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Aims: Multiple risk scores to predict ischaemic stroke (IS) in patients with atrial fibrillation (AF) have been developed. This study aims to systematically review these scores, their validations and updates, assess their methodological quality, and calculate pooled estimates of the predictive performance.Methods and Results: We searched PubMed and Web of Science for studies developing, validating, or updating risk scores for IS in AF patients. Methodological quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). To assess discrimination, pooled c-statistics were calculated using random-effects meta-analysis. We identified 19 scores, which were validated and updated once or more in 70 and 40 studies, respectively, including 329 validations and 76 updates-nearly all on the CHA2DS2-VASc and CHADS2. Pooled c-statistics were calculated among 6 267 728 patients and 359 373 events of IS. For the CHA2DS2-VASc and CHADS2, pooled c-statistics were 0.644 [95% confidence interval (CI) 0.635-0.653] and 0.658 (0.644-0.672), respectively. Better discriminatory abilities were found in the newer risk scores, with the modified-CHADS2 demonstrating the best discrimination [c-statistic 0.715 (0.674-0.754)]. Updates were found for the CHA2DS2-VASc and CHADS2 only, showing improved discrimination. Calibration was reasonable but available for only 17 studies. The PROBAST indicated a risk of methodological bias in all studies.Conclusion: Nineteen risk scores and 76 updates are available to predict IS in patients with AF. The guideline-endorsed CHA2DS2-VASc shows inferior discriminative abilities compared with newer scores. Additional external validations and data on calibration are required before considering the newer scores in clinical practice.Clinical Trial Registration: ID CRD4202161247 (PROSPERO). [ABSTRACT FROM AUTHOR]

Background The risk–benefit ratio of continuing with renin–angiotensin system inhibitors (RASi) after an episode of acute kidney injury (AKI) is unclear. While stopping RASi may prevent recurrent AKI or hyperkalaemia, it may deprive patients of the cardiovascular benefits of using RASi. Methods We analysed outcomes of long-term RASi users experiencing AKI (stage 2 or 3, or clinically coded) during hospitalization in Stockholm and Sweden during 2007–18. We compared stopping RASi within 3 months after discharge with continuing RASi. The primary study outcome was the composite of all-cause mortality, myocardial infarction (MI) and stroke. Recurrent AKI was our secondary outcome and we considered hyperkalaemia as a positive control outcome. Propensity score overlap weighted Cox models were used to estimate hazard ratios (HRs), balancing 75 confounders. Weighted absolute risk differences (ARDs) were also determined. Results We included 10 165 individuals, of whom 4429 stopped and 5736 continued RASi, with a median follow-up of 2.3 years. The median age was 78 years; 45% were women and median kidney function before the index episode of AKI was 55 mL/min/1.73 m2. After weighting, those who stopped had an increased risk [HR, 95% confidence interval (CI)] of the composite of death, MI and stroke [1.13, 1.07–1.19; ARD 3.7, 95% CI 2.6–4.8] compared with those who continued, a similar risk of recurrent AKI (0.94, 0.84–1.05) and a decreased risk of hyperkalaemia (0.79, 0.71–0.88). Discussion Stopping RASi use among survivors of moderate-to-severe AKI was associated with a similar risk of recurrent AKI, but higher risk of the composite of death, MI and stroke. [ABSTRACT FROM AUTHOR]

Background: External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing events occur, these competing risks should be accounted for when comparing predicted risks to observed outcomes.Methods: We discuss existing measures of calibration and discrimination that incorporate competing events for time-to-event models. These methods are illustrated using a clinical-data example concerning the prediction of kidney failure in a population with advanced chronic kidney disease (CKD), using the guideline-recommended Kidney Failure Risk Equation (KFRE). The KFRE was developed using Cox regression in a diverse population of CKD patients and has been proposed for use in patients with advanced CKD in whom death is a frequent competing event.Results: When validating the 5-year KFRE with methods that account for competing events, it becomes apparent that the 5-year KFRE considerably overestimates the real-world risk of kidney failure. The absolute overestimation was 10%age points on average and 29%age points in older high-risk patients.Conclusions: It is crucial that competing events are accounted for during external validation to provide a more reliable assessment the performance of a model in clinical settings in which competing risks occur. [ABSTRACT FROM AUTHOR]

