| مستخلص: |
• Indirect derivation of reference intervals (RIs) from routine data by cutting out the distribution peak is being advocated. • For validation, RIs were derived by four indirect methods using data matched to those used for determining RIs directly. • The shapes of the two datasets showed a variable degree of mismatch in peak locations and skewness. • As a result, RIs by indirect methods showed various patterns of biases from those derived directly. • It is required to make a stricter data-cleaning before applying indirect methods that rely on the central peak. 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. 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 (λ) reported by the IFCC global RI study (the first two methods) or using a λ predicted (the last two). 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). To reduce prediction biases that remained after age adjustment, it is necessary to apply more rigorous data-cleaning before applying indirect methods. [ABSTRACT FROM AUTHOR] |
