Development and Videographic Evaluation of a Vascular Access Simulation-Based Curriculum for Surgical and Medical Trainees.

Background: There is a paucity of data evaluating femoral arterial access training, despite significant morbidity/mortality associated with incorrect femoral arterial access. The aim of this study was to develop and evaluate a novel 2-component simulation-based curriculum to address a lack of standardized access training and identify the most frequent errors in access. Methods: The femoral arterial access curriculum was developed through a multi-disciplinary collaboration and utilized in-person simulation sessions in conjunction with online and in-person didactic training. Access errors and curriculum efficacy were assessed using mixed-methodology evaluation of video recordings of trainee arterial access pre- and postcurriculum. All recordings were reviewed and scored by 2 blinded, independent investigators. Results: Twenty-six participants completed the curriculum with pre- and postcurriculum recordings. Sixteen participants (62%) were in their first year of residency training. Fifteen participants (58%) belonged to general surgery residency, 9 (35%) to emergency medicine, 1 to vascular surgery, and 1 to interventional radiology residency programs. The global rating for the overall ability to obtain femoral arterial access under ultrasound guidance (0 = fail, 4 = excellent) improved following the curriculum (0.87 ± 0.15, 2.79 ± 1.26, P < 0.0001). Fourteen participants (54%) were unable to independently complete the procedure before training, compared to only 2 participants (8%) following the curriculum. Procedural completion time decreased from 7.14 ± 4.26 to 3.81 ± 2.53 minutes (P < 0.001). Most frequent errors, determined through qualitative analysis, included difficulty using the ultrasound and unsafe maneuvers. Conclusions: Before the curriculum, there were significant frequent errors in junior resident femoral arterial access with major patient safety concerns. A novel simulation-based femoral arterial access curriculum resulted in improved procedural skills across all metrics.

Pilot Study to Improve Resident Experience on Vascular Surgery by Standardizing Dissemination of Operative Steps.

OBJECTIVE: Many surgical residencies have passed along attendings preferences and procedural knowledge as a highly utilized but informal resource. The objective was to assess the effect of providing operative steps and attending preferences on surgical resident performance. DESIGN: This was a prospective observational study with a survey-based design. SETTING: We created and shared vascular surgery operative steps including institutional and attending preferences with junior residents at the Massachusetts General Hospital. PARTICIPANTS: There were a total of 31 residents who completed a survey to assess self-perception of performance in operative knowledge and Accreditation Council for Graduate Medical Education (ACGME) Milestone criteria. RESULTS: Advice from colleagues was the most utilized resource, followed by web-based materials. Of the web-based materials, almost all residents utilized Google searches over other web-based resources designed to specifically help surgical trainees. The vascular surgery resource was used by 90% of residents more than 3 times per week to prepare for operative cases. There was significant improvement in patient positioning, instrument selection, operative field exposure, anatomy, sequence of procedure, procedure choices, and peri-operative care knowledge. CONCLUSIONS: Development of institutional resources that specifically capture attending surgeon procedural variations can improve resident performance, encourage resident autonomy, and provide a catalog of approaches to challenging operative situations.

A comprehensive analysis of leadership attributes, discrepancies, and implications for gender equity in vascular surgery.

