BACKGROUND: The use of Reference Change Values (RCV) has been advocated as very useful for monitoring individuals. Most of these are performed for monitoring individuals in acute situations and for following up the improvement or deterioration of chronic diseases. In our study, we aimed at evaluating the RCV calculation for 24 clinical chemistry analytes widely used in clinical laboratories and the utilization of this data. METHODS: Twenty-four serum samples were analyzed with Abbott kits (Abbott Laboratories, Abbott Park, IL, USA), manufactured for use with the Architect c8000 (Abbott Laboratories, Abbott Park, IL, USA) auto-analyzer. We calculated RCV using the following formula: RCV = Z x 2 1/2x (CVA2 + CVw2)1/2. Four reference change values (RCV) were calculated for each analyte using four statistical probabilities (0.95, and 0.99, unidirectional and bidirectional). Moreover, by providing an interval after identifying upper and lower limits with the Reference Change Factor (RCF), serially measured tests were calculated by using two formulas: exp (Z x 2 1/2 x (CV(A)2 + CVw2)½/100) for RCF(UP) and (1/RCF(UP)) for RCF(DOWN). RESULTS: RCVs of these analytes were calculated as 14.63% for glucose, 29.88% for urea, 17.75% for ALP, 53.39% for CK, 46.98% for CK-MB, 21.00% amylase, 8.00% for total protein, 8.70% for albumin, 51.08% for total bilirubin, 86.34% for direct bilirubin, 6.40% for calcium, 15.03% for creatinine, 21.47% for urate, 14.19% for total cholesterol, 46.62% for triglyceride, 20.51% for HDL-cholesterol, 29.59% for AST, 46.31% for ALT, 31.54% for GGT, 20.92% for LDH, 19.75% for inorganic phosphate, 3.05% for sodium, 11.75% for potassium, 4.44% for chloride (RCV, p < 0.05, unidirectionally). CONCLUSIONS: We suggest using RCV as well as using population-based reference intervals in clinical laboratories. RCV could be available as a tool for making clinical decision, especially when monitoring individuals.
BACKGROUND: The use of Reference Change Values (RCV) has been advocated as very useful for monitoring individuals. Most of these are performed for monitoring individuals in acute situations and for following up the improvement or deterioration of chronic diseases. In our study, we aimed at evaluating the RCV calculation for 24 clinical chemistry analytes widely used in clinical laboratories and the utilization of this data. METHODS: Twenty-four serum samples were analyzed with Abbott kits (Abbott Laboratories, Abbott Park, IL, USA), manufactured for use with the Architect c8000 (Abbott Laboratories, Abbott Park, IL, USA) auto-analyzer. We calculated RCV using the following formula: RCV = Z x 2 1/2x (CVA2 + CVw2)1/2. Four reference change values (RCV) were calculated for each analyte using four statistical probabilities (0.95, and 0.99, unidirectional and bidirectional). Moreover, by providing an interval after identifying upper and lower limits with the Reference Change Factor (RCF), serially measured tests were calculated by using two formulas: exp (Z x 2 1/2 x (CV(A)2 + CVw2)½/100) for RCF(UP) and (1/RCF(UP)) for RCF(DOWN). RESULTS: RCVs of these analytes were calculated as 14.63% for glucose, 29.88% for urea, 17.75% for ALP, 53.39% for CK, 46.98% for CK-MB, 21.00% amylase, 8.00% for total protein, 8.70% for albumin, 51.08% for total bilirubin, 86.34% for direct bilirubin, 6.40% for calcium, 15.03% for creatinine, 21.47% for urate, 14.19% for total cholesterol, 46.62% for triglyceride, 20.51% for HDL-cholesterol, 29.59% for AST, 46.31% for ALT, 31.54% for GGT, 20.92% for LDH, 19.75% for inorganic phosphate, 3.05% for sodium, 11.75% for potassium, 4.44% for chloride (RCV, p < 0.05, unidirectionally). CONCLUSIONS: We suggest using RCV as well as using population-based reference intervals in clinical laboratories. RCV could be available as a tool for making clinical decision, especially when monitoring individuals.