https://scholarlypublications.universiteitleiden.nl/access/item%3A2868076/view

Sodium Restriction in Patients With CKD : A Randomized Controlled Trial of Self-management Support

Background and objectives Patients with ESRD face significant challenges to remaining employed. It is unknown when in the course of kidney disease patients stop working. Design, setting, participants, & measurements We examined employment trends over time among patients ages 18–54 years old who initiated dialysis in the United States between 1996 and 2013 from a national ESRD registry. We compared unadjusted trends in employment at the start of dialysis and 6 months before ESRD and used linear probability models to estimate changes in employment over time after adjusting for patient characteristics and local unemployment rates in the general population. We also examined employment among selected vulnerable patient populations and changes in employment in the 6 months preceding dialysis initiation. Results Employment was low among patients starting dialysis throughout the study period at 23%–24%, and 38% of patients who were employed 6 months before ESRD stopped working by dialysis initiation. However, after adjusting for observed characteristics, the probability of employment increased over time; patients starting dialysis between 2008 and 2013 had a 4.7% (95% confidence interval, 4.3% to 5.1%) increase in the absolute probability of employment at the start of dialysis compared with patients starting dialysis between 1996 and 2001. Black and Hispanic patients were less likely to be employed than other patients starting dialysis, but this gap narrowed during the study period. Conclusions Although working-aged patients in the United States starting dialysis have experienced increases in the adjusted probability of employment over time, employment at the start of dialysis has remained low.

Employment among Patients Starting Dialysis in the United States

Objective
To identify racial and ethnic differences in mortality and cardiovascular (CV) risk among patients with end-stage renal disease (ESRD) due to lupus nephritis (LN).

Methods
Within the US ESRD registry (1995–2008), we identified individuals ages >17 years with incident ESRD due to systemic lupus erythematosus. We ascertained demographics, clinical factors, and deaths from registry patient files and CV events (myocardial infarction, heart failure, and hemorrhagic and ischemic strokes) from inpatient Medicare claims. We calculated incidence rates (95% confidence intervals [95% CIs]) per 1,000 person-years for study events, stratified by race and ethnicity. We compared probabilities of the events among racial and ethnic groups using cumulative incidence function curves and multivariable-adjusted subdistribution proportional hazard ratios (HRsd), taking into account the competing events of kidney transplantation and death (for nonfatal CV events).

Results
Of 12,533 patients with LN-associated ESRD, the mean ± SD age was 40.7 ± 14.9 years, 82% were women, and 49% were African American. The overall mortality rate was 98.1/1,000 person-years (95% CI 95.3–100.9). In multivariable models, Asian and Hispanic LN-associated ESRD patients had lower mortality than whites (HRsd 0.70 [95% CI 0.58–0.84] and 0.79 [95% CI 0.71–0.88], respectively), whereas African Americans had higher mortality (HRsd 1.27 [95% CI 1.18–1.36]). African American patients >40 years old had higher mortality than their white counterparts (HRsd 1.67 [95% CI 1.44–1.93]). African Americans were more likely to be admitted for heart failure or hemorrhagic stroke.

Conclusion
Among patients with LN-associated ESRD, Asians and Hispanics experienced lower mortality and CV event risks than whites, and African Americans had higher mortality and CV event risks than whites.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/acr.22562

Racial and Ethnic Differences in Mortality and Cardiovascular Events Among Patients With End-Stage Renal Disease Due to Lupus Nephritis.

Aims End-stage renal disease (ESRD) is a strong cardiovascular risk factor. We aimed to determine the extent to which cause of kidney disease independently contributes to this risk. Methods and results Using a national US ESRD registry, we selected patients with eight different causes of ESRD who initiated dialysis 1997-2014. We used proportional sub-distribution hazard models, with non-cardiovascular death or kidney transplantation as competing risks, to estimate hazard ratios (HRs) for a first composite cardiovascular event (myocardial infarction, ischaemic stroke, or cardiovascular or cerebrovascular death), by cause of ESRD. The population was restricted to those using Medicare insurance at Day 91 after dialysis initiation (when most patients become Medicare eligible). Outcomes were ascertained from Medicare claims or Death Notifications. Among the 658 168 patients identified, composite event rates ranged from 3.5/100 person-years in IgA nephropathy to 14.6/100 person-years in diabetic nephropathy (DN). After adjusting for demographics, socioeconomic factors, comorbidities, dialysis modality, and laboratory values, cardiovascular event HRs differed significantly by cause of ESRD. Comparing to IgA nephropathy, the adjusted HR was highest for DN [aHR = 2.97, 95% confidence interval (CI) 2.77-3.20], next highest for lupus nephritis (aHR = 1.86, 95% CI 1.71-2.03), and thereafter ranged from 1.29 (95% CI 1.19-1.39) in autosomal dominant polycystic kidney disease to 1.67 (95% CI 1.52-1.83) in membranous nephropathy. Conclusion High cardiovascular event rates in dialysis patients vary considerably by cause of ESRD. Determining underlying reasons for these differences might provide new insights in to cardiovascular disease mechanisms as well as inform future drug development and clinical trial design.

Cause of kidney disease and cardiovascular events in a national cohort of US patients with end-stage renal disease on dialysis: a retrospective analysis

Functions shipped with R base can fulfill many tasks of missing data handling. However, because the data volume of electronic medical record (EMR) system is always very large, more sophisticated methods may be helpful in data management. The article focuses on missing data handling by using advanced techniques. There are three types of missing data, that is, missing completely at random (MCAR), missing at random (MAR) and not missing at random (NMAR). This classification system depends on how missing values are generated. Two packages, Multivariate Imputation by Chained Equations (MICE) and Visualization and Imputation of Missing Values (VIM), provide sophisticated functions to explore missing data pattern. In particular, the VIM package is especially helpful in visual inspection of missing data. Finally, correlation analysis provides information on the dependence of missing data on other variables. Such information is useful in subsequent imputations.

Missing data exploration: highlighting graphical presentation of missing pattern

Missing data constitute a problem present in all studies of medical research. The most common approach to handling missing data—complete case analysis—relies on assumptions about missing data that rarely hold in practice. The implications of this approach are biased and inefficient descriptions of relationships of interest. Here, various approaches for handling missing data in clinical studies are described. In particular, this work promotes the use of multiple imputation methods that rely on assumptions about missingness that are more flexible than those assumptions relied on by the most common method in use. Furthermore, multiple imputation methods are becoming increasingly more accessible in mainstream statistical software packages, making them both a sound and practical choice. The use of multiple imputation methods is illustrated with examples pertinent to kidney research, and concrete guidance on their use is provided. Clin J Am Soc Nephrol 9: 1328–1335, 2014. doi: 10.2215/CJN.10141013

Addressing Missing Data in Clinical Studies of Kidney

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Addressing Missing Data in Clinical Studies of Kidney