Objective: We compared the diagnostic performance of a novel point-ofcare duplex ultrasound test (podiatry ankle duplex scan; PAD-scan) against commonly used bedside tests for the detection of PAD in diabetes. Background: PAD is a major risk factor for diabetic foot ulceration and amputation. Its diagnosis is fundamental though challenging. Although a variety of bedside tests are available, there is no agreement as to which is the most useful. PAD-scan may be advantageous over current tests as it allows for vessel visualization and more accurate arterial waveform assessment. However, its accuracy has not been previously evaluated. Methods: From March to October 2019, we recruited 305 patients from 2 diabetic foot clinics. The diagnostic performance of ankle-brachial pressure index, toe-brachial pressure index, transcutaneous pressure of oxygen, pulse palpation, and ankle waveform assessment using PADscan and Doppler devices (audible and visual waveform assessment) were assessed. The reference test was a full lower limb duplex ultrasound. Results: Based on the reference test, 202 (66.2%) patients had evidence of PAD. PAD-scan had a significantly higher sensitivity [95%, confidence interval (CI) 90%-97%) as compared to all other tests. Particularly low sensitivities were seen with pulse palpation (43%, CI 36%-50%) and transcutaneous pressure of oxygen (31%, CI 24%-38%). PAD-scan had a lower specificity (77%, CI 67%-84%) compared to toe-brachial pressure index (86%, CI 78%-93%; P < 0.001), but not statistically different when compared to all other tests. Conclusions: PAD-scan has superior diagnostic utility and is a valid first line investigation.

IntroductionIn the UK, over 7000 amputations are performed each year because of diabetes. Up to 80% of these are preceded by a foot ulcer and could therefore be prevented with improvements in ulcer care. Peripheral arterial disease is an important risk factor for the development of diabetic foot ulceration. However, its diagnosis in diabetes is challenging due to the presence of neuropathy and arterial calcification. Commonly used bedside tests either have low sensitivities or little supporting evidence to justify their use. Duplex ultrasound (DUS) has good correlation to angiography findings but a full scan is difficult to learn and time consuming to perform. We have previously demonstrated that a focused DUS of the distal anterior and posterior tibial arteries at the ankle (podiatry ankle duplex scan (PAD-scan)) can be readily learnt by novices and performed rapidly and accurately. The primary aim of this study is to determine the diagnostic accuracy of the PAD-scan and other commonly used bedside tests in detecting arterial disease in diabetes.Methods and analysisThe study will include 305 patients presenting to diabetic foot clinics at two centres. Arterial assessment will be performed using the following index tests: the PAD-scan, pulse palpation, audible handheld Doppler, Ankle Brachial Pressure Index, Toe Brachial Pressure Index and transcutaneous pressure of oxygen. Patients will then undergo a full lower limb arterial DUS by a blinded vascular scientist as a reference test.Ethics and disseminationApproval was gained from NRES Committee London (REC reference 17/LO/1447). Findings will be disseminated by various methods including international presentations and publication in a peer-reviewed journal.Trial registration numberClinicalTrials.gov Registry (NCT04058626).

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Objective This review aims to summarise the contemporary uses of intraoperative completion Duplex ultrasound (IODUS) for the assessment of lower extremity bypass surgery (LEB) and carotid artery endarterectomy (CEA). Methods We performed a systematic literature search using the databases of MEDLINE. Eligible studies evaluated the use of IODUS during LEB or CEA. Results We found 22 eligible studies; 16 considered the use of IODUS in CEA and 6 in LEB. There was considerable heterogeneity between studies in terms of intervention, outcome measures and follow-up. In the assessment of CEA, there is conflicting evidence regarding the benefits of completion imaging. However, analysis from the largest study suggests a modest reduction in adjusted risk of stroke/mortality when using IODUS selectively (RR 0.74, CI 0.63–0.88, p = 0.001). Evidence also suggests that uncorrected residual flow abnormalities detected on IODUS are associated with higher rates of restenosis (range 2.1% to 20%). In the assessment of LEB, we found a paucity of evidence when considering the benefit of IODUS on patency rates or when considering its utility as compared to other imaging modalities. However, the available evidence suggests higher rates of thrombosis or secondary intervention in grafts with uncorrected residual flow abnormalities (up to 36% at 3 months). Conclusions IODUS can be used to detect defects in both CEA and LEB procedures. However, there is a need for more robust prospective studies to determine the best scanning strategy, criteria for intervention and the impact on clinical outcomes.

Duplex ultrasound (DUS) is the most widely used method for surveillance of arteriovenous fistulae (AVF) created for dialysis. However, DUS is poor at predicting AVF outcomes and there is a need for novel methods that can more accurately evaluate multidirectional AVF flow. In this study we aimed to evaluate the feasibility of detecting AVF stenosis using a novel method combining tensor-decomposition of B-mode ultrasound cine loops (videos) of blood flow and machine learning classification. Classification of stenosis was based on the DUS assessment of blood flow volume, vessel diameter size, flow velocity, and spectral waveform features. Real-time B-mode cine loops of the arterial inflow, anastomosis, and venous outflow of the AVFs were analysed. Tensor decompositions were computed from both the ‘full-frame’ (whole-image) videos and ‘cropped’ videos (to include areas of blood flow only). The resulting output were labelled for the presence of stenosis, as per the DUS findings, and used as a set of features for classification using a Long Short-Term Memory (LSTM) neural network. A total of 61 out of 66 available videos were used for analysis. The whole-image classifier failed to beat random guessing, achieving a mean area under the receiver operating characteristics (AUROC) value of 0.49 (CI 0.48 to 0.50). In contrast, the ‘cropped’ video classifier performed better with a mean AUROC of 0.82 (CI 0.66 to 0.96), showing promising predictive power despite the small size of the dataset. The combined application of tensor decomposition and machine learning are promising for the detection of AVF stenosis and warrant further investigation.