Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness.

Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term "manifestations," as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.

Evaluation of large language models as a diagnostic aid for complex medical cases.

Background: The use of large language models (LLM) has recently gained popularity in diverse areas, including answering questions posted by patients as well as medical professionals. Objective: To evaluate the performance and limitations of LLMs in providing the correct diagnosis for a complex clinical case. Design: Seventy-five consecutive clinical cases were selected from the Massachusetts General Hospital Case Records, and differential diagnoses were generated by OpenAI's GPT3.5 and 4 models. Results: The mean number of diagnoses provided by the Massachusetts General Hospital case discussants was 16.77, by GPT3.5 30 and by GPT4 15.45 (p < 0.0001). GPT4 was more frequently able to list the correct diagnosis as first (22% versus 20% with GPT3.5, p = 0.86), provide the correct diagnosis among the top three generated diagnoses (42% versus 24%, p = 0.075). GPT4 was better at providing the correct diagnosis, when the different diagnoses were classified into groups according to the medical specialty and include the correct diagnosis at any point in the differential list (68% versus 48%, p = 0.0063). GPT4 provided a differential list that was more similar to the list provided by the case discussants than GPT3.5 (Jaccard Similarity Index 0.22 versus 0.12, p = 0.001). Inclusion of the correct diagnosis in the generated differential was correlated with PubMed articles matching the diagnosis (OR 1.40, 95% CI 1.25-1.56 for GPT3.5, OR 1.25, 95% CI 1.13-1.40 for GPT4), but not with disease incidence. Conclusions and relevance: The GPT4 model was able to generate a differential diagnosis list with the correct diagnosis in approximately two thirds of cases, but the most likely diagnosis was often incorrect for both models. In its current state, this tool can at most be used as an aid to expand on potential diagnostic considerations for a case, and future LLMs should be trained which account for the discrepancy between disease incidence and availability in the literature.

Prevalence of all epilepsies in urban informal settlements in Nairobi, Kenya: a two-stage population-based study.

BACKGROUND: WHO estimates that more than 50 million people worldwide have epilepsy and 80% of cases are in low-income and middle-income countries. Most studies in Africa have focused on active convulsive epilepsy in rural areas, but there are few data in urban settings. We aimed to estimate the prevalence and spatial distribution of all epilepsies in two urban informal settlements in Nairobi, Kenya. METHODS: We did a two-stage population-based cross-sectional study of residents in a demographic surveillance system covering two informal settlements in Nairobi, Kenya (Korogocho and Viwandani). Stage 1 screened all household members using a validated epilepsy screening questionnaire to detect possible cases. In stage 2, those identified with possible seizures and a proportion of those screening negative were invited to local clinics for clinical and neurological assessments by a neurologist. Seizures were classified following the International League Against Epilepsy recommendations. We adjusted for attrition between the two stages using multiple imputations and for sensitivity by dividing estimates by the sensitivity value of the screening tool. Complementary log-log regression was used to assess prevalence differences by participant socio-demographics. FINDINGS: A total of 56 425 individuals were screened during stage 1 (between Sept 17 and Dec 23, 2021) during which 1126 were classified as potential epilepsy cases. A total of 873 were assessed by a neurologist in stage 2 (between April 12 and Aug 6, 2022) during which 528 were confirmed as epilepsy cases. 253 potential cases were not assessed by a neurologist due to attrition. 30 179 (53·5%) of the 56 425 individuals were male and 26 246 (46·5%) were female. The median age was 24 years (IQR 11-35). Attrition-adjusted and sensitivity-adjusted prevalence for all types of epilepsy was 11·9 cases per 1000 people (95% CI 11·0-12·8), convulsive epilepsy was 8·7 cases per 1000 people (8·0-9·6), and non-convulsive epilepsy was 3·2 cases per 1000 people (2·7-3·7). Overall prevalence was highest among separated or divorced individuals at 20·3 cases per 1000 people (95% CI 15·9-24·7), unemployed people at 18·8 cases per 1000 people (16·2-21·4), those with no formal education at 18·5 cases per 1000 people (16·3-20·7), and adolescents aged 13-18 years at 15·2 cases per 1000 people (12·0-18·5). The epilepsy diagnostic gap was 80%. INTERPRETATION: Epilepsy is common in urban informal settlements of Nairobi, with large diagnostic gaps. Targeted interventions are needed to increase early epilepsy detection, particularly among vulnerable groups, to enable prompt treatment and prevention of adverse social consequences. FUNDING: National Institute for Health Research using Official Development Assistance.

