Thyroid-stimulating hormone (TSH) receptor antibody (TSH-R-Ab or TRAb) testing plays a pivotal role in arriving at the aetiological diagnosis in patients with thyrotoxicosis. A positive test establishes the diagnosis of Graves’ disease (GD) while a negative result in conjunction with imaging studies supports other possible aetiologies. In patients with GD, TRAb levels at diagnosis and at the time of withdrawal of antithyroid drugs can identify patients who are unlikely to achieve remission and guide clinical management decisions. We provide an algorithm that incorporates TRAb in the decision-making process for the management of thyrotoxicosis. The utility of TRAb in predicting the risk of fetal and neonatal thyroid dysfunction is established and widely accepted in guidelines. TRAb may also help in the diagnosis of Graves’ orbitopathy, especially in euthyroid or hypothyroid patients and its role in guiding its management is evolving as a useful adjunct to the clinical parameters used in making therapeutic decisions. Anti-thyroid peroxidase antibodies (TPOAb) and anti-thyroglobulin antibodies (TgAb) indicate thyroid autoimmunity. The most common use of TPOAb is to identify patients at a higher risk of progression to treatment-requiring hypothyroidism. They also aid the diagnosis of immune thyroiditis and Hashimoto’s encephalopathy. Thyroglobulin measurement is used to help guide differentiated thyroid cancer treatment. TgAb is used as an accompanying test with thyroglobulin measurement as its presence can interfere with the thyroglobulin assay. A negative TgAb result reduces the likelihood of, but does not exclude, interference with thyroglobulin assay.

The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief.

Aims To evaluate 99th percentile upper reference limits (URLs) and investigate ethnic differences for the Abbott Architect high-sensitivity cardiac troponin I (hs-cTnI) in a middle-aged to elderly cosmopolitan population. Methods In subjects without cardiovascular disease and after outlier exclusion, data on hs-cTnI from 149 white men, 150 white women, 150 South Asian (SA) men and 150 SA women in their sixth, seventh and eight decades were analysed. Each ethnicity–gender–decade subgroup consisted of 50 patients except white men in their sixth decade (n=49). Results The overall, women and men hs-cTnI 99th percentile URLs were 22.1, 17.9 and 24.8 ng/L, respectively. Median (IQR) hs-cTnI was higher in men (2.7 (1.8–4.1) ng/L) than in women (1.9 (1.1–3.2) ng/L; p<0.001). White men (3.2 (2.2–4.4) ng/L) had higher hs-cTnI than SA men (2.5 (1.6–3.6) ng/L; p<0.001), white women (2.1 (1.3–3.3) ng/L; p<0.001) and SA women (1.6 (1.0–3.0) ng/L; p<0.001). Hs-cTnI in white women was similar to SA women (p=0.07) and SA men (p=0.07). Patients in the eighth decade had higher hs-cTnI (p<0.05) than those in sixth decade within each ethnicity–gender subgroup. Of significant associations, age had the greatest impact on hs-cTnI followed by gender and then ethnicity. Conclusion We report white–SA differences in hs-cTnI in men and a similar trend in women. We confirm age and gender differences in hs-cTnI, irrespective of ethnicity. Further studies are required to determine whether ethnicity-specific age and gender 99th percentile URLs improve detection or exclusion of myocardial injury.

Method-related variations in the measurement of hormones and the reference intervals used in the clinical laboratory can have a significant, but often under-appreciated, impact on the diagnosis and management of endocrine disorders. This variation in laboratory practice has the potential to lead to an errant approach to patient care and thus could cause harm. It may also be the source of confusion or result in excessive or inadequate investigation. It is important that laboratory professionals and clinicians know about these impacts, their sources, and how to detect and mitigate them when they do arise. In this review article, we describe the historical and scientific context from which inconsistency in the clinical laboratory arises. Examples from the published literature of the impact of the method, reference interval, and clinical decision threshold-related discordances on the assessment and monitoring of various endocrine disorders are discussed to illustrate the sources, causes, and effects of this variability. Its potential impact on the evaluation of growth hormone deficiency and excess, thyroid and parathyroid disorders, hyperandrogenism, hypogonadism, glucocorticoid excess and deficiency, and diabetes mellitus is elaborated. Strategies for assessment and mitigation of the discordance are discussed. The clinical laboratory has a responsibility to recognise and address these issues, and although a lot has been accomplished in this area already, there remains more to be done.