The optimal dosing scheme for levothyroxine after thyroidectomy: A comprehensive comparison and evaluation

Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial

Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high-interindividual differences in effective dosage and the narrow therapeutic window. Model-informed precision dosing (MIPD) using machine learning could assist general practitioners (GPs), but no such models exist for primary care. Furthermore, introduction of decision-support algorithms in healthcare is limited due to the substantial gap between developers and clinicians' perspectives. We report the development, validation, and a clinical simulation trial of the first MIPD application for primary care. Stable maintenance dosage of levothyroxine was the model target. The multiclass model generates predictions for individual patients, for different dosing classes. Random forest was trained and tested on a national primary care database (n = 19,004) with a final weighted AUC across dosing options of 0.71, even in subclinical hypothyroidism. TSH, fT4, weight, and age were most predictive. To assess the safety, feasibility, and clinical impact of MIPD for levothyroxine, we performed clinical simulation studies in GPs and compared MIPD to traditional prescription. Fifty-one GPs selected starting dosages for 20 primary hypothyroidism cases without and then with MIPD 2 weeks later. Overdosage and underdosage were defined as higher and lower than 12.5 μg relative to stable maintenance dosage. MIPD decreased overdosage in number (30.5 to 23.9%, P < 0.01) and magnitude (median 50 to 37.5 μg, P < 0.01) and increased optimal starting dosages (18.3 to 30.2%, P < 0.01). GPs considered lab results more often with MIPD and most would use the model frequently. This study demonstrates the clinical relevance, safety, and effectiveness of MIPD for levothyroxine in primary care.

Artificial intelligence in endocrinology: a comprehensive review

BACKGROUND AND AIM

Artificial intelligence (AI) has emerged as a promising technology in the field of endocrinology, offering significant potential to revolutionize the diagnosis, treatment, and management of endocrine disorders. This comprehensive review aims to provide a concise overview of the current landscape of AI applications in endocrinology and metabolism, focusing on the fundamental concepts of AI, including machine learning algorithms and deep learning models.

METHODS

The review explores various areas of endocrinology where AI has demonstrated its value, encompassing screening and diagnosis, risk prediction, translational research, and "pre-emptive medicine". Within each domain, relevant studies are discussed, offering insights into the methodology and main findings of AI in the treatment of different pathologies, such as diabetes mellitus and related disorders, thyroid disorders, adrenal tumors, and bone and mineral disorders.

RESULTS

Collectively, these studies show the valuable contributions of AI in optimizing healthcare outcomes and unveiling new understandings of the intricate mechanisms underlying endocrine disorders. Furthermore, AI-driven approaches facilitate the development of precision medicine strategies, enabling tailored interventions for patients based on their individual characteristics and needs.

CONCLUSIONS

By embracing AI in endocrinology, a future can be envisioned where medical professionals and AI systems synergistically collaborate, ultimately enhancing the lives of individuals affected by endocrine disorders.

A Clinical Audit of Thyroid Hormonal Replacement After Total Thyroidectomy

Background Thyroid hormone replacement (THR) in athyreotic patients post-thyroidectomy due to thyroid cancer might seem like a straightforward clinical issue to address. To investigate the impact of THR on enhancing thyroid-stimulating hormone (TSH) levels, we conducted a clinical audit, tailoring the dosage based on patient weight and aligning with the standards outlined by the National Institute for Health and Care Excellence and the American Thyroid Association guidelines. Methodology This retrospective and prospective audit analyzed outpatient clinic records for hormone replacement therapy (HRT) post-total thyroidectomy. Retrospective data from March to May 2022 were collected, followed by prospective data after interventions adjusting HRT based on patient weight to digitize clinic notes. The second phase involved changes for 20 scheduled thyroidectomy patients among the total 37 included in the study. Results The thyroid profiles of both groups in the initial and subsequent cycles, treated with adjusted doses of THR, exhibited normal levels of thyroid hormones and calcium. No substantial differences were observed between the groups. On multivariate logistic regression analysis, we found that older age, male sex, body mass index, and preoperative TSH level were the only significant predictors of the need for hormonal therapy. Conclusions Optimal dose of THR after total thyroidectomy had a positive effect on TSH levels in hypothyroidism patients. Hence, THR should be prescribed according to patient weight based on standards and guidelines.