COVID-19 Vaccine Side Effects and Long-Term Neutralizing Antibody Response : A Prospective Cohort Study.

BACKGROUND: Concern about side effects is a common reason for SARS-CoV-2 vaccine hesitancy. OBJECTIVE: To determine whether short-term side effects of SARS-CoV-2 messenger RNA (mRNA) vaccination are associated with subsequent neutralizing antibody (nAB) response. DESIGN: Prospective cohort study. SETTING: San Francisco Bay Area. PARTICIPANTS: Adults who had not been vaccinated against or exposed to SARS-CoV-2, who then received 2 doses of either BNT162b2 or mRNA-1273. MEASUREMENTS: Serum nAB titer at 1 month and 6 months after the second vaccine dose. Daily symptom surveys and objective biometric measurements at each dose. RESULTS: 363 participants were included in symptom-related analyses (65.6% female; mean age, 52.4 years [SD, 11.9]), and 147 were included in biometric-related analyses (66.0% female; mean age, 58.8 years [SD, 5.3]). Chills, tiredness, feeling unwell, and headache after the second dose were each associated with 1.4 to 1.6 fold higher nAB at 1 and 6 months after vaccination. Symptom count and vaccination-induced change in skin temperature and heart rate were all positively associated with nAB across both follow-up time points. Each 1 °C increase in skin temperature after dose 2 was associated with 1.8 fold higher nAB 1 month later and 3.1 fold higher nAB 6 months later. LIMITATIONS: The study was conducted in 2021 in people receiving the primary vaccine series, making generalizability to people with prior SARS-CoV-2 vaccination or exposure unclear. Whether the observed associations would also apply for neutralizing activity against non-ancestral SARS-CoV-2 strains is also unknown. CONCLUSION: Convergent self-report and objective biometric findings indicate that short-term systemic side effects of SARS-CoV-2 mRNA vaccination are associated with greater long-lasting nAB responses. This may be relevant in addressing negative attitudes toward vaccine side effects, which are a barrier to vaccine uptake. PRIMARY FUNDING SOURCE: National Institute on Aging.

Patients Undergoing Reverse Total Shoulder Arthroplasty Have Less Pain and Require Fewer Opioid Pain Medications Compared to Anatomic Total Shoulder Arthroplasty in the Early Postoperative Period: A Retrospective Review.

BACKGROUND: Recovery after anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA) has many similarities; however, recently surgeons have suggested patients undergoing rTSA have a less difficult postoperative course with less pain compared with aTSA patients. Given the heightened awareness to postoperative pain control and opioid consumption, as well as the expanding indications for rTSA, we sought to determine the differences in pain and opioid consumption between aTSA and rTSA patients in a 12-week postoperative period. METHODS: A retrospective chart review was performed to identify all patients who underwent a primary aTSA or rTSA from January 2013 to April 2018 at a single institution. Patients with recorded visual analog scale (VAS) and American Shoulder and Elbow Surgeons (ASES) scores were included for analysis. Revision arthroplasties were excluded. VAS scores were recorded preoperatively and at standard 2-week, 6-week, and 12-week postoperative time points. P-values < 0.05 were considered statistically significant, except where Bonferroni corrections were applied. RESULTS: A total of 690 patients underwent TSA (278 aTSA, 412 rTSA). Preoperatively, aTSA and rTSA patient groups had similar VAS scores (6 vs 6, P = 0.38). Postoperatively, the aTSA group had a higher VAS at the 6-week visit, compared with rTSA patients (2.8 vs 2.2, P = 0.003). aTSA patients remained on opioids at a higher rate at the 2-week (62.4% vs 45.6%, P = < 0.001) time period. aTSA patients needed more opioid prescription refills before the 2-week (61.7% vs 45.5%, P = <0.001) and 6-week (40.4% vs 30.7%, P = 0.01) follow-up visits. CONCLUSIONS: Despite similar preoperative VAS and rates of preoperative opioid use, aTSA patients required more opioid medication refills and remained on opioids for a longer duration in the early postoperative period to achieve similar postoperative pain control as indicated by similar VAS. This study suggests that the recovery from rTSA is less difficult compared with aTSA as indicated by VAS and opioid consumption.