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 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. [ABSTRACT FROM AUTHOR]

Background Cardiovascular disease is the major cause of death in end-stage renal disease (ESRD). To develop better means to assess cardiovascular risk in these patients, we compared conventional echocardiography-derived left ventricular ejection fraction (EF) with the novel method of 2D speckle-tracking echocardiography to determine cardiac strain. Methods Predictive performances of conventional EF and speckle-tracking echocardiography-derived global longitudinal strain (GLS) were compared using receiver-operator curve (ROC) analyses and calibration by calibration plots. We also took into account other known cardiovascular risk factors through multivariable logistic regression analysis. Results The study comprised 171 ESRD patients (mean age 64 years, 64% male) on maintenance dialysis therapy (93% haemodialysis, 7% peritoneal dialysis) for an average period of 39 months. During 2.1 years of follow-up, 42 patients (25%) died from cardiovascular disease. ROC analysis of GLS resulted in an area under the curve of 0.700 [95% confidence interval (CI) 0.603–0.797] compared with an area under the curve of EF of 0.615 (95% CI 0.514–0.716) (P = 0.059 for difference). The total absolute deviation between predicted and observed outcome frequencies obtained by calibration plots were 13.8% for EF compared with only 6.4% for GLS. Best results of ROC analysis (area under the curve = 0.759; P = 0.06), calibration and goodness-of-fit (χ2 = 28.34, P ≤ 0.0001, R 2 = 0.25) were achieved for GLS added to a baseline model consisting of known cardiovascular risk factors in a multivariate regression analysis. Conclusions In summary, in chronic dialysis patients, GLS is a more precise predictor of cardiovascular mortality than conventional echocardiography-derived EF. [ABSTRACT FROM AUTHOR]

Background Initiation of renal replacement therapy often results from a combination of kidney function deterioration and symptoms related to chronic kidney disease (CKD) progression. We investigated the association between kidney function decline and symptom development in patients with advanced CKD. Methods In the European Quality study on treatment in advanced CKD (EQUAL study), a European prospective cohort study, patients with advanced CKD aged ≥65 years and a kidney function that dropped <20 mL/min/1.73 m2 were followed for 1 year. Linear mixed-effects models were used to assess the association between kidney function decline and symptom development. The sum score for symptom number ranged from 0 to 33 and for overall symptom severity from 0 to 165, using the Dialysis Symptom Index. Results At least one kidney function estimate with symptom number or overall symptom severity was available for 1109 and 1019 patients, respectively. The mean (95% confidence interval) annual kidney function decline was 1.70 (1.32; 2.08) mL/min/1.73 m2. The mean overall increase in symptom number and severity was 0.73 (0.28; 1.19) and 2.93 (1.34; 4.52) per year, respectively. A cross-sectional association between the level of kidney function and symptoms was lacking. Furthermore, kidney function at cohort entry was not associated with symptom development. However, each mL/min/1.73 m2 of annual kidney function decline was associated with an extra annual increase of 0.23 (0.07; 0.39) in the number of symptoms and 0.87 (0.35; 1.40) in overall symptom severity. Conclusions A faster kidney function decline was associated with a steeper increase in both symptom number and severity. Considering the modest association, our results seem to suggest that repeated thorough assessment of symptom development during outpatient clinic visits, in addition to the monitoring of kidney function decline, is important for clinical decision-making. [ABSTRACT FROM AUTHOR]

