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Introduction: Spontaneous coronary artery dissection is an exceptionally rare cause of acute coronary syndrome (ACS) with a reported incidence of 1–4% of all ACS cases. It is most prevalent in young women with the left anterior descending artery and left main are the most common arteries involved. Case Report: The patient is a 75-year-old man with a prior medical history of hypertension, gastroesophageal reflux disease (GERD), and pulmonary fibrosis presented to the emergency department (ED) with syncope. The patient reported a burning sensation over the central chest different from his GERD. On physical exam, the patient had a 3/6 holosystolic murmur throughout the left pericardium. His laboratory workup was benign with negative troponins and normal electrocardiogram (EKG). Chest X-ray during this time revealed no acute processes and computed tomography angiography (CTA) of the chest was positive for a 4.1 mm fusiform ascending aortic aneurysm. 2D Echo revealed new cardiomyopathy with ejection fraction (EF) of 35–40% with trivial mitral regurgitation (MR) and mild aortic regurgitation. Left heart catheterization revealed left main stenosis of 75%, left anterior descending (LAD) with ostial stenosis of 80% and 30% stenosis of the left circumflex and right coronary artery (RCA). The LAD had a proximal eccentric plaque which under intravascular ultrasound (IVUS) revealed a dissection. The patient underwent a coronary artery bypass grafting (CABG) and did well until discharge home. His postoperative course was complicated by new onset of atrial fibrillation/flutter. The patient was discharged to rehab with Eliquis, baby aspirin, Coreg, and Digoxin. Conclusion: Our case is unique in view of the unconventional age group, gender, and odd symptomatology. We hope to increase the awareness among medical practitioners for this high morbidity diagnosis if not identified correctly.

Objectives Medical coding, or the translation of healthcare information into numeric codes, is expensive and time intensive. This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statistical healthcare surveys.

MotivationAs the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned.ResultsDeveloped through the National Heart, Lung, and Blood Institute’s (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15,911 study variables from public datasets. On a manually curated search dataset, Dug’s total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch’s total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results.Availability and ImplementationDug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/.Contactawaldrop@rti.org or scox@renci.org

Motivation As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. Results Developed through the National Heart, Lung and Blood Institute’s (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug’s total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch’s total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. Availability and implementation Dug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND Previous qualitative studies and data science studies using Reddit for tobacco research are limited by the lack of available demographic information. Social media investigations are often limited to manual qualitative coding or machine learning classification in isolation. OBJECTIVE This study combines both machine learning methods and manual qualitative coding to provide contextual age nuance to social media analysis. By being able to predict a Redditor’s age using publicly available data, the most popular posts can be analyzed and qualitatively coded to provide nuanced comparisons on thematic topics by age group. METHODS The current study combines these two methods to 1) predict Reddit users’ age into two categories (13-20, 21-54) and 2) qualitatively code Electronic Nicotine Delivery System [ENDS] related Reddit posts within the two age groups. We identified Reddit posts on three topics: Vaping in General, Tobacco 21 Minimum Age Laws, and Flavor Restriction Policies. An age algorithm was used to predict Reddit users’ ages (13-20 or 21-54 year old users). The 25 posts with the highest karma score (number of upvotes minus number of downvotes) for each query and each predicted age group were qualitatively coded. RESULTS The top three, two of which were part of the query, out of nine, topics that emerged were “Flavor Restriction Policies”, “Tobacco 21 Policies”, and “Use”. Tobacco 21 and Flavor Restriction Policy posts were prominent coding categories. Opposition to flavor restriction policies was a prominent sub-category for both groups, but more common in the 21-54 group. The 13-20 group was more likely to discuss opposition to minimum age laws as well as access to flavored ENDS products. The 21-54 group more commonly mentioned general vaping use behavior. CONCLUSIONS Users predicted to be in the 13-20 age group posted about different ENDS-related topics on Reddit than users predicted to be in the 21-54 age group. Future studies could use these complementary methods with social media data to gain insights from target audiences.

UNSTRUCTURED REMOVE

BACKGROUND Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users’ demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. OBJECTIVE We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. METHODS This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users’ age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. RESULTS The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. CONCLUSIONS We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users’ posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users' demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users.We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data.This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users' age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature.The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group.We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users' posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