Machine learning in medication prescription: A systematic review

BACKGROUND

Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications "off-label" as children are still underrepresented in clinical studies, which leads to a high risk of an incorrect dose and adverse drug effects.

METHODS

PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription.

RESULTS

The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance.

CONCLUSIONS

By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.

Robotic system for magnetic resonance imaging-guided focused ultrasound treatment of thyroid nodules

BACKGROUND

Herein, a robotic system offering Magnetic Resonance-guided Focused Ultrasound (MRgFUS) therapy of thyroid nodules was developed.

METHODS

The robotic system offers linear motion in 2 PC-controlled axes that navigate a 3 MHz single-element focused transducer. The system, through a C-arm structure attaches to the table of Magnetic Resonance Imaging (MRI) scanners and couples to the neck of patients lying in the supine position. The MRI compatibility of the developed system was assessed inside a 3 T scanner. Benchtop and MRI feasibility studies evaluating the heating performance of the system were executed on excised pork tissue and on homogeneous and thyroid model agar-based phantoms.

RESULTS

The MRI compatibility of the system was successfully established. Grid sonications executed using robotic motion inflicted discrete and overlapping lesions on the excised tissue, while magnetic resonance (MR) thermometry successfully monitored thermal heating in agar-based phantoms.

CONCLUSIONS

The developed system was found to be efficient with ex-vivo evaluation. The system can perform clinical MRgFUS therapy of thyroid nodules and other shallow targets after further in-vivo evaluation.

The Use of Levothyroxine Absorption Tests in Clinical Practice

Although levothyroxine (LT4) is a widely prescribed drug, more than 30% of LT4-treated patients fail to achieve the recommended serum level of thyrotropin with a body weight-based dose of LT4. An LT4 absorption test (LT4AT) is part of the workup for confirming normal LT4 absorption or diagnosing malabsorption. We searched PubMed with the terms levothyrox*, L-T4, LT4, TT4, FT4, FT3, TT3, test, loading, uptake, absorp*, "absorb*, bioavailab*, bioequiv* malabsorb*, and pseudomalabsorb*. A total of 43 full-text publications were analyzed. The published procedures for LT4AT differ markedly in the test dose, formulation, test duration, frequency of blood collection, analyte (total thyroxine [TT4] or free thyroxine [FT4]), metric (absolute or relative peak or increment, or area under the curve) and the threshold for normal absorption. In a standardized LT4AT for routine use, the physician could advise the patient to not consume food, beverages, or medications the morning of the test; administer 1000 µg of LT4 in the patient's usual formulation as the test dose; ensure that the patient is supervised throughout the LT4AT; perform a 4-hour test, with hourly blood samples; assay FT4; and consider that normal LT4 absorption corresponds to an FT4 increment of more than 0.40 ng/dL (5.14 pmol/L) or a TT4 increment of more than 6 μg/dL (77.23 nmol/L) for a test dose of at least 300 µg, or a percentage TT4 absorption of more than 60%. If the test indicates abnormal LT4 absorption, the physician can increase the LT4 dose, change the formulation or administration route, and/or refer the patient to a gastroenterologist.