Observational pharmacoepidemiological studies using routinely collected healthcare data are increasingly being used in the field of nephrology to answer questions on the effectiveness and safety of medications. This review discusses a number of biases that may arise in such studies and proposes solutions to minimize them during the design or statistical analysis phase. We first describe designs to handle confounding by indication (e.g. active comparator design) and methods to investigate the influence of unmeasured confounding, such as the E-value, the use of negative control outcomes and control cohorts. We next discuss prevalent user and immortal time biases in pharmacoepidemiology research and how these can be prevented by focussing on incident users and applying either landmarking, using a time-varying exposure, or the cloning, censoring and weighting method. Lastly, we briefly discuss the common issues with missing data and misclassification bias. When these biases are properly accounted for, pharmacoepidemiological observational studies can provide valuable information for clinical practice. [ABSTRACT FROM AUTHOR]

Aims  The increasing prevalence of ischaemic stroke (IS) can partly be explained by the likewise growing number of patients with chronic kidney disease (CKD). Risk scores have been developed to identify high-risk patients, allowing for personalized anticoagulation therapy. However, predictive performance in CKD is unclear. The aim of this study is to validate six commonly used risk scores for IS in atrial fibrillation (AF) patients across the spectrum of kidney function. Methods and results  Overall, 36 004 subjects with newly diagnosed AF from SCREAM (Stockholm CREAtinine Measurements), a healthcare utilization cohort of Stockholm residents, were included. Predictive performance of the AFI, CHADS2, Modified CHADS2, CHA2DS2-VASc, ATRIA, and GARFIELD-AF risk scores was evaluated across three strata of kidney function: normal kidney function [estimated glomerular filtration rate (eGFR) >60 mL/min/1.73 m2], mild CKD (eGFR 30–60 mL/min/1.73 m2), and advanced CKD (eGFR <30 mL/min/1.73 m2). Predictive performance was assessed by discrimination and calibration. During 1.9 years, 3069 (8.5%) patients suffered an IS. Discrimination was dependent on eGFR: the median c-statistic in normal eGFR was 0.75 (range 0.68–0.78), but decreased to 0.68 (0.58–0.73) and 0.68 (0.55–0.74) for mild and advanced CKD, respectively. Calibration was reasonable and largely independent of eGFR. The Modified CHADS2 score showed good performance across kidney function strata, both for discrimination [c-statistic: 0.78 (95% confidence interval 0.77–0.79), 0.73 (0.71–0.74) and 0.74 (0.69–0.79), respectively] and calibration. Conclusion  In the most clinically relevant stages of CKD, predictive performance of the majority of risk scores was poor, increasing the risk of misclassification and thus of over- or undertreatment. The Modified CHADS2 score performed good and consistently across all kidney function strata, and should therefore be preferred for risk estimation in AF patients. [ABSTRACT FROM AUTHOR]

Background Dialysis patients have an increased bleeding risk as compared with the general population. However, there is limited information whether bleeding risks are different for patients treated with haemodialysis (HD) or peritoneal dialysis (PD). From a clinical point of view, this information could influence therapy choice. Therefore the aim of this study was to investigate the association between dialysis modality and bleeding risk. Methods Incident dialysis patients from the Netherlands Cooperative Study on the Adequacy of Dialysis were prospectively followed for major bleeding events over 3 years. Hazard ratios with 95% confidence intervals (CIs) were calculated for HD compared with PD using a time-dependent Cox regression analysis, with updates on dialysis modality. Results In total, 1745 patients started dialysis, of whom 1211 (69.4%) received HD and 534 (30.6%) PD. The bleeding rate was 60.8/1000 person-years for HD patients and 34.6/1000 person-years for PD patients. The time-dependent Cox regression analysis showed that after adjustment for age, sex, primary kidney disease, prior bleeding, cardiovascular disease, antiplatelet drug use, vitamin K antagonist use, erythropoietin use, arterial hypertension, residual glomerular filtratin rate, haemoglobin and albumin levels, bleeding risk for HD patients compared with PD increased 1.5-fold (95% CI 1.0–2.2). Conclusions In this large prospective cohort of incident dialysis patients, HD patients had an increased bleeding risk compared with PD patients. In particular, HD patients with a history of prior bleeding had an increased bleeding risk. [ABSTRACT FROM AUTHOR]

