Vascular最新文献

ObjectivesThis study aimed to assess whether Large Language Models (LLMs), like ChatGPT-4, could simplify discharge summaries for vascular surgery patients while maintaining accuracy and completeness, ultimately improving patient comprehension and engagement in their postoperative care.MethodsIn this cross-sectional multicentric study, discharge summaries from 90 vascular surgery patients across three centers were collected. These were divided into three groups based on patient pathology: aortic pathology, peripheral artery disease, and carotid artery disease. Summaries were processed by LLMs to create patient-friendly versions with a target reading level suitable for a 6th-grade education. The readability of the original and AI-generated summaries was evaluated using the Flesch-Kincaid Grade Level and Ease Score. Understandability and actionability were assessed with the Patient Education Materials Assessment Tool for Print (PEMAT-P), which evaluates the clarity, organization, and actionable nature of the text. Accuracy and completeness were rated using a 6-point Likert scale and a 3-point Likert scale, respectively. Statistical analyses, including paired-samples t-tests, ANOVA, and post-hoc tests, were performed to assess the differences between the original and AI-modified summaries.ResultsAI-generated summaries demonstrated significant improvements in readability, with a 39.6% reduction in Flesch-Kincaid Grade Level and a 106.37% increase in Ease Score. The mean understandability score based on PEMAT-P was 77.71, while the actionability score was 52.12. Accuracy was rated highly (mean score of 5.21), and completeness had a mean score of 2.61. However, 10.8% of summaries had omissions, and 7.5% had hallucinations, with corrections made in some cases.ConclusionsLLMs like ChatGPT-4 can significantly improve the readability and accessibility of discharge summaries for vascular surgery patients, enhancing their understanding and engagement in postoperative care. While the summaries were accurate and complete, the occurrence of errors suggests the need for further refinement to minimize omissions and hallucinations. These findings indicate that AI can be a valuable tool in improving communication between healthcare providers and patients. Future research should focus on reducing errors and enhancing actionability.

BackgroundThe prevalence of peripheral arterial disease (PAD) is on the rise globally, leading to adverse clinical outcomes. Our aim was to investigate the causal relationship between apolipoprotein and PAD, as well as the potential mediating role of smoking, diabetes, hypertension, myocardial infarction, and ischemic stroke.MethodsWe employed two-sample Mendelian randomization (TSMR) to assess the causal effect of apoB/A1 on the risk of PAD and potential mediators (smoking, diabetes mellitus, hypertension, myocardial infarction, and ischemic stroke), as well as the causal effect of those mediators on PAD. The use of multivariate MR (MVMR) allowed us to explore and quantify the mediating role of these factors in the causal association between apoB/A1 and the risk of PAD.ResultsOur MR analysis showed that each standard deviation increase in apoB/A1 increased the risk of PAD by 46% (OR = 1.460, 95% CI: 1.255-1.697, P = 8.74E-07). Hypertension, myocardial infarction, and ischemic stroke were ultimately recognized as the mediators of the causal relationship between apoB/A1 and PAD, explaining 7.5%, 19.7%, and 62.5% of the causal effect, respectively, and the proportion combined of the three together was 81%.ConclusionsThese studies demonstrated that elevated apoB/A1 increases the risk of developing PAD and that this association may be mediated by hypertension, myocardial infarction, and ischemic stroke.

ObjectivesEmbolizing an abdominal aortic aneurysm sac through a transcaval approach is a novel approach to treat type-II endoleaks that occur following aortic endografting. This study reviews the outcomes of this treatment in one of the few centres in Australia that offers this procedure.MethodsA retrospective cohort study was conducted, including patients who had received transcaval embolisation of type-II endoleak over a 9-year period. The primary outcome was clinical success, defined as the absence of endoleak on post-procedural ultrasonography at 6-weeks, provided the procedure was successfully completed. Other outcomes included clinical success at 1-year, aneurysm size, rupture and aneurysm-related mortality.ResultsTwelve patients with type-II endoleak and AAA sac growth received transcaval embolisation. Technical success was achieved in 91.7% of cases, in which both Onyx 34 and coils were deployed into the sac. Clinical success, defined as an absence of endoleak on ultrasonography 6-weeks after a technically successful procedure, was achieved in 66.7% of patients, none of whom had evidence of endoleak at 1-year post-procedure. In patients whom clinical success was achieved, there was a decrease in sac size during follow-up, from 72.5 ± 13.1 at 6-weeks to 66.0 ± 12.1 at 1-year post-procedure. Aneurysm sac size continued to enlarge if clinical success was not observed, with an average sac size of 72.0 ± 14.1 at 6-weeks increasing to 76.9 ± 15.2 at 1-year. No patients suffered AAA rupture or AAA-related mortality during follow-up.ConclusionsThis study reports a high technical and clinical success rate, with outcomes that could suggest that this procedure could induce sac regression.