Computer-Assisted Levothyroxine Dose Selection for the Treatment of Postoperative Hypothyroidism

Background: Thyroid hormone replacement with levothyroxine (LT4) is a recommended treatment for patients undergoing thyroidectomy. The starting LT4 dose is frequently calculated based on the patient's weight. However, the weight-based LT4 dosing performs poorly in clinical practice, with only ∼30% of patients achieving target thyrotropin (TSH) levels at the first thyroid function testing after treatment initiation. A better way to calculate the LT4 dose for patients with postoperative hypothyroidism is needed. Methods: In this retrospective cohort study we used demographic, clinical, and laboratory data for 951 patients after thyroidectomy and several regression and classification machine learning methods to develop an LT4 dose calculator for treating postoperative hypothyroidism targeting the desired TSH level. We compared the accuracy with the current standard-of-care practice and other published algorithms and evaluated generalizability with fivefold cross-validation and out-of-sample testing. Results: The retrospective clinical chart review showed that only 285/951 (30%) patients met their postoperative TSH goal. Obese patients were overtreated with LT4. An ordinary least squares regression based on weight, height, age, sex, calcium supplementation, and height:sex interaction predicted prescribed LT4 dose in 43.5% of all patients and 45.3% of patients with normal postoperative TSH (0.45-4.5 mIU/L). The ordinal logistic regression, artificial neural networks regression/classification, and random forest methods achieved comparable performance. LT4 calculator recommended lower LT4 doses to obese patients. Conclusions: The standard-of-care LT4 dosing does not achieve the target TSH in most thyroidectomy patients. Computer-assisted LT4 dose calculation performs better by considering multiple relevant patient characteristics and providing personalized and equitable care to patients with postoperative hypothyroidism. Prospective validation of LT4 calculator performance in patients with various TSH goals is needed.

Efficacy of machine learning to identify clinical factors influencing levothyroxine dosage after total thyroidectomy

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.

In silico dose adjustment of levothyroxine after total thyroidectomy using fuzzy logic methodology: A proof-of-concept study

Thyroid hormone replacement therapy is used to raise undesirably low concentrations of natural thyroid hormones, commonly by administrating levothyroxine (LT4). Finding the appropriate LT4 dose regime, particularly for patients undergone thyroidectomy, is still demanding more effort, and much research has been conducted. Providing a new fuzzy logic system, a useful control algorithm, we aim to introduce a proper LT4 dosing regimen for every thyroidectomized patient in a computerized environment. Consequently, we contrast the differences between our proposed dose regime and conventional monotherapy methods using THYROSIM, a thyroid simulation application. Considering our nine defined comparative criteria, results reveal that the FLS dose regime is dominant in terms of six indexes, while the discrepancies are not noticeable in the other three indexes. A great superiority of FLS dose regime is its ability to reduce the time to reach desirable thyrotropin (Thyroid Stimulating Hormone, TSH) serum concentration to 6 days post-thyroidectomy, and keep the T4, T3, and TSH values in the normal window afterward. The proposed FLS could be an applicable decision support system for physicians as they can define their intended Individual Target Value of TSH for each patient to optimize LT4 dose adjustment.

Factors influencing the levothyroxine dose in the hormone replacement therapy of primary hypothyroidism in adults

Levothyroxine (LT4) is a safe, effective means of hormone replacement therapy for hypothyroidism. Here, we review the pharmaceutical, pathophysiological and behavioural factors influencing the absorption, distribution, metabolism and excretion of LT4. Any factor that alters the state of the epithelium in the stomach or small intestine will reduce and/or slow absorption of LT4; these include ulcerative colitis, coeliac disease, bariatric surgery, Helicobacter pylori infection, food intolerance, gastritis, mineral supplements, dietary fibre, resins, and various drugs. Once in the circulation, LT4 is almost fully bound to plasma proteins. Although free T4 (FT4) and liothyronine concentrations are extensively buffered, it is possible that drug- or disorder-induced changes in plasma proteins levels can modify free hormone levels. The data on the clinical significance of genetic variants in deiodinase genes are contradictory, and wide-scale genotyping of hypothyroid patients is not currently justified. We developed a decision tree for the physician faced with an abnormally high thyroid-stimulating hormone (TSH) level in a patient reporting adequate compliance with the recommended LT4 dose. The physician should review medications, the medical history and the serum FT4 level and check for acute adrenal insufficiency, heterophilic anti-TSH antibodies, antibodies against gastric and intestinal components (gastric parietal cells, endomysium, and tissue transglutaminase 2), and Helicobacter pylori infection. The next step is an LT4 pharmacodynamic absorption test; poor LT4 absorption should prompt a consultation with a gastroenterologist and (depending on the findings) an increase in the LT4 dose level. An in-depth etiological investigation can reveal visceral disorders and, especially, digestive tract disorders.