Prognostic models that aim to improve the prediction of clinical events, individualized treatment and decision-making are increasingly being developed and published. However, relatively few models are externally validated and validation by independent researchers is rare. External validation is necessary to determine a prediction model's reproducibility and generalizability to new and different patients. Various methodological considerations are important when assessing or designing an external validation study. In this article, an overview is provided of these considerations, starting with what external validation is, what types of external validation can be distinguished and why such studies are a crucial step towards the clinical implementation of accurate prediction models. Statistical analyses and interpretation of external validation results are reviewed in an intuitive manner and considerations for selecting an appropriate existing prediction model and external validation population are discussed. This study enables clinicians and researchers to gain a deeper understanding of how to interpret model validation results and how to translate these results to their own patient population. [ABSTRACT FROM AUTHOR]

Background Conservative care (CC) may be a valid alternative to dialysis for certain older patients with advanced chronic kidney disease (CKD). A model that predicts patient prognosis on both treatment pathways could be of value in shared decision-making. Therefore, the aim is to develop a prediction tool that predicts the mortality risk for the same patient for both dialysis and CC from the time of treatment decision. Methods CKD Stage 4/5 patients aged ≥70 years, treated at a single centre in the Netherlands, were included between 2004 and 2016. Predictors were collected at treatment decision and selected based on literature and an expert panel. Outcome was 2-year mortality. Basic and extended logistic regression models were developed for both the dialysis and CC groups. These models were internally validated with bootstrapping. Model performance was assessed with discrimination and calibration. Results In total, 366 patients were included, of which 126 chose CC. Pre-selected predictors for the basic model were age, estimated glomerular filtration rate, malignancy and cardiovascular disease. Discrimination was moderate, with optimism-corrected C-statistics ranging from 0.675 to 0.750. Calibration plots showed good calibration. Conclusions A prediction tool that predicts 2-year mortality was developed to provide older advanced CKD patients with individualized prognosis estimates for both dialysis and CC. Future studies are needed to test whether our findings hold in other CKD populations. Following external validation, this prediction tool could be used to compare a patient's prognosis on both dialysis and CC, and help to inform treatment decision-making. [ABSTRACT FROM AUTHOR]

Background Survival among dialysis patients with diabetes mellitus (DM) is inferior to survival of non-diabetic dialysis patients, probably due to the higher prevalence of diabetes-related comorbid conditions. One could hypothesize that these comorbid conditions also contribute to a decreased survival after amputation in diabetic patients compared with non-diabetic patients on dialysis. Methods Data were collected from the Netherlands Cooperative Study on the Adequacy of Dialysis, a multicentre, prospective cohort study in which new patients with end-stage renal disease were monitored until transplantation or death. Amputation rates (incident cases) were calculated in patients with and without DM. The primary endpoint was all-cause survival after first amputation during dialysis therapy in diabetic patients compared with non-diabetic dialysis patients with an amputation. This was formally assessed using interaction analysis (Poisson regression). Results During follow-up (mean duration 2.9 years), 50 of the 413 diabetic patients had a new amputation (12.1%), compared with 20 of 1553 non-diabetic patients (1.2%). Amputation rates/1000 person-years were 47.9 [95% confidence interval (CI) 36.3–63.2] and 4.1 (95% CI 2.7–6.4), respectively, for diabetic patients and non-diabetic patients. Amputation increased mortality risk more than 4-fold in patients without diabetes [hazard ratio (HR) 4.6 (95% CI 2.8–7.6)] as well as in patients with diabetes [HR 4.6 (95% CI 3.3–6.4)]. No formal interaction between diabetes and amputation was found (P = 0.12). Conclusions Amputation in dialysis patients is associated with a 4-fold increased mortality risk; this mortality risk was similar for diabetes and non-diabetes patients. Importantly, the risk for amputation is 10-fold higher in DM compared with non-diabetic dialysis patients. [ABSTRACT FROM AUTHOR]

