Comparison of the new and traditional CKD-EPI GFR estimation equations with urinary inulin clearance: A study of equation performance.

BACKGROUND: Diagnosis, prognostication and treatment in chronic kidney disease is often informed by an estimate of the glomerular filtration rate (GFR). Commonly used GFR estimation (eGFR) equations are based on serum creatinine (Cr) concentrations and display suboptimal precision and accuracy. Newer equations incorporating additional endogenous markers such as ß-Trace Protein (BTP), ß2-Microglobulin (B2M) and cystatin C (cysC) have been developed but require validation. METHODS: This prospective cohort study evaluated the performance of 6 eGFR equations developed by the chronic kidney disease - epidemiology collaboration group (CKD-EPI) against urinary inulin clearance GFR in patients recruited from outpatient nephrology clinics. RESULTS: Mean biases were negligible and similar between equations. The eGFR-EPI Cr/cysC had the best precision and accuracy of all the equations and the best agreement with inulin mGFR when classifying participants into GFR categories. The BTP and B2M equations displayed the worst precisions and accuracies and showed the least consistent performance across levels of GFR. Thus, the eGFR-EPI Cr/cysC is the least biased, most precise and has the highest accuracy as compared to other eGFR-EPI equations. CONCLUSIONS: The BTP and B2M equations are the worst performing of the eGFR-EPI equations, and no benefit is observed with the addition of BTP or B2M to Cr/cysC.

Adequacy of glycemic control in hemodialysis patients with diabetes.

OBJECTIVE: We sought to measure the prevalence of inadequate glycemic control in prevalent hemodialysis patients with diabetes and to examine independent predictors of inadequate glycemic control in these patients. RESEARCH DESIGN AND METHODS: This is a cross-sectional study of prevalent hemodialysis patients with diabetes in southeastern Ontario (n = 100). Data were collected by chart review and interview. The outcome variable was inadequate glycemic control defined as HbA1c (A1C) >0.07. Other measured variables were diabetes type, diabetes duration, diabetes physician, blood glucose monitoring, diabetes medications, BMI, time on dialysis, and other demographic, clinical, and laboratory variables. RESULTS: Fifty-four patients had A1C >0.07. In bivariate analysis, these patients had a longer diabetes duration (23.6 vs. 14.7 years, P < 0.001), higher proportion with insulin use (81.5 vs. 58.7%, P = 0.012), higher proportion with microvascular complications (66.7 vs. 43.5%, P = 0.017), and lower erythropoietin (EPO) dose (7.0 vs. 11.9 x 10(3) units/week, P < 0.01) than patients with adequate glycemic control. There was no difference between the two groups in terms of macrovascular complications (59.3 vs. 65.2%, P = 0.54). In multiple logistic regression controlling for age and diabetes type, the diabetes duration (odds ratio 1.09 [95% CI 1.04-1.15], P < 0.001), EPO dose (0.90 [0.85-0.97], P < 0.01), and blood glucose monitoring (10.06 [1.03-98.74], P = 0.05) were the only significant independent predictors of A1C >0.07. CONCLUSIONS: A high proportion of hemodialysis patients with diabetes had inadequate glycemic control, particularly those with longstanding disease. Patients with inadequate glycemic control had a significantly higher burden of microvascular complications.

Quality of sleep in patients with chronic kidney disease.

BACKGROUND: Sleep disorders are common in patients with renal failure on dialysis; however, the prevalence of "poor sleep" in patients with chronic kidney disease (CKD) not yet on dialysis is not known. This study aimed to measure the prevalence of "poor sleep" in CKD patients and to examine the association between quality of sleep and the degree of renal impairment in this population. METHODS: Quality of sleep was measured using the Pittsburgh Sleep Quality Index (PSQI) in 120 prevalent CKD patients. RESULTS: Sixty-three subjects (53%) had "poor sleep" defined as a global PSQI score >5. There was no statistically significant relationship between the global PSQI score and the blood urea nitrogen level (BUN), serum creatinine level or calculated creatinine clearance, but the sleep efficiency component score correlated with BUN (r = 0.19, P = 0.04) and serum creatinine (r = 0.20, P = 0.03). A history of depression was the only independent predictor of "poor sleep" (global PSQI >5). CONCLUSIONS: "Poor sleep" is common in CKD patients. Quality of sleep decreases in the early stages of CKD and does not appear to be associated with the subsequent degree of renal failure. Large prospective longitudinal studies of quality of sleep in CKD patients are needed to confirm the high prevalence of impaired quality of sleep in this population and examine the association between renal function and quality of sleep while controlling for potential confounding variables.

Pre-dialysis clinic attendance improves quality of life among hemodialysis patients.

BACKGROUND: Although previous research has demonstrated that referral to pre-dialysis clinics is associated with favourable objective outcomes, the benefit of a pre-dialysis clinic from the perspective of patient-perceived subjective outcomes, such as quality of life (QOL), is less well defined. METHODS: A retrospective incident cohort study was conducted to determine if pre-dialysis clinic attendance was a predictor of better QOL scores measured within the first six months of hemodialysis (HD) initiation. Inclusion criteria were HD initiation from January 1 1998 to January 1 2000, diagnosis of chronic renal failure, and completion of the QOL questionnaire within six months of HD initiation. Patients receiving HD for less than four weeks were excluded. An incident cohort of 120 dialysis patients was identified, including 74 patients who attended at least one pre-dialysis clinic and 46 patients who did not. QOL was measured using the SF 36-Item Health Survey. Independent variables included age, sex, diabetes, pre-dialysis clinic attendance and length of attendance, history of ischemic heart disease, stroke, peripheral vascular disease, heart failure, malignancy, and chronic lung disease, residual creatinine clearance at dialysis initiation, and kt/v, albumin and hemoglobin at the time of QOL assessment. Bivariate and multivariate linear regression analyses were used to identify predictors of QOL scores. RESULTS: Multivariate analysis suggested that pre-dialysis clinic attendance was an independent predictor of higher QOL scores in four of eight health domains (physical function, p < 0.01; emotional role limitation, p = 0.01; social function, p = 0.01; and general health, p = 0.03), even after statistical adjustment for age, sex, residual renal function, kt/v, albumin, and co-morbid disease. Pre-dialysis clinic attendance was also an independent predictor of the physical component summary score (p = 0.03). CONCLUSIONS: We conclude that pre-dialysis clinic attendance favourably influences patient-perceived quality of life within six months of dialysis initiation.