Identification of Spontaneous Shoulder Hemarthrosis with Point-of-Care Ultrasound in the Emergency Department.

CASE PRESENTATION: A 32-year-old man with a history of hemophilia A presented to the emergency department with right shoulder pain, swelling, and decreased range of motion. DISCUSSION: Emergency physicians can use ultrasound to quickly and accurately identify hemarthrosis at the bedside.

Development of a simulation-based curriculum for Pediatric prehospital skills: a mixed-methods needs assessment.

BACKGROUND: The assessment and treatment of pediatric patients in the out-of-hospital environment often presents unique difficulties and stress for EMS practitioners. OBJECTIVE: Use a mixed-methods approach to assess the current experience of EMS practitioners caring for critically ill and injured children, and the potential role of a simulation-based curriculum to improve pediatric prehospital skills. METHODS: Data were obtained from three sources in a single, urban EMS system: a retrospective review of local pediatric EMS encounters over one year; survey data of EMS practitioners' comfort with pediatric skills using a 7-point Likert scale; and qualitative data from focus groups with EMS practitioners assessing their experiences with pediatric patients and their preferred training modalities. RESULTS: 2.1% of pediatric prehospital encounters were considered "critical," the highest acuity level. A total of 136 of approximately 858 prehospital providers responded to the quantitative survey; 34.4% of all respondents either somewhat disagree (16.4%), disagree (10.2%), or strongly disagree (7.8%) with the statement: "I feel comfortable taking care of a critically ill pediatric patient." Forty-seven providers participated in focus groups that resulted in twelve major themes under three domains. Specific themes included challenges in medication dosing, communication, and airway management. Participants expressed a desire for more repetition and reinforcement of these skills, and they were receptive to the use of high-fidelity simulation as a training modality. CONCLUSIONS: Critically ill pediatric prehospital encounters are rare. Over one third of EMS practitioners expressed a low comfort level in managing critically ill children. High-fidelity simulation may be an effective means to improve the comfort and skills of prehospital providers.

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning.

PURPOSE: To investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation. MATERIALS AND METHODS: Hip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics. RESULTS: The binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification. CONCLUSION: A deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article.© RSNA, 2020.

Evaluating the predictability of medical conditions from social media posts.

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients' consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients' Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.

Variations in Facebook Posting Patterns Across Validated Patient Health Conditions: A Prospective Cohort Study.

BACKGROUND: Social media is emerging as an insightful platform for studying health. To develop targeted health interventions involving social media, we sought to identify the patient demographic and disease predictors of frequency of posting on Facebook. OBJECTIVE: The aims were to explore the language topics correlated with frequency of social media use across a cohort of social media users within a health care setting, evaluate the differences in the quantity of social media postings across individuals with different disease diagnoses, and determine if patients could accurately predict their own levels of social media engagement. METHODS: Patients seeking care at a single, academic, urban, tertiary care emergency department from March to October 2014 were queried on their willingness to share data from their Facebook accounts and electronic medical records (EMRs). For each participant, the total content of Facebook posts was extracted. Using the latent Dirichlet allocation natural language processing technique, Facebook language topics were correlated with frequency of Facebook use. The mean number of Facebook posts over 6 months prior to enrollment was then compared across validated health outcomes in the sample. RESULTS: A total of 695 patients consented to provide access to their EMR and social media data. Significantly correlated language topics among participants with the highest quartile of posts contained health terms, such as "cough," "headaches," and "insomnia." When adjusted for demographics, individuals with a history of depression had significantly higher posts (mean 38, 95% CI 28-50) than individuals without a history of depression (mean 22, 95% CI 19-26, P=.001). Except for depression, across prevalent health outcomes in the sample (hypertension, diabetes, asthma), there were no significant posting differences between individuals with or without each condition. CONCLUSIONS: High-frequency posters in our sample were more likely to post about health and to have a diagnosis of depression. The direction of causality between depression and social media use requires further evaluation. Our findings suggest that patients with depression may be appropriate targets for health-related interventions on social media.

Twitter as a Tool for Health Research: A Systematic Review.

BACKGROUND: Researchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose. OBJECTIVES: To systematically review the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research. SEARCH METHODS: We performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015. SELECTION CRITERIA: We searched for peer-reviewed original research studies that primarily used Twitter for health research. DATA COLLECTION AND ANALYSIS: Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source. MAIN RESULTS: Of 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%). CONCLUSIONS: We identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user's Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research.

Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department.