Outpatient versus inpatient shoulder arthroplasty outcomes using an updated patient-selection algorithm: minimum two-year follow-up.

BACKGROUND: Previous studies have demonstrated the safety and cost-effectiveness of outpatient total shoulder arthroplasty (TSA), with the majority of studies focusing on 90-day outcomes and complications. Patient selection algorithms have helped appropriately choose patients for an outpatient TSA setting. This study aimed to determine the outcomes of TSA between outpatient and inpatient cohorts with at least a 2-year follow-up. METHODS: A retrospective review identified patients older than 18 years who underwent a TSA with a minimum of 2-year follow-up in either an inpatient or outpatient setting. Using a previously published outpatient TSA patient-selection algorithm, patients were allocated into three groups: outpatient, inpatient due to insurance requirements, and inpatient due to not meeting algorithm criteria. Outcomes evaluated included visual analog scale (VAS) pain, American Shoulder and Elbow Surgeons (ASES) score, Single Assessment Numeric Evaluation (SANE) score, range of motion (ROM), strength, complications, re-admissions, and re-operations. Analysis was performed between the outpatient and inpatient groups to demonstrate the safety and efficacy of outpatient TSA with midterm follow-up. RESULTS: A total of 779 TSA were included in this study, allocated into the outpatient (N = 108), inpatient due to insurance (N = 349), and inpatient due to algorithm (N = 322). The average age between these groups was significantly different (59.4 ± 7.4, 66.5 ± 7.5, and 72.5 ± 8.7, respectively; P < 0.0001). All patient groups demonstrated significant improvements in preoperative to final patient-outcomes scores, ROM, and strength. Analysis between cohorts showed similar final follow-up outcome scores, ROM, and strength, with few significant differences that are likely not clinically different, regardless of surgical location, insurance status, or meeting patient-selection algorithm. Complications, reoperations, and readmissions between all three groups were not significantly different. CONCLUSION: This study reaffirms prior short-term follow-up literature. Transitioning appropriate patients to outpatient TSA results in similar outcomes and complications compared to inpatient cohorts with mid-term follow-up.

Development and validation of a clinical breast cancer tool for accurate prediction of recurrence.

Given high costs of Oncotype DX (ODX) testing, widely used in recurrence risk assessment for early-stage breast cancer, studies have predicted ODX using quantitative clinicopathologic variables. However, such models have incorporated only small cohorts. Using a cohort of patients from the National Cancer Database (NCDB, n = 53,346), we trained machine learning models to predict low-risk (0-25) or high-risk (26-100) ODX using quantitative estrogen receptor (ER)/progesterone receptor (PR)/Ki-67 status, quantitative ER/PR status alone, and no quantitative features. Models were externally validated on a diverse cohort of 970 patients (median follow-up 55 months) for accuracy in ODX prediction and recurrence. Comparing the area under the receiver operating characteristic curve (AUROC) in a held-out set from NCDB, models incorporating quantitative ER/PR (AUROC 0.78, 95% CI 0.77-0.80) and ER/PR/Ki-67 (AUROC 0.81, 95% CI 0.80-0.83) outperformed the non-quantitative model (AUROC 0.70, 95% CI 0.68-0.72). These results were preserved in the validation cohort, where the ER/PR/Ki-67 model (AUROC 0.87, 95% CI 0.81-0.93, p = 0.009) and the ER/PR model (AUROC 0.86, 95% CI 0.80-0.92, p = 0.031) significantly outperformed the non-quantitative model (AUROC 0.80, 95% CI 0.73-0.87). Using a high-sensitivity rule-out threshold, the non-quantitative, quantitative ER/PR and ER/PR/Ki-67 models identified 35%, 30% and 43% of patients as low-risk in the validation cohort. Of these low-risk patients, fewer than 3% had a recurrence at 5 years. These models may help identify patients who can forgo genomic testing and initiate endocrine therapy alone. An online calculator is provided for further study.