OBJECTIVE: Well-developed leadership skills have been associated with a better understanding of health care context, increased team performance, and improved patient outcomes. Surgeons, in particular, stand to benefit from leadership development. Although studies have focused on investigating knowledge gaps and needs of surgeons in leadership roles, there is a noticeable gap in the literature concerning leadership in vascular surgery. The goal of this study was to characterize current leadership attributes of vascular surgeons and understand demographic influences on leadership patterns. METHODS: This retrospective cohort study was a descriptive analysis of vascular surgeons and their observers who took the Leadership Practices Inventory (LPI) from 2020 to 2023. The LPI is a 30-question inventory that measures the frequency of specific leadership behaviors across five practices of leadership. RESULTS: A total of 110 vascular surgeons completed the LPI. The majority of participants were White (56%) and identified as male (60%). Vascular surgeons most frequently observed the "enabling others to act" leadership practice style (8.90 ± 0.74) by all evaluators. Vascular surgeons were most frequently above the 70th percentile in the "challenge the process" leadership practice style (49%) compared with the average of other leaders worldwide. Observers rated vascular surgeons as displaying significantly more frequent leadership behaviors than vascular surgeons rated themselves in every leadership practice style (P < .01). The only demographic variable associated with a significantly increased occurrence of achieving 70th percentile across all five leadership practice styles was the male gender: a multivariable model adjusting for objective experience showed that men were at least 3.5 times more likely to be rated above the 70th percentile than women. CONCLUSIONS: Vascular surgeons under-report the frequency at which they practice leadership skills across all five leadership practice styles and should recognize their strengths of enabling others to act and challenging the process. Men are recognized as exhibiting all five leadership practices more frequently than women, regardless of current position or experience level. This observation may reflect the limited leadership positions available for women, thereby restricting their opportunities to demonstrate leadership practices as frequently or recognizably as their male counterparts.

Complications of Carotid Interventions for Symptomatic Lesions after Systemic Thrombolysis.

BACKGROUND: Series detailing complications after carotid endarterectomy (CEA) and transfemoral carotid stenting (tfCAS) for patients presenting with neurologic symptoms that are treated with systemic thrombolysis (ST) are sparse. We sought to determine if treatment with ST was associated with a higher rate of post-carotid intervention complications. METHODS: A multispecialty, institutional, prospectively maintained database was queried for symptomatic patients treated with CEA or tfCAS from 2007 to 2019. The primary outcomes of interest were bleeding complications (access/wound complications, hematuria, intracranial hemorrhage) or need for reintervention, stroke, and death. We compared rates of these outcomes between patients who were and were not treated with ST. To adjust for preoperative patient factors and confounding variables, propensity scores for assignment to ST and non-ST were calculated. RESULTS: There were 1,139 patients included (949 [82%] CEA and 190 [17%] tfCAS. All treated lesions were symptomatic (550 [48%] stroke, 603 [52%] transient ischemic attack). Fifty-six patients (5%) were treated with ST. Fifteen of 56 patients also underwent catheter-based intervention for stroke. ST was administered 0 to 1 day preoperatively in 21 (38%) patients, 2 to 6 days preoperatively in 27 (48%) patients, and greater than 6 days preoperatively in 8 (14%) patients. ST patients were more likely to present with stroke (93% vs. 45%; P < 0.001) and have higher preoperative Rankin scores. Unadjusted rate of bleeding/return to operating room was 3% for ST group and 3% for non-ST group (P = 0.60). Unadjusted rate of stroke was 4% for ST group and 3% for the non-ST group (P = 0.91), while perioperative mortality was 5% for ST group and 1% for non-ST group (P = 0.009). After adjusting for patient factors, preoperative antiplatelet/anticoagulation, and operative factors, ST was not associated with an increased odds of perioperative bleeding/return to the operating room (odds ratio 0.37; 95% confidence interval: 0.02-1.63; P = 0.309) or stroke (odds ratio 0.62; 95% confidence interval: 0.16-2.40; P = 0.493). CONCLUSIONS: ST does not convey a higher risk of complications after CEA or tfCAS. After controlling for other factors, patients that received ST had similar rates of local complications and stroke when compared to non-ST patients. Early carotid intervention is safe in patients that have received ST, and delays should be avoided in symptomatic patients given the high risk of recurrent stroke.

Percent Thrombus Predicts Popliteal Artery Aneurysm Related Limb Threatening Events.

