Background Patients with chronic kidney disease are often requested to engage in self-monitoring sodium (i.e. salt) intake, but it is currently unknown how self-monitoring would empower them. This... Show moreBackground Patients with chronic kidney disease are often requested to engage in self-monitoring sodium (i.e. salt) intake, but it is currently unknown how self-monitoring would empower them. This study aims to assess: (1) how frequent self-monitoring tools are being used during low-sodium diet self-management interventions; (2) whether self-efficacy (i.e. trust in own capability to manage the chronic disease) is associated with self-monitoring frequency; and (3) whether higher self-monitoring frequency is associated with an improvement in self-efficacy over time.Method Data from two multicenter randomized controlled trials (ESMO [n = 151] and SUBLIME [n = 99]) among adult Dutch patients with chronic kidney disease (eGFR >= 20-25 mL/min/1.73 m(2)) were used. In both studies, routine care was compared to a 3-month low-sodium diet self-management intervention with several self-monitoring tools (online food diary, home blood pressure monitor, and urinary sodium measurement device [only ESMO]). Data was collected on usage frequency of self-monitoring tools. Frequencies during the interventions were compared between low and high baseline self-efficacy groups using the Mann-Whitney U test and T-test and associated with changes in self-efficacy during the interventions using Spearman correlation coefficients.Results Large variations in self-monitoring frequency were observed. In both interventions, usage of self-monitoring tools was highest during the first month with sharp drops thereafter. The online food diary was the most frequently used tool. In the ESMO intervention, low baseline self-efficacy was associated with a higher usage frequency of self-monitoring tools. This finding was not confirmed in the SUBLIME intervention. No significant associations were found between usage frequency of self-monitoring tools and changes in self-efficacy over time.Conclusion Patients with low self-efficacy might benefit most from frequent usage of self-monitoring tools when sufficient guidance and support is provided. Show less
Lay Summary Our study compared the incidence of dialysis, the most common treatment modality for kidney failure, in the Netherlands with 11 other European countries/regions between 2001 and 2019.... Show moreLay Summary Our study compared the incidence of dialysis, the most common treatment modality for kidney failure, in the Netherlands with 11 other European countries/regions between 2001 and 2019. We observed a decline in the dialysis incidence in the last decade, most marked among patients aged >= 65 years. Similar trends were found in Austria, Denmark, England/Wales, Finland, Scotland, and Sweden. Although the number of kidney transplantations increased significantly, this can only explain a minor part of the observed decrease in the dialysis incidence among these older patients. An increase in the number of patients choosing to forego dialysis (i.e. conservative care) or improvement of preventive care for cardiovascular- or kidney disease, are likely explanations. Our study stresses the need for registration of patients with earlier stages of chronic kidney disease and patients that choose conservative care. This would allow for better assessment of healthcare utilization and outcomes in these patient groups.Introduction After decades of increasing dialysis incidence, we observed a decreasing trend in the Netherlands in the last decade. We compared this trend with trends in other European countries. Materials and Methods Aggregated data for calendar years 2001-2019 from the Dutch registries of kidney replacement therapy patients and the European Renal Association Registry were used. Dialysis incidence in the Netherlands was compared with that in 11 other European countries/regions using three age groups: 20-64, 65-74, and >= 75 years, taking into account pre-emptive kidney transplantation (PKT) incidence. Time trends were assessed as annual percentage change (APC) with 95% confidence intervals (CI) using joinpoint regression analysis. Results Between 2001 and 2019 the Dutch dialysis incidence decreased slightly among patients aged 20-64 years (APC -0.9, 95% CI -1.4; -0.5). For patients 65-74 and >= 75 years old, a peak was seen in 2004 and 2009, respectively. Afterwards, the decrease was most marked in patients aged >= 75 years: APC -3.2 (-4.1; -2.3) versus APC -1.8 (-2.2; -1.3) for patients 65-74 years old. PKT incidence increased significantly during the study period but remained limited compared to the observed decrease in dialysis incidence, especially among older patients. Large differences in dialysis incidence were observed among European countries/regions. A decreasing dialysis incidence among older patients was also seen in Austria, Denmark, England/Wales, Finland, Scotland, and Sweden. Conclusions The Dutch dialysis incidence decreased most profoundly among older patients. This was also observed in several other European countries/regions. Although PKT incidence increased, it can only explain a minor part of the decrease in dialysis incidence. Show less
Background: Dialysis is associated with frequent hospitalisations. Studies comparing hospitalisations between peritoneal dialysis (PD) and haemodialysis (HD) report conflicting results and mostly... Show moreBackground: Dialysis is associated with frequent hospitalisations. Studies comparing hospitalisations between peritoneal dialysis (PD) and haemodialysis (HD) report conflicting results and mostly analyse data of patients that remain on their initial dialysis modality. This cohort study compares hospitalisations between PD and HD patients taking into account transitions between modalities. Methods: The Dutch nOcturnal and hoME dialysis Study To Improve Clinical Outcomes collected hospitalisation data of patients who started dialysis between 2012 and 2017. Primary outcome was hospitalisation rate, analysed with a multi-state model that attributed each hospitalisation to the current dialysis modality. Results: In total, 695 patients (252 PD, 443 HD) treated in 31 Dutch hospitals were included. The crude hospitalisation rate for PD was 2.3 ( +/- 5.0) and for HD 1.4 ( +/- 3.2) hospitalisations per patient-year. The adjusted hazard ratio for hospitalisation rate was 1.1 (95%CI 1.02-1.3) for PD compared with HD. The risk for first hospitalisation was 1.3 times (95%CI 1.1-1.6) higher for PD compared with HD during the first year after dialysis initiation. The number of hospitalisations and number of hospital days per patient-year were significantly higher for PD. The most common causes of PD and HD hospitalisations were peritonitis (23%) and vascular access-related problems (33%). Conclusion: PD was associated with higher hospitalisation rate, higher risk for first hospitalisation and higher number of hospitalisations compared with HD. Since the PD hospitalisations were mainly caused by peritonitis, more attention to infection prevention is necessary for reducing the number of hospitalisations in the future. Show less