Five million nights: temporal dynamics in human sleep phenotypes.

Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse factors can cause combinations of sleep parameters to differ both between people and within people over time. Researchers have attempted to combine sleep parameters to improve detecting similarities between nights of sleep. The cluster of similar combinations of sleep parameters from a night of sleep defines that night's sleep phenotype. To date, quantitative models of sleep phenotype made from data collected from large populations have used cross-sectional data, which preclude longitudinal analyses that could better quantify differences within individuals over time. In analyses reported here, we used five million nights of wearable sleep data to test (a) whether an individual's sleep phenotype changes over time and (b) whether these changes elucidate new information about acute periods of illness (e.g., flu, fever, COVID-19). We found evidence for 13 sleep phenotypes associated with sleep quality and that individuals transition between these phenotypes over time. Patterns of transitions significantly differ (i) between individuals (with vs. without a chronic health condition; chi-square test; p-value < 1e-100) and (ii) within individuals over time (before vs. during an acute condition; Chi-Square test; p-value < 1e-100). Finally, we found that the patterns of transitions carried more information about chronic and acute health conditions than did phenotype membership alone (longitudinal analyses yielded 2-10× as much information as cross-sectional analyses). These results support the use of temporal dynamics in the future development of longitudinal sleep analyses.

Epidemiology of Injuries Among National Basketball Association Players: 2013-2014 Through 2018-2019.

BACKGROUND: Understanding the epidemiology of injuries to athletes is essential to informing injury prevention efforts. HYPOTHESIS: The incidence and impact of basketball-related injuries among National Basketball Association (NBA) players from 2013-2014 through 2018-2019 is relatively stable over time. STUDY DESIGN: Descriptive epidemiology study. LEVEL OF EVIDENCE: Level 3. METHODS: Injuries from 2013-2014 through 2018-2019 were analyzed using the NBA Injury and Illness Database from an electronic medical record system. Descriptive statistics were calculated for injuries by season, game-loss, and onset. Incidence rates were estimated using Poisson models and linear trend tests. RESULTS: Between 552 and 606 players participated in ≥1 game per season during the study. Annual injury incidence ranged from 1550 to 1892, with 33.6% to 38.5% resulting in a missed NBA game. Game-loss injury rates ranged from 5.6 to 7.0 injuries per 10,000 player-minutes from 2014-2015 through 2018-2019 (P = 0.19); the rate was lower in 2013-2014 (5.0 injuries per 10,000 player-minutes), partly due to increased preseason injury rates and transition of reporting processes. The 6-year game-loss injury rate in preseason and regular season games was 6.9 (95% CI 6.0, 8.0) and 6.2 (95% CI 6.0, 6.5) injuries per 10,000 player-minutes; the rate in playoff games was lower (P < 0.01) at 2.8 (95% CI 2.2, 3.6). Most (73%) game-loss injuries had acute onset; 44.4% to 52.5% of these involved contact with another player. CONCLUSION: From 2013-2014 through 2018-2019, over one-third of injuries resulted in missed NBA games, with highest rates of game-loss injuries in preseason games and lowest rates in playoff games. Most game-loss injuries had acute onset, and half of those involved contact with another player. CLINICAL RELEVANCE: These findings - through reliable data reporting by team medical staff in an audited system - can guide evidence-based injury reduction strategies and inform player health priorities.

Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts From 2021 to 2023.