BackgroundVascular graft infection (VGI) is a devastating complication that necessitates immediate diagnosis to reduce morbidity and mortality. In 2016, The Management of Aortic Graft Infection Collaboration (MAGIC) proposed a diagnostic algorithm for identifying aortic graft infection which uses clinical, radiological, and laboratory findings to determine risk. The purpose of this study was to retrospectively validate the MAGIC criteria for VGI diagnosis and potentially broaden its application.MethodsAn IRB approved retrospective cohort study was performed with 28 suspected VGI patients from our institution. A blinded chart review categorized patients based on MAGIC criteria into "infected," "suspected infection," or "insufficient evidence" groups. Due to the MAGIC criteria's placement of a third outcome, "suspected infection," the results were dichotomized into two systems whereby "suspected infection" patients were considered as infected (S = I) or uninfected (S = NI). Both dichotomized systems from the MAGIC criteria were then compared to the clinical diagnosis (with surgical evidence when possible) from which sensitivity and specificity were calculated.ResultsA total of 15 patients (54%) presented with definitive VGI, two patients (7%) presented with suspicion for VGI, and 11 patients (39%) presented with insufficient evidence to suspect VGI; placement in these categories were determined by the MAGIC criteria at entry to the study. When using clinical diagnoses, 17 out of 28 had VGI (61%). When using the categorization system S = I, sensitivity was 88.2% and specificity of 100%, for S = NI, sensitivity dropped to 76.4%, but specificity remained at 100%. These results were recomputed using only 25 of the 28 patients who were good surgical candidates. Using this new surgical threshold and smaller cohort to compare MAGIC against, sensitivity was calculated to be 93.3% with a specificity of 100% when S = I. When S = NI, the sensitivity was calculated to be 86.7% with specificity remaining at 100%.ConclusionsThe MAGIC criteria provide excellent specificity and reasonable sensitivity for diagnosis of VGI for peripheral and aortic revascularization.

ObjectivesFemoral vein transposition (tFV) is a complex procedure that provides high patency rates for patients with exhausted upper-limb vascular access. Traditionally, the procedure involves a long single incision in the thigh to harvest the femoral vein, but this approach is associated with increased risks of local complications such as infections and haematomas. Skip incisions have shown to lower complication rates and shorten maturation times. We present an optimised, minimally invasive technique that transposes the femoral vein through three short skip incisions on the anterior thigh.MethodsWe present the technical details and outcomes of three patients who underwent femoral vein transposition (tFV) for haemodialysis access. Additionally, we conducted a literature review on lower-limb vascular access options for haemodialysis.ResultsPre-operative clinical and ultrasound assessments confirmed femoral vein transposition (tFV) eligibility. The procedure involved three short skip incisions along the medial thigh to harvest the femoral vein. Subcutaneous tunnelization was performed through the anterior thigh. The arteriovenous anastomosis was sized at 3-4 mm to minimise the risk of lower-limb steal syndrome. The average procedure duration was 120 min. Postoperative recovery was uneventful, with patients discharged an average of 6 days after surgery. Wound healing was satisfactory, and the access was cannulated approximately 3 weeks post-procedure.ConclusionsThis method reduces wound complications and significantly improves the comfort of patients and dialysis nursing staff by enhancing cannulation access and increasing the availability of puncture sites, thereby improving the overall patient care experience.

BackgroundPatients suffering from lower extremity venous ulcers typically undergo prolonged dressing changes, entailing extended treatment cycles and significant costs, creating an urgent need for effective continuous care. There is scarce literature reporting on the preferences and requirements for wound care within continuous care services for such conditions. Discrete choice experiments serve as an innovative method to elicit patient preferences, where the development of attributes and levels is a critically important process.ObjectiveTo identify attributes and levels patients with venous leg ulcers consider when making decisions about continued care services for venous leg ulcers.MethodsIn this study, we employed four steps to devise service features (i.e., attributes) and their potential manifestations (i.e., levels). Firstly, we conducted a systematic literature search with articles screened and content compiled by two researchers to identify possible service attributes. Secondly, we conducted in-depth interviews with patients suffering from venous leg ulcers to gather personal experiences and expectations for quality care, and used Nvivo11.0 for data management. Thirdly, focus group discussions were held to assess general viewpoints from various perspectives. Finally, expert meetings were organized to refine our research tools further. To narrow down the attributes to a manageable number for the discrete choice experiment, focus groups and expert meetings performed ranking exercises, calculating the average importance scores by dividing the total score by the number of participants and ranking attributes from highest to lowest average scores.ResultsThrough literature reviews, qualitative data acquisition, expert meetings, and ranking exercises, a total of six attributes were finalized, each with two to three levels. The attributes included: 1) service cost (Ave score: 4.7); 2) Mode of service delivery (Ave score: 4.5); 3) Service type (Ave score: 4.4); 4) Consistency of caregiver (Ave score: 4.4); 5) Category of service providers (Ave score: 4.3); and 6) Appointment scheduling (Ave score: 4.3). These attributes encompassed key aspects related to wound care in the continuous care services for patients with venous leg ulcers.ConclusionsThe mixed-methods approach adopted in this study has proven particularly suitable for identifying, refining, and selecting attributes and levels for discrete choice experiments. By leveraging the advantages and limitations of the four steps, and especially through qualitative data analysis, a more profound and comprehensive understanding of the attributes and levels was achieved. This approach has facilitated the practicality and accuracy in attribute construction, enhancing the overall efficacy of the DCE design.

