ContextAmiodarone-induced thyrotoxicosis (AIT) is a difficult diagnostic and management challenge, especially during severe thyrotoxicosis accompanied by cardiovascular compromise.ObjectiveTo evaluate thyroidectomy for the management of AIT.DesignRetrospective cohort study of adults with noncongenital heart disease with AIT after >3 months of amiodarone who underwent thyroidectomy from 1 November 2002 to 31 December 2016.SettingReferral center.PatientsThe group was comprised of 17 patients.Main Outcome MeasuresThyroid function, left ventricular ejection fraction (LVEF), and surgical complications were the main outcome measures.ResultsPatients had median age of 60 years, 82.4% were male, and 47% had systolic heart failure. At diagnosis, median TSH was 0.005 mIU/L, median free T4 was 3.25 ng/dL, and total T3 was 198.5 ng/dL. We classified five patients as type 1 and type 2 and two patients as mixed; five patients remained undefined. The most common surgical indications were medically refractory disease, worsening cardiac status, and severe thyrotoxicosis requiring prompt resolution. Within 1 week post-thyroidectomy, median TSH was 0.565 mIU/L, and free T4 was 1.8 ng/dL. Median LVEF improved by 8% in patients with systolic heart failure. Seven patients had a complication within 30 days postsurgery (rehospitalization, n = 4; cervical hematoma, n = 2; recurrent arrhythmia, n = 2; symptomatic hypocalcemia, n = 1; death, n = 1). A larger thyroid gland was a risk factor for complications.ConclusionsThyroidectomy resulted in rapid resolution of thyrotoxicosis. Its complication rate was higher than for non-AIT indications but lower than previously reported in a similar population of high-risk surgical patients.
Use of a composite based on guidelines proposed by the AACE, TOS, and the ASMBS appears to be effective for preventing iron and vitamins A, B1, B12, and D deficiencies in the LRYGB patients during the first postoperative year. Separation of calcium and Fe does not need to be mandatory. Even with simplification, compliance is far from universal.
Objective and Background The clinical trials community has been hesitant to adopt Bayesian statistical methods, which are often more flexible and efficient with more naturally interpretable results than frequentist methods. We aimed to identify self-reported barriers to implementing Bayesian methods and preferences for becoming comfortable with them. Methods We developed a 22-question survey submitted to medical researchers (non-statisticians) from industry, academia, and regulatory agencies. Question areas included demographics, experience, comfort levels with Bayesian analyses, perceived barriers to these analyses, and preferences for increasing familiarity with Bayesian methods. Results Of the 323 respondents, most were affiliated with pharmaceutical companies (33.4%), clinical research organizations (29.7%), and regulatory agencies (18.6%). The rest represented academia, medical practice, or other. Over 56% of respondents expressed little to no comfort in interpreting Bayesian analyses. “Insufficient knowledge of Bayesian approaches” was ranked the most important perceived barrier to implementing Bayesian methods by a plurality (48%). Of the approaches listed, in-person training was the most preferred for gaining comfort with Bayesian methods. Conclusions Based on these survey results, we recommend that introductory level training on Bayesian statistics be presented in an in-person workshop that could also be broadcast online with live Q&A. Other approaches such as online training or collaborative projects may be better suited for higher-level trainings where instructors may assume a baseline understanding of Bayesian statistics. Increased coverage of Bayesian methods at medical conferences and medical school trainings would help improve comfort and overcome the substantial knowledge barriers medical researchers face when implementing these methods. Supplementary Information The online version contains supplementary material available at 10.1007/s43441-021-00357-x.
Aims To evaluate the effectiveness of automated symptom and side effect monitoring on quality of life among individuals with symptomatic diabetic peripheral neuropathy. Methods We conducted a pragmatic, cluster randomized controlled trial (July 2014 to July 2016) within a large healthcare system. We randomized 1834 primary care physicians and prospectively recruited from their lists 1270 individuals with neuropathy who were newly prescribed medications for their symptoms. Intervention participants received automated telephone-based symptom and side effect monitoring with physician feedback over 6 months. The control group received usual care plus three non-interactive diabetes educational calls. Our primary outcomes were quality of life (EQ-5D) and select symptoms (e.g. pain) measured 4-8 weeks after starting medication and again 8 months after baseline. Process outcomes included receiving a clinically effective dose and communication between individuals with neuropathy and their primary care provider over 12 months. Interviewers collecting outcome data were blinded to intervention assignment.Results Some 1252 participants completed the baseline measures [mean age (SD): 67 (11.7), 53% female, 57% white, 8% Asian, 13% black, 20% Hispanic]. In total, 1179 participants (93%) completed follow-up (619 control, 560 intervention). Quality of life scores (intervention: 0.658 AE 0.094; control: 0.653 AE 0.092) and symptom severity were similar at baseline. The intervention had no effect on primary [EQ-5D: À0.002 (95% CI À0.01, 0.01), P = 0.623; pain: 0.295 (À0.75, 1.34), P = 0.579; sleep disruption: 0.342 (À0.18, 0.86), P = 0.196; lower extremity functioning: À0.079 (À1.27, 1.11), P = 0.896; depression: À0.462 (À1.24, 0.32); P = 0.247] or process outcomes.Conclusions Automated telephone monitoring and feedback alone were not effective at improving quality of life or symptoms for people with symptomatic diabetic peripheral neuropathy.Trial Registration: ClinicalTrials.gov (NCT02056431).
The Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) of the U.S. Food and Drug Administration (FDA) have been leaders in protecting and promoting the U.S. public health by helping to ensure that safe and effective drugs and biological products are available in the United States for those who need them. The null hypothesis significance testing approach, along with other considerations, is typically used to demonstrate the effectiveness of a drug or biological product. The Bayesian framework presents an alternative approach to demonstrate the effectiveness of a treatment. This article discusses the Bayesian framework for drug and biological product development, highlights key settings in which Bayesian approaches may be appropriate, and provides recent examples of the use of Bayesian approaches within CDER and CBER.
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