OBJECTIVE: The objective of this study was to identify clinical and anatomic characteristics of Popliteal artery aneurysms (PAAs) associated with acutely limb threatening events. SUMMARY BACKGROUND DATA: Popliteal artery aneurysms (PAAs) are associated with high morbidity and mortality. Current guidelines recommend operative repair for PAAs with a diameter greater than 20 mm based on very limited evidence. METHODS: This retrospective cross-sectional cohort was derived from a multi-institutional database queried for all patients with a PAA from 2008 to 2022. Duplex ultrasound (DUS) characteristics of PAAs were abstracted by registered physicians in vascular interpretation. Symptom status at the time of DUS was divided into three categories: asymptomatic PAA, symptomatic PAA with claudication or chronic limb ischemia, and acutely limb threatening PAAs with a thromboembolic event, acute limb ischemia, or rupture. RESULTS: There were 470 PAAs identified in 331 patients. The mean age was 74 years at diagnosis, 94% of patients were white, and 97% of patients were male. In a univariate analysis, patient comorbidities and medications were not associated with symptom status. In a multivariate analysis including age, higher percent thrombus was significantly associated with symptomatic PAAs (RRR 15.2; CI 2.69-72.3; P<0.01) and PAAs with an acutely limb threatening event (RRR 17.9; CI 3.76-85.0; P<0.01). All other anatomic characteristics were not associated with symptom status. CONCLUSION: Percent thrombus was significantly associated with symptomatic PAAs and acutely limb threatening events, whereas diameter was not significantly associated with any symptom group. This analysis supports the use of percent thrombus in identifying high risk PAAs that warrant repair.

Outcomes of symptomatic penetrating aortic ulcer and intramural hematoma in the endovascular era.

BACKGROUND: Although endovascular technology has resulted in a paradigm shift in treatment, medical management remains the standard of care for penetrating aortic ulcer (PAU) and intramural hematoma (IMH). This study aimed to detail the short- and long-term outcomes of symptomatic PAU/IMH. METHODS: Institutional data on symptomatic PAU/IMH were gathered (2005-2020). The primary outcome was the composite of recurrent symptoms, radiographic progression, intervention, rupture, and death from related or unknown cause. Factors associated with the primary outcome were determined using a Fine-Gray model with death from an unrelated cause as a competing risk. RESULTS: A total of 83 symptomatic patients treated with medical management aside from ruptures and type A dissections: 21 isolated PAU, 30 isolated IMH, and 32 IMH and PAU. Adverse outcomes included symptom recurrence in 14 (16.9%), radiographic progression to dissection or saccular aneurysm in 17 (20.5%), surgery in 20 (24.1%) (17 thoracic endovascular aortic repair, 1 endovascular aortic repair, 1 frozen elephant trunk, and 1 open repair), and rupture in 4 (4.8%). Twenty-seven patients (32.5%) died during follow-up: 6 from IMH treatment complications, 8 from an unknown cause, and 13 from other causes. The 30-day, 1-year, and 5-year cumulative incidences of the primary outcome was 26.5% (95% confidence interval [CI], 16.9%-37.0%), 44.9% (95% CI, 32.8%-56.2%), and 57.5% (95% CI, 42.4%-69.9%), respectively. IMH with PAU was associated with a significantly higher risk of the primary outcome compared with isolated IMH (subdistribution hazard ratio, 2.21; 95% CI, 1.09-4.50; P = .027) and isolated PAU (subdistribution hazard ratio, 3.58; 95% CI, 1.44-8.88; P = .006). CONCLUSIONS: Complications from symptomatic PAU and IMH are frequent, with intervention, recurrent symptoms, radiographic progression, rupture, or death affecting 25% of patients at 30 days after diagnosis and almost one-half of patients 1 year after diagnosis. Given the high rate of adverse events in this population, investigation into a more aggressive interventional strategy may warranted, especially in patients with a combined IMH and PAU.

Implementation of a Consensus-Driven Quality Improvement Protocol to Decrease Length of Stay after Elective Carotid Endarterectomy.