PURPOSE: Artificial intelligence (AI) models can generate scientific abstracts that are difficult to distinguish from the work of human authors. The use of AI in scientific writing and performance of AI detection tools are poorly characterized. METHODS: We extracted text from published scientific abstracts from the ASCO 2021-2023 Annual Meetings. Likelihood of AI content was evaluated by three detectors: GPTZero, Originality.ai, and Sapling. Optimal thresholds for AI content detection were selected using 100 abstracts from before 2020 as negative controls, and 100 produced by OpenAI's GPT-3 and GPT-4 models as positive controls. Logistic regression was used to evaluate the association of predicted AI content with submission year and abstract characteristics, and adjusted odds ratios (aORs) were computed. RESULTS: Fifteen thousand five hundred and fifty-three abstracts met inclusion criteria. Across detectors, abstracts submitted in 2023 were significantly more likely to contain AI content than those in 2021 (aOR range from 1.79 with Originality to 2.37 with Sapling). Online-only publication and lack of clinical trial number were consistently associated with AI content. With optimal thresholds, 99.5%, 96%, and 97% of GPT-3/4-generated abstracts were identified by GPTZero, Originality, and Sapling respectively, and no sampled abstracts from before 2020 were classified as AI generated by the GPTZero and Originality detectors. Correlation between detectors was low to moderate, with Spearman correlation coefficient ranging from 0.14 for Originality and Sapling to 0.47 for Sapling and GPTZero. CONCLUSION: There is an increasing signal of AI content in ASCO abstracts, coinciding with the growing popularity of generative AI models.

Unique Challenges for WHO Emergency Medical Teams in Ukraine: Field Assessment & Global Recommendations.

The purpose of WHO Emergency Medical Teams is to Minor wording edit recommendations: 'to provide timely, high-quality health services.' (removed 'of') 'in the immediate aftermath of disasters and during disease outbreaks and other emergencies, including conflict and insecurity.'improve the timeliness and quality of health services by national and international Emergency Medical Teams in the immediate aftermath of a disaster, outbreak, and other emergencies, including war and conflicts. 'The war in Ukraine has presented all healthcare providers with many unique challenges'. What is meant by this? All Ukrainian HCPs, all HCP operating in Ukraine? Can you be more specific regarding what you are trying to say.The war in Ukraine has presented all healthcare providers with many unique challenges. This assessment addresses would edit to: 'the importance and the complexities of' the importance of and the complexities of the global spread of the Emergency Medical Team system challenges to meet a wide variety of crises including war, those that are unique to this very complex crisis in Ukraine, and the essential role of educational initiatives, not only in professional development but also in teamwork and cultural integration.

Model compounds for evaluating the reactivity of amphetamine-type stimulants.

The use of controlled precursors for reaction optimisation is not always practical. One approach to limiting the use of controlled substances is to instead use 'model compounds'. Herein, two model compounds resembling norephedrine and ephedrine were selected based on their (i) structural similarity (i.e., presence of key functional groups) and (ii) availability from multiple suppliers without restriction. Model compounds 2-amino-1-phenylethanol and 2-(methylamino)-1-phenylethanol (halostachine), were compared to norephedrine and pseudoephedrine by firstly subjecting them to transformations known in the synthesis of amphetamines, and secondly, comparing the compounds using colourimetric spot tests, FTIR and NMR.

Feasibility and acceptability of an integrated mind-body intervention for depression: whole-body hyperthermia (WBH) and cognitive behavioral therapy (CBT).

OBJECTIVE: To examine the feasibility of an integrated mind-body MDD treatment combining cognitive behavioral therapy (CBT) and whole-body hyperthermia (WBH). METHODS: In this single-arm trial, 16 adults with MDD initially received 8 weekly CBT sessions and 8 weekly WBH sessions. Outcomes included WBH sessions completed (primary), self-report depression assessments completed (secondary), and pre-post intervention changes in depression symptoms (secondary). We also explored changes in mood and cognitive processes and assessed changes in mood as predictors of overall treatment response. RESULTS: Thirteen participants (81.3%) completed ≥ 4 WBH sessions (primary outcome); midway through the trial, we reduced from 8 weekly to 4 bi-weekly WBH sessions to increase feasibility. The n = 12 participants who attended the final assessment visit completed 100% of administered self-report depression assessments; all enrolled participants (n = 16) completed 89% of these assessments. Among the n = 12 who attended the final assessment visit, the average pre-post-intervention BDI-II reduction was 15.8 points (95% CI: -22.0, -9.70), p = 0.0001, with 11 no longer meeting MDD criteria (secondary outcomes). Pre-post intervention improvements in negative automatic thinking, but not cognitive flexibility, achieved statistical significance. Improved mood from pre-post the initial WBH session predicted pre-post treatment BDI-II change (36.2%; rho = 0.60, p = 0.038); mood changes pre-post the first CBT session did not. LIMITATIONS: Small sample size and single-arm design limit generalizability. CONCLUSION: An integrated mind-body intervention comprising weekly CBT sessions and bi-weekly WBH sessions was feasible. Results warrant future larger controlled clinical trials.Clinivaltrials.gov Registration: NCT05708976.