SummaryObjective High-attrition rates have been observed in long-term clinical trials of weight loss agents. We evaluated the impact of an innovative retention programme on 1-year retention. Methods Three Phase 3 global multicentre clinical trials evaluated the efficacy and safety of a CB1 receptor antagonist in subjects with BMI ≥ or = 27 kg/m2. The impact of a multifaceted retention programme including a dietitian screening interview, a comprehensive culturally adapted lifestyle modification programme, and a dietitian support system to maximize lifestyle adherence, was evaluated in 4,410 subjects from four subpopulations (non-US English-speaking, non-English-speaking, US-without dietitian screening and US-with dietitian screening) comprising 208 centres from 15 countries. Results The median proportion retained over the first year among subjects in three protocols was 82%. Non-English-speaking countries showed higher retention rates (89%) compared with the USA (73%) and non-US English-speaking (81%) countries. Within the USA, behavioural screening was associated with 29% reduction in dropout rate; for every five monthly teleconferences attended above 11, there was a 32% decrease in dropout rate. Conclusions This novel retention programme greatly improved upon reported retention rates of studies conducted with other weight loss agents in long-term clinical trials. Its effectiveness should be confirmed in future trials.

BACKGROUND JUUL is an electronic nicotine delivery system (ENDS) resembling a USB device that has become rapidly popular among youth. Recent studies suggest that social media may be contributing to its popularity. JUUL company claims their products are targeted for adult current smokers but recent surveillance suggests youth may be exposed to JUUL products online. To date, there has been little attention on restricting youth exposure to age restricted products on social media. OBJECTIVE The objective of this study was to utilize a computational age prediction algorithm to determine the extent to which underage youth are being exposed to JUUL’s marketing practices on Twitter. METHODS We examined all of @JUULvapor’s Twitter followers in April 2018. For followers with a public account, we obtained their metadata and last 200 tweets using the Twitter application programming interface. We ran a series of classification models to predict whether the account following @JUULvapor was an underage youth or an adult. RESULTS Out of 9,077 individuals following @JUULvapor Twitter account, a three-age category model predicted that 44.9% are 13 to 17 years old (N=4,078), 43.6% are 18 to 24 years old (N=3,957), and 11.5% are 25 years old or older (N=1,042); and a two-age category model predicted that 80.6% (N=7,313) are under 21 years old. CONCLUSIONS Despite a disclaimer that followers must be of legal age to purchase tobacco products, the majority of JUUL followers on Twitter are under age. This suggests that ENDS brands and social media networks need to implement more stringent age-verification methods to protect youth from age-restricted content.

The reactivation of varicella zoster virus (VZV), also known as herpes zoster (HZ) or shingles, is of substantial public health importance, as up to one-third of adults in the US will develop HZ within their lifetime (1–4). Nearly 100% of individuals in the US age 40 years or older have been exposed to this virus, and its reactivation is related to diminished VZV-specific cell-mediated immunity associated with increasing age and immunosuppressive conditions. Among patients who develop HZ, postherpetic neuralgia occurs in ∼15%, causing long-term disability. More rarely, disseminated forms of HZ occur, potentially leading to encephalitis and death. In the US, the annual incidence rates (IRs) of HZ vary between 0.4 and 1.1 per 100 patient-years in patients ages 50–80 years, with the highest rates seen in women (1). In patients with rheumatoid arthritis (RA), the risk of HZ is elevated 2–3-fold (5,6). Although certain disease-modifying therapies, such as anti–tumor necrosis factor α (anti-TNFα) therapies, have been linked to further increases in risk, studies evaluating this have produced conflicting results. To date, only prednisone has been consistently shown to increase the risk of shingles by an additional 1.5–2-fold (7). Furthermore, although disseminated HZ is much more common in immunosuppressed populations, it is unclear whether RA and/or its disease-modifying therapies increase the risk (7,8). Tofacitinib is an oral JAK inhibitor for the treatment of RA. Tofacitinib preferentially inhibits JAK-3 and/or JAK-1, modulating the immune response via down-regulation of several cytokines (e.g., interleukins 2, 4, 7, 9, 15, and 21) that are integral to lymphocyte development and function (9). Given its mechanism of action and the increased baseline risk of HZ among patients with RA, an increased risk of HZ is a theoretical concern. Accordingly, we undertook a retrospective evaluation of HZ cases as reported within the Tofacitinib RA Development Program, with the objectives of describing the outcomes of and identifying risk factors for HZ among tofacitinib-treated patients, as well as evaluating the relative incidence of HZ in tofacitinib-treated patients as compared with those receiving placebo.

