Highlights
- •Retrospective study on patients who underwent total or completion thyroidectomy.
- •Machine learning utilized to find predictors of levothyroxine dosing.
- •Algorithms found race and ethnicity significantly associated with dosing.
- •Alcohol use and osteoarthritis were also associated with dosing.
- •Confirmed previously known factors: weight, BMI, age, and sex.
Abstract
Background
We employed Machine Learning (ML) to evaluate potential additional clinical factors
influencing replacement dosage requirements of levothyroxine.
Method
This was a retrospective study of patients who underwent total or completion thyroidectomy
with benign pathology. Patients who achieved an euthyroid state were included in three
different ML models.
Results
Of the 487 patients included, mean age was 54.1 ± 14.1 years, 86.0% were females,
39.0% were White, 53.0% Black, 2.7% Hispanic, 1.4% Asian, and 3.9% Other. The Extreme
Gradient Boosting (XGBoost) model achieved the highest accuracy at 61.0% in predicting
adequate dosage compared to 47.0% based on 1.6 mcg/kg/day (p < 0.05). The Poisson
regression indicated non-Caucasian race (p < 0.05), routine alcohol use (estimate = 0.03,
p = 0.02), and osteoarthritis (estimate = −0.10, p < 0.001) in addition to known factors
such as age (estimate = −0.003, p < 0.001), sex (female, estimate = −0.06, p < 0.001),
and weight (estimate = 0.01, p < 0.001) were associated with the dosing of levothyroxine.
Conclusions
Along with weight, sex, age, and BMI, ML algorithms indicated that race, ethnicity,
lifestyle and comorbidity factors also may impact levothyroxine dosing in post-thyroidectomy
patients with benign conditions.
Abbreviations:
BMI (Body Mass Index), BSA (Body Surface Area), LBM (Lean Body Mass), ML (Machine Learning), TSH (Thyroid Stimulating Hormone), XGBoost (Extreme Gradient Boost), FNN (Feedforward Neural Network), HTN (Hypertension), GERD (Gastroesophageal Reflux Disease), COPD (Chronic Obstructive Pulmonary Disease)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: November 20, 2022
Accepted:
November 19,
2022
Received in revised form:
October 26,
2022
Received:
September 10,
2022
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