Simulated Sea Surface Salinity Data from a 1/48° Ocean Model.

To study the validation process for sea surface salinity (SSS) we have generated one year (November 2011- October 2012) of simulated satellite and in situ "ground truth" data. This was done using the ECCO (Estimating the Circulation and Climate of the Oceans) 1/48° simulation, the highest resolution global ocean model currently available. The ground tracks of three satellites, Aquarius, SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were extracted and used to sample the model with a gaussian weighting similar to that of the spaceborne sensor ground footprint. This produced simulated level 2 (L2) data. Simulated level 3 (L3) data were then produced by averaging L2 data onto a regular grid. The model was sampled to produce simulated Argo and tropical mooring SSS datasets. The Argo data were combined into a simulated gridded monthly 1° Argo product. The simulated data produced from this effort have been used to study sampling errors, matchups, subfootprint variability and the validation process for SSS at L2 and L3.

Aspirin vs Placebo as Adjuvant Therapy for Breast Cancer: The Alliance A011502 Randomized Trial.

Importance: Observational studies of survivors of breast cancer and prospective trials of aspirin for cardiovascular disease suggest improved breast cancer survival among aspirin users, but prospective studies of aspirin to prevent breast cancer recurrence are lacking. Objective: To determine whether aspirin decreases the risk of invasive cancer events among survivors of breast cancer. Design, Setting, and Participants: A011502, a phase 3, randomized, placebo-controlled, double-blind trial conducted in the United States and Canada with 3020 participants who had high-risk nonmetastatic breast cancer, enrolled participants from 534 sites from January 6, 2017, through December 4, 2020, with follow-up to March 4, 2023. Interventions: Participants were randomized (stratified for hormone receptor status [positive vs negative], body mass index [≤30 vs >30], stage II vs III, and time since diagnosis [<18 vs ≥18 months]) to receive 300 mg of aspirin (n = 1510) or placebo once daily (n = 1510) for 5 years. Main Outcomes and Measures: The primary outcome was invasive disease-free survival. Overall survival was a key secondary outcome. Results: A total of 3020 participants were randomized when the data and safety monitoring committee recommended suspending the study at the first interim analysis because the hazard ratio had crossed the prespecified futility bound. By median follow-up of 33.8 months (range, 0.1-72.6 months), 253 invasive disease-free survival events were observed (141 in the aspirin group and 112 in the placebo group), yielding a hazard ratio of 1.27 (95% CI, 0.99-1.63; P = .06). All invasive disease-free survival events, including death, invasive progression (both distant and locoregional), and new primary events, were numerically higher in the aspirin group, although the differences were not statistically significant. There was no difference in overall survival (hazard ratio, 1.19; 95% CI, 0.82-1.72). Rates of grades 3 and 4 adverse events were similar in both groups. Conclusion and Relevance: Among participants with high-risk nonmetastatic breast cancer, daily aspirin therapy did not improve risk of breast cancer recurrence or survival in early follow-up. Despite its promise and wide availability, aspirin should not be recommended as an adjuvant breast cancer treatment. Trial Registration: ClinicalTrials.gov Identifier: NCT02927249.

Takotsubo cardiomyopathy as the primary manifestation of decompensated Graves' disease.

Takotsubo cardiomyopathy (TCM) is characterized by transient left ventricular dysfunction with apical ballooning, usually observed in postmenopausal women after a stressful event. We discuss a rare presentation of TCM induced by thyrotoxicosis secondary to Graves' disease. This case raises interesting questions about the pathogenesis, diagnosis, and management of TCM. Learning objectives: 1. To recognize hyperthyroidism as a possible etiology of takutsubo cardiomyopathy.2. To identify the effect of radioiodine contrast on diagnosis of some types of takutsubo cardiomyopathy.