Optimal Thyroid Hormone Replacement

Hypothyroidism is a common endocrinopathy, and levothyroxine is frequently prescribed. Despite the basic tenets of initiating and adjusting levothyroxine being agreed on, there are many nuances and complexities to consistently maintaining euthyroidism. Understanding the impact of patient weight and residual thyroid function on initial levothyroxine dosage and consideration of age, comorbidities, thyrotropin goal, life stage, and quality of life as levothyroxine is adjusted can be challenging and continually evolving. Because levothyroxine is a lifelong medication, it is important to avoid risks from periods of overtreatment or undertreatment. For the subset of patients not restored to baseline health with levothyroxine, causes arising from all aspects of the patient's life (coexistent medical conditions, stressors, lifestyle, psychosocial factors) should be broadly considered. If such factors do not appear to be contributing, and biochemical euthyroidism has been successfully maintained, there may be benefit to a trial of combination therapy with levothyroxine and liothyronine. This is not supported by the majority of randomized clinical trials, but may be supported by other studies providing lower-quality evidence and by animal studies. Given this discrepancy, it is important that any trial of combination therapy be continued only as long as a patient benefit is being enjoyed. Monitoring for adverse effects, particularly in older or frail individuals, is necessary and combination therapy should not be used during pregnancy. A sustained-release liothyronine preparation has completed phase 1 testing and may soon be available for better designed and powered studies assessing whether combination therapy provides superior therapy for hypothyroidism.

Optimized Replacement T4 and T4+T3 Dosing in Male and Female Hypothyroid Patients With Different BMIs Using a Personalized Mechanistic Model of Thyroid Hormone Regulation Dynamics

OBJECTIVE

A personalized simulation tool, p-THYROSIM, was developed (1) to better optimize replacement LT4 and LT4+LT3 dosing for hypothyroid patients, based on individual hormone levels, BMIs, and gender; and (2) to better understand how gender and BMI impact thyroid dynamical regulation over time in these patients.

METHODS

p-THYROSIM was developed by (1) modifying and refining THYROSIM, an established physiologically based mechanistic model of the system regulating serum T3, T4, and TSH level dynamics; (2) incorporating sex and BMI of individual patients into the model; and (3) quantifying it with 3 experimental datasets and validating it with a fourth containing data from distinct male and female patients across a wide range of BMIs. For validation, we compared our optimized predictions with previously published results on optimized LT4 monotherapies. We also optimized combination T3+T4 dosing and computed unmeasured residual thyroid function (RTF) across a wide range of BMIs from male and female patient data.

RESULTS

Compared with 3 other dosing methods, the accuracy of p-THYROSIM optimized dosages for LT4 monotherapy was better overall (53% vs. 44%, 43%, and 38%) and for extreme BMI patients (63% vs. ~51% low BMI, 48% vs. ~36% and 22% for high BMI). Optimal dosing for combination LT4+LT3 therapy and unmeasured RTFs was predictively computed with p-THYROSIM for male and female patients in low, normal, and high BMI ranges, yielding daily T3 doses of 5 to 7.5 μg of LT3 combined with 62.5-100 μg of LT4 for women or 75-125 μg of LT4 for men. Also, graphs of steady-state serum T3, T4, and TSH concentrations vs. RTF (range 0%-50%) for untreated patients showed that neither BMI nor gender had any effect on RTF predictions for our patient cohort data. Notably, the graphs provide a means for estimating unmeasurable RTFs for individual patients from their hormone measurements before treatment.

