Sevelamer hydrochloride is used widely, but its impact upon cardiovascular calcification, cardiovascular mortality, all-cause mortality and hospitalization is not known. Primary outcome was cardiovascular calcification (coronary artery calcification scores (CACS) and aortic calcification scores (ACS)). Secondary outcomes were serum characteristics, hospitalization, cardiovascular mortality and all-cause mortality. Risk ratio (RR), mean differences and standard mean difference with 95% confidence intervals (CIs) were pooled using random- or fixed-effects models. We identified 31 studies (on 23 randomized controlled trials with 4395 participants). An analysis pooling showed a significant decrease in serum levels of phosphate with calcium-based phosphate binders (CBPBs) by 0.17 mg/dL [mean difference (MD), 95% CI, 0.03, 0.31] than sevelamer. A significant difference in the change of CACS by –102.66 [MD: 95% CI, –159.51, –45.80] and ACS by –1008.73 [MD, 95% CI, –1664.75, –352.72] between sevelamer and CBPBs was observed. Prevalence of hypercalcemia (serum levels of calcium >10.2–10.5 mg/dL and >11.0 mg/dL) was significantly smaller for sevelamer (RR = 0.44, 95% CI, 0.33, 0.58; RR = 0.24, 95% CI, 0.14, 0.40). No significant difference was found in hospitalization, all-cause mortality or cardiovascular mortality. This meta-analysis suggests that sevelamer benefits dialysis patients in terms of CACS, ACS and hypercalcemia.

The diagnostic value of serum creatinine and cystatin c in evaluating glomerular filtration rate in patients with chronic kidney disease: a systematic literature review and meta-analysis The diagnostic value of serum creatinine and cystatin c in evaluating glomerular filtration rate in patients with chronic kidney disease: a systematic literature review and meta-analysis

Accurate assessment of kidney function is clinically important, but estimates of glomerular filtration rate (GFR) by regression are imprecise. We hypothesized that ensemble learning could improve precision. A total of 1419 participants were enrolled, with 1002 in the development dataset and 417 in the external validation dataset. GFR was independently estimated from age, sex and serum creatinine using an artificial neural network (ANN), support vector machine (SVM), regression, and ensemble learning. GFR was measured by 99mTc-DTPA renal dynamic imaging calibrated with dual plasma sample 99mTc-DTPA GFR. Mean measured GFRs were 70.0 ml/min/1.73 m2 in the developmental and 53.4 ml/min/1.73 m2 in the external validation cohorts. In the external validation cohort, precision was better in the ensemble model of the ANN, SVM and regression equation (IQR = 13.5 ml/min/1.73 m2) than in the new regression model (IQR = 14.0 ml/min/1.73 m2, P < 0.001). The precision of ensemble learning was the best of the three models, but the models had similar bias and accuracy. The median difference ranged from 2.3 to 3.7 ml/min/1.73 m2, 30% accuracy ranged from 73.1 to 76.0%, and P was > 0.05 for all comparisons of the new regression equation and the other new models. An ensemble learning model including three variables, the average ANN, SVM, and regression equation values, was more precise than the new regression model. A more complex ensemble learning strategy may further improve GFR estimates.