BACKGROUND: Social media may offer insight into the relationship between an individual's health and their everyday life, as well as attitudes towards health and the perceived quality of healthcare services. OBJECTIVE: To determine the acceptability to patients and potential utility to researchers of a database linking patients' social media content with their electronic medical record (EMR) data. METHODS: Adult Facebook/Twitter users who presented to an emergency department were queried about their willingness to share their social media data and EMR data with health researchers for the purpose of building a databank for research purposes. Shared posts were searched for select terms about health and healthcare. RESULTS: Of the 5256 patients approached, 2717 (52%) were Facebook and/or Twitter users. 1432 (53%) of those patients agreed to participate in the study. Of these participants, 1008 (71%) consented to share their social media data for the purposes of comparing it with their EMR. Social media data consisted of 1 395 720 posts/tweets to Facebook and Twitter. Participants sharing social media data were slightly younger (29.1±9.8 vs 31.9±10.4 years old; p<0.001), more likely to post at least once a day (42% vs 29%; p=0.003) and more likely to present to the emergency room via self-arrival mode and have private insurance. Of Facebook posts, 7.5% (95% CI 4.8% to 10.2%) were related to health. Individuals with a given diagnosis in their EMR were significantly more likely to use terms related to that diagnosis on Facebook than patients without that diagnosis in their EMR (p<0.0008). CONCLUSIONS: Many patients are willing to share and link their social media data with EMR data. Sharing patients have several demographic and clinical differences compared with non-sharers. A database that merges social media with EMR data has the potential to provide insights about individuals' health and health outcomes.

Bedside ultrasound identification of infectious flexor tenosynovitis in the emergency department.

Infectious flexor tenosynovitis (FTS) is a serious infection of the hand and wrist that can lead to necrosis and amputation without prompt diagnosis and surgical debridement. Despite the growing use of point-of-care ultrasound (POCUS) by emergency physicians there is only one reported case of the use of POCUS for the diagnosis of infectious FTS in the emergency department setting. We present a case of a 58 year-old man where POCUS identified tissue necrosis and fluid along the flexor tendon sheath of the hand. Subsequent surgical pathology confirmed the diagnosis of infectious FTS.

Return visits to the emergency department: the patient perspective.

STUDY OBJECTIVE: Reasons for recurrent emergency department (ED) visits have been examined primarily through administrative data review. Inclusion of patients' perspectives of reasons for ED return may help inform future initiatives aimed at reducing recurrent utilization. The objective of this study is to describe the personal experiences and challenges faced by patients transitioning home after an ED discharge. METHODS: We performed semistructured qualitative interviews of adult patients with an unscheduled return to the ED within 9 days of an index ED discharge. Questions focused on problems with the initial discharge process, medications, outpatient care access, social support, and health care decisionmaking. Themes were identified with a modified grounded theory approach. RESULTS: Sixty interviews were performed. Most patients were satisfied with the discharge process at the index discharge, but many had complaints about the clinical care delivered, including insufficient evaluation and treatment. The primary reason for returning to the ED was fear or uncertainty about their condition. Most patients had a primary care physician, but they rarely visited a physician before returning to the ED. Patients cited convenience and more expedited evaluations as primary reasons for seeking care in the ED versus the clinic. CONCLUSION: Postdischarge factors, including perceived inability to access timely follow-up care and uncertainty and fear about disease progression, are primary motivators for return to the ED. Many patients prefer hospital-based care because of increased convenience and timely results. Further work is needed to develop alternative pathways for patients to ask questions and seek guidance when and where they want.

Hidden in plain sight: a crowdsourced public art contest to make automated external defibrillators more visible.

OBJECTIVES: We sought to explore the feasibility of using a crowdsourcing study to promote awareness about automated external defibrillators (AEDs) and their locations. METHODS: The Defibrillator Design Challenge was an online initiative that asked the public to create educational designs that would enhance AED visibility, which took place over 8 weeks, from February 6, 2014, to April 6, 2014. Participants were encouraged to vote for AED designs and share designs on social media for points. Using a mixed-methods study design, we measured participant demographics and motivations, design characteristics, dissemination, and Web site engagement. RESULTS: Over 8 weeks, there were 13 992 unique Web site visitors; 119 submitted designs and 2140 voted. The designs were shared 48 254 times on Facebook and Twitter. Most designers-voters reported that they participated to contribute to an important cause (44%) rather than to win money (0.8%). Design themes included: empowerment, location awareness, objects (e.g., wings, lightning, batteries, lifebuoys), and others. CONCLUSIONS: The Defibrillator Design Challenge engaged a broad audience to generate AED designs and foster awareness. This project provides a framework for using design and contest architecture to promote health messages.