Oevelen, M. van; Abrahams, A.C.; Bos, W.J.W.; Hoekstra, T.; Hemmelder, M.H.; Dam, M. ten; Buren, M. van 2021
Background. Dialysis withdrawal is a common cause of death in dialysis-dependent patients. This study aims to describe dialysis withdrawal practice in The Netherlands, focussing on time trends,... Show moreBackground. Dialysis withdrawal is a common cause of death in dialysis-dependent patients. This study aims to describe dialysis withdrawal practice in The Netherlands, focussing on time trends, risk factors and centre variation.Methods. Data were retrieved from the Dutch registry of kidney replacement therapy patients. All patients who started maintenance dialysis and died in the period 2000-2019 were included. The main outcome was death after dialysis withdrawal; all other causes of death were used for comparison. Time trends were analysed as unadjusted data (proportion per year) and the year of death was included in a multivariable logistic model. Univariable and multivariable analyses were performed to identify factors associated with withdrawal. Centre variation was compared using funnel plots.Results. A total of 34 692 patients started dialysis and 18 412 patients died while on dialysis. Dialysis withdrawal was an increasingly common cause of death, increasing from 18.3% in 2000-2004 to 26.8% in 2015-2019. Of all patients withdrawing, 26.1% discontinued treatment within their first year. In multivariable analysis, increasing age, female sex, haemodialysis as a treatment modality and year of death were independent factors associated with death after dialysis withdrawal. Centre variation was large (80.7 and 57.4% within 95% control limits of the funnel plots for 2000-2009 and 2010-2019, respectively), even after adjustment for confounding factors.Conclusions. Treatment withdrawal has become the main cause of death among dialysis-dependent patients in The Netherlands, with large variations between centres. These findings emphasize the need for timely advance care planning and improving the shared decision-making process on choosing dialysis or conservative care. Show less
Janse, R.J.; Hoekstra, T.; Jager, K.J.; Zoccali, C.; Tripepi, G.; Dekker, F.W.; Diepen, M. van 2021
The correlation coefficient is a statistical measure often used in studies to show an association between variables or to look at the agreement between two methods. In this paper, we will discuss... Show moreThe correlation coefficient is a statistical measure often used in studies to show an association between variables or to look at the agreement between two methods. In this paper, we will discuss not only the basics of the correlation coefficient, such as its assumptions and how it is interpreted, but also important limitations when using the correlation coefficient, such as its assumption of a linear association and its sensitivity to the range of observations. We will also discuss why the coefficient is invalid when used to assess agreement of two methods aiming to measure a certain value, and discuss better alternatives, such as the intraclass coefficient and Bland-Altman's limits of agreement. The concepts discussed in this paper are supported with examples from literature in the field of nephrology. Show less
AimSubclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups... Show moreAimSubclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D.MethodsThe study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders.ResultsAt baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose.ConclusionsDifferent glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk. Show less
Obura, M.; Beulens, J.W.J.; Slieker, R.; Koopman, A.D.M.; Hoekstra, T.; Nijpels, G.; ... ; Rutters, F. 2020
Aim To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with... Show moreAim To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration.Methods We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months.Results At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (beta = 0.36, 95% CI 0.13-0.58), Subgroup 3 (beta = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (beta = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months.Conclusions Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified. Show less
A funnel plot is a graphical method to evaluate health-care quality by comparing hospital performances on certain outcomes. So far, in nephrology, this method has been applied to clinical outcomes... Show moreA funnel plot is a graphical method to evaluate health-care quality by comparing hospital performances on certain outcomes. So far, in nephrology, this method has been applied to clinical outcomes like mortality and complications. However, patient-reported outcomes (PROs; eg, health-related quality of life [HRQOL]) are becoming increasingly important and should be incorporated into this quality assessment. Using funnel plots has several advantages, including clearly visualized precision, detection of volume-effects, discouragement of ranking hospitals and easy interpretation of results. However, without sufficient knowledge of underlying methods, it is easy to stumble into pitfalls, such as overinterpretation of standardized scores, incorrect direct comparisons of hospitals and assuming a hospital to be in-control (ie, to perform as expected) based on underpowered comparisons. Furthermore, application of funnel plots to PROs is accompanied by additional challenges related to the multidimensional nature of PROs and difficulties with measuring PROs. Before using funnel plots for PROs, high and consistent response rates, adequate case mix correction and high-quality PRO measures are required. In this article, we aim to provide insight into the use and interpretation of funnel plots by presenting an overview of the basic principles, pitfalls and considerations when applied to PROs, using examples from Dutch routine dialysis care. Show less
