Using Non-Linear Programming Solvers to Generate Hypothalamus-Pituitary Curves for Patients With Hypothyroidism

TL;DR

This paper introduces two novel methods using non-linear programming to generate personalized hypothalamus-pituitary curves for hypothyroidism treatment, aiming to improve hormone level management over traditional approaches.

Contribution

It proposes and compares two new non-linear programming techniques for creating patient-specific HP curves, enhancing treatment customization in hypothyroidism.

Findings

Proposed methods outperform existing techniques in accuracy.

Validated on literature and patient data.

Potential to improve personalized treatment strategies.

Abstract

Common practice in treating primary hypothyroidism is to use only Thyrothropin (TSH) to adjust the dose of thyroid replacement. In this paper, it is argued that using both TSH and free Thyroxine (FT4) values in the replacement may be beneficial in the treatment of hypothyroidism. The tool to determine the optimal value of TSH and FT4 is the Hypothalamus-Pituitary (HP) curve. These curves are models of thyroid hormone concentrations that can be used to determine patient-specific treatment strategies for individuals with hypothyroidism. By generating an HP curve for an individual with hypothyroidism, a set point is determined that represents the optimal levels of thyroid hormones in the blood. A graphical method for set point determination is proposed. A physician can then prescribe a thyroid replacement strategy to achieve this set point. In this paper, two methods for generating HP curves are proposed using non-linear programming solvers and compared with the existing methods. The proposed methods are tested using datasets from the literature, as well as measurements of patients being treated for hypothyroidism.