BACKGROUND: A proposal to calculate and use the reference change value (RCV) as an objective guide for interpreting the numerical results obtained in clinical laboratory serial testing is introduced in this study. METHODS: A database showing the results of a compilation of 191 publications on biological variation and including information on a number of analytes provided the standardized criterion based on biology for calculating the RCVs. RESULTS: For each of the 261 analytes included in the study, the RCV was determined using Harris's formula, replacing analytical imprecision with the desirable specification of analytical quality based on half the within-subject biological variation at 95% probability levels. The result is a guide for a common criterion to identify clinically significant changes in serial results. CONCLUSIONS: The RCV concept is an approach that can be offered by laboratories to assess changes in serial results. The RCV data in this study are presented as a point of departure for a widely applicable objective guide to interpret changes in serial results.
BACKGROUND: A proposal to calculate and use the reference change value (RCV) as an objective guide for interpreting the numerical results obtained in clinical laboratory serial testing is introduced in this study. METHODS: A database showing the results of a compilation of 191 publications on biological variation and including information on a number of analytes provided the standardized criterion based on biology for calculating the RCVs. RESULTS: For each of the 261 analytes included in the study, the RCV was determined using Harris's formula, replacing analytical imprecision with the desirable specification of analytical quality based on half the within-subject biological variation at 95% probability levels. The result is a guide for a common criterion to identify clinically significant changes in serial results. CONCLUSIONS: The RCV concept is an approach that can be offered by laboratories to assess changes in serial results. The RCV data in this study are presented as a point of departure for a widely applicable objective guide to interpret changes in serial results.
Authors: Gabriel Lima-Oliveira; Giuseppe Lippi; Gian Luca Salvagno; Marise Danielle Raulino Campelo; Katharyne Soares Adala Tajra; Flavio dos Santos Gomes; Carlos David Valentim; Sylvio José Colonna Romano; Geraldo Picheth; Gian Cesare Guidi Journal: Blood Transfus Date: 2013-07-18 Impact factor: 3.443
Authors: Mark V Thomas; Adam Branscum; Craig S Miller; Jeffrey Ebersole; Mohanad Al-Sabbagh; Julie L Schuster Journal: J Periodontol Date: 2009-07 Impact factor: 6.993
Authors: Gabriel Lima-Oliveira; Giuseppe Lippi; Gian Luca Salvagno; Martina Montagnana; Geraldo Picheth; Gian Cesare Guidi Journal: Indian J Clin Biochem Date: 2013-04-03
Authors: Gabriel Lima-Oliveira; Gian Luca Salvagno; Giuseppe Lippi; Matteo Gelati; Martina Montagnana; Elisa Danese; Geraldo Picheth; Gian Cesare Guidi Journal: Ann Lab Med Date: 2012-06-20 Impact factor: 3.464