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OBJECTIVES The effectiveness of proximal thoracic aortic aneurysm (TAA) surgery in preventing acute aortic syndromes, such as dissection and rupture, is unknown at the populational level. This study evaluated trends in acute aortic syndrome operation incidence relative to proximal aortic surgical volume in the USA. METHODS A retrospective analysis of the National Inpatient Sample in 2005–2014 was performed. Acute aortic syndrome and TAA were identified with International Classification of Diseases, 9th edition diagnosis codes. Proximal aortic surgery was defined as the diagnosis of acute aortic syndrome or TAA with an aortic procedure and either cardioplegia, cardiopulmonary bypass or other cardiac operation. Annual rates of acute aortic syndrome surgery and proximal thoracic aneurysm surgery were adjusted for US population. Trends were evaluated using linear regression. RESULTS We identified 38 442 operations for acute aortic diagnoses and 74 953 operations for TAAs. Case volume for acute aortic syndromes increased from 0.93 to 1.63 per 100 000 (P = 0.001), and aneurysm surgery increased from 1.75 to 3.19 per 100 000 (P < 0.001). Patient and hospital characteristics differed between acute aortic and aneurysm operations, with black patients being most notably underrepresented in the aneurysm population (4.9% vs 17.0%, P < 0.001). CONCLUSIONS Acute aortic syndrome operative volume increased from 2005 to 2014 despite increasing rates of proximal aortic aneurysm surgery. Patient characteristic discrepancies were observed between the 2 groups of hospitalizations, highlighting the need for continued efforts to minimize sociodemographic disparities.

Magnetic resonance (MR) images and computed tomograms of 25 patients with head trauma were compared. MR proved to be superior in many ways for demonstrating extracerebral as well as intracerebral traumatic lesions. Isodense subdural hematomas, which present a diagnostic dilemma on CT images were clearly seen on MR, regardless of their varying CT densities. In a case of epidural hematoma, the dura mater was shown directly as nearly devoid of signal on MR. Direct coronal images provided excellent visualization of extracerebral collections along the peritentorial space and subtemporal area. In a patient with intracerebral hematoma, CT failed to demonstrate residual parenchymal changes in a 3-month follow-up study, but MR clearly depicted the abnormalities. The superiority of MR over CT was also well illustrated in a patient with post-traumatic osteomyelitis of the calvarium.

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Background Screening protocols do not exist for ascending thoracic aortic aneurysms (ATAAs). A risk prediction algorithm may aid targeted screening of patients with an undiagnosed ATAA to prevent aortic dissection. We aimed to develop and validate a risk model to identify those at increased risk of having an ATAA, based on readily available clinical information. Methods and Results This is a cross‐sectional study of computed tomography scans involving the chest at a tertiary care center on unique patients aged 50 to 85 years between 2013 and 2016. These criteria yielded 21 325 computed tomography scans. The double‐oblique technique was used to measure the ascending thoracic aorta, and an ATAA was defined as >40 mm in diameter. A logistic regression model was fitted for the risk of ATAA, with readily available demographics and comorbidity variables. Model performance was characterized by discrimination and calibration metrics via split‐sample testing. Among the 21 325 patients, there were 560 (2.6%) patients with an ATAA. The multivariable model demonstrated that older age, higher body surface area, history of arrhythmia, aortic valve disease, hypertension, and family history of aortic aneurysm were associated with increased risk of an ATAA, whereas female sex and diabetes were associated with a lower risk of an ATAA. The C statistic of the model was 0.723±0.016. The regression coefficients were transformed to scores that allow for point‐of‐care calculation of patients' risk. Conclusions We developed and internally validated a model to predict patients' risk of having an ATAA based on demographic and clinical characteristics. This algorithm may guide the targeted screening of an undiagnosed ATAA.