Estimating Hormone Concentrations in the Pituitary-Thyroid Feedback Loop from Irregularly Sampled Measurements

TL;DR

This paper develops and tests a method to estimate internal hormone levels in thyroid disease models using irregular blood sample data, demonstrating robustness and improved accuracy with increased sampling frequency.

Contribution

It empirically verifies sample-based detectability and implements a moving horizon estimator for irregularly sampled hormone data in thyroid models.

Findings

Estimator remains robust across various sampling schemes.

More frequent sampling reduces estimation error.

Method works for both hypo- and hyperthyroidism models.

Abstract

Model-based control techniques have recently been investigated for the recommendation of medication dosages to address thyroid diseases. These techniques often rely on knowledge of internal hormone concentrations that cannot be measured from blood samples. Moreover, the measurable concentrations are typically only obtainable at irregular sampling times. In this work, we empirically verify a notion of sample-based detectability that accounts for irregular sampling of the measurable concentrations on two pituitary-thyroid loop models representing patients with hypo- and hyperthyroidism, respectively, and include the internal concentrations as states. We then implement sample-based moving horizon estimation for the models, and test its performance on virtual patients across a range of sampling schemes. Our study shows robust stability of the estimator across all scenarios, and that more frequent sampling leads to less estimation error in the presence of model uncertainty and misreported dosages.