Background Prediction tools that identify chronic kidney disease (CKD) patients at a high risk of developing kidney failure have the potential for great clinical value, but limited uptake. The aim of the current study is to systematically review all available models predicting kidney failure in CKD patients, organize empirical evidence on their validity and ultimately provide guidance in the interpretation and uptake of these tools. Methods PubMed and EMBASE were searched for relevant articles. Titles, abstracts and full-text articles were sequentially screened for inclusion by two independent researchers. Data on study design, model development and performance were extracted. The risk of bias and clinical usefulness were assessed and combined in order to provide recommendations on which models to use. Results Of 2183 screened studies, a total of 42 studies were included in the current review. Most studies showed high discriminatory capacity and the included predictors had large overlap. Overall, the risk of bias was high. Slightly less than half the studies (48%) presented enough detail for the use of their prediction tool in practice and few models were externally validated. Conclusions The current systematic review may be used as a tool to select the most appropriate and robust prognostic model for various settings. Although some models showed great potential, many lacked clinical relevance due to being developed in a prevalent patient population with a wide range of disease severity. Future research efforts should focus on external validation and impact assessment in clinically relevant patient populations. [ABSTRACT FROM AUTHOR]

Background Acute kidney injury (AKI) can affect hospitalized patients with coronavirus disease 2019 (COVID-19), with estimates ranging between 0.5% and 40%. We performed a systematic review and meta-analysis of studies reporting incidence, mortality and risk factors for AKI in hospitalized COVID-19 patients. Methods We systematically searched 11 electronic databases until 29 May 2020 for studies in English reporting original data on AKI and kidney replacement therapy (KRT) in hospitalized COVID-19 patients. Incidences of AKI and KRT and risk ratios for mortality associated with AKI were pooled using generalized linear mixed and random-effects models. Potential risk factors for AKI were assessed using meta-regression. Incidences were stratified by geographic location and disease severity. Results A total of 3042 articles were identified, of which 142 studies were included, with 49 048 hospitalized COVID-19 patients including 5152 AKI events. The risk of bias of included studies was generally low. The pooled incidence of AKI was 28.6% [95% confidence interval (CI) 19.8–39.5] among hospitalized COVID-19 patients from the USA and Europe (20 studies) and 5.5% (95% CI 4.1–7.4) among patients from China (62 studies), whereas the pooled incidence of KRT was 7.7% (95% CI 5.1–11.4; 18 studies) and 2.2% (95% CI 1.5–3.3; 52 studies), respectively. Among patients admitted to the intensive care unit, the incidence of KRT was 20.6% (95% CI 15.7–26.7; 38 studies). Meta-regression analyses showed that age, male sex, cardiovascular disease, diabetes mellitus, hypertension and chronic kidney disease were associated with the occurrence of AKI; in itself, AKI was associated with an increased risk of mortality, with a pooled risk ratio of 4.6 (95% CI 3.3–6.5). Conclusions AKI and KRT are common events in hospitalized COVID-19 patients, with estimates varying across geographic locations. Additional studies are needed to better understand the underlying mechanisms and optimal treatment of AKI in these patients. [ABSTRACT FROM AUTHOR]