BACKGROUND: National guidelines stipulate that postoperative length-of-stay (LOS) after elective carotid endarterectomy (CEA) should not exceed 1 day on average, yet perioperative care coordination gaps may limit the ability for institutions to achieve this goal. Internal review determined that increased LOS after CEA at our institution was frequently attributable to urinary retention or postoperative hypertension. We designed and implemented a quality improvement (QI) protocol aiming to better our institutional performance in postoperative LOS after CEA, consisting of 2 Plan-Do-Study-Act (PDSA) cycles. METHODS: In the first PDSA cycle, a division-wide standardized protocol was developed by which antihypertensive medications were managed preoperatively and through postoperative day (POD) 1. This protocol included dedicated patient outreach with instructions for at-home antihypertensive management through the morning of POD 0. Second, alpha-1-blockade was administered to all male patients preoperatively. All patients receiving an elective CEA performed at our institution by vascular surgeons were included in the protocol. The primary outcome measure was defined percent failure of the LOS >1 day metric, with raw LOS as a secondary outcome measure. Process measures included adherence to the antihypertensive medication protocol and adherence to preoperative alpha-1 blockade. Balance measures included documented intraoperative hypotension and 30-day readmission. Fisher's exact test was used to evaluate relationships between preintervention and postintervention cohorts and the outcome measure. Wilcoxon rank-sum tests were used to evaluate relationships between cohorts and total LOS. RESULTS: Baseline performance on the LOS >1 day metric after elective CEA was 58.3% in the 8 months prior to intervention, across 48 patients. Both PDSA interventions were implemented simultaneously. In the 12 months after intervention, 64 patients met protocol inclusion criteria, including 19 symptomatic patients (29.7%). Process measure success for preoperative antihypertensive regimen adherence was 89.8%. For males not chronically prescribed alpha-1 blockade preoperatively, process measure success for adherence to preoperative alpha-1 blockade was 78.8%. The intraoperative hypotension balance measure occurred in 1 patient (1.6%). Performance on the LOS >1 day outcome measure was improved to 32.8% (P = 0.01). Performance on the raw LOS outcome measure was similar between the preintervention cohort (median 2 days, interquartile range [IQR] 1-2) and postintervention cohort (median 1 day, IQR 1-2, P = 0.07). Performance on the 30-day readmission balance measure was similar between preintervention (6.3%) and postintervention cohorts (9.4%, P = 0.73). CONCLUSIONS: The consensus-driven development and implementation of a QI protocol to reduce postoperative LOS after CEA showed promising results in our institution, with approximately 40% improvement in the primary outcome measure. Wider efforts to improve LOS after CEA should include a focus on minimization of postoperative hypertension and urinary retention.

Feasibility of Encord Artificial Intelligence Annotation of Arterial Duplex Ultrasound Images.

DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making.

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach.

The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients' health report and provides better treatment.

Segmentation of Optic Disc and Cup Using Modified Recurrent Neural Network.

Glaucoma is one of the leading factors of vision loss, where the people tends to lose their vision quickly. The examination of cup-to-disc ratio is considered essential in diagnosing glaucoma. It is hence regarded that the segmentation of optic disc and cup is useful in finding the ratio. In this paper, we develop an extraction and segmentation of optic disc and cup from an input eye image using modified recurrent neural networks (mRNN). The mRNN use the combination of recurrent neural network (RNN) with fully convolutional network (FCN) that exploits the intra- and interslice contexts. The FCN extracts the contents from an input image by constructing a feature map for the intra- and interslice contexts. This is carried out to extract the relevant information, where RNN concentrates more on interslice context. The simulation is conducted to test the efficacy of the model that integrates the contextual information for optimal segmentation of optical cup and disc. The results of simulation show that the proposed method mRNN is efficient in improving the rate of segmentation than the other deep learning models like Drive, STARE, MESSIDOR, ORIGA, and DIARETDB.

An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections.

