Emphysematous aortitis is a rare but lethal form of infectious vasculitis. This condition was found incidentally on computed tomography of the chest during the evaluation of a patient presenting with pneumonia coincident with adynamic ileus. The patient did not have a history of malignancy. While colon cancer could not be ruled out, it is possible that ileus may have contributed to or resulted in bacterial translocation in this case. Appropriate investigations and empirical therapy against Clostridium septicum should be initiated in the presence of clinical and radiological findings suggestive of emphysematous aortitis.
In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.
Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.
BackgroundAntibiotic-resistant infections are one of the greatest public health issues with more than 2 million infections and 23,000 deaths per year in the United States. Reducing inappropriate antibiotic use is essential to reduce both antibiotic resistance and adverse events. The most important modifiable risk factor for antibiotic resistance is inappropriate prescribing of antibiotics. At least 30% of outpatient antibiotic prescriptions in the United States are unnecessary. We aimed to pilot our outpatient antimicrobial stewardship initiative to track and reduce antibiotic prescriptions among adult patients presenting with common acute respiratory infections in our hospital’s outpatient primary care settings.MethodsA retrospective and prospective cohort study from October, 2017 to March, 2019. Implemented a robust outpatient antimicrobial stewardship initiative with a dedicated team and data analyst based on CDC core elements for outpatient antimicrobial stewardship and a prior UHF initiative. Data of common respiratory tract infections and the respective rates of antibiotic prescriptions from 3 adult primary care sites were collected from the EHR. Serials of educational interventions were performed between June, 2018 to September, 2018. We disseminated resources from the CDC and DOH like brochures, posters, viral prescription pads, pocket guidelines, grand rounds and electronic lectures for providers and periodic provider feedback reports.ResultsOur findings revealed that the physician compliance rate of antibiotics not prescribed for common respiratory tract infections remarkably improved from 72% to 85% after implementing our interventions (Figure 1). The chi-square test showed 40, and P value is 0.000034 which is less than 0.05. Thus, we are 95% confident that there is a significant association between our interventions and reduction of inappropriate antibiotic use (Figure 2).ConclusionIntroduction of a robust and multifaceted Outpatient Antimicrobial Stewardship initiative with a dedicated team can substantially decrease outpatient antibiotic prescription rates for respiratory tract infections in metropolitan community hospital-based primary care settings.
Disclosures
All authors: No reported disclosures.
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