Multidisciplinary approach to redefining thyroid hormone reference intervals with big data analysis

IF 2.5 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinical biochemistry Pub Date : 2024-10-22 DOI:10.1016/j.clinbiochem.2024.110835

Cody W. Lewis , Joshua E. Raizman , Victoria Higgins , Jessica L. Gifford , Christopher Symonds , Gregory Kline , Jacques Romney , Manpreet Doulla , Carol Huang , Allison A. Venner

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引用次数: 0

Abstract

Objectives

This study aimed to employ big data analysis to harmonize reference intervals (RI) for thyroid function tests, with refinement to the TSH upper reference limit, and to optimize the TSH reflex algorithm to improve clinical management and test utilization.

Design & methods

TSH, free T4, and free T3 results tested in Alberta, Canada, on Roche Cobas and Siemens Atellica were extracted from the laboratory information system (N = 1,144,155 for TSH, N = 183,354 for free T4 and N = 92,632 for free T3). Results from specialists, inpatients, or repeat testing, as well as from positive thyroid disease, autoimmune disease, and pregnancy biomarkers were excluded. RIs were derived using statistical models (Bhattacharya, refineR, and simple non-parametric) followed by endocrinology and laboratory review.

Results

The TSH RIs for 0 to 7 days, 8 days to 1 year, and ≥1 year were 1.23 to 25.0 mIU/L, 1.00 to 6.80 mIU/L and 0.20 to 6.50 mIU/L, respectively. The free T4 RIs for 0 to 14 days, 15 to 29 days, and ≥30 days were 13.5 to 50.0 pmol/L, 8.7 to 32.5 pmol/L, and 10.0 to 25.0 pmol/L, respectively. An updated TSH reflex algorithm was developed based on the optimized TSH and free T4 RIs, with free T4 reflexed only at a TSH of <0.1 mIU/L.

Conclusions

The collaboration of a multidisciplinary team and the utilization of big data analysis led to the enhancement of thyroid function RIs, specifically resulting in the widening of the upper TSH reference limit to 6.50. Application of these optimized RIs with the TSH reflex algorithm will serve as a guide for improvement in interpretation of thyroid function tests.

查看原文本刊更多论文

利用大数据分析重新定义甲状腺激素参考区间的多学科方法。

研究目的本研究旨在利用大数据分析来统一甲状腺功能检测的参考区间（RI），完善 TSH 参考上限，并优化 TSH 反射算法，以改善临床管理和检测利用率：从实验室信息系统中提取加拿大阿尔伯塔省使用罗氏Cobas和西门子Atellica检测的促甲状腺激素、游离T4和游离T3结果（促甲状腺激素结果为1,144,155，游离T4结果为183,354，游离T3结果为92,632）。专家、住院病人或重复检测的结果，以及甲状腺疾病、自身免疫性疾病和妊娠生物标记物的阳性结果均被排除在外。使用统计模型（Bhattacharya、refineR 和简单非参数）得出相关指数，然后由内分泌科和实验室进行复核：0至7天、8天至1年和≥1年的促甲状腺激素相关指数分别为1.23至25.0 mIU/L、1.00至6.80 mIU/L和0.20至6.50 mIU/L。0至14天、15至29天和≥30天的游离T4 RI分别为13.5至50.0 pmol/L、8.7至32.5 pmol/L和10.0至25.0 pmol/L。根据优化后的促甲状腺激素和游离 T4 RI，制定了最新的促甲状腺激素反射算法，游离 T4 仅在促甲状腺激素达到结论时反射：多学科团队的合作和大数据分析的应用提高了甲状腺功能相关指数，特别是将 TSH 参考上限扩大到了 6.50。将这些优化的RI与促甲状腺激素反射算法相结合，将为改进甲状腺功能检测的解释提供指导。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical biochemistry 医学-医学实验技术

CiteScore

5.10

自引率

0.00%

发文量

151

审稿时长

25 days

期刊介绍： Clinical Biochemistry publishes articles relating to clinical chemistry, molecular biology and genetics, therapeutic drug monitoring and toxicology, laboratory immunology and laboratory medicine in general, with the focus on analytical and clinical investigation of laboratory tests in humans used for diagnosis, prognosis, treatment and therapy, and monitoring of disease.