In the past few years, remote monitoring technologies have grown increasingly important in the delivery of healthcare. According to healthcare professionals, a variety of factors influence the public perception of connected healthcare systems in a variety of ways. First and foremost, wearable technology in healthcare must establish better bonds with the individuals who will be using them. The emotional reactions of patients to obtaining remote healthcare services may be of interest to healthcare practitioners if they are given the opportunity to investigate them. In this study, we develop an artificial intelligence-based classification system that aims to detect the emotions from the input data using metaheuristic feature selection and machine learning classification. The proposed model is made to undergo series of steps involving preprocessing, feature selection, and classification. The simulation is conducted to test the efficacy of the model on various features present in a dataset. The results of simulation show that the proposed model is effective enough to classify the emotions from the input dataset than other existing methods.

Healthcare Biclustering-Based Prediction on Gene Expression Dataset.

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing.

Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data.

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipresent monitoring. Easily accessible biomarkers for neurodegenerative disorders, namely, Alzheimer's disease (AD) are needed urgently to assist the diagnoses at its early stages. Due to the severe situations, these systems demand high-quality qualities including availability and accuracy. Deep learning algorithms are promising in such health applications when a large amount of data is available. These solutions are ideal for a distributed blockchain-based IoT system. A good Internet connection is critical to the speed of these system responses. Due to their limited processing capabilities, smart gateway devices cannot implement deep learning algorithms. In this paper, we investigate the use of blockchain-based deep neural networks for higher speed and delivery of healthcare data in a healthcare management system. The study exhibits a real-time health monitoring for classification and assesses the response time and accuracy. The deep learning model classifies the brain diseases as benign or malignant. The study takes into account three different classes to predict the brain disease as benign or malignant that includes AD, mild cognitive impairment, and normal cognitive level. The study involves a series of processing where most of the data are utilized for training these classifiers and ensemble model with a metaclassifier classifying the resultant class. The simulation is conducted to test the efficacy of the model over that of the OASIS-3 dataset, which is a longitudinal neuroimaging, cognitive, clinical, and biomarker dataset for normal aging and AD, and it is further trained and tested on the UDS dataset from ADNI. The results show that the proposed method accurately (98%) responds to the query with high speed retrieval of classified results with an increased training accuracy of 0.539 and testing accuracy of 0.559.

Improved Handover Authentication in Fifth-Generation Communication Networks Using Fuzzy Evolutionary Optimisation with Nanocore Elements in Mobile Healthcare Applications.

Authentication is a suitable form of restricting the network from different types of attacks, especially in case of fifth-generation telecommunication networks, especially in healthcare applications. The handover and authentication mechanism are one such type that enables mitigation of attacks in health-related services. In this paper, we model an evolutionary model that uses a fuzzy evolutionary model in maintaining the handover and key management to improve the performance of authentication in nanocore technology-based 5G networks. The model is designed in such a way that it minimizes the delays and complexity while authenticating the networks in 5G networks. The attacks are mitigated using an evolutionary model when it is trained with the relevant attack datasets, and the model is validated to mitigate the attacks. The simulation is conducted to test the efficacy of the model, and the results of simulation show that the proposed method is effective in improving the handling and authentication and mitigation against various types of attacks in mobile health applications.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems.

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

A systematic review of spinal cord ischemia prevention and management after open and endovascular aortic repair.

