Objective: Abdominal aortic aneurysm (AAA)-related mortality is decreasing since the introduction of endovascular aneurysm repair, but mortality from AAAs by age, sex, and race has not been recently reported.Methods: We abstracted crude mortality rates by International Classification of Diseases, Tenth Revision code from the Centers for Disease Control and Prevention Wonder database from 1999 to 2015 and stratified these by year, diagnosis, age, sex, race, and state.Results: We found that overall AAA-related mortality decreased steadily from a maximum 2.83 annual deaths per 100,000 patients in 1999 to 1.2 deaths per 100,000 patients in 2015. This decrease occurred among intact and ruptured AAAs (Fig 1) and among white men, black men, white women, and black women. AAA-related mortality reached a nadir in 2015, with rates by age, sex, and race summarized in Table . Notably, crude AAA-related mortality was higher in males than in females and higher in white compared with black compared with Asian Americans. Mortality similarly increased with age. Finally, mortality from intact and ruptured (Fig 2) aneurysms varied dramatically across the United States during the study period.Conclusions: AAA-related mortality continues to decline dramatically, from both intact and ruptured AAA, which is likely secondary to the increased use of endovascular aneurysm repair. This decrease occurs across all gender and racial groups, but there remains a geographic disparity in AAA-related mortality that should be addressed in future studies.
Background: Groin wound infections represent a substantial source of patients' morbidity and resource utilization. Definitions and reporting times of groin infections are poorly standardized, which limits our understanding of the true scope of the problem and potentially leads to event under-reporting. Our objective was to investigate the timing and variation of groin wound complications after vascular surgery. Methods:We reviewed all patients who underwent vascular surgery with a groin incision at our institution during 2013 (N ¼ 256; 32% female; mean age, 68.8 years). We analyzed patient-and procedure-level variables. Our primary outcome was any groin complication within 180 days. We classified groin-related events as major (hospital readmission or reoperation for groin wound) or minor (wound opened in clinic, initiation of antibiotics specifically for a groin wound, or new groin hematoma or wound drainage).Results: The Kaplan-Meier estimated rate of groin complications at 180 days was 23% (n ¼ 53/256); 29 (54%) were major and 24 (46%) were minor. The Kaplan-Meier 30-day event rate was 13% for any complication and only 3% for major complications, indicating that most events occurring within the first 30 days did not require readmission or reoperation. By 180 days, the overall complication rate rose to 23% and the major event rate to 14%, indicating that nearly all complications occurring after 30 days required readmission or reoperation. Those with a groin complication more commonly had tissue loss (23% vs 12%; P ¼ .05), underwent infrainguinal bypass (42% vs 22%; P¼.004), had a redo incision (32% vs 18%; P ¼ .03), and had a longer operation (77% vs 65% surgery >200 minutes; P ¼ .07). There were no significant differences in patients' comorbidities, skin closure, dressing type, prosthetic implants, hemostatic agents, or discharge status.Conclusions: Whereas >20% of patients suffered a groin complication, nearly half of these events occurred after 30 days. Standardized reporting measures limited to 30-day events or infection definitions that are limited to the need for antibiotic use may misrepresent the true infection rate and thus highlight the need for uniform reporting standards.
Background: Deep learning has great potential to assist with detecting and triaging critical findings such as pneumoperitoneum on medical images. To be clinically useful, the performance of this technology still needs to be validated for generalizability across different types of imaging systems.Purpose: To evaluate the performance of deep learning to detect pneumoperitoneum in chest radiographs and to conduct sensitivity and specificity analysis of common deep learning architectures across different types of imaging systems at various institutions.Materials and Methods: This retrospective study included 1,287 chest Xray images of patients who underwent initial chest radiography at 13 different hospitals between 2011 and 2019. State-of-the-art deep learning models were trained on a subset of this dataset, and the automated classification performance was evaluated on the rest of the dataset by measuring the AUC, sensitivity, and specificity. Furthermore, the generalizability of these deep learning models was assessed by stratifying the test dataset according to the type of imaging systems utilized.Results: All deep learning models performed well for identifying radiographs
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