Background Quality of life (QoL) is an important outcome in chronic kidney disease (CKD). Patients feel that symptoms are an important determinant of QoL. However, this relation is unknown. The aims of this study were to investigate the impact of the number and severity of symptoms on QoL in elderly pre-dialysis patients, assessed by both the effect of symptoms and their importance relative to kidney function, and other clinical variables on QoL. Methods The European Quality study (EQUAL study) is an ongoing European prospective follow-up study in late Stage 4/5 CKD patients aged ≥65 years. We used patients included between March 2012 and December 2015. Patients scored their symptoms with the Dialysis Symptom Index, and QoL with the research and development-36 (RAND-36) item Health Survey (RAND-36). The RAND-36 results in a physical component summary (PCS) and a mental component summary (MCS). We used linear regression to estimate the relation between symptoms and QoL at baseline and after 6 months, and to calculate the variance in QoL explained by symptoms. Results The baseline questionnaire was filled in by 1079 (73%) patients (median age 75 years, 66% male, 98% Caucasian), and the follow up questionnaire by 627 (42%) patients. At baseline, every additional symptom changed MCS with −0.81 [95% confidence interval (CI): −0.91 to −0.71] and PCS with −0.50 (95% CI: −0.62 to −0.39). In univariable analyses, number of symptoms explained 22% of MCS variance and 11% of PCS variance, whereas estimated glomerular filtration rate only explained 1%. Conclusions In elderly CKD Stage 4/5 patients, symptoms have a substantial impact on QoL. This indicates symptoms should have a more prominent role in clinical decision-making. [ABSTRACT FROM AUTHOR]

Proper adjustment for confounding is essential when estimating the effects of treatments or risk factors on health outcomes in observational data. To this end, various statistical methods have been developed. In the past couple of years, the use of propensity scores (PSs) to control for confounding has increased. Proper understanding of this method is necessary to critically appraise research in which it is applied. In this article, we provide an overview of PS methods, explaining their concept, advantages and possible disadvantages. Furthermore, the use of PS matching, PS adjustment and PS weighting is illustrated using data from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) cohort of dialysis patients. [ABSTRACT FROM AUTHOR]

Background Bleeding risk scores have been created to identify patients with an increased bleeding risk, which could also be useful in dialysis patients. However, the predictive performances of these bleeding risk scores in dialysis patients are unknown. Therefore, the aim of this study was to validate existing bleeding risk scores in dialysis patients. Methods A cohort of 1745 incident dialysis patients was prospectively followed for 3 years during which bleeding events were registered. We evaluated the discriminative performance of the Hypertension, Abnormal kidney and liver function, Stroke, Bleeding, Labile INR, Elderly and Drugs or alcohol (HASBLED), the AnTicoagulation and Risk factors In Atrial fibrillation (ATRIA), the Hepatic or kidney disease, Ethanol abuse, Malignancy, Older age, Reduced platelet count or Reduced platelet function, Hypertension, Anaemia, Genetic factors, Excessive fall risk and Stroke (HEMORR2HAGES) and the Outcomes Registry for Better Informed Treatment (ORBIT) bleeding risk scores by calculating C-statistics with 95% confidence intervals (CI). In addition, calibration was evaluated by comparing predicted and observed risks. Results Of the 1745 dialysis patients, 183 patients had a bleeding event, corresponding to an incidence rate of 5.23/100 person-years. The HASBLED [C-statistic of 0.58 (95% CI 0.54–0.62)], ATRIA [C-statistic of 0.55 (95% CI 0.51–0.60)], HEMORR2HAGES [C-statistic of 0.56 (95% CI 0.52–0.61)] and ORBIT [C-statistic of 0.56 (95% CI 0.52–0.61)] risk scores had poor discriminative performances in dialysis patients. Furthermore, the calibration analyses showed that patients with a low risk of bleeding according to the HASBLED, ATRIA, HEMORR2HAGES and ORBIT bleeding risk scores had higher incidence rates for bleeding in our cohort than predicted. Conclusions The HASBLED, ATRIA, HEMORR2HAGES and ORBIT bleeding risk scores had poor predictive abilities in dialysis patients. Therefore, these bleeding risk scores may not be useful in this population. [ABSTRACT FROM AUTHOR]

