TEVAR for complicated and uncomplicated type B aortic dissection-Systematic review and meta-analysis.

BACKGROUND: Type B aortic dissection (TBAD), is defined as a dissection involving the aorta distal to left subclavian artery with the ascending aorta and the aortic arch not affected. TBAD is classified due to the time frame and presence of complications. Complicated TBAD (co-TBAD) patients have a greater mortality rate than uncomplicated TBAD (un-TBAD) and thoracic endovascular aortic repair (TEVAR) is considered the gold-standard intervention for these clinical challenges. METHODS: We undertook a systematic review of the literature regarding TEVAR intervention in co-TBAD and un-TBAD. A comprehensive search was undertaken across four major databases and was evaluated and assessed until June 2020. RESULTS: A total of 16,104 patients were included in the study (7772 patients co-TBAD and 8352 un-TBAD). A significantly higher proportion of comorbidities were seen in co-TBAD patients compared with un-TBAD. Acute dissection was more frequent in the co-TBAD group (73.55% vs. 66.91%), while chronic dissection was more common in un-TBAD patients (33.8% vs. 70.73%). Postprocedure stroke was higher in co-TBAD (5.85% vs. 3.92%; p < .01), while postprocedural renal failure was higher in un-TBAD patients (7.23 vs. 11.38%; p < .01). No difference was observed in in-hospital mortality however the 30 days mortality was higher in the co-TBAD group. One-year survival was higher in the uncomplicated group but this difference was not observed in the 5-year survival. CONCLUSION: In our analysis we can appreciate that despite significantly higher comorbidities in the co-TBAD cohort, there was no difference in in-hospital mortality between the two groups and the 5-year survival did not have any difference.

The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery.

BACKGROUND: Artificial intelligence (AI) is a field involving computational simulation of human intelligence processes; these applications of deep learning could have implications in the specialty of emergency surgery (ES). ES is a rapidly advancing area, and this review will outline the most recent advances. METHODS: A literature search encompassing the uses of AI in surgery was conducted across large databases (Pubmed, OVID, SCOPUS). Two doctors (LR, CH) both collated relevant papers and appraised them. Papers included were published within the last 5 years, and a "snowball effect" used to collate further relevant literature. RESULTS: AI has been shown to provide value in predicting surgical outcomes and giving personalised patient risks based on inputted data. Further to this, image recognition technology within AI has showed success in fracture identification and breast cancer diagnosis. Regarding theatre presence, supervised robots have carried out suturing and anastomosis of bowel in controlled environments to a high standard. CONCLUSION: AI has potential for integration across surgical services, from diagnosis to treatment, and aiding the surgeon in key decision-making for risks per patient. Fully automated surgery may be the future, but at present, AI needs human supervision.

The bicuspid aortic valve: Is it an immunological disease process?

Bicuspid aortic valves (BAVs) are the most common congenital cardiac condition and are characterized by a structural abnormality whereby the aortic valve is composed of two leaflets instead of being trileaflet. It is linked to an increased risk for a variety of complications of the aorta, many with an immunological pathogenesis. The aim of this study is to review and analyze the literature regarding immunological processes involving BAVs, associated common pathologies, and their incidence in the population. This study will also examine current trends in surgical and therapeutic approaches to treatment and discuss the future direction of BAV treatment.