Effect of Low-Sodium versus Conventional Sodium Dialysate on Left Ventricular Mass in Home and Self-Care Satellite Facility Hemodialysis Patients: A Randomized Clinical Trial.

BACKGROUND: Fluid overload in patients undergoing hemodialysis contributes to cardiovascular morbidity and mortality. There is a global trend to lower dialysate sodium with the goal of reducing fluid overload. METHODS: To investigate whether lower dialysate sodium during hemodialysis reduces left ventricular mass, we conducted a randomized trial in which patients received either low-sodium dialysate (135 mM) or conventional dialysate (140 mM) for 12 months. We included participants who were aged >18 years old, had a predialysis serum sodium ≥135 mM, and were receiving hemodialysis at home or a self-care satellite facility. Exclusion criteria included hemodialysis frequency >3.5 times per week and use of sodium profiling or hemodiafiltration. The main outcome was left ventricular mass index by cardiac magnetic resonance imaging. RESULTS: The 99 participants had a median age of 51 years old; 67 were men, 31 had diabetes mellitus, and 59 had left ventricular hypertrophy. Over 12 months of follow-up, relative to control, a dialysate sodium concentration of 135 mmol/L did not change the left ventricular mass index, despite significant reductions at 6 and 12 months in interdialytic weight gain, in extracellular fluid volume, and in plasma B-type natriuretic peptide concentration (ratio of intervention to control). The intervention increased intradialytic hypotension (odds ratio [OR], 7.5; 95% confidence interval [95% CI], 1.1 to 49.8 at 6 months and OR, 3.6; 95% CI, 0.5 to 28.8 at 12 months). Five participants in the intervention arm could not complete the trial because of hypotension. We found no effect on health-related quality of life measures, perceived thirst or xerostomia, or dietary sodium intake. CONCLUSIONS: Dialysate sodium of 135 mmol/L did not reduce left ventricular mass relative to control, despite improving fluid status. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: The Australian New Zealand Clinical Trials Registry, ACTRN12611000975998.

Evolving connectionist system versus algebraic formulas for prediction of renal function from serum creatinine.

BACKGROUND: In clinical trials, equation 7 from the Modification of Diet in Renal Disease (MDRD) Study is the most accurate formula for the prediction of glomerular filtration rate (GFR) from serum creatinine. An alternative approach has been developed using evolving connectionist systems (ECOS), which are novel computing structures that can be trained to generate accurate output from a given set of input variables. This study aims to compare the prediction errors associated with each method, using data that reproduce routine clinical practice as opposed to the artificial setting of clinical trials. METHODS: The methods were compared using 441 radioisotope measurements of GFR in 178 chronic kidney disease patients from 12 centers in Australia and New Zealand. All clinical and laboratory measurements were obtained from the patients' center rather than central laboratories, as would be the case in routine clinical practice. Both the MDRD formula and ECOS used the same predictive variables, and both were optimized to the study cohort by stepwise regression and training, respectively. RESULTS: Mean measured GFR in the cohort was 22.6 mL/min/1.73 m(2). The bias and precision of the MDRD formula were -3.5 mL/min/1.73 m(2) and 34.5%, respectively, improving to -1.2 mL/min/1.73 m(2) and 31.1% after maximal optimization of the formula to study data. The bias and precision of the ECOS were 0.7 mL/min/1.73 m(2) and 32.6%, respectively, improving to -0.1 mL/min/1.73 m(2) and 16.6% after maximal optimization of the system to study data. The prediction of GFR using ECOS was improved by accounting for the center from where clinical and laboratory measurements originated within the connectionist model. CONCLUSION: Algebraic formulas will be associated with greater prediction error in routine clinical practice than in the original trials, and machine intelligence is more likely to predict GFR accurately in this setting.