Automatic Levothyroxine Dosing Algorithm for Patients Suffering from Hashimoto's Thyroiditis

Hypothyroidism is a condition where the patient's thyroid gland cannot produce sufficient thyroid hormones (mainly triiodothyronine and thyroxine). The primary cause of hypothyroidism is autoimmune-mediated destruction of the thyroid gland, referred to as Hashimoto's thyroiditis. A patient's desired thyroid hormone concentration is achieved by oral administration of thyroid hormone, usually levothyroxine. Establishing individual levothyroxine doses to achieve desired thyroid hormone concentrations requires several patient visits. Additionally, clear guidance for the dosing regimen is lacking, and significant inter-individual differences exist. This study aims to design a digital automatic dosing algorithm for patients suffering from Hashimoto's thyroiditis. The dynamic behaviour of the relevant thyroid function is mathematically modelled. Methods of automatic control are exploited for the design of the proposed robust model-based levothyroxine dosing algorithm. Numerical simulations are performed to evaluate the mathematical model and the dosing algorithm. With the help of the developed controller thyroid hormone concentrations of patients, emulated using Thyrosim, have been regulated under the euthyroid state. The proposed concept demonstrates reliable responses amidst varying patient parameters. Our developed model provides a useful basis for the design of automatic levothyroxine dosing algorithms. The proposed robust feedback loop contributes to the first results for computer-assisted thyroid dosing algorithms.

Mathematical Modeling of Free Thyroxine Concentrations During Methimazole Treatment for Graves' Disease: Development and Validation of a Computer-Aided Thyroid Treatment Method

BACKGROUND

Methimazole (MMI) is the first-line treatment for patients with Graves' disease (GD). While there are empirical recommendations for initial MMI doses, there is no clear guidance for subsequent MMI dose titrations. We aimed to (a) develop a mathematical model capturing the dynamics of free thyroxine (FT4) during MMI treatment (b), validate this model by use of numerical simulation in comparison with real-life patient data (c), develop the software application Digital Thyroid (DigiThy) serving either as a practice tool for treating virtual patients or as a decision support system with dosing recommendations for MMI, and (d) validate this software framework by comparing the efficacy of its MMI dosing recommendations with that from clinical endocrinologists.

METHODS

Based on concepts of automatic control and by use of optimization techniques, we developed two first order ordinary differential equations for modeling FT4 dynamics during MMI treatment. Clinical data from patients with GD derived from the outpatient clinic of Endocrinology at the Medical University of Graz, Austria, were used to develop and validate this model. It was subsequently used to create the web-based software application DigiThy as a simulation environment for treating virtual patients and an autonomous computer-aided thyroid treatment (CATT) method providing MMI dosing recommendations.

RESULTS

Based on MMI doses, concentrations of FT4, thyroid-stimulating hormone (TSH), and TSH-receptor antibodies (TRAb), a mathematical model with 8 patient-specific constants was developed. Predicted FT4 concentrations were not significantly different compared to the available consecutively measured FT4 concentrations in 9 patients with GD (52 data pairs, p=0.607). Treatment success of MMI dosing recommendations in 41 virtually generated patients defined by achieved target FT4 concentrations preferably with low required MMI doses was similar between CATT and usual care. Statistically, CATT was significantly superior (p<0.001).

CONCLUSIONS

Our mathematical model produced valid FT4 predictions during MMI treatment in GD and provided the basis for the DigiThy application already serving as a training tool for treating virtual patients. Clinical trial data are required to evaluate whether DigiThy can be approved as a decision support system with automatically generated MMI dosing recommendations.

Associations of Thyroid Hormones and Resting Heart Rate in Patients Referred to Coronary Angiography

Resting heart rate (RHR) is associated with increased risk of cardiovascular morbidity and mortality. Thyroid hormones exert several effects on the cardiovascular system, but the relation between thyroid function and RHR remains to be further established. We evaluated whether measures of thyroid hormone status are associated with RHR in patients referred to coronary angiography. Thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxin (FT4), and RHR were determined in 2795 participants of the Ludwigshafen Risk and Cardiovascular Health (LURIC) Study. Median (25th to 75th percentile) serum concentrations were 1.25 (0.76-1.92) mU/l for TSH, 4.8 (4.2-5.3) pmol/l for FT3 and 17.1 (15.4-19.0) pmol/l for FT4, and mean (±standard deviation) RHR was 68.8 (±11.7) beats/min. Comparing the highest versus the lowest quartile, RHR (beats/min) was significantly higher in the fourth FT4 quartile [3.48, 95% confidence interval (CI): 2.23-4.73; p <0.001] and in the fourth FT3 quartile (2.30, 95% CI: 1.06-3.55; p <0.001), but there was no significant difference for TSH quartiles. In multiple linear regression analyses adjusting for various potential confounders, FT3 and FT4 were significant predictors of RHR (p <0.001 for both). In subgroups restricted to TSH, FT3, and FT4 values within the reference range, both FT3 and FT4 remained significant predictors of RHR (p <0.001 for all). In conclusion, in patients referred to coronary angiography, FT3 and FT4 but not TSH were positively associated with RHR. The relationship between free thyroid hormones and RHR warrants further investigations regarding its diagnostic and therapeutic implications.