Profiling retrospective thyroid function data in complete thyroidectomy patients to investigate the HPT axis set point (PREDICT-IT).

Journal Article (Journal Article)

BACKGROUND: The homeostatic euthyroid set point of the hypothalamus-pituitary-thyroid axis of any given individual is unique and oscillates narrowly within substantially broader normal population ranges of circulating free thyroxine (FT4) and thyroid-stimulating hormone (TSH), otherwise termed 'thyroid function test (TFT)'. We developed a mathematical algorithm codenamed Thyroid-SPOT that effectively reconstructs the personalized set point in open-loop situations and evaluated its performance in a retrospective patient sample. METHODS: We computed the set points of 101 patients who underwent total thyroidectomy for non-functioning thyroid disease using Thyroid-SPOT on each patient's own serial post-thyroidectomy TFT. Every predicted set point was compared against its respective healthy pre-operative euthyroid TFT per individual and their separation (i.e. predicted-observed TFT) quantified. RESULTS: Bland-Altman analysis to measure the agreement between each pair of an individual's predicted and actual set points revealed a mean difference in FT4 and TSH of + 3.03 pmol/L (95% CI 2.64, 3.43) and - 0.03 mIU/L (95% CI - 0.25, 0.19), respectively. These differences are small compared to the width of the reference intervals. Thyroid-SPOT can predict the euthyroid set point remarkably well, especially for TSH with a 10-16-fold spread in magnitude between population normal limits. CONCLUSION: Every individual's equilibrium euthyroid set point is unique. Thyroid-SPOT serves as an accurate, precise and reliable targeting system for optimal personalized restoration of euthyroidism. This algorithm can guide clinicians in L-thyroxine dose titrations to resolve persistent dysthyroid symptoms among challenging cases harbouring "normal TFT" within the laboratory ranges but differing significantly from their actual euthyroid set points.

Full Text

Duke Authors

Cited Authors

  • Li, E; Yen, PM; Dietrich, JW; Leow, MK-S

Published Date

  • May 1, 2021

Published In

Volume / Issue

  • 44 / 5

Start / End Page

  • 969 - 977

PubMed ID

  • 32808162

Electronic International Standard Serial Number (EISSN)

  • 1720-8386

Digital Object Identifier (DOI)

  • 10.1007/s40618-020-01390-7


  • eng

Conference Location

  • Italy