Point-of-Care Ultrasound Overview and Curriculum Implementation in Internal Medicine Residency Training Programs in the United States.

Point-of-care ultrasonography (POCUS) augments physical examination and expedites diagnostic care and clinical decision-making. The use of POCUS in internal medicine (IM) appears inconsistent despite its commendable benefits. It is not fully incorporated into the IM residency core competency skills or academic curriculum. This narrative literature review explores the benefits of POCUS and evaluates the need for an IM-focused POCUS curriculum. The obstacles and a proposed curriculum are also described.

Use of Point-of-Care Ultrasound for Early Identification of Acute Aortic Root Dissection.

Point-of-care ultrasound (POCUS) is becoming a frequently utilized imaging tool in the emergency department (ED) as it can aid in early diagnosis of many pathologies. This is a case report of a 55-year-old male who presented to the emergency department by ambulance for sudden onset chest pain followed by a syncopal episode. Point-of-care echocardiogram revealed a large pericardial effusion with a significantly dilated aortic root, concerning for aortic dissection. Patient was emergently taken for a computed tomography (CT) scan, which was only remarkable for an ascending thoracic aortic aneurysm but failed to show an aortic dissection flap. On repeat POCUS, a dissection intimal flap, large pericardial effusion with tamponade physiology, and aortic regurgitation were identified and later confirmed on transesophageal echocardiogram. This case report details a rare pathology that was correctly identified on initial POCUS before it was seen on CT scan.

Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach

IntroductionWithin the UK, COVID-19 has contributed towards over 103,000 deaths. Multiple risk factors for COVID-19 have been identified including various demographics, co-morbidities, biochemical parameters, and physical assessment findings. However, using this vast data to improve clinical care has proven challenging. Aimsto develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, to aid risk-stratification and earlier clinical decision-making. MethodsAnonymized data regarding 44 independent predictor variables of 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-controlled analysis. Primary outcomes included inpatient mortality, level of ventilatory support and oxygen therapy required, and duration of inpatient treatment. Secondary pulmonary embolism was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were created using Bayesian Networks, and cross-validated. ResultsOur multivariable models were able to predict, using feature selected risk factors, the probability of inpatient mortality (F1 score 83.7%, PPV 82%, NPV 67.9%); level of ventilatory support required (F1 score varies from 55.8% "High-flow Oxygen level" to 71.5% "ITU-Admission level"); duration of inpatient treatment (varies from 46.7% for "[≥] 2 days but < 3 days" to 69.8% "[≤] 1 day"); and risk of pulmonary embolism sequelae (F1 score 85.8%, PPV of 83.7%, and NPV of 80.9%). ConclusionOverall, our findings demonstrate reliable, multivariable predictive models for 4 outcomes, that utilize readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as clinical decision-making tools. HighlightsO_LIUsing COVID-19 risk-factor data to assist clinical decision making is a challenge C_LIO_LIAnonymous data from 355 COVID-19 inpatients was collected & balanced C_LIO_LIKey independent variables were feature selected for 4 different outcomes C_LIO_LIAccurate, multi-variable predictive models were computed, using Bayesian Networks C_LIO_LIFuture research should externally validate our models & demonstrate clinical utility C_LI

A prospective evaluation of emergency department bedside ultrasonography for the detection of acute cholecystitis.

STUDY OBJECTIVE: We assess the diagnostic accuracy of emergency physician-performed bedside ultrasonography and radiology ultrasonography for the detection of cholecystitis, as determined by surgical pathology. METHODS: We conducted a prospective, observational study on a convenience sample of emergency department (ED) patients presenting with suspected cholecystitis from May 2006 to February 2008. Bedside gallbladder ultrasonography was performed by emergency medicine residents and attending physicians at an academic institution. Emergency physicians assessed for gallstones, a sonographic Murphy's sign, gallbladder wall thickness, and pericholecystic fluid, and the findings were recorded before formal imaging. The test characteristics of bedside and radiology ultrasonography were determined by comparing their respective results to pathology reports and clinical follow-up at 2 weeks. RESULTS: Of the 193 patients enrolled, 189 were evaluated by bedside ultrasonography. Forty-three emergency physicians conducted the ultrasonography, and each physician performed a median of 2 tests. After the bedside ultrasonography, 125 patients received additional radiology ultrasonography. Twenty-six patients underwent cholecystectomy, 23 had pathology-confirmed cholecystitis, and 163 were discharged home to follow-up. Twenty-five were excluded (23 lost to follow-up and 2 unavailable pathology). The test characteristics of bedside ultrasonography were sensitivity 87% (95% confidence interval [CI] 66% to 97%), specificity 82% (95% CI 74% to 88%), positive likelihood ratio 4.7 (95% CI 3.2 to 6.9), negative likelihood ratio 0.16 (95% CI 0.06 to 0.46), positive predictive value 44% (95% CI 29% to 59%), and negative predictive value 97% (95% CI 93% to 99%). The test characteristics of radiology ultrasonography were sensitivity 83% (95% CI 61% to 95%), specificity 86% (95% CI 77% to 92%), positive likelihood ratio 5.7 (95% CI 3.3 to 9.8), negative likelihood ratio 0.20 (95% CI 0.08 to 0.50), positive predictive value 59% (95% CI 41% to 76%), and negative predictive value 95% (95% CI 88% to 99%). CONCLUSION: The test characteristics of emergency physician-performed bedside ultrasonography for the detection of acute cholecystitis are similar to the test characteristics of radiology ultrasonography. Patients with a negative ED bedside ultrasonography result are unlikely to require cholecystectomy or admission for cholecystitis within 2 weeks of their initial presentation.