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Aim: To establish Reference intervals (RIs) of thyroid-related hormones in older adults using five data mining algorithms and to assess the applicability of each algorithm., Methods: RIs for thyroid-related hormones in older adults were established using five data mining algorithms based on physical examination and patient data. The results of these algorithms were compared to those of RIs established using healthy older adults recruited following strict inclusion and exclusion criteria. The bias ratio (BR) matrix was used to compare the limits of RIs established using different algorithms., Results: Consistency across different algorithms in physical examination data was found to be greater than that of outpatient data. The transformed Hoffmann, transformed Bhattacahrya, kosmic and refineR algorithms showed good performance in calculating RIs from physical examination data. The RIs of Thyroid Stimulating Hormone (TSH) established using Expectation maximization (EM) and patient data were highly consistent with the RIs established using data from healthy older adults., Conclusion: This study recommends the use of transformed Hoffmann, transformed Bhattacahrya, kosmic, and refineR algorithms which are based on physical examination data to establish RIs for thyroid-related hormones in older adults. However, if patient data is used, then an EM algorithm combined with Box-Cox transformation is recommended for data with obvious skewness., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: While many studies have established reference intervals (RIs) for thyroid hormones using patient data, this approach has not been validated. Therefore, in this study, we aimed to validate an approach for establishing RIs for thyroid hormones only using patient data from clinical laboratories., Methods: We established two derived databases: derived database* and derived database#. Reference individuals in derived database* were selected using strict exclusion criteria, and the RIs established by the database were considered standard RIs (RIs*). Individuals in derived database# were the physical examination population, whose information was downloaded directly from the Laboratory Information System, and RIs established from this database were evaluated (RIs#). The comparative confidence interval (CI) method and consistency of the decision results based on external databases were used to compare RIs* and RIs#., Results: RIs# and RIs* for the thyroid hormones tested were similar. The 90% CIs of the upper and lower limits of RIs for most thyroid hormones overlapped between RIs# and RIs*, and the limit of RIs# was within the 90% CI of RIs*. The consistency rates for the results of the RIs* and RIs# in the external database were greater than 98% for all thyroid hormones tested., Conclusion: It was possible to establish RIs for thyroid hormones using only patient data from clinical laboratories after adopting appropriate statistical methods., (Copyright © 2020 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

Background: Currently, the direct method is the main approach for establishment of reference interval (RI). However, only a handful of studies have described the effects of sample size on establishment of RI and estimation of sample size. We describe a novel approach for estimation of the sample size when establishing RIs using the transformed parametric and non-parametric methods.Methods: A total of 3,697 healthy participants were enrolled in this study. We adopted a two-layer nested loop sample size estimation method to determine the effects of sample size on RI, using thyroid-related hormone as an example. The sample size was selected as the calculation result when the width of the confidence interval (CI) of the upper and lower limit of the RI were both stably < 0.2 times the width of RI. Then, we calculated the sample size for establishing RIs via transformed parametric and non-parametric methods for thyroid-related hormones.Results: Sample sizes for thyroid stimulating hormone (TSH), as required by parametric and non-parametric methods to establish RIs were 239 and 850, respectively. Sample sizes required by the transformed parametric method for free triiodothyronine (FT3), free thyroxine (FT4), total triiodothyronine (TT3) and total thyroxine (TT4) were all less than 120, while those required by the non-parametric method were more than 120.Conclusion: We describe a novel approach for estimating sample sizes for establishment of RI. A corresponding open-source code has been developed and is available for applications. The established method is suitable for most analytes, with evidence based on thyroid-related hormones indicating that different sample sizes are required to establish RIs using different methods for analytes with different variations. [ABSTRACT FROM AUTHOR]

Thyroglobulin antibody (TG-Ab) and thyroid peroxidase antibody (TPO-Ab) are cornerstone biomarkers for autoimmune thyroid diseases, and establishment of appropriate thresholds is crucial for physicians to appropriately interpret test results. Therefore, we established the thresholds of TG-Ab and TPO-Ab in the Chinese population through analysis of real-world big data, and explored the influence of age, gender, and seasonal factors on their levels. The data of 35,869 subjects downloaded from electronic health records were analyzed after filtering based on exclusion criteria and outliers. The influence of each factor on antibody levels was analyzed by stratification. Thresholds of TG-Ab and TPO-Ab were established through Clinical Laboratory Standards Institute document C28-A3 and National Academy of Clinical Biochemistry (NACB) guidelines, respectively. There were significant differences according to gender after age stratification; the level of TG-Ab gradually increased with age in females. There were significant differences in TG-Ab and TPO-Ab distributions with respect to age after gender stratification. Moreover, differences were observed between seasons for TG-Ab and TPO-Ab. The thresholds of TG-Ab and TPO-Ab were 107 [90% confidence interval (CI):101–115] IU/mL and 29 (90% CI: 28–30) IU/mL, respectively, using C28-A3 guidelines, but were 84 (90% l CI: 50–126) IU/mL and 29 (90% CI: 27–34) IU/mL, respectively, using NACB guidelines. The levels of TG-Ab and TPO-Ab were significantly affected by gender, age, and season. The thresholds for TG-Ab and TPO-Ab for the Chinese population were established by big data analysis. [ABSTRACT FROM AUTHOR]

Abstract Background Thyroid-stimulating hormone (TSH) plays a key role in maintaining normal thyroid function. Here, we used “big data” to analyze the effects of seasonality and temperature on TSH concentrations to understand factors affecting the reference interval. Methods Information from 339,985 patients at Peking Union Medical College Hospital was collected from September 1st, 2013, to August 31st, 2016, and retrospectively analyzed. A statistical method was used to exclude outliers, with data from 206,486 patients included in the final analysis. The research period was divided into four seasons according to the National Weather Service. Correlations between TSH concentrations and season and temperature were determined. Results Median TSH levels during spring, summer, autumn, and winter were 1.88, 1.86, 1.87, and 1.96 μIU/L, respectively. TSH fluctuation was larger in winter (±0.128) than in summer (±0.125). After normalizing the data from each year to the lowest TSH median value (summer), TSH appeared to peak in winter and trough in summer, showing a negative correlation with temperature. Pearson correlation analysis indicated that the monthly median TSH values were negatively correlated with temperature (r = −0.663, p <.001). Conclusions This study showed significant seasonal- and temperature-dependent variation in TSH concentrations. Thus, these might be important factors to consider when diagnosing thyroid function disorders. Highlights • “Big data” from clinical studies can be used to help establish reference intervals. • Season and temperature affect TSH levels, which are the highest in winter. • The gender differences may be a factor when establishing the TSH reference interval. [ABSTRACT FROM AUTHOR]