Interconversion of Plasma Free Thyroxine Values from Assay Platforms with Different Reference Intervals Using Linear Transformation Methods

Impact of regression modeling on the assessment and harmonization of a point-of-care analyzer and commercial laboratory analyzer for feline plasma biochemistry testing

BACKGROUND

Regression describes the relationship of results from two analyzers, and the generated equation can be used to harmonize results. Point-of-care (POC) analyzers cannot be calibrated by the end user, so regression offers an opportunity for calculated harmonization. Harmonization (uniformity) of laboratory results facilitates the use of common reference intervals and medical decision thresholds.

OBJECTIVE

Our aims were to characterize the relationship of results for multiple biochemistry analytes on a POC and a commercial laboratory analyzer (CL) with three regression techniques and to use regression equations to harmonize the POC results with those of the CL. Harmonized results were assessed by recognized quality goals. We used harmonized results to assess the regression techniques.

METHODS

After analyzer imprecision assessments, paired clinical samples were assessed with one dataset to calculate regression parameters that were applied to a second dataset. Three regression techniques were performed, and each was used to harmonize the POC results with those from the CL. POC results were assessed for bias and the number of results reaching quality goals before and after harmonization.

RESULTS

All regression techniques could be used to harmonize most analytes so that 95% of results were within ASVCP TEa guidelines. Harmonization could be further improved with alternate regression techniques or exclusions.

CONCLUSIONS

Regression offers a means to harmonize POC and CL analyzers. Further work is needed to assess how few samples can reliably be used and to assess likely species differences. No regression technique reliably describes the relationship between methods when correlation is poor.

A mathematical algorithm to harmonize measurements for thyroid-stimulating hormone between instruments

BACKGROUND

Thyroid-stimulating hormone (TSH) is measured differently between diagnostic units using different devices, which makes cross-comparisons challenging. Here, we have developed a mathematical algorithm to harmonize TSH measurements between 2 instruments, the Abbott ISR2000 and the Siemens ADVIA Centaur XP.

METHODS

Applying the principle of the maximum allowable error between the standard curve and real signal values, the minimum number of comparison samples required for TSH hormone detection was calculated for both instruments. Next, a mathematical algorithm describing the relationship between TSH standard curves from both instruments was established. The algorithm was then tested on sample measurements from both instruments, with signals transformed to Siemens ADVIA Centuar XP-type data. Finally, test results were assessed where the relative error was 

RESULTS

Before conversion, the mean percentage error between the TSH results of samples measured on both instruments was 23.20% (>1/2 TEa). After algorithmic transformation, the average percentage error was reduced to 7.93% (<1/2 TEa).

CONCLUSIONS

Our algorithm enables TSH measurements across different instruments to be comparable, and provides a method to harmonize TSH data between laboratories that utilize different instrumentation.

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Key Words

• Comparability
• Conversion
• Determination
• Mathematical algorithm
• Thyroid-stimulating hormone (TSH)

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MESH Headings

• Humans
• Thyrotropin
• Algorithms

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Affiliation(s)

Xin Zhou Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China; Jintang First People's Hospital, Chengdu 610400, China
Zaishuan Liu Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China
Yining Ma College of Mathematics of Sichuan University, Chengdu 610065, China
Chongwei Zhang Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China
Yongkang Wu Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China; Jintang First People's Hospital, Chengdu 610400, China.

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How to approach clinically discordant FT4 results when changing testing platforms: real-world evidence

PURPOSE

Measurement of thyroid-stimulating hormone (TSH) and free thyroxine (FT4) is important for assessing thyroid dysfunction. After changing assay manufacturer, high FT4 versus TSH levels were reported at Ente Ospedaliero Cantonale (EOC; Bellinzona, Switzerland).

METHODS

Exploratory analysis used existing TSH and FT4 measurements taken at EOC during routine clinical practice (February 2018-April 2020) using Elecsys® TSH and Elecsys FT4 III immunoassays on cobas® 6000 and cobas 8000 analyzers (Roche Diagnostics). Reference intervals (RIs) were estimated using both direct and indirect (refineR algorithm) methods.

RESULTS

In samples with normal TSH levels, 90.9% of FT4 measurements were within the normal range provided by Roche (12-22 pmol/L). For FT4 measurements, confidence intervals (CIs) for the lower end of the RI obtained using direct and indirect methods were lower than estimated values in the method sheet; the estimated value of the upper end of the RI (UEoRI) in the method sheet was within the CI for the UEoRI using the direct method but not the indirect method. CIs for the direct and indirect methods overlapped at both ends of the RI. The most common cause of increased FT4 with normal TSH was identified in a subset of patients as use of thyroxine therapy (72.6%).

CONCLUSIONS

It is important to verify RIs for FT4 in the laboratory population when changing testing platforms; indirect methods may constitute a convenient tool for this. Applying specific RIs for selected subpopulations should be considered to avoid misinterpretations and inappropriate clinical actions.