METHODS: MEDLINE and Embase databases were searched from inception up to September 2019 to identify all studies that compared the predictive performance of cystatin C- and/or creatinine-based eGFR in predicting the clearance of vancomycin. The prediction errors (PEs) (the value of eGFR equations minus vancomycin clearance) were quantified for each equation and were pooled using a random-effects model. The root mean squared errors were also quantified to provide a metric for imprecision.
RESULTS: This meta-analysis included evaluations of seven different cystatin C- and creatinine-based eGFR equations in total from 26 studies and 1,234 patients. The mean PE (MPE) for cystatin C-based eGFR was 4.378 mL min-1 (95% confidence interval [CI], -29.425, 38.181), while the creatinine-based eGFR provided an MPE of 27.617 mL min-1 (95% CI, 8.675, 46.560) in predicting clearance of vancomycin. This indicates the presence of unbiased results in vancomycin clearance prediction by the cystatin C-based eGFR equations. Meanwhile, creatinine-based eGFR equations demonstrated a statistically significant positive bias in vancomycin clearance prediction.
CONCLUSION: Cystatin C-based eGFR equations are better than creatinine-based eGFR equations in predicting the clearance of vancomycin. This suggests that utilising cystatin C-based eGFR equations could result in better accuracy and precision to predict vancomycin pharmacokinetic parameters.
METHODS: Study subjects include patients with various levels of renal function recruited from the nephrology clinic and wards of a tertiary hospital. The blood samples collected were analyzed for serum cystatin C and creatinine levels by particle-enhanced turbidimetric immunoassay and kinetic alkaline picrate method, respectively. DNA was extracted using a commercially available kit. -Polymerase chain reaction results were confirmed by direct DNA Sanger sequencing.
RESULTS: The genotype percentage (G/G = 73%, G/A = 24.1%, and A/A = 2.9%) adhere to the Hardy-Weinberg equilibrium. The dominant allele found in our population was CST3 73G allele (85%). The regression lines' slope of serum cystatin C against creatinine and cystatin C-based eGFR against creatinine-based eGFR, between G and A allele groups, showed a statistically significant difference (z-score = 3.457, p < 0.001 and z-score = 2.158, p = 0.015, respectively). Patients with A allele had a lower serum cystatin C level when the values were extrapolated at a fixed serum creatinine value, suggesting the influence of genetic factor.
CONCLUSION: Presence of CST3 gene G73A polymorphism affects serum cystatin C levels.
METHODS: The current study was conducted at the Hospital University Sains Malaysia, Kelantan. A total of 300 elderly Malay participants ≥ 65 years, with CKD, were taken in study. Demographic data, blood pressure, weight, and height were documented. Serum creatinine was assayed by Chemistry Analyzer Model Architect-C8000 (Jaffe Method), while serum cystatin C was examined by Human cystatin C ELISA kit (Sigma-Aldrich) using Thermo Scientific Varioskan Flash ELISA reader.
RESULTS: The study participants were divided into three groups on the basis of age. There was a statistically significant difference at the p value cystatin C levels were observed on the basis of patient's age and BMI.
CONCLUSION: Cystatin C is not related to BMI and age among elderly chronic kidney disease patients. The study clearly evaluates the role of serum cystatin C as a good competitor of creatinine among the elderly CKD patients.