OBJECTIVE: Spinal cord ischemia (SCI) is one of the most devastating complications after descending thoracic aortic (DTA) and thoracoabdominal aortic (TAA) repairs. Patients who develop SCI have a poor prognosis, with mortality rates reaching 75% within the first year after surgery. Many factors have been shown to increase the risk of this complication, including the extent of TAA repair, length of aortic and collateral network coverage, embolization, and reduced spinal cord perfusion pressure. As a result, a variety of treatment strategies have been developed. We aimed to provide an up-to-date review of SCI rates with associated treatment algorithms from open and endovascular DTA and TAA repair. METHODS: Using PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines, a literature review with the MeSH (medical subject headings) terms "spinal cord ischemia," "spinal cord ischemia prevention and mitigation strategies," "spinal cord ischemia rates," and "spinal cord infarction" was performed in the Cochrane and PubMed databases to find all peer-reviewed studies of DTA and TAA repair with SCI complications reported. The search was limited to 2012 to 2021 and English-language reports. MeSH subheadings, including diagnosis, complications, physiopathology, surgery, mortality, and therapy, were used to further restrict the included studies. Studies were excluded if they were not of humans, had not pertained to SCI after DTA or TAA operative repair, and if the study had primarily discussed neuromonitoring techniques. Additionally, studies with <40 patients or limited information regarding SCI protection strategies were excluded. Each study was individually reviewed by two of us (S.L. and A.D.) to assess the type and extent of aortic pathology, operative technique, SCI protection or mitigation strategies, rates of overall and permanent SCI symptoms, associations with SCI on multivariate analysis, and mortality. RESULTS: Of the 450 studies returned by the MeSH search strategy, 41 met the inclusion criteria and were included in the final analysis. For the endovascular DTA repair patients, the overall SCI rates ranged from 0% to 10.6%, with permanent SCI symptoms ranging from 0% to 5.1%. The rate of overall SCI after endovascular and open TAA repair was 0% to 35%. The permanent SCI symptom rate was reported by only one study of open repair at 1.1%. The permanent SCI symptom rate after endovascular TAA repair was 2% to 20.5%. CONCLUSIONS: The present review has provided an up-to-date review of the current rates of SCI and the prevention and mitigation strategies used during DTA and TAA repair. We found that a multimodal approach, including a bundled institutional protocol, staging of multiple repairs, preservation of the collateral blood flow network, augmented spinal cord perfusion, selective cerebrospinal fluid drainage, and distal aortic perfusion during open TAA repairs, appears to be important in reducing the risk of SCI.

A systematic review of thromboelastography utilization in vascular and endovascular surgery.

OBJECTIVE: Thromboelastography (TEG) is diagnostic modality that analyzes real-time blood coagulation parameters. Clinically, TEG primarily allows for directed blood component resuscitation among patients with acute blood loss and coagulopathy. The utilization of TEG has been widely adopted in among other surgical specialties; however, its use in vascular surgery is less prominent. We aimed to provide an up-to-date review of TEG utilization in vascular and endovascular surgery. METHODS: Using Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, a literature review with the Medical Subject Headings (MeSH) terms "TEG and arterial events", "TEG and vascular surgery", "TEG and vascular", "TEG and endovascular surgery", "TEG and endovascular", "TEG and peripheral artery disease", "TEG and prediction of arterial events", "TEG and prediction of complications ", "TEG and prediction of thrombosis", "TEG and prediction of amputation", and "TEG and amputation" was performed in Cochrane and PubMed databases to identify all peer-reviewed studies of TEG utilization in vascular surgery, written between 2000 and 2021 in the English language. The free-text and MeSH subheadings search terms included diagnosis, complications, physiopathology, surgery, mortality, and therapy to further restrict the articles. Studies were excluded if they were not in humans or pertaining to vascular or endovascular surgery. Additionally, case reports and studies with limited information regarding TEG utilization were excluded. Each study was independently reviewed by two researchers to assess for eligibility. RESULTS: Of the 262 studies identified through the MeSH strategy, 15 studies met inclusion criteria and were reviewed and summarized. Literature on TEG utilization in vascular surgery spanned cerebrovascular disease (n = 3), peripheral arterial disease (n = 3), arteriovenous malformations (n = 1), venous thromboembolic events (n = 7), and perioperative bleeding and transfusion (n = 1). In cerebrovascular disease, TEG may predict the presence and stability of carotid plaques, analyze platelet function before carotid stenting, and compare efficacy of antiplatelet therapy after stent deployment. In peripheral arterial disease, TEG has been used to predict disease severity and analyze the impact of contrast on coagulation parameters. In venous disease, TEG may predict hypercoagulability and thromboembolic events among various patient populations. Finally, TEG can be utilized in the postoperative setting to predict hemorrhage and transfusion requirements. CONCLUSIONS: This systematic review provides an up-to-date summarization of TEG utilization in multiple facets of vascular and endovascular surgery.