BackgroundPeripheral artery disease (PAD) outcomes often rely on the expertise of individual vascular units, introducing potential subjectivity into disease staging. This retrospective, multicenter cohort study aimed to demonstrate the ability of artificial intelligence (AI) to provide disease staging based on inter-institutional expertise by predicting limb outcomes in post-interventional pedal angiograms of PAD patients, specifically in comparison to the inframalleolar modifier in the Global Limb Anatomic Staging System (IM GLASS).MethodsWe used computer vision (CV) based on the MobileNetV2 model, implemented via TensorFlow.js library, for transfer learning and feature extraction from 518 pedal angiograms of PAD patients with known 3-month limb outcomes: 218 salvaged limbs, 140 minor amputations, and 160 major amputations.ResultsAfter 43 epochs of training with a learning rate of 0.001 and a batch size of 16, the model achieved a validation accuracy of 95% and a test accuracy of 93% in differentiating salvaged limbs from amputations. In manual testing with 45 angiograms excluded from the training, validation, and test processes, the AI predicted mean limb salvage probabilities of 96% for actual salvaged limbs, 27% for minor amputations, and 17% for major amputations (p-value < .001). The correlation coefficient between the CV model-predicted outcome and the actual outcome for these 45 angiograms was 0.7, nearly five times higher than that between the IM GLASS pattern and the actual outcome (0.14).ConclusionComputer vision can analyze angiograms and predict disease outcomes, demonstrating a significant correlation between predicted and actual limb salvage rates, outperforming IM GLASS segmentation by a vascular specialist. It has the potential to provide immediate and precise treatment results during vascular interventions, tailored to (inter)institutional expertise, and enhance individualized decision-making.

ObjectivesMal-deployment of the thoracic endovascular aortic repair (TEVAR) stent graft during a frozen elephant trunk (FET) procedure for an acute type A aortic dissection (ATAAD) leads to devastating complications. We report a hemiarch replacement with TEVAR stent graft covering the aortic arch vessels salvaged through an endovascular approach.MethodsA 69-year-old man with ATAAD in 2018, status post-hemiarch repair with TEVAR, presented in 2023 with progressive dizziness/syncope and lower extremity hypertension with inability to tolerate anti-hypertensives. A CT scan revealed TEVAR stent graft coverage of the brachiocephalic, left common carotid (LCA), and left subclavian artery (LSA), with retrograde flow into the arch vessels through a false lumen fed by a dissection fenestration at the aortic bifurcation. Given an elevated peri-operative death and stroke risk with an open arch revision, we performed an in situ anterograde laser fenestration of his FET with LCA and LSA stenting via the Culotte technique.ResultsOn 1 year follow-up, the patient's dizziness has resolved, and he has maintained normotension via anti-hypertensives.ConclusionLaser fenestration and the Culotte technique can be used to salvage a TEVAR causing coverage of the arch vessels, showing that endovascular techniques can be a safe alternative for rescuing mal-positioned FETs.

ObjectivesCarotid stenosis plays a significant role in stroke burden. Surgical intervention in the form of carotid endarterectomy or carotid artery stenting is an important stroke risk reduction strategy. Careful patient selection with identification of high-risk individuals is crucial to operative planning given perioperative risks including stroke, myocardial infarction, and death. Machine learning (ML) is a subset of artificial intelligence (AI) consisting of mathematical algorithms that can learn from datasets to perform particular tasks. These algorithms offer a tool for prediction of patient outcomes by analysis of preoperative data leading to improved patient selection. This systematic review aims to assess the use of artificial intelligence in risk stratification for carotid endarterectomy and carotid artery stenting.MethodsPubMed, Web of Knowledge, EMBASE, and the Cochrane Library were systematically searched to identify any articles utilising artificial intelligence in predicting surgical outcomes in carotid endarterectomy or carotid artery stenting. After duplicate removal, all studies underwent independent title and abstract screening followed by quality assessment using the PROBAST tool. Data extraction was then carried out for synthesis and comparison of study outcomes including accuracy, area under receiver operator curve (AUC), sensitivity, and specificity.ResultsAfter duplicate processing, a total of 100 articles underwent title and abstract screening resulting in 11 clinical studies published between 2008 and 2023 that fit eligibility criteria. Surgical outcomes assessed included haemodynamic instability, shunt requirement, hyperperfusion syndrome, stroke, myocardial infarction, and death. Artificial intelligence models were able to accurately predict major adverse cardiovascular events (AUC 0.84), postoperative haemodynamic instability (AUC 0.86), shunt requirement (AUC 0.87), and postoperative hyperperfusion syndrome (AUC 0.95). However, many studies had a high risk of bias due to lack of external validation.ConclusionThis systematic review highlights the potential application of machine learning in prediction of surgical outcomes in carotid artery intervention. However, use of these tools in a clinical setting requires further robust study with use of external validation and larger patient datasets.