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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]

• This review describes the definition, advantages and challenge of real world big data study in laboratory medicine. • This review summarizes the current application and methods of real world big data study in laboratory medicine from seven different perspectives. • The future development of real world big data study in laboratory medicine is prospected from artificial intelligence. With the recent developments in information technology, real world big data studies (RWBDSs) have attracted increasing attention in the field of medicine. In RWBDSs, clinical laboratory data is an important part of the wider scope of real-world medical data, and its standardized use is critical for the generation of high-quality real-world evidence. To improve the core functioning and competitiveness of clinical laboratories as well as provide high-quality medical services for patients, it is important to construct an information analysis model and perform RWBDSs. However, among the majority of developing countries, as well as in some developed countries, due to the poorly developed neglect of data formatting standards information construction and the lack of consideration for, and experience with, the ideas and methods of RWBDSs, many clinical laboratories are unable to make use of the vast amount of data stored in their systems. Additionally, in the literature, there remain many areas that require improvements, such as the correct misuse of research methods, appropriate unreasonable data presentation methods, and optimal opaque methods for data cleaning, storage, and mining. In this review, we describe both the advantages and disadvantages of RWBDSs in laboratory medicine. In addition, we summarize the current application and methods of RWBDS in laboratory medicine from seven different perspectives: the establishment of a reference interval, patient data-based real time quality control, diagnostic or prognostic modeling, epidemiological investigation, laboratory management, analysis of sources of variations for analytes, and external quality assessment. Finally, we discuss the future prospects of this research. This review can provide the basis for clinical laboratories to carry out real world research; additionally, it promotes and standardizes RWBDS in laboratory medicine. [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]

• Indirect methods can be used to establish tCO2 RI for the population in Tibet. • The tCO2 RI of residents in Tibet was significantly lower than that of plainsmen. • Labs in high-altitude areas should establish tCO2 RIs basing on their population. Hypoxia leads to different concentrations of the bicarbonate buffer system in Tibetan people. Indirect methods were used to establish the reference interval (RI) for total carbon dioxide (tCO2) based on big data from the adult population of Tibet, a high-altitude area in Western China. Anonymous tCO2 test data (n = 442,714) were collected from the People's Hospital of the Tibet Autonomous Region from January 2018, to December 2021. Multiple linear regression and variance component analyses were performed to assess the effects of sex, age, and race on tCO2 levels. Indirect methods, including Hoffmann, Bhattacharya, expectation maximization (EM), kosmic and refineR, were used to calculate the total RI and ethnicity-partitioned RI. A total of 230,821 real-world tCO2 test results were eligible. Sex, age, and race were significantly associated with the tCO2 levels. The total and ethnically-partitioned RIs estimated using the five indirect methods were comparable. The total RI of tCO2 was 14–24 mmol/L (calculated using Hoffmann and refineR) and 15–24 mmol/L (Bhattacharya, EM and kosmic). For Han nationality, the RIs were 14–25 mmol/L (calculated using Hoffmann and Bhattacharya), 16–23 mmol/L (EM), 15–24 mmol/L (kosmic), and 14.2–24.5 mmol/L (refineR). For the Tibetan population, the RIs were 14–24 mmol/L (calculated using Hoffmann and refineR), 15–24 mmol/L (Bhattacharya and kosmic), and 15–23 mmol/L (EM). The established RIs were significantly lower than those living at lower altitudes area (22–29 mmol/L) that was provided by the manufacturer. The tCO2 RI of the populations living on the Tibetan Plateau was significantly lower than those at the lower altitudes. The RIs established using indirect methods are suitable for clinical applications in Tibet. [ABSTRACT FROM AUTHOR]