The Local Market of Major Teaching Hospitals.

OBJECTIVE: The purpose of this study was to describe the local communities served by major teaching hospitals. METHODS: Using a dataset of hospitals around the United States provided by the Association of American Medical Colleges, we identified major teaching hospitals (MTHs) using the Association of American Medical Colleges' definition of those with an intern-to-resident bed ratio above 0.25 and more than 100 beds. We defined the local geographic market surrounding these hospitals as the Dartmouth Atlas hospital service area (HSA). Using MATLAB R2020b software, data from each ZIP Code Tabulation Area from the US Census Bureau's 2019 American Community Survey 5-Year Estimate Data tables were grouped by HSA and attributed to each MTH. One-sample t tests were used to evaluate for statistical differences between the HSAs and the US average data. We further stratified the data into regions as defined by the US Census Bureau: West, Midwest, Northeast, and South. One-sample t tests were used to evaluate for statistical differences between MTH HSA regional populations with their respective US regional population. RESULTS: The local population surrounding 299 unique MTHs covered 180 HSAs and was 57% White, 51% female, 14% older than 65 years old, 37% with public insurance coverage, 12% with any disability, and 40% with at least a bachelor's degree. Compared with the overall US population, HSAs surrounding MTHs had higher percentages of female residents, Black/African American residents, and residents enrolled in Medicare. In contrast, these communities also showed higher average household and per capita income, higher percentages of bachelor's degree attainment, and lower rates of any disability or Medicaid insurance. CONCLUSIONS: Our analysis suggests that the local population surrounding MTHs is representative of the wide-ranging ethnic and economic diversity of the US population that is advantaged in some ways and disadvantaged in others. MTHs continue to play an important role in caring for a diverse population. To support and improve policy related to the reimbursement of uncompensated care and care of underserved populations, researchers and policy makers must work to better delineate and make transparent local hospital markets.

Optimizing the Synergistic Potential of Pseudo-Labels from Radiology Notes and Annotated Ground Truth in Identifying Pulmonary Opacities on Chest Radiographs for Early Detection of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury, hallmarks of which are bilateral radiographic opacities. Studies have shown that early recognition of ARDS could reduce severity and lethal clinical sequela. A Convolutional Neural Network (CNN) model that can identify bilateral pulmonary opacities on chest x-ray (CXR) images can aid early ARDS recognition. Obtaining large datasets with ground truth labels to train CNNs is challenging, as medical image annotation requires clinical expertise and meticulous consideration. In this work, we implement a natural language processing pipeline that extracts pseudo-labels CXR images by parsing radiology notes for abnormal findings. We obtain ground-truth annotations from clinicians for the presence of pulmonary opacities for a subset of these images. A knowledge distillation-based teacher-student training framework is implemented to leverage the larger dataset with noisy pseudo-labels. Our results show an AUC of 0.93 (95%CI 0.92-0.94) for the prediction of bilateral opacities on chest radiographs.

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or 'equivocal' images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the 'equivocal' class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems.

Hair-Dye-Related Accidental Poisoning and Death.

Para phenylenediamine (PPD) is a common component of hair dye as well as temporary tattoos and is a well-known cause of type 4 hypersensitivity reactions from topical exposure. While there have been several cases reported in the literature describing toxicities following ingestion, there are a paucity of reports of severe systemic disease following topical exposure. Cases of PPD ingestion have been reported to present with angioedema-like reactions, often progressing to rhabdomyolysis and renal failure. To our knowledge, there have only been two reported cases of severe reactions following topical exposure to PPD. We present a case of a 59-year-old man with topical exposure to hair dye who presented with an angioedema-like reaction shortly after topical exposure to PPD containing hair dye that rapidly progressed to rhabdomyolysis, renal failure, and eventually death.

Frequency and outcomes of gastrointestinal symptoms in patients with Corona Virus Disease-19.

OBJECTIVES: To characterize the frequency and association of gastrointestinal (GI) symptoms with outcomes in patients with corona virus disease 2019  (COVID-19) admitted to the hospital. METHODS: Records were retrospectively collected from patients admitted to a tertiary care center in Washington, D.C., with confirmed COVID-19 from March 15, 2020  to July 15, 2020. After adjusting for clinical demographics and comorbidities, multivariate logistic regression analysis was performed. RESULTS: The most common  presenting symptoms of COVID-19 in patients that were admitted to the hospital were cough (38.4%), shortness of breath (37.5%), and fever (34.3%), followed by GI symptoms in 25.9% of patients. The most common GI symptom was diarrhea (12.8%) followed by nausea or vomiting (10.5%), decreased appetite (9.3%), and abdominal pain (3.8%). Patients with diarrhea were more likely to die (odds ratio [OR] 2.750; p = 0.006; confidence interval [CI] 1.329-5.688), be admitted to the intensive care unit (ICU) (OR 2.242; p = 0.019; CI 1.139-4.413), and be intubated (OR 3.155; p = 0.002; CI 1.535-6.487). Additional outcomes analyzed were need for vasopressors, presence of shock, and acute kidney injury. Patients with  diarrhea  were 2.738 (p = 0.007; CI 1.325-5.658), 2.467 (p = 0.013; CI 1.209-5.035), and 2.694 (p = 0.007; CI 1.305-5.561) times more likely to experience these outcomes, respectively. CONCLUSIONS: Screening questions should be expanded to include common GI symptoms in patients with COVID-19. Health care providers should note whether their patient is presenting with diarrhea due to the potential implications on disease severity and outcomes.

Emphysematous osteomyelitis of the clavicle: a pleural process?

Emphysematous osteomyelitis (EO) is a rare infection associated with intraosseous gas. EO is an often fatal disease with an estimated 34% mortality. We present a case of a 63-year-old man with sternoclavicular EO with pleural involvement and significant subcutaneous emphysema diagnosed by CT. Extension of intraosseous gas into the pleural cavity is an extremely interesting presentation that has not been previously reported. The patient underwent a multidisciplinary treatment approach with surgical debridement and an extended antibiotic course. Intraoperative cultures of the pectoralis muscle and bone biopsy grew pan-sensitive Escherichia coli Prompt recognition and treatment are paramount to avoid a potentially fatal outcome. A review of the literature of the previous 46 cases of EO is presented for associated risk factors, the role of surgical management and antibiotic therapy.

Catheter-Directed Thrombolytic Therapy in the Management of Massive Pulmonary Embolism in Pregnancy.

BACKGROUND: Pulmonary embolism is one of the most common causes of maternal mortality and can be classified into low-risk, submassive, and massive. Three treatment options exist for massive pulmonary embolism in nonpregnant patients: thrombolysis, percutaneous catheter-based embolectomy, or surgical embolectomy; however, there is limited evidence to guide management of pulmonary embolism in pregnancy. CASE: We present a case of massive pulmonary embolism in pregnancy. Our patient presented with pulmonary embolism with biomarker and imaging evidence of right heart strain. She developed hypotension and an increased oxygen requirement and was subsequently treated with ultrasound-assisted catheter-directed thrombolysis. She was discharged on low-molecular-weight heparin and had a normal spontaneous vaginal delivery at 39 weeks of gestation. CONCLUSIONS: Catheter-directed thrombolysis is preferred to systemic thrombolytic therapy in pregnant patients with massive pulmonary embolism requiring thrombus removal.