Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China.
Department of Laboratory Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China; State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing 100730, China.
Clin Biochem. 2022 Sep;107:40-49. doi: 10.1016/j.clinbiochem.2022.05.008. Epub 2022 May 27.
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.
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.
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.
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.
使用五种数据挖掘算法建立老年人甲状腺相关激素的参考区间（RI），并评估每种算法的适用性。
基于体检和患者数据，使用五种数据挖掘算法建立老年人甲状腺相关激素的 RI。将这些算法的结果与根据严格的纳入和排除标准招募的健康老年人的 RI 进行比较。使用偏比（BR）矩阵比较不同算法建立的 RI 范围。
发现体检数据在不同算法之间的一致性大于门诊数据。从体检数据计算 RI 时，转换的 Hoffmann、转换的 Bhattacahrya、kosmic 和 refineR 算法表现出良好的性能。使用期望最大化（EM）和患者数据建立的促甲状腺激素（TSH）RI 与使用健康老年人数据建立的 RI 高度一致。
本研究建议使用基于体检数据的转换的 Hoffmann、转换的 Bhattacahrya、kosmic 和 refineR 算法来建立老年人甲状腺相关激素的 RI。但是，如果使用患者数据，并且数据存在明显的偏态性，则建议使用 Box-Cox 转换的 EM 算法。