Clinical epidemiological studies often focus on investigating the underlying causes of disease. For instance, a nephrologist may be interested in the association between blood pressure and the development of chronic kidney disease (CKD). However, instead of focusing on the mere occurrence of CKD, the decline of kidney function over time might be the outcome of interest. For examining this kidney function trajectory, patients are typically followed over time with their kidney function estimated at several time points. During follow-up, some patients may drop out earlier than others and for different reasons. Furthermore, some patients may have greater kidney function at study entry or faster kidney function decline than others. Also, a substantial heterogeneity may exist in the number of kidney function estimates available for each patient. This heterogeneity with respect to kidney function, dropout and number of kidney function estimates is important to take into account when estimating kidney function trajectories. In general, two methods are used in the literature to estimate kidney function trajectories over time: linear regression to estimate individual slopes and the linear mixed-effects model (LMM), i.e. repeated measures analysis. Importantly, the linear regression method does not properly take into account the above-mentioned heterogeneity, whereas the LMM is able to retain all information and variability in the data. However, the underlying concepts, use and interpretation of LMMs are not always straightforward. Therefore we illustrate this using a clinical example and offer a framework of how to model and interpret the LMM. [ABSTRACT FROM AUTHOR]

Background Chronic kidney disease (CKD) is frequently accompanied by thyroid hormone dysfunction. It is currently unclear whether these alterations are the cause or consequence of CKD. This study aimed at studying the effect of thyroid hormone alterations on renal function in cross-sectional and longitudinal analyses in individuals from all adult age groups. Methods Individual participant data (IPD) from 16 independent cohorts having measured thyroid stimulating hormone, free thyroxine levels and creatinine levels were included. Thyroid hormone status was defined using clinical cut-off values. Estimated glomerular filtration rates (eGFR) were calculated by means of the four-variable Modification of Diet in Renal Disease (MDRD) formula. For this IPD meta-analysis, eGFR at baseline and eGFR change during follow-up were computed by fitting linear regression models and linear mixed models in each cohort separately. Effect estimates were pooled using random effects models. Results A total of 72 856 individuals from 16 different cohorts were included. At baseline, individuals with overt hypothyroidism (n  = 704) and subclinical hypothyroidism (n  = 3356) had a average (95% confidence interval) −4.07 (−6.37 to −1.78) and −2.40 (−3.78 to −1.02) mL/min/1.73 m2 lower eGFR as compared with euthyroid subjects (n  = 66 542). In (subclinical) hyperthyroid subjects (n  = 2254), average eGFR was 3.01 (1.50–4.52) mL/min/1.73 m2 higher. During 329 713 patient years of follow-up, eGFR did not decline more rapidly in individuals with low thyroid function compared with individuals with normal thyroid function. Conclusions Low thyroid function is not associated with a deterioration of renal function. The cross-sectional association may be explained by renal dysfunction causing thyroid hormone alterations. [ABSTRACT FROM AUTHOR]

Background An easy-to-use prediction model for long-term renal patient survival based on only four predictors [age, primary renal disease, sex and therapy at 90 days after the start of renal replacement therapy (RRT)] has been developed in The Netherlands. To assess the usability of this model for use in Europe, we externally validated the model in 10 European countries. Methods Data from the European Renal Association–European Dialysis and Transplant Association (ERA-EDTA) Registry were used. Ten countries that reported individual patient data to the registry on patients starting RRT in the period 1995–2005 were included. Patients <16  years of age and/or with missing predictor variable data were excluded. The external validation of the prediction model was evaluated for the 10- (primary endpoint), 5- and 3-year survival predictions by assessing the calibration and discrimination outcomes. Results We used a data set of 136 304 patients from 10 countries. The calibration in the large and calibration plots for 10 deciles of predicted survival probabilities showed average differences of 1.5, 3.2 and 3.4% in observed versus predicted 10-, 5- and 3-year survival, with some small variation on the country level. The concordance index, indicating the discriminatory power of the model, was 0.71 in the complete ERA-EDTA Registry cohort and varied according to country level between 0.70 and 0.75. Conclusions A prediction model for long-term renal patient survival developed in a single country, based on only four easily available variables, has a comparably adequate performance in a wide range of other European countries. [ABSTRACT FROM AUTHOR]

