dialysis have a generally increased risk of death that is not specifically caused by excess cardiovascular mortality.

Risk prediction models

Prognostic research focuses on the prediction of the future course of a given disease in probability terms. Prognostication is performed by clinical decision makers by using risk prediction models that allow us to estimate the probability that a specific event occurs in a given patient over a predefined time period conditional on prognostic factors (predictors). Before application in clinical practice, risk prediction models should be properly validated by assessing their discrimination and calibration, or explained variation. Reclassification analyses allow us to evaluate the gain in risk prediction by using a new model compared with an established one. We discuss the concepts of developing and validating risk prediction models by means of two examples, the Framingham risk calculator for prediction of coronary heart disease (CHD), and the recently published Renal Risk Score to predict progression of chronic kidney disease (CKD).

When do we need competing risks methods for survival analysis in nephrology

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.

Predictors of poor outcome in chronic dialysis patients: The Netherlands cooperative study on the adequacy of dialysis

In a prospective cohort study, we constructed a composite index of poor outcome that incorporates survival, morbidity, and quality of life (QL). We identified baseline patient and treatment characteristics that predicted poor outcome 1 year after the start of chronic dialysis. Outcome was classified as poor if a patient had died or if at least two of the following criteria were present: (1) 30 days or greater of hospitalization per year, (2) serum albumin level of 30 g/L or less or a malnutrition index score of 11 or greater, (3) a 36-item Medical Outcomes Study (MOS)-Short Form Health Survey Questionnaire (SF-36) physical summary QL score of 2 or more SDs less than the general population mean score, and (4) an SF-36 mental summary QL score of 2 or more SDs less than the general population mean score. Multivariate logistic regression analysis was used to identify independent predictors of poor outcome. Of 250 included patients, 189 were assessable with respect to poor outcome. Of these patients, 47 (25%) were classified as poor. A baseline presence of comorbidity, serum albumin level of 30 g/L or less, physical or mental QL score 2 or more SDs less than the general population mean score, and, to a lesser extent, residual glomerular filtration rate of 2.5 mL/min/1.73 m(2) or less were independently associated with a greater risk for poor outcome. A post hoc analysis indicated a mean arterial blood pressure greater than 107 mm Hg was predictive of poor outcome in patients undergoing peritoneal dialysis. In conclusion, our prognostic model provides a useful tool to identify chronic dialysis patients at risk for poor health status. Strategies aimed at preserving residual renal function, controlling blood pressure, monitoring QL, and consequently giving psychosocial support may reduce the risk for poor outcome.

Residual renal function at the start of dialysis and clinical outcomes

BACKGROUND This study evaluates the association between estimated GFR (eGFR) at the start of dialysis and mortality within Europe. METHODS Renal registries participating in the ERA-EDTA Registry were asked to provide data on serum creatinine recorded 0-4 weeks before the start of dialysis in incident dialysis patients in 1999 and 2003. Within this cohort study, data were available in 11 472 patients from nine national or regional European renal registries. Cox regression analyses were performed to examine the association between GFR estimated by the four-variable MDRD equation (eGFR) and all-cause mortality, using a follow-up through 31 December 2005. RESULTS In the 2003 data, the mean eGFR was 8.6 ml/min/1.73 m(2). The unadjusted survival analyses showed that an increase in eGFR of 1 ml/min/1.73 m(2) was associated with a higher mortality risk (HR = 1.03; 95% CI: 1.03-1.04) that remained similar after adjustment for age, gender, primary renal disease, treatment modality, country and comorbidity. The findings were consistent across gender, treatment modalities, geographical regions and time periods (2003 versus 1999), but the association between a higher eGFR at the start of dialysis and mortality was the strongest in the youngest age groups and in patients with glomerulonephritis. Analyses at centre level showed that a 10% increase in the percentage of patients starting dialysis at high eGFR levels (>or=10.5 ml/min) was associated with a 22% higher mortality risk (HR = 1.22; 95% CI: 1.18-1.26). CONCLUSIONS This European study showed that a higher eGFR at the start of dialysis was associated with a higher mortality risk. However, an answer to the question when to start dialysis needs to come from randomized controlled trials.

CKD Prevalence Varies across the European General Population

CKD prevalence estimation is central to CKD management and prevention planning at the population level. This study estimated CKD prevalence in the European adult general population and investigated international variation in CKD prevalence by age, sex, and presence of diabetes, hypertension, and obesity. We collected data from 19 general-population studies from 13 European countries. CKD stages 1-5 was defined as eGFR<60 ml/min per 1.73 m(2), as calculated by the CKD-Epidemiology Collaboration equation, or albuminuria >30 mg/g, and CKD stages 3-5 was defined as eGFR<60 ml/min per 1.73 m(2) CKD prevalence was age- and sex-standardized to the population of the 27 Member States of the European Union (EU27). We found considerable differences in both CKD stages 1-5 and CKD stages 3-5 prevalence across European study populations. The adjusted CKD stages 1-5 prevalence varied between 3.31% (95% confidence interval [95% CI], 3.30% to 3.33%) in Norway and 17.3% (95% CI, 16.5% to 18.1%) in northeast Germany. The adjusted CKD stages 3-5 prevalence varied between 1.0% (95% CI, 0.7% to 1.3%) in central Italy and 5.9% (95% CI, 5.2% to 6.6%) in northeast Germany. The variation in CKD prevalence stratified by diabetes, hypertension, and obesity status followed the same pattern as the overall prevalence. In conclusion, this large-scale attempt to carefully characterize CKD prevalence in Europe identified substantial variation in CKD prevalence that appears to be due to factors other than the prevalence of diabetes, hypertension, and obesity.

