Reference Intervals Generated by Electronic Medical Record Data Mining with Clinical Exclusions: Age-Specific Intervals for Thyroid-Stimulating Hormone from 33038 Euthyroid Patients

Establishing laboratory-specific reference intervals for TSH and fT4 by use of the indirect Hoffman method

BACKGROUND

The results of examinations of laboratory parameters are the basis of appropriate medical decisions. The availability of reliable and accurate reference intervals (RIs) for each laboratory parameter is an integral part of its appropriate interpretation. Each medical laboratory should confirm their RIs. Up-to-date reference intervals for thyroid function hormones are still a matter of ongoing controversy. The aim of the study was the application of the indirect Hoffman method to determine RIs for TSH and fT4 based on the large data pools stored in laboratory information systems and the comparison of these RIs to generally used RIs.

MATERIAL AND METHODS

The TSH and fT4 routine examination results of hospitalized and outpatient populations were collected over five years (2015-2019), and reference limits were established by the improved Hoffmann method after the exclusion of outliers. Comparative verification of established RIs was conducted with the RIs values provided by test manufacturers and literature data.

RESULTS

Various RIs were observed in different age groups in the examined populations. For TSH, RIs varied between different age groups, with a narrower range of RIs in the studied adult population and a shift of both reference boundaries toward higher values in comparison to manufacturers' data among children. RIs estimated for fT4 were very similar to the manufacturer and literature data.

CONCLUSION

Thyroid hormone levels change during a person's lifetime and vary between sexes, but this difference does not always influence the clinical interpretation of laboratory results in the context of RIs. The use of indirect methods is justified due to the ease and low cost of their application.

Clinical thresholds for diagnosing iron deficiency: comparison of functional assessment of serum ferritin to population based centiles

Low serum ferritin is diagnostic of iron deficiency, yet its published lower cut-off values are highly variable, particularly for pediatric populations. Lower cut-off values are commonly reported as 2.5th percentiles, and is based on the variation of ferritin values in the population. Our objective was to determine whether a functional approach based on iron deficient erythropoiesis could provide a better alternative. Utilizing 64,443 ferritin test results from pediatric electronic health records, we conducted various statistical techniques to derive 2.5th percentiles, and also derived functional reference limits through the association between ferritin and erythrocyte parameters: hemoglobin, mean corpuscular volume, mean cell hemoglobin concentration, and red cell distribution width. We find that lower limits of reference intervals derived as centiles are too low for clinical interpretation. Functional limits indicate iron deficiency anemia starts to occur when ferritin levels reach 10 µg/L, and are largely similar between genders and age groups. In comparison, centiles (2.5%) presented with lower limits overall, with varying levels depending on age and gender. Functionally-derived limits better reflects the underlying physiology of a patient, and may provide a basis for deriving a threshold related to treatment of iron deficiency and any other biomarker with functional outcomes.

Data-driven quality assurance to prevent erroneous test results

Increasing laboratory automation and efficiency requires quality assurance (QA) approaches to ensure that reported results are precise and accurate. Prerequisites for designing optimal QA strategies include an in-depth understanding of the laboratory processes, the expected results, and of the mechanisms that can cause erroneous results. Oftentimes, a laboratory's own data, extracted from the laboratory information system, electronic medical record, and/or clinical data warehouse are necessary to master the aforementioned requirements. Data-driven QA utilizes retrospective and/or prospective laboratory results to minimize errors in the clinical laboratory due to pre-analytical or analytical vulnerabilities. Additionally, exploitation of this data may improve result interpretation. The objective of this review is to illustrate specific examples of data-driven QA approaches for several areas of the clinical laboratory and for different phases of the testing cycle.