Experimental pain and opioid analgesia in volunteers at high risk for obstructive sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) is characterized by recurrent nocturnal hypoxia and sleep disruption. Sleep fragmentation caused hyperalgesia in volunteers, while nocturnal hypoxemia enhanced morphine analgesic potency in children with OSA. This evidence directly relates to surgical OSA patients who are at risk for airway compromise due to postoperative use of opioids. Using accepted experimental pain models, we characterized pain processing and opioid analgesia in male volunteers recruited based on their risk for OSA. METHODS: After approval from the Intitutional Review Board and informed consent, we assessed heat and cold pain thresholds and tolerances in volunteers after overnight polysomnography (PSG). Three pro-inflammatory and 3 hypoxia markers were determined in the serum. Pain tests were performed at baseline, placebo, and two effect site concentrations of remifentanil (1 and 2 µg/ml), an µ-opioid agonist. Linear mixed effects regression models were employed to evaluate the association of 3 PSG descriptors [wake after sleep onset, number of sleep stage shifts, and lowest oxyhemoglobin saturation (SaO(2)) during sleep] and all serum markers with pain thresholds and tolerances at baseline, as well as their changes under remifentanil. RESULTS: Forty-three volunteers (12 normal and 31 with a PSG-based diagnosis of OSA) were included in the analysis. The lower nadir SaO(2) and higher insulin growth factor binding protein-1 (IGFBP-1) were associated with higher analgesic sensitivity to remifentanil (SaO(2), P = 0.0440; IGFBP-1, P = 0.0013). Other pro-inflammatory mediators like interleukin-1ß and tumor necrosis factor-α (TNF-α) were associated with an enhanced sensitivity to the opioid analgesic effect (IL-1ß, P = 0.0218; TNF-α, P = 0.0276). CONCLUSIONS: Nocturnal hypoxemia in subjects at high risk for OSA was associated with an increased potency of opioid analgesia. A serum hypoxia marker (IGFBP-1) was associated with hypoalgesia and increased potency to opioid analgesia; other pro-inflammatory mediators also predicted an enhanced opioid potency. TRIAL REGISTRATION: Clinicaltrials.gov NCT00672737.

The effect of relaxin on the female anterior cruciate ligament: Analysis of mechanical properties in an animal model.

BACKGROUND: The peptide hormone relaxin, found in pregnant and non-pregnant females, has been shown to have collagenolytic effects on ligamentous tissue. Relaxin receptors have recently been identified on the human female anterior cruciate ligament (ACL). Relaxin may affect the load bearing properties of the female ACL and contribute to non-contact ACL injuries. HYPOTHESIS: The administration of recombinant relaxin+/-estrogen will lead to a significant decrease in ACL integrity in the guinea pig model. STUDY DESIGN: Controlled laboratory study. METHODS: Adult female guinea pigs were divided into three experimental groups. Group 1 (n=4) was administered only 20 microg/h of recombinant porcine relaxin for 3 weeks. Group 2 (n=4) was administered 20 microg/h of recombinant porcine relaxin and 5 microg/h of estradiol for 3 weeks. Group 3 (n=4) served as both a normal control before surgical transection of the ACL and a positive ACL tear control after transection. All hormones were administered using separate implanted osmotic pumps. ACL laxity was tested by implanting radio-opaque markers in the femur and tibia of each leg. After applying a standard anterior force (22 N), the distance between markers was measured radiographically at day 0 and day 21. The animals were then sacrificed and the ACL's were analyzed for load-to-failure using a material testing machine. RESULTS: Load-to-failure testing indicated that animals treated with relaxin only had significantly weaker ACL's (micro=40.4 N, p=0.001) compared to controls (micro=64.1 N). The relaxin+estrogen group (micro=32.7 N) was also significantly weaker than controls (p=0.007). There were no statistical differences between relaxin and relaxin+estrogen groups. Both relaxin only and relaxin+estrogen groups showed an increase in anterior translation of the tibia after 3 weeks of infusion, but it did not achieve statistical significance. CONCLUSIONS: Relaxin significantly alters the mechanical properties of the ACL in an animal model. CLINICAL RELEVANCE: The effects of relaxin, possibly in conjunction with estrogen, may contribute to a comprehensive etiology for human female non-contact ACL injuries.