International Differences in Dialysis Mortality Reflect Background General Population Atherosclerotic Cardiovascular Mortality

Existing national, racial, and ethnic differences in dialysis patient mortality rates largely are unexplained. This study aimed to test the hypothesis that mortality rates related to atherosclerotic cardiovascular disease (ASCVD) in dialysis populations (DP) and in the background general populations (GP) are correlated. In a cross-sectional, multinational study, all-cause and ASCVD mortality rates were compared between GP and DP using the most recent data from the World Health Organization mortality database (67 countries; 1,571,852,000 population) and from national renal registries (26 countries; 623,900 population). Across GP of 67 countries (14,082,146 deaths), all-cause mortality rates (median 8.88 per 1000 population; range 1.93 to 15.40) were strongly related to ASCVD mortality rates (median 3.21; range 0.53 to 8.69), with Eastern European countries clustering in the upper and Southeast and East Asian countries in the lower rate ranges. Across DP (103,432 deaths), mortality rates from all causes (median 166.20; range 54.47 to 268.80) and from ASCVD (median 63.39 per 1000 population; range 21.52 to 162.40) were higher and strongly correlated. ASCVD mortality rates in DP and in the GP were significantly correlated; the relationship became even stronger after adjustment for age (R(2) = 0.56, P < 0.0001). A substantial portion of the variability in mortality rates that were observed across DP worldwide is attributable to the variability in background ASCVD mortality rates in the respective GP. Genetic and environmental factors may underlie these differences.

Sample size calculations: basic principles and common pitfalls

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.

An update on renal replacement therapy in Europe: ERA–EDTA Registry data from 1997 to 2006

BACKGROUND Recent studies have indicated a stabilization in the incidence rates of renal replacement therapy (RRT) for end-stage renal disease (ESRD) in a number of European countries. The aim of this study was to provide an update on the incidence, prevalence and outcomes of RRT in Europe over the past decade. METHODS Nineteen European national or regional renal registries with registry data from 1997 to 2006 participated in the study. Incidence and prevalence trends were analysed with Poisson and Joinpoint regression. Cox regression methods were used to examine patient survival. RESULTS The total adjusted incidence rate of RRT for ESRD increased from 109.9 per million population (pmp) in 1997 to 119.7 pmp in 2000, i.e. an average annual percentage change (AAPC) of 2.9% (95% CI 2.1-3.8%). Thereafter, the incidence increased at a much lower rate to 125.4 pmp in 2006 [AAPC 0.6% (95% CI 0.3-0.8%)]. This change in the trend of the incidence of RRT was largely due to a stabilization in the incidence rates of RRT for females aged 65-74 years, males aged 75-84 years and patients receiving RRT for ESRD due to hypertension/renal vascular disease. The overall adjusted prevalence in Europe continued to increase linearly at 2.7% per year. Between the periods 1997-2001 and 2002-2006, the risk of death decreased for all treatment modalities, with the most substantial improvement in patients starting peritoneal dialysis [19% (95% CI 15-22%)] and in patients receiving a kidney transplant [17% (95% CI 11-23%)]. CONCLUSION This European study shows that the annual rise of the overall incidence rate of RRT for ESRD has diminished and that in several age groups the incidence rates have now stabilized. The survival of dialysis patients and kidney transplant recipients has continued to improve.

Renal replacement therapy in Europe: a summary of the 2012 ERA-EDTA Registry Annual Report

Background This article summarizes the 2012 European Renal Association—European Dialysis and Transplant Association Registry Annual Report (available at www.era-edta-reg.org) with a specific focus on older patients (defined as ≥65 years). Methods Data provided by 45 national or regional renal registries in 30 countries in Europe and bordering the Mediterranean Sea were used. Individual patient level data were received from 31 renal registries, whereas 14 renal registries contributed data in an aggregated form. The incidence, prevalence and survival probabilities of patients with end-stage renal disease (ESRD) receiving renal replacement therapy (RRT) and renal transplantation rates for 2012 are presented. Results In 2012, the overall unadjusted incidence rate of patients with ESRD receiving RRT was 109.6 per million population (pmp) (n = 69 035), ranging from 219.9 pmp in Portugal to 24.2 pmp in Montenegro. The proportion of incident patients ≥75 years varied from 15 to 44% between countries. The overall unadjusted prevalence on 31 December 2012 was 716.7 pmp (n = 451 270), ranging from 1670.2 pmp in Portugal to 146.7 pmp in the Ukraine. The proportion of prevalent patients ≥75 years varied from 11 to 32% between countries. The overall renal transplantation rate in 2012 was 28.3 pmp (n = 15 673), with the highest rate seen in the Spanish region of Catalonia. The proportion of patients ≥65 years receiving a transplant ranged from 0 to 35%. Five-year adjusted survival for all RRT patients was 59.7% (95% confidence interval, CI: 59.3–60.0) which fell to 39.3% (95% CI: 38.7–39.9) in patients 65–74 years and 21.3% (95% CI: 20.8–21.9) in patients ≥75 years.