Browsing by Author "Streichert, Thomas"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Publication Comparison of reference intervals derived by direct and indirect methods based on compatible datasets obtained in Turkey(Elsevier, 2021-05-26) Özarda, Yeşim; Ichihara, Kiyoshi; Jones, Graham; Streichert, Thomas; Ahmadian, Robab; Ahmadian, Robab; Bursa Uludağ Üniversitesi/Tıp Fakültesi/İstatistik Anabilim Dalı.; 0000-0003-1550-639X; AAE-5602-2019Background: Indirect derivation of reference intervals (RIs) from the laboratory information system (LIS) has been recently pursued. We aimed at evaluating the accuracy of indirectly predicted RIs compared to the RIs established directly from healthy subjects in the nationwide RI study in Turkey, targeting 25 major chemistry analytes.Methods: LIS data were retrieved from the laboratory that performed measurements for the direct study. They were cleaned by limiting to outpatients with age 18-65 years, and by allowing only one record per year per patient. Evaluated were four indirect methods of univariate approach: Hoffmann, Bhattacharya, Arzideh, and Wosniok methods. Power transformation of the LIS dataset was performed either using the power (lambda) reported by the IFCC global RI study (the first two methods) or using a lambda predicted (the last two).Results: Compared to the direct study dataset, the LIS dataset showed a variable degree of alterations in peak location and shape. Consequently, lower-side peak-shifts observed in sodium, albumin, etc. led to lowered RI limits, whereas higher-side peak-shift observed in triglyceride, low-density lipoprotein cholesterol, etc. led to raised RI limits. Overall, 72% (62-81) of the RI limits predicted by indirect methods showed significant biases from direct RIs. However, the biases observed in total cholesterol, lactic dehydrogenase, etc. were attributed to a higher-side age-bias in LIS dataset. After excluding them, the overall proportion of biased RIs was reduced to 47% (38-54).Conclusion: To reduce prediction biases that remained after age adjustment, it is necessary to apply more rigorous data-cleaning before applying indirect methods.Item Distinguishing reference intervals and clinical decision limits – A review by the IFCC committee on reference intervals and decision limits(Taylor and Francis, 2018-05-18) Sikaris, Ken Andrew; Streichert, Thomas; Macri, Joseph; İlçöl, Yeşim Özarda; Uludağ Üniversitesi/Tıp Fakültesi/Tıbbı Biyokimya Anabilim Dalı.; AAL-8873-2021; 35741320500Reference Intervals (RIs) and clinical decision limits (CDLs) are a vital part of the information supplied by laboratories to support the interpretation of numerical clinical pathology results. RIs describe the typical distribution of results seen in a healthy reference population while CDLs are associated with a significantly higher risk of adverse clinical outcomes or are diagnostic for the presence of a specific disease. However, as the two concepts are sometimes confused, there is a need to clarify the differences between these terms and to ensure they are easily distinguished, especially because CDLs have a clinical association with specific diseases and risks, thereby implying that effective clinical interventions are available. It is important to note that, because population-based RIs are derived from the range of values expected in a typical community population, laboratory results that fall outside a RI do not necessarily indicate a disease but rather that additional medical follow-up and/or treatment may be warranted. In contrast, CDLs are associated with a risk of specific adverse outcomes, and are commonly used to interpret laboratory test results, including lipid parameters, glucose, hemoglobin A1c (HbA1c), and tumor markers, to determine risk of disease, to diagnose or to treat. In recent years, the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Reference Intervals and Decision Limits (C-RIDL) has focused primarily on RIs and has performed multicenter studies to obtain common RIs. However, the broader responsibility of the Committee, from its name, includes decision limits. C-RIDL now aims to emphasize the importance of the correct use of both RIs and CDLs and to encourage laboratories to specify the appropriate information to clinicians as needed. This review discusses RIs and CDLs in detail, describes the similarities and the differences between these two important tools in laboratory medicine, and clearly explains the processes used to define them. C-RIDL encourages the involvement of laboratory professionals in the establishment of both RIs and CDLs.Item Indirect methods for reference interval determination - Review and recommendations(Walter de Gruyter, 2019-01) Jones, Graham R. D.; Haeckel, Rainer; Loh, Tze Ping; Sikaris, Ken; Streichert, Thomas; Katayev, Alex; Barth, Julian H; Özarda, Yeşim; Bursa Uludağ Üniversitesi/Tıp Fakültesi/Biyokimya Anabilim Dalı.; 0000-0003-0532-789X; AAL-8873-2021; 35741320500Reference intervals are a vital part of the information supplied by clinical laboratories to support interpretation of numerical pathology results such as are produced in clinical chemistry and hematology laboratories. The traditional method for establishing reference intervals, known as the direct approach, is based on collecting samples from members of a preselected reference population, making the measurements and then determining the intervals. An alternative approach is to perform analysis of results generated as part of routine pathology testing and using appropriate statistical techniques to determine reference intervals. This is known as the indirect approach. This paper from a working group of the International Federation of Clinical Chemistry (IFCC) Committee on Reference Intervals and Decision Limits (C-RIDL) aims to summarize current thinking on indirect approaches to reference intervals. The indirect approach has some major potential advantages compared with direct methods. The processes are faster, cheaper and do not involve patient inconvenience, discomfort or the risks associated with generating new patient health information. Indirect methods also use the same preanalytical and analytical techniques used for patient management and can provide very large numbers for assessment. Limitations to the indirect methods include possible effects of diseased subpopulations on the derived interval. The IFCC C-RIDL aims to encourage the use of indirect methods to establish and verify reference intervals, to promote publication of such intervals with clear explanation of the process used and also to support the development of improved statistical techniques for these studies.