ArticleThe Journal of surgical research2019
Optimizing Levothyroxine Dose Adjustment After Thyroidectomy With a Decision Tree.
Article in The Journal of surgical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Pooled it
- Pooled it
- The effect of reminder mobile application use on medication adherence after total thyroidectomy: a randomized controlled trial.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Trial
- Levothyroxine dose prediction post-thyroidectomy for differentiated thyroid carcinoma.Frontiers in endocrinology · 2025Article
- Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Article
- Levothyroxine Dosing for Thyroid-Stimulating Hormone Suppression in Patients with Differentiated Thyroid Cancer after Total Thyroidectomy.Endocrinology and metabolism (Seoul, Korea) · 2024Article
- Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence.Annals of general psychiatry · 2024Article
- A predictive model for L-T4 dose in postoperative DTC after RAI therapy and its clinical validation in two institutions.Frontiers in endocrinology · 2024Article
- Efficacy of Levothyroxine Sodium Soft Gelatin Capsules in Thyroidectomized Patients Taking Proton Pump Inhibitors: An Open-Label Study.Thyroid : official journal of the American Thyroid Association · 2023Article
- Impact of thyroid hormone replacement on the risk of second cancer after thyroidectomy: a Korean National Cohort Study.Scientific reports · 2023Article
- Article
- Machine learning identifies baseline clinical features that predict early hypothyroidism in patients with Graves' disease after radioiodine therapy.Endocrine connections · 2022Article
- Optimal Thyroid Hormone Replacement.Endocrine reviews · 2022Article
- Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models.BMC public health · 2022Article
- Article
- A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters.Scientific reports · 2021Article
- Hyperthyroidism.Gland surgery · 2020Review
- Levothyroxine Therapy in Thyrodectomized Patients.Frontiers in endocrinology · 2020Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAfter thyroidectomy, patients require Levothyroxine (LT4). It may take years of dose adjustments to achieve euthyroidism. During this time, patients encounter undesirable symptoms associated with hypo- or hyper-thyroidism. Currently, providers adjust LT4 dose by clinical estimation, and no algorithm exists. The objective of this study was to build a decision tree that could estimate LT4 dose adjustments and reduce the time to euthyroidism.
methodsWe performed a retrospective cohort analysis on 320 patients who underwent total or completion thyroidectomy at our institution. All patients required one or more LT4 dose adjustments from their initial postoperative dose before attaining euthyroidism. Using the Classification and Regression Tree algorithm, we built various decision trees from patient characteristics, estimating the dose adjustment to reach euthyroidism.
resultsThe most accurate decision tree used thyroid-stimulating hormone values at first dose adjustment (mean absolute error = 13.0 μg). In comparison, the expert provider and naïve system had a mean absolute error of 11.7 μg and 17.2 μg, respectively. In the evaluation dataset, the decision tree correctly predicted the dose adjustment within the smallest LT4 dose increment (12.5 μg) 79 of 106 times (75%, confidence interval = 65%-82%). In comparison, expert provider estimation correctly predicted the dose adjustment 76 of 106 times (72%, confidence interval = 62%-80%).
conclusionsA decision tree predicts the correct LT4 dose adjustment with an accuracy exceeding that of a completely naïve system and comparable to that of an expert provider. It can assist providers inexperienced with LT4 dose adjustment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.