José Luis Teruel

Healthcare for patients with advanced chronic kidney disease (ACKD) on conservative treatment very often poses healthcare problems that are difficult to solve. At the end of 2011, we began a program based on the care and monitoring of these patients by Primary Care Teams. ACKD patients who opted for conservative treatment were offered the chance to be cared for mainly at home by the Primary Care doctor and nurse, under the coordination of the Palliative Care Unit and the Nephrology Department. During 2012, 2013, and 2014, 76 patients received treatment in this program (mean age: 81 years; mean Charlson age-comorbidity index: 10, and mean glomerular filtration rate: 12.4 mL/min/1.73 m²). The median patient follow-up time (until death or until 31 December 2014) was 165 days. During this period, 51% of patients did not have to visit the hospital’s emergency department and 58% did not require hospitalization. Forty-eight of the 76 patients died after a median time of 135 days in the program; 24 (50%) died at home. Our experience indicates that with the support of the Palliative Care Unit and the Nephrology Department, ACKD patients who are not dialysis candidates may be monitored at home by Primary Care Teams.

The values of body composition provided by the two most commonly used bioelectrical impedance systems in Spain, single-frequency bioelectrical impedance vector analysis (SF-BIVA) and multi-frequency bioelectrical impedance spectroscopy (MF-BIS) are different and not comparable. Analyse whether the inter-method variability is due to bioelectrical variables measured by the different monitors, or rather due to the equations used to calculate body volume and mass. Another objective was to determine whether, despite the inter-method variability, the classification of hydration status by the two methods is consistent. Bioelectrical impedance was measured by SF-BIVA and MF-BIS immediately before a dialysis session in 54 patients on haemodialysis. In 38 patients, the study was repeated by SF-BIVA at the end of the same dialysis session. Resistance and phase angle values provided by the two monitors at a frequency of 50kHz were consistent. For resistance, variability was 1.3% and the intra-class correlation coefficient was 0.99. For phase angle, variability and the intra-class correlation coefficient were 11.5% and 0.92, respectively. The volume values for total body water, extracellular water, fat mass and body cell mass were biased, with a level of variability that would not be acceptable in clinical practice. The intra-class correlation coefficient also suggested a poor level of agreement. SF-BIVA systems define overhydration or dehydration as a vector below or above the tolerance ellipse of 75% on the longitudinal axis. MF-BIS uses two criteria for pre-dialysis hyper-hydration: overhydration (OH) greater than 2.5 litres, or greater than 15% of extracellular water. The degree of equivalence with the results of the SF-BIVA monitor was better with the second criterion (kappa: 0.81, excellent agreement) than with the first one (kappa: 0.71, acceptable agreement). The MF-BIS system defines post-dialysis normal hydration as a difference between OH and ultrafiltratation volume between –1.1 and 1.1 litres and agreement with the SF-BIVA system for this parameter was acceptable (weighted kappa index: 0.64). The MF-BIS and SF-BIVA systems provide similar readings for bioelectrical parameters, and the wide variation in the quantification of volume and body mass must be attributed to the different equations used for calculation. Furthermore, the criteria used by both systems to define both pre- and post-dialysis hydration have an acceptable level of equivalence.

The aim of this work was to study the accuracy of the CKD-EPI equation to estimate the glomerular filtrate in patients with advanced chronic renal failure. We compared the estimations of Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and Cockcroft-Gault (CG) equations to a glomerular filtration rate measured as the arithmetic mean of the urea and creatinine clearances (ClUrCr). The study was made in 89 nondialyzed patients with chronic renal disease in stage 4 or 5. Serum creatinine values were recalibrated to standardized creatinine measurements. In each patient, the difference between each estimating equation and the measured glomerular filtration rate was calculated. The absolute difference expressed as a percentage of the measured glomerular filtration rate indicates the intermethod variability. Overall, the glomerular filtration rate measured as the ClUrCr was 14.5 ± 5.5 ml/min/1.73 m(2); and the results of the estimating equations were: MDRD 14.3 ± 5.5 (p = NS); CKD-EPI 13.6 ± 5.4 (p <0.01) and CG 16.8 ± 6.5 ml/min/1.73 m(2) (p <0.001). The variability of the estimating equations was 16 ± 12.2%, 16.7 ± 12,1% and 22 ± 15.6% (p <0.05), for MDRD, CKD-EPI and CG. The percentage of estimates within 30% above or below the measured glomerular filtration rate was 85% for MDRD, 88% for CKD-EPI and 70% for CG. The CG variability, but not MDRD variability or CKD-EPI variability, was influenced by gender (19.3 ± 15.1% in males vs 27.3 ± 15.5% in females, p <0.05) and showed a negative correlation with the glomerular filtration rate (r = -0.23, p <0.05) and the age (r = -0.24, p <0.05) and positive correlation with the body mass index (r = 0.37, p <0.001). In patients with chronic renal disease in stage 5, the variability of the different estimating equations was similar. We conclude that in our population with advanced chronic renal failure, the CKD-EPI equation is as accuracy as the MDRD equation. With standardized creatinine the CG equation has a lower accuracy and its utilization may be reconsiderated.