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Öğe Biological variation estimates of prothrombin time, activated partial thromboplastin time, and fibrinogen in 28 healthy individuals(Wiley, 2018) Falay, Mesude; Senes, Mehmet; Korkmaz, Selcuk; Turhan, Turan; Okay, Murat; Ozturk, Berna Afacan; Yucel, Dogan; Ozet, GulsumBackground Although tests of global hemostasis prothrombin time (PT) and activated partial thromboplastin time (aPTT) should not be used for prediction of bleeding risk, these tests are often used by many clinicians in daily practice particularly as a preoperative screening test. Robust biological variation (BV) data are needed for safe clinical applications of these tests. In this study, a stringent protocol was followed to estimate the BV's for PT, aPTT, and fibrinogen levels. Methods Results Weekly blood samples were obtained from 28 healthy individuals (18 females, 10 males) during 10 weeks study period. All measurements were performed with Stago STA-R coagulation analyzer. Prior to coefficient of variation (CV)-analysis of variance (ANOVA), the data were assessed for normality, trends, outliers, and variance homogeneity. Sex-stratified within-individual (CVI) and between-individual (CVG) BV estimates were determined for PT, aPTT, and fibrinogen tests. No difference was found between male and female estimates of BV. The observed CVI and CVG estimates were found to be lower than those previously published. Only for fibrinogen, CVI was higher than CVG. Conclusion Following a meticulous protocol, our study results provide up-to-date and more stringent BV estimates of global hemostasis tests.Öğe Biological variation of peripheral blood T-lymphocytes(Elsevier, 2019) Falay, Mesude; Senes, Mehmet; Korkmaz, Selcuk; Zararsiz, Gokmen; Turhan, Turan; Okay, Murat; Yucel, CigdemBackground: Flow cytometric analysis of the lymphocyte subsets has become one of the most commonly used techniques in the routine clinical laboratory. It is frequently used in monitoring lymphocyte recovery after hematopoietic stem cell transplantation (HSCT), as well as diagnosis and treatment of acquired immunodeficiency syndrome (AIDS). Reliable biological variation (BV) data is needed for safe clinical application of these tests. In this study, similar preanalytical and analytical protocols to the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) checklist were followed and a stringent statistical approach was applied to define BV of T-lymphocytes. Methods: During the 10 weeks study period, weekly blood samples were obtained from 30 healthy individuals (20 females, 10 males) and analyzed with Facs Canto (BD Biosciences, San Jose, CA, USA) analyzer using 4colour BD Multitest CD3/CD8/CD45/CD4 reagents. Data were assessed in terms of normality, tendencies, outliers and variance homogeneity prior to applying coefficient of variance (CV)- analysis of variance (ANOVA) test. Sex-stratified within-individual (CVI) and between-individual (CVG) BV estimates of CD3+, CD3 + CD4 +, CD3 + CD8 +, and CD3 + CD4 + CD8 + T lymphocytes were calculated. Results: No difference was found between males and females. Except for the CD3 + CD4 + CD8 + subset, stable BV was found for CD3+, CD3 + CD4 +, and CD3 + CD8 + subsets. Conclussion: Instead of using the conventional reference ranges of CD3+, CD3 + CD4 + and CD3 + CD8 + counts for monitoring HIV positive or post-HSCT patients, RCV should be used. Because individualityis characteristic of lymphocytes subsets RCVS should be used instead of RIs for patient monitoring.Öğe BioVar: an online biological variation analysis tool(Walter De Gruyter Gmbh, 2020) Korkmaz, Selcuk; Zarasiz, Gokmen; Goksuluk, Dincer; Senes, Mehmet; Sonmez, Cem; Yucel, DoganObjectives: Biological variation (BV) analysis of laboratory tests gets increased attention due to its practical applications. These applications include correct interpretation of laboratory tests, the decision on the availability of reference intervals, contributions to clinical decision-making. It is critical to derive the BV information accurately and reliably. Another crucial step is to perform the statistical analysis of the BV data. Although there are updated and comprehensive guidelines, there is no reliable and comprehensive tool to perform statistical analysis of BV data. Methods: We presented BioVar, an online tool for statistical analysis of the BV data based on available and updated guidelines. Results: This tool can be used (i) to detect outliers, (ii) to control normality assumption, (iii) to check steady-state condition, (iv) to test homogeneity assumptions, (v) to perform subset analysis for genders, (vi) to perform analysis of variance to estimate components of variation and (vii) to identify analytical performance specifications of laboratory tests. Moreover, plots can be created at each step of outlier detection to inspect outliers and compare gender groups visually. An automatic report can be generated and downloaded. Conclusion: The tool is freely available through turcosa. shinyapps.io/biovar/, and source code is available on the Github: github.com/selcukorkmaz/BioVar.