Computed tomography angiography is associated with low added utility for detecting clinically relevant vascular injuries among patients with extremity trauma

BackgroundExtremity CT angiography (CTA) is frequently used to assess for vascular injury among patients with extremity trauma. The injured extremity index (IEI), defined as the ratio of systolic occlusion pressure between injured and uninjured extremities, has been implemented to screen patients being considered for CTA. Physical examination together with IEI is extremely sensitive for significant extremity vascular injury. Unfortunately, IEI cannot always be calculated. This study aimed to determine whether patients with normal pulse examinations and no hard signs of vascular injury benefitted from further imaging with CTA. We hypothesized that CTA has become overused among patients with extremity trauma, as determined by the outcome of vascular abnormalities that underwent vascular intervention but were missed by physical examination.MethodsThe charts of traumatically injured patients who underwent extremity CTA were retrospectively reviewed. This study was performed at a level 1 trauma center for patients who presented as trauma activations from September 1, 2019 to September 1, 2020.ResultsOne hundred and thirty-six patients with 167 injured limbs were included. Eight limbs (4.8%) underwent an open vascular operation, whereas five limbs (3.0%) underwent an endovascular procedure. One of the 167 limbs (0.6%) had a vascular injury seen on CTA and underwent intervention that was not associated with a pulse abnormality or hard signs of vascular injury. This patient presented in a delayed fashion after an initially normal IEI and examination. Proximity injuries and fractures alone were not highly associated with vascular injuries.DiscussionMany patients with normal pulse examination and no hard signs of vascular injury underwent CTA; the vast majority of these patients did not then have a vascular intervention. Given the consequences of missed vascular injuries, further work is required to prospectively assess the utility of CTA among patients with extremity trauma.Level of evidenceIII.

Hemorrhagic complications after endovascular interventions and the effectiveness of vascular closure devices

Aim. To identify the factors associated with hemorrhagic complications after endovascular interventions, as well as to assess the effectiveness of vascular closure devices.Material and methods. The study included 423 patients after endo - vascular intervention with femoral arterial access: 118 — manual compression, 305 — hemostasis using vascular closure devices (VCDs). The development of following complications was recorded: retroperitoneal hematoma, false aneurysm, arteriovenous fistula, bleeding, thigh soft-tissue hematoma. Time to hemostasis, immobilization period, and length of stay were assessed.Results. The complication rate was lower in VCD group compared to manual hemostasis (2,95 vs 11%, p=0,021). In VCD group, hemostasis characteristics significantly differed from the manual compression group: time to hemostasis — 3,1 vs 22,3 min (p=0,001), immobilization duration — 4,1 vs 20 hours (p=0,001), length of stay — 4 vs 8 days (p=0,001), respectively. The risk of complications increased with following factors: anticoagulant therapy, female sex, age >65 years, diabetes, body mass index >30 kg/m2 , vascular access calcification, introducer diameter >6 Fr, prior puncture. There were following independent predictors of complications: glycoprotein IIb/IIIa inhibitor therapy, superficial or deep femoral artery puncture.Conclusion. VCDs significantly reduces the hemorrhagic complication rate and improves hemostasis parameters as compared to manual compression.

ADRC-Based Control Method for the Vascular Intervention Master–Slave Surgical Robotic System

In vascular interventional surgery, surgeons operate guidewires and catheters to diagnose and treat patients with the assistance of the digital subtraction angiography (DSA). Therefore, the surgeon will be exposed to X-rays for extended periods. To protect the surgeon, the development of a robot-assisted surgical system is of great significance. The displacement tracking accuracy is the most important issue to be considered in the development of the system. In this study, the active disturbance rejection control (ADRC) method is applied to guarantee displacement tracking accuracy. First, the core contents of the proportional–integral–derivative (PID) and ADRC methods are analyzed. Second, comparative evaluation experiments for incremental PID and ADRC methods are presented. The results show that the ADRC method has better performance of than that of the incremental PID method. Finally, the calibration experiments for the ADRC control method are implemented using the master–slave robotic system. These experiments demonstrate that the maximum tracking error is 0.87 mm using the ADRC method, effectively guaranteeing surgical safety.

Augmented reality in vascular and endovascular surgery: a scoping review (Preprint)

BACKGROUND Technological advances have transformed vascular intervention over recent decades. Augmented reality (AR) is a subject of growing interest within surgery, with potential to improve the clinicians’ understanding of 3D anatomy and their processing of real-time information. The aim of this review was to summarise the fundamental concepts of these technologies and to systematically assess the literature currently applying AR to vascular surgery. METHODS A systematic literature review of ‘Medline,’ ‘Scopus’ and ‘Embase’ was performed according to PRISMA guidelines. Studies were selected by a blinded process between two investigators and assessed with data quality tools. RESULTS AR technologies have had a number of applications across vascular and endovascular surgery. The majority of studies use 3D imaging (e.g) CT angiogram derived images of vascular anatomy to augment the clinicians anatomical understanding during procedures. A wide range of AR technologies have been employed with ‘heads up’ fusion imaging and AR head-mounted displays the most commonly clinically applied. AR applications have included guiding open, robotic and endovascular surgery while minimising dissection, improving procedural times and reducing radiation and contrast exposure. Additionally, AR has been successfully applied to surgical training, with scope to improve technical and team communication skills. CONCLUSIONS AR has shown promising developments in the field of vascular and endovascular surgery, with potential benefits to surgeons and patients alike. These include reductions in patient risk and operating times while optimising contrast and radiation exposure for radiological interventions. While more technological advances are required to overcome current limitations, it is likely that AR will be a regular feature of vascular surgery clinical practice and training in the future. CLINICALTRIAL Not suitable for PROSPERO registration due to scoping nature of review, without specific intervention or population study.

Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters

Background: Peripheral artery disease (PAD) is a significant burden, particularly among patients with severe disease requiring invasive treatment. We applied a general Machine Learning (ML) workflow and investigated if a multi-dimensional marker set of standard clinical parameters can identify patients in need of vascular intervention without specialized intra–hospital diagnostics. Methods: This is a retrospective study involving patients with stable PAD (sPAD, Fontaine Class I and II, n = 38) and unstable PAD (unPAD, Fontaine Class III and IV, n = 18) in need of invasive therapeutic measures. ML algorithms such as Random Forest were utilized to evaluate a matrix consisting of multiple routinely clinically available parameters (age, complete blood count, inflammation, lipid, iron metabolism). Results: ML has enabled a generation of an Artificial Intelligence (AI) PAD score (AI-PAD) that successfully divided sPAD from unPAD patients (high AI-PAD in sPAD, low AI-PAD in unPAD, cutoff at 50 AI-PAD units). Furthermore, the probability score positively coincided with gold-standard intra-hospital mean ankle-brachial index (ABI). Conclusion: AI-based tools may be promising to enable the correct identification of patients with unstable PAD by using existing clinical information, thus supplementing clinical decision making. Additional studies in larger prospective cohorts are necessary to determine the usefulness of this approach in comparison to standard diagnostic measures.