CONCLUSIONS

p-THYROSIM can provide accurate monotherapies for male and female hypothyroid patients, personalized with their BMIs. Where combination therapy is warranted, our results predict that not much LT3 is needed in addition to LT4 to restore euthyroid levels, suggesting opportunities for further research exploring combination therapy with lower T3 doses and slow-releasing T3 formulations.

A Bibliometric Analysis of 8271 Publications on Thyroid Nodules From 2000 to 2021

INTRODUCTION

Thyroid nodules (TNs) are a common clinical condition. The probability of thyroid nodules being malignant is 7-15%. However, in recent decades, a number of publications on TNs have not been well summarized and discussed. The aim of this study was to summarize and sort out medical publications on TNs over the past 2 decades using a bibliometric method.

MATERIALS AND METHODS

Medical publications from January 1^st, 2000, to November 1^st, 2021, were searched in the Web of Science Core Collection database using the Medical Subject Heading (MeSH) term "thyroid nodule". Full associated data were downloaded, and detailed information was extracted using the bibliometric analysis platform VOSviewer.

RESULTS

A total of 8271 publications related to TNs from the last 2 decades were found and included in this study. An increasing trend was presented in the annual number of publications. The United States, China and Italy contributed the most publications. Carcinoma, management, ultrasound, and fine-needle aspiration were the most popular subjects in the field of TNs. The topics of the studies could be stratified into four clusters. The first cluster was using ultrasound to evaluate the nodules, including the thyroid imaging reporting and data system (TI-RADS), elastography and benign features. The second cluster was the fine-needle aspiration method, including the Bethesda system, cytology and BRAF mutations. The third cluster was the management of nodules, including radiofrequency and thermal ablation, surgery, and consensus statements. The last cluster was carcinoma, which is correlated with all three clusters described above. The preoperative diagnosis of cytologically indeterminate nodules was particularly highlighted in the top 10 most cited publications in recent years.

CONCLUSION

How to diagnose thyroid nodules as malignant or benign, especially in cytologically indeterminate nodules, is still the most concerning topic in TN research. Although the fine-needle aspiration method and gene-expression classifiers show promising results, there is still a crucial need for translations from fundamental studies to clinical applications.

Patient-Tailored Levothyroxine Dosage with Pharmacokinetic/Pharmacodynamic Modeling: A Novel Approach After Total Thyroidectomy

Background: After seven decades of levothyroxine (LT4) replacement therapy, dosage adjustment still takes several months. We have developed a decision aid tool (DAT) that models LT4 pharmacometrics and enables patient-tailored dosage. The aim of this was to speed up dosage adjustments for patients after total thyroidectomy. Methods: The DAT computer program was developed with a group of 46 patients post-thyroidectomy, and it was then applied in a prospective randomized multicenter validation trial in 145 unselected patients admitted for total thyroidectomy for goiter, differentiated thyroid cancer, or thyrotoxicosis. The LT4 dosage was adjusted after only two weeks, with or without application of the DAT, which calculated individual free thyroxine (fT4) targets based on four repeated measurements of fT4 and thyrotropin (TSH) levels. The individual TSH target was either <0.1, 0.1-0.5, or 0.5-2.0 mIU/L, depending on the diagnosis. Initial postoperative LT4 dosage was determined according to clinical routine without using algorithms. A simplified DAT with a population-based fT4 target was used for thyrotoxic patients who often went into surgery after prolonged TSH suppression. Subsequent LT4 adjustments were carried out every six weeks until target TSH was achieved. Results: When clinicians were guided by the DAT, 40% of patients with goiter and 59% of patients with cancer satisfied the narrow TSH targets eight weeks after surgery, as compared with only 0% and 19% of the controls, respectively. The TSH was within the normal range in 80% of DAT/goiter patients eight weeks after surgery as compared with 19% of controls. The DAT shortened the average dosage adjustment period by 58 days in the goiter group and 40 days in the cancer group. For thyrotoxic patients, application of the simplified DAT did not improve the dosage adjustment. Conclusions: Application of the DAT in combination with early postoperative TSH and fT4 monitoring offers a fast approach to LT4 dosage after total thyroidectomy for patients with goiter or differentiated thyroid cancer. Estimation of individual TSH-fT4 dynamics was crucial for the model to work, as removal of this feature in the applied model for thyrotoxic patients also removed the benefit of the DAT.

