Contemporary national outcomes of hyperbaric oxygen therapy in necrotizing soft tissue infections.

BACKGROUND: The role of hyperbaric oxygen therapy (HBOT) in necrotizing soft tissue infections (NSTI) is mainly based on small retrospective studies. A previous study using the 1998-2009 National Inpatient Sample (NIS) found HBOT to be associated with decreased mortality in NSTI. Given the argument of advancements in critical care, we aimed to investigate the continued role of HBOT in NSTI. METHODS: The 2012-2020 National Inpatient Sample (NIS) was queried for NSTI admissions who received surgery. 60,481 patients between 2012-2020 were included, 600 (<1%) underwent HBOT. Primary outcome was in-hospital mortality. Secondary outcomes included amputation, hospital length of stay, and costs. A multivariate model was constructed to account for baseline differences in groups. RESULTS: Age, gender, and comorbidities were similar between the two groups. On bivariate comparison, the HBOT group had lower mortality rate (<2% vs 5.9%, p<0.001) and lower amputation rate (11.8% vs 18.3%, p<0.001) however, longer lengths of stay (16.9 days vs 14.6 days, p<0.001) and higher costs ($54,000 vs $46,000, p<0.001). After multivariate analysis, HBOT was associated with decreased mortality (Adjusted Odds Ratio (AOR) 0.22, 95% CI 0.09-0.53, P<0.001) and lower risk of amputation (AOR 0.73, 95% CI 0.55-0.96, P = 0.03). HBO was associated with longer stays by 1.6 days (95% CI 0.4-2.7 days) and increased costs by $7,800 (95% CI $2,200-$13,300), they also had significantly lower risks of non-home discharges (AOR 0.79, 95%CI 0.65-0.96). CONCLUSIONS: After correction for differences, HBOT was associated with decreased mortality, amputations, and non-home discharges in NSTI with the tradeoff of increase to costs and length of stay.

Biatrial Volumetric Assessment by Non-ECG-Gated CT Pulmonary Angiography Correlated with Transthoracic Echocardiography in Patients with Normal Diastology.

Atrial size is a predictor of cardiovascular mortality. Non-ECG-gated computed tomography pulmonary angiography (CTPA) is a common test for cardiopulmonary evaluation but normative values for biatrial volumes are lacking. We derived normal CT biatrial volumes using manual and semiautomated segmentation with contemporaneous transthoracic echocardiography (TTE) to confirm normal diastology. Thirty-five consecutive cases in sinus rhythm with no history of cardio-vascular, renal, or pulmonary disease and normal diastolic function were selected. Planimetric CTPA measurements were compared to TTE volumes measured using area length method. TTE and CTPA derived normal LAVi and RAVi were 27 + 5 and 20 + 6 mL/m2, and 30 + 8 and 29 + 9 mL/m2, respectively. Bland-Altman analysis revealed an underestimation of biatrial volumes by TTE. TTE-CT mean biases for LAV and RAV were -5.7 + 12.0 mL and -16.2 + 14.8 mL, respectively. The CT intraclass correlation coefficients (ICC 95% CI) for LA and RA volumes were 0.99 (0.96-1.00) and 0.96 (0.76-0.99), respectively. There was excellent correlation (p < 0.001) between the semiautomated and manual measurements for LA (r 0.99, 95% CI 0.98-0.99) and RA (r 0.99, 95% CI 0.99-1.00). Atrial volumetric assessment on CTPA is easy and reproducible and can provide additional metric in cardiopulmonary assessment.

Abnormal Pulmonary Venous Filling: An Adjunct Feature in the Computed Tomography Pulmonary Angiogram Assessment of Chronic Thromboembolic Pulmonary Hypertension.

Background Hypodense filling defects within the pulmonary veins on computed tomography described as pulmonary vein sign (PVS) have been noted in acute pulmonary embolism and shown to be associated with poor prognosis. We evaluated venous flow abnormalities in chronic thromboembolic pulmonary hypertension (CTEPH) to determine its usefulness in the computed tomography assessment of CTEPH. Methods and Results Blinded retrospective computed tomography analysis of 50 proximal CTEPH cases and 3 control groups-50 acute pulmonary embolism, 50 nonthromboembolic cohort, and 50 pulmonary arterial hypertension. Venous flow reduction was assessed by the following: (1) presence of a filling defect of at least 2 cm in a pulmonary vein draining into the left atrium, and (2) left atrium attenuation (>160 Hounsfield units). PVS was most prevalent in CTEPH. Compared with all controls, sensitivity and specificity of PVS for CTEPH is 78.0% and 85.3% (95% CI, 64.0-88.5 and 78.6-90.6, respectively) versus 34.0% and 70.7% (95% CI, 21.2-48.8 and 62.7-77.8) in acute pulmonary embolism, 8.0% and 62% (95% CI, 2.2-19.2 and 53.7-69.8) in nonthromboembolic and 2.0% and 60% (95% CI, 0.1-10.7 and 51.7-67.9) in pulmonary arterial hypertension. In CTEPH, lobar and segmental arterial occlusive disease was most commonly associated with corresponding absent venous flow. PVS detection was highly reproducible (Kappa=0.96, 95% CI, 0.90-1.01, P<0.001). Conclusions PVS is easy to detect with higher sensitivity and specificity in CTEPH compared with acute pulmonary embolism and is not a feature of pulmonary arterial hypertension. Asymmetric enhancement of pulmonary veins may serve as an additional parameter in the computed tomography assessment of CTEPH and can be used to differentiate CTEPH from pulmonary arterial hypertension.

Diving after SARS-CoV-2 (COVID-19) infection: Fitness to dive assessment and medical guidance.

Scuba diving is a critical activity for commercial industry, military activities, research, and public safety, as well as a passion for many recreational divers. Physicians are expected to provide return-to-diving recommendations after SARS-CoV-2 (COVID-19) infection based upon the best available evidence, often drawn from experience with other, similar diseases. Scuba diving presents unique physiologic challenges to the body secondary to immersion, increased pressure and increased work of breathing. The long-term sequelae of COVID-19 are still unknown, but if they are proven to be similar to other coronaviruses (such as Middle East respiratory syndrome or SARS-CoV-1) they may result in long-term pulmonary and cardiac sequelae that impact divers' ability to safely return to scuba diving. This review considers available literature and the pathophysiology of COVID-19 as it relates to diving fitness, including current recommendations for similar illnesses, and proposes guidelines for evaluation of divers after COVID-19. The guidelines are based upon best available evidence about COVID-19, as well as past experience with determination of diving fitness. It is likely that all divers who have contracted COVID-19 will require a medical evaluation prior to return to diving with emphasis upon pulmonary and cardiac function as well as exercise capacity.

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.