The Optimal Dosing Scheme for Levothyroxine After Thyroidectomy: A Comprehensive Comparison and Evaluation

Background: Patients often struggle to attain euthyroidism after thyroidectomy, and multiple dosing schemes have been proposed to supplant the standard weight-based approach for initial levothyroxine dosing after thyroidectomy. The objectives of this study were to review the literature for existing levothyroxine dosing schemes and compare estimation accuracies with novel schemes developed with machine learning.

Methods: This study retrospectively analyzed 598 patients who attained euthyroidism after total or completion thyroidectomy for benign disease. A scoping review identified existing levothyroxine dosing schemes. Thirteen machine learning algorithms estimated euthyroid dose. Using 10-fold cross-validation, we compared schemes by the proportion of patients having a predicted dose within 12.5 µg/day of their euthyroid dose.

Results: Of 264 reviewed articles, 7 articles proposed retrospectively implementable dosing schemes. A novel Poisson regression model proved most accurate, correctly predicting 64.8% of doses. Incorporating 7 variables, Poisson regression was significantly more accurate than the best scheme in the literature (body mass index/weight based) that correctly predicted 60.9% of doses (P = .031). Standard weight-based dosing (1.6 µg/kg/day) correctly predicted 51.3% of doses, and the least effective scheme (age/sex/weight based) correctly predicted 27.4% of doses.

Conclusion: Using readily available variables, a novel Poisson regression dosing scheme outperforms other machine learning algorithms and all existing schemes in estimating levothyroxine dose.