Purpose: A post hoc analysis of a recent trial on direct oral anticoagulants versus vitamin K antagonists showed that amongst patients with mildly decreased kidney function, use of vitamin K antagonists was associated with a greater decline in renal function than use of direct oral anticoagulants. Whether these vitamin K antagonist effects are the same in pre-dialysis patients is unknown. Therefore, the aim of this study was to investigate the association between vitamin K antagonist use and the rate of renal function decline and time until start of dialysis in incident pre-dialysis patients.Methods: Data from 984 patients from the PREdialysis PAtient REcord study, a multicenter follow-up study of patients with chronic kidney disease who started pre-dialysis care in the Netherlands (1999��2011), were analyzed. Of these patients, 101 used a vitamin K antagonist. Linear mixed models were used to compare renal function decline between vitamin K antagonist users and non-users. Cox proportional hazards models were used to estimate the HR with 95% CI for starting dialysis.Results: Vitamin K antagonist use was associated with an extra change in renal function of ��0.09 (95% CI ��1.32 to 1.13) mL/min/1.73 m2 per year after adjustment for confounding. The adjusted HR for the start of dialysis was 1.20 (95% CI 0.85 to 1.69) in vitamin K antagonist users, compared to non-users. Conclusion: In incident pre-dialysis patients, the use of vitamin K antagonists was not associated with an accelerated kidney function decline or an earlier start of dialysis compared to non-use. The lack of knowledge on the indication for vitamin K antagonist use could lead to confounding by indication. [ABSTRACT FROM AUTHOR]

Purpose: Substantial heterogeneity exists in reported kidney function decline in pre-dialysis chronic kidney disease (CKD). By design, kidney function decline can be studied in CKD 3–5 cohorts or dialysis-based studies. In the latter, patients are selected based on the fact that they initiated dialysis, possibly leading to an overestimation of the true underlying kidney function decline in the pre-dialysis period. We performed a systematic review and meta-analysis to compare the kidney function decline during pre-dialysis in CKD stage 3–5 patients, in these two different study types. Patients and methods: We searched PubMed, EMBASE, Web of Science and Cochrane to identify eligible studies reporting an estimated glomerular filtration rate (eGFR) decline (mL/min/1.73 m2) in adult pre-dialysis CKD patients. Random-effects meta-analysis was performed to obtain weighted mean annual eGFR decline. Results: We included 60 studies (43 CKD 3–5 cohorts and 17 dialysis-based studies). The meta-analysis yielded a weighted annual mean (95% CI) eGFR decline during pre-dialysis of 2.4 (95% CI: 2.2, 2.6) mL/min/1.73 m2 in CKD 3–5 cohorts compared to 8.5 (95% CI: 6.8, 10.1) in dialysis-based studies (difference 6.0 [95% CI: 4.8, 7.2]). Conclusion: To conclude, dialysis-based studies report faster mean annual eGFR decline during pre-dialysis than CKD 3–5 cohorts. Thus, eGFR decline data from CKD 3–5 cohorts should be used to guide clinical decision making in CKD patients and for power calculations in randomized controlled trials with CKD progression during pre-dialysis as the outcome. [ABSTRACT FROM AUTHOR]

In the originally published version of this manuscript, the following brackets were missing in Box 1, which affects the data of the paper: Risk at 1 year after inclusion in exposed group (R1) = a/(a + b) Risk at 1 year after inclusion in unexposed group (R0) = c/(c + d) These details have been corrected only in this erratum to preserve the published version of record. [Extracted from the article]