Weight and Body Mass Index for Predicting Thyroxine Dose in Primary Hypothyroidism

Background

The treatment of primary hypothyroidism with thyroxine is weight-based or body mass index (BMI)-based. However, significant variation in the dose and the consequent delay in achieving euthyroid state is observed along the spectrum of patient body weights.

Objectives

To determine the weight and BMI-based dosing of thyroxine in primary hypothyroidism to achieve euthyroidism.

Material and methods

It was a retrospective review of the patient records conducted in the department of endocrinology, Shifa International Hospital, Islamabad, from July 1, 2014, to June 30, 2019 (five-year period). Patients with clinical and biochemical hypothyroidism were enrolled and initiated on thyroxine replacement to achieve euthyroid status. A total of 504 patients were included in the study.

Results

The mean age was 44.5 ±13.6 standard deviation. Females were 83.5%. The mean dose of thyroxine to achieve euthyroid status was 107.7 ± 39.3 mean standard deviation mcg/day, i.e. 1.4 (0.5) mcg/kg/day. Euthyroid status was achieved in 264 (52.4%) of patients at three months. The mean TSH level after treatment was 2.09 (1.2) mU/L. The linear regression model showed that BMI and weight are independent predictors of the required thyroxine dose (R and Rsquare values are .274 and 0.075 for BMI and .319 and .102 for weight, respectively (P-value <.0001). There was no impact of age, gender, height, and duration of disease on achieving euthyroid at six months after treatment (P values: .85, .394, .827, and .105, respectively).

Conclusion

The optimum dose in primary hyperthyroidism can be determined with body weight and BMI-based calculations.

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.

Levothyroxine Therapy in Thyrodectomized Patients

Administration of the optimal dose of levothyroxine (LT4) is crucial to restore euthyroidism after total thyroidectomy. An insufficient or excessive dosage may result in hypothyroidism or thyrotoxicosis, either one associated with a number of symptoms/complications. Most literature regarding the LT4 dosage deals with the treatment of primary hypothyroidism, whereas a limited number of studies handle the issue of thyroxin replacement after total thyroidectomy. A literature review was performed focusing on all papers dealing with this topic within the last 15 years. Papers that reported a scheme to calculate the proper LT4 dose were collected and compared to set up a review exploring limits and drawbacks of LT4 replacement therapy in the wide population of patients who had undergone thyroidectomy. Most of the methods for monitoring and adjusting thyroid hormone replacement after thyroidectomy for benign disease use LT4 at an empirical dose of approximately 1.6 μg/kg, with subsequent changes according to thyroid function test results and assessments of the patient's symptoms. Approximately 75% of patients require a dose adjustment, suggesting that factors other than body weight play a role in the determination of the proper LT4 dose. Hence, several schemes are reported in the literature for the proper initial dose of LT4. An inadequate level of thyroid hormone levels in these patients can be due to several factors. The most common ones that lead to the necessity of LT4 dose adjustments include lack of compliance, changes in LT4 formulation, dosage errors, increased serum levels of T4-binding globulin, body mass changes, and dietary habits. Moreover, concomitant ingestion of calcium supplements, ferrous sulfate, proton-pump inhibitors, bile acid sequestrants, and sucralfate might influence LT4 absorption and/or metabolism. Furthermore, some gastrointestinal conditions and their treatments can contribute to suboptimal LT4 performance by altering gastric acidity and thereby reducing its bioavailability, particularly in the solid form. Beyond the classic tablet form, new formulations of LT4, such as a soft gel capsule and an oral solution, recently became available. The liquid formulation is supposed to overcome the food and beverages interference with absorption of LT4 tablets.