Faculty Bibliography

Common in-flight emergencies include syncope, respiratory symptoms, nausea/vomiting, cardiac symptoms, and seizures. Flight conditions, such as changes in air pressure and humidity, can exacerbate existing chronic medical conditions. In 2017, US airlines carried 849.3 million passengers. Undoubtedly, there were many requests for in-flight medical assistance. Whenever a medical event occurs, it is standard procedure that an announcement be made by a flight attendant, requesting medical personnel to identify themselves. The 1998 Aviation Medical Assistance Act provides liability protection for a healthcare professional (HCP) acting as a good Samaritan. Nevertheless, HCPs may initially experience trepidation providing care in an aircraft. They may be unaware that a first aid kit, a emergency medical kit, and an automatic external defibrillator are on every plane. Flight crews have been trained in cardiopulmonary resuscitation, and a support system, including a ground-based consultation service, is available to provide radio assistance from an on-call physician. When multiple HCPs volunteer, the most experienced should assume leadership of care. After evaluating the ill passenger, the HCP communicates the assessment to the crew and, when necessary, to the ground-based physician. The goal of in-flight care is to medically stabilize the ill passenger and facilitate the individual's arrival at the scheduled destination for continued medical care. When unable to stabilize the passenger's condition, the decision to divert the plane rests with the flight's captain. Our article helps HCPs to best understand their resources, structured support, liability, and role during an in-flight medical event. With this knowledge of resources, a good Samaritan can confidently attend to an ill airline passenger in flight.

Antithrombotic therapy for stroke prevention in patients with atrial fibrillation (AF) has dramatically shifted from warfarin, a vitamin K antagonist, to the direct oral anticoagulants (DOACs) such as dabigatran, apixaban, and rivaroxaban. In patients with contraindications to oral anticoagulation, left atrial appendage occlusion (LAAO) devices, such as the Watchmanâ„¢ device, may be considered; however, temporary postimplantation antithrombotic therapy is still a recommended practice. We present a case of complex antithrombotic management, post LAAO device implantation, designed to avoid drug interactions with concomitant rifampin use and remained necessary secondary to subsequent device leak. This case highlights the challenges of antithrombotic therapy post LAAO device placement in a complex, but representative, patient.

BACKGROUND:Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. The ability to predict obesity before age five could be a useful tool, allowing prevention strategies to focus on high risk children. The few existing prediction models for obesity in childhood have primarily employed data from longitudinal cohort studies, relying on difficult to collect data that are not readily available to all practitioners. Instead, we utilized real-world unaugmented electronic health record (EHR) data from the first two years of life to predict obesity status at age five, an approach not yet taken in pediatric obesity research. METHODS AND FINDINGS/RESULTS:We trained a variety of machine learning algorithms to perform both binary classification and regression. Following previous studies demonstrating different obesity determinants for boys and girls, we similarly developed separate models for both groups. In each of the separate models for boys and girls we found that weight for length z-score, BMI between 19 and 24 months, and the last BMI measure recorded before age two were the most important features for prediction. The best performing models were able to predict obesity with an Area Under the Receiver Operator Characteristic Curve (AUC) of 81.7% for girls and 76.1% for boys. CONCLUSIONS:We were able to predict obesity at age five using EHR data with an AUC comparable to cohort-based studies, reducing the need for investment in additional data collection. Our results suggest that machine learning approaches for predicting future childhood obesity using EHR data could improve the ability of clinicians and researchers to drive future policy, intervention design, and the decision-making process in a clinical setting.

Purpose: To explore the experiences of transgender and gender nonbinary (TGNB) medical students and physicians in the United States. Methods: The authors conducted a 79-item online survey using Likert-type and open-ended questions to assess the experiences of TGNB-identified U.S. medical students and physicians. Variables included demographic data, disclosure of TGNB status, exposure to transphobia, and descriptions of educational and professional experiences. Recruitment was conducted using snowball sampling through Lesbian, Gay, Bisexual, Transgender, Queer professional groups, list-servs, and social media. The survey was open from June 2017 through November 2017. Results: Respondents included 21 students and 15 physicians (10 transgender women, 10 transgender men, and 16 nonbinary participants). Half (50%; 18) of the participants and 60% (9) of physicians had not disclosed their TGNB identity to their medical school or residency program, respectively. Respondents faced barriers on the basis of gender identity/expression when applying to medical school (22%; 11) and residency (43%; 6). More than three-quarters (78%; 28) of participants censored speech and/or mannerisms half of the time or more at work/school to avoid unintentional disclosure of their TGNB status. More than two-thirds (69%; 25) heard derogatory comments about TGNB individuals at medical school, in residency, or in practice, while 33% (12) witnessed discriminatory care of a TGNB patient. Conclusion: TGNB medical students and physicians faced significant barriers during medical training, including having to hide their identities and witnessing anti-TGNB stigma and discrimination. This study, the first to exclusively assess experiences of TGNB medical students and physicians, reveals that significant disparities still exist on the basis of gender identity.