Extraction of the molecular level biomedical event trigger based on gene ontology using radial belief neural network techniques.

Detection of molecular level biomedical event extraction plays a vital role in creating and visualizing the applications related to natural language processing. Cystic Fibrosis is an inherited genetic and debilitating pathology involving the respiratory and digestive systems. The excessive production of thick sticky mucus on the outside of the cells is the main consequence of such disease. This includes disease prevention and medical search to signify the occurrence and detection of event triggers, which is regarded as a proper step in an event extraction of molecular level in biomedical applications. In this model, use a rich set of extracted features to feed the machine learning classifier that helps in better extraction of events. The study uses an automatic feature selection and a classification model using Radial Belief Neural Network (RBNN) for the optimal detection of molecular biomedical event detection. The Radial Belief Neural Network (RBNN) is the proposed system is implemented and it is the classifier to give accurate result of the disease detection. These three algorithms are used to enhance the generalization performance and scalability of detecting the molecular event triggers. The validation is conducted on the cystic fibrosis event trigger based on the gene ontology bio system using the RBNN model with a lung molecular event-level extraction dataset. The extensive computation shows that the Radial Belief Neural Network (RBNN) is proposed to given the better performance results like Accuracy, Sensitivity, Specificity, F-measure and Execution time.

Does recipient work status pre-transplant affect post-heart transplant survival? A United Network for Organ Sharing database review.

BACKGROUND: Recipient-related factors, such as education level and type of health insurance, are known to affect heart transplantation outcomes. Pre-operative employment status has shown an association with survival in abdominal organ transplant patients. We sought to evaluate the effect of work status of heart transplant (HTx) recipients at the time of listing and at the time of transplantation on short- and long-term survival. METHODS: We evaluated the United Network for Organ Sharing (UNOS) registry for all adult HTx recipients from 2001 to 2014. Recipients were grouped based on their work status at listing and at heart transplantation. Kaplan-Meier estimates illustrated 30-day, 1-year, 5-year, and 10-year survival comparing working with non-working groups. The Cox proportional hazards regression model was applied to adjust for covariates that could potentially confound the post-transplantation survival analysis. RESULTS: Working at listing for HTx was not significantly associated with 30-day and 1-year survival. However, 5- and 10-year mortality were 14.5% working vs 19.8% not working (p < 0.0001) and 16% working vs 26% not working (p < 0.0001), respectively. Working at HTx appeared to be associated with a survival benefit at every time interval, with a trend toward improved survival at 30 days and 1 year and a significant association at 5 and 10 years. Kaplan-Meier analysis demonstrated a 5% and 10% decrease in 5- and 10-year mortality, respectively, for the working group compared with the group not working at transplantation. The Cox proportional hazards regression model showed that working at listing and working at transplantation were each associated with decreased mortality (hazard ratio [HR] = 0.8, 95% confidence interval [CI] 0.71 to 0.91; and HR = 0.76, 95% CI 0.65 to 0.89, respectively). CONCLUSIONS: This study is the first analysis of UNOS STAR data on recipient work status pre-HTx demonstrating: (1) an improvement in post-transplant survival for working HTx candidates; and (2) an association between working pre-HTx and longer post-HTx survival. Given that work status before HTx may be a modifiable risk factor for better outcomes after HTx, we strongly recommend that UNOS consider these important findings for moving forward this patient-centered research on work status. Working at listing and working at HTx are associated with long-term survival benefits. The association may be reciprocal, where working identifies less ill patients and also improves well-being. Consideration should be given to giving additional weight to work status during organ allocation. Work status may also be a modifiable factor associated with better post-HTx outcomes.