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ObjectiveRecent cohort studies have identified obesity as a risk factor for poor outcomes in coronavirus disease 2019 (COVID-19). To further explore the relationship between obesity and critical illness in COVID-19, the association of BMI with baseline demographic and intensive care unit (ICU) parameters, laboratory values, and outcomes in a critically ill patient cohort was examined.MethodsIn this retrospective study, the first 277 consecutive patients admitted to Massachusetts General Hospital ICUs with laboratory-confirmed COVID-19 were examined. BMI class, initial ICU laboratory values, physiologic characteristics including gas exchange and ventilatory mechanics, and ICU interventions as clinically available were measured. Mortality, length of ICU admission, and duration of mechanical ventilation were also measured.ResultsThere was no difference found in respiratory system compliance or oxygenation between patients with and without obesity. Patients without obesity had higher initial ferritin and D-dimer levels than patients with obesity. Standard acute respiratory distress syndrome management, including prone ventilation, was equally distributed between BMI groups. There was no difference found in outcomes between BMI groups, including 30- and 60-day mortality and duration of mechanical ventilation.ConclusionsIn this cohort of critically ill patients with COVID-19, obesity was not associated with meaningful differences in respiratory physiology, inflammatory profile, or clinical outcomes.

Immunocompromised individuals (patients with cancer, diabetes, HIV/AIDS, transplant recipients) and pregnant women are at greater risk of complicated foodborne illness than the general population. Though rare, Campylobacter enteritis-associated acute pancreatitis has not been reported in an immunocompromised host to our knowledge. Herein, we describe a case of Campylobacter infection-associated pancreatitis in a renal transplant recipient. This case highlights the need for food safety education for the immunocompromised, emphasizes the role of health care providers in encouraging adherence to food safety guidelines, and stresses the need to maintain broad infectious differentials for immunocompromised patient populations, even for conditions which are not commonly associated with an infectious etiology. [ABSTRACT FROM AUTHOR]

The article presents a case study of a patient presenting with dyspnea and hemoptysis while on warfarin therapy, highlighting the importance of recognizing associated bleeding risks. Topics include the phenomenon of Black Bronchoscopy, describing its rarity and various etiologies, including infections, metabolic disorders, and malignancies, potential causes of black bronchoscopy such as occupational exposures, substance use, and malignancy.

Objective: The aim of this study was to develop and evaluate the performance of artificial intelligence (AI) models that can identify safe and dangerous zones of dissection, and anatomical landmarks during laparoscopic cholecystectomy (LC). Summary Background Data: Many adverse events during surgery occur due to errors in visual perception and judgment leading to misinterpretation of anatomy. Deep learning, a subfield of AI, can potentially be used to provide real-time guidance intraoperatively. Methods: Deep learning models were developed and trained to identify safe (Go) and dangerous (No-Go) zones of dissection, liver, gallbladder, and hepatocystic triangle during LC. Annotations were performed by 4 high-volume surgeons. AI predictions were evaluated using 10-fold cross-validation against annotations by expert surgeons. Primary outcomes were intersection- over-union (IOU) and F1 score (validated spatial correlation indices), and secondary outcomes were pixel-wise accuracy, sensitivity, specificity, ± standard deviation. Results: AI models were trained on 2627 random frames from 290 LC videos, procured from 37 countries, 136 institutions, and 153 surgeons. Mean IOU, F1 score, accuracy, sensitivity, and specificity for the AI to identify Go zones were 0.53 (±0.24), 0.70 (±0.28), 0.94 (±0.05), 0.69 (±0.20). and 0.94 (±0.03), respectively. For No-Go zones, these metrics were 0.71 (±0.29), 0.83 (±0.31), 0.95 (±0.06), 0.80 (±0.21), and 0.98 (±0.05), respectively. Mean IOU for identification of the liver, gallbladder, and hepatocystic triangle were: 0.86 (±0.12), 0.72 (±0.19), and 0.65 (±0.22), respectively. Conclusions: AI can be used to identify anatomy within the surgical field. This technology may eventually be used to provide real-time guidance and minimize the risk of adverse events. [ABSTRACT FROM AUTHOR]

The article focuses on SVC syndrome, which is associated with lung malignancy and leads to neovascularization and collateralization of vascular beds, causing unusual radiographic findings that may be mistaken for other conditions. It mentions the case study discusses a 72-year-old female with SVC syndrome and hepatic pseudolesion due to collateralization of the superior vein of Sappey, highlighting the importance of recognizing these findings for timely diagnosis.