The risk of harm whilst waiting for varicose veins procedure.

INTRODUCTION: Varicose veins (VV) negatively impact quality of life (QoL) and have risks of major complications including bleeding, ulceration and phlebitis. During the COVID-19 pandemic, the VSGBI (Vascular Society of Great Britain and Ireland) and GIRFT (Get It Right First Time) classified VVs as lowest priority for intervention. OBJECTIVE: This study aims to determine harm caused and the impact on the QoL on patients waiting for their VVs procedures for more than 1 year. METHODS: This was a prospective study conducted at the Norfolk and Norwich University Hospital (NNUH). Patients with VVs awaiting intervention for >1 year were included in the study. Patients with CEAP C6 disease were considered to be too high risk to be invited for treatment during the Covid-19 pandemic. Patients were sent QoL questionnaires and underwent a telephone consultation to assess harm. Both generic (EQ-VAS and EQ-5D) and disease-specific (AVVQ and CIVIQ-14) instruments were utilised. There were no control groups available for comparison. RESULTS: 275 patients were identified (37.1% male) with median time on waiting list of 60 weeks (IQR 56-65). 19 patients (6.9%) came to major harm, including phlebitis (3.6%), bleeding (1.8%) and ulceration (1.8%). Fifty-two patients (18.9%) had minor harm, including worsening pain (12.7%) and swelling (6.2%). 6.9% reported psychological harm. Rising CEAP stage was also associated with worsening level of harm in patients with C5-6 disease (p < 0.0001). Only 8.7% stated they would decline surgery during the pandemic. 104 QoL questionnaires were returned. Median EQ-VAS and EQ-5D was 75 (IQR: 60-85) and 0.685 (0.566-0.761), respectively. Median AVVQ score was 23.2 (14.9-31.0) and CIVIQ-14 score was 33 (21-44).ConclusionsThis study highlights the impact of delaying VVs surgery during a pandemic. A significant rate of both major and minor as well as psychological harm was reported. In addition, VVs had a significant detriment to quality of life

Device Evolution and New Concepts to Preserve Renal Artery Patency in Challenging Infrarenal Aortic Necks

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An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data