Background: PF-04447943 is a potent, selective phosphodiesterase 9A (PDE9A) inhibitor that elevates guanoscine 3’,5’ – cyclic monophosphate (cGMP) in brain and cerebrospinal fluid. PDE9A inhibition enhances synaptic plasticity and improves memory in preclinical cognition models, and prevents decreases in dendritic spine density in transgenic mice that overexpress amyloid precursor protein (APP) leading to high levels of amyloid beta (A β) production (Tg2576). Objective: This Phase 2 multicenter study was designed to assess the efficacy, safety and pharmacokinetics of PF-04447943 compared with placebo in mild to moderate probable Alzheimer’s disease (AD). Methods: Subjects in overall good health with Mini Mental State Examination (MMSE) scores of 14-26 were randomized to 12 weeks treatment with PF-04447943 25 mg q12h (n=91) or placebo (n=100). Concomitant acetylcholinesterase inhibitor or memantine use was excluded. The primary outcome was the Alzheimer’s Disease Assessment Scale – cognitive subscale (ADAS-cog). The Neuropsychiatric Inventory (NPI), Clinical Global Impression-Improvement scale (CGI-I) and standard safety measures were secondary outcomes. Results: Completion rates were similar, 87% PF-04447943 vs 92% placebo. At week 12 the mean (SE) baseline adjusted decrease from baseline in ADAS cog for PF-04447943-treated patients was -1.91 (0.54). Placebo treated patients had a change of -1.60 (0.50). The difference between treatments was -0.31 (90% CI of -1.52, 0.90). Corresponding values for the NPI were -2.86 (0.72) vs -2.70 (0.67) with a treatment difference of -0.16 (90% CI of -1.78, 1.48). Neither these changes nor the distribution of CGI-I scores were statistically significantly different between groups. The incidence of serious adverse events (AEs) was similar between groups with 2 deaths in the placebo group. The PF-04447943 group reported more gastrointestinal AEs including diarrhea (5.5% vs 3%) and nausea (5.5% vs 1%) and had a higher rate of discontinuation due to AEs (6.6% vs 2%). Conclusions: Although generally safe and well-tolerated, 12 weeks PF-04447943 treatment did not improve cognition, behavior, and global change compared with placebo.

Three double-blind, placebo-controlled, three-parallel-group, multicenter phase 3 trials were conducted to assess the efficacy and safety of CP-945,598 for weight loss and weight-loss maintenance. Two trials were designed to be 2 years in duration (in obese and overweight patients) and one as a 1-year study (in obese and overweight patients with type 2 diabetes). However, the 2-year trials and the CP-945,598 development program were terminated before completion due to changing regulatory perspectives of CB1 receptor-related drugs. In total, 1,253 and 2,536 participants in the two 2-year multinational and North American studies were randomized to 10-mg CP-945,598 (n = 360; 718); 20-mg CP-945,598 (n = 534, 1,084) and placebo (n = 359, 734), respectively; and 975 participants were randomized to 10-mg CP-945,598 (n = 318); 20-mg CP-945,598 (n = 320); and placebo (n = 337) in the 1-year multinational diabetes trial. Baseline demographics were similar between treatment groups within each trial. One year of treatment with CP-945,598 resulted in a dose-related mean percentage reduction from baseline body-weight in all trials. A significant proportion of all participants also achieved 5% and 10% weight loss after 1 year. In participants with mainly well-controlled type 2 diabetes, the combination of lifestyle and CP-945,598 induced substantial improvements in glycemic control. The most frequent adverse events (AEs) for CP-945,598 were: diarrhea, nausea, nasopharyngitis, and headache. Self-reported experiences of anxiety and suicidal thoughts were higher with CP-945,598 than placebo, as were the incidence of depression and depressed mood. However, the reported increases in psychiatric symptoms were not consistently dose dependent.

Background End of life care is currently a government and national priority. The Liverpool Care Pathway for the Dying Patient (LCP) is an evidence-based multiprofessional document which aims to optimise end of life care. In May 2006 the LCP was introduced into a primary care locality within a London PCT. An audit carried out on the first 20 patients to be placed on the LCP noted that the ‘do not attempt resuscitation’ (DNAR) order had been signed by their general practitioner (GP) in only seven out of the 20 patients. Aims and objectives To explore the factors influencing whether GPs sign the DNAR order of the LCP. To explore what helps or hinders GPs to sign DNAR orders and to propose actions facilitating completion of such orders. Method Semi-structured interviews with seven GPs and four nurses were carried out between April 2008 and September 2008. Grounded theory analysis was used. Results The LCP is a nurse led process in the primary care setting that was studied. Facilitatory factors for obtaining a signed DNAR order were a multiprofessional approach to the care of palliative care patients, a good working relationship between GP and district nurse, the GP and district nurse working from the same premises and district nurse confidence in commencing the LCP. Inhibitory factors were initial GP ambivalence towards the LCP, ambivalence of the district nurse to completing the LCP process, difficulties with accessing a GP to sign the DNAR order and commencing the LCP out of hours. Conclusions The study demonstrates the importance of a co-ordinated multi-professional approach to achieve a signed DNAR order of the LCP. It highlights the need for the on-going education of healthcare professionals on end of life issues.