Understanding AAV vector immunogenicity: from particle to patient.

Gene therapy holds promise for patients with inherited monogenic disorders, cancer, and rare genetic diseases. Naturally occurring adeno-associated virus (AAV) offers a well-suited vehicle for clinical gene transfer due to its lack of significant clinical pathogenicity and amenability to be engineered to deliver therapeutic transgenes in a variety of cell types for long-term sustained expression. AAV has been bioengineered to produce recombinant AAV (rAAV) vectors for many gene therapies that are approved or in late-stage development. However, ongoing challenges hamper wider use of rAAV vector-mediated therapies. These include immunity against rAAV vectors, limited transgene packaging capacity, sub-optimal tissue transduction, potential risks of insertional mutagenesis and vector shedding. This review focuses on aspects of immunity against rAAV, mediated by anti-AAV neutralizing antibodies (NAbs) arising after natural exposure to AAVs or after rAAV vector administration. We provide an in-depth analysis of factors determining AAV seroprevalence and examine clinical approaches to managing anti-AAV NAbs pre- and post-vector administration. Methodologies used to quantify anti-AAV NAb levels and strategies to overcome pre-existing AAV immunity are also discussed. The broad adoption of rAAV vector-mediated gene therapies will require wider clinical appreciation of their current limitations and further research to mitigate their impact.

A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection.

Voice is an essential component of human communication, serving as a fundamental medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory patterns can lead to voice disorders, which can have a profound impact on interpersonal interactions. Early detection of voice disorders is crucial for improving voice health and quality of life. This research proposes a novel methodology called VDDMFS [voice disorder detection using MFCC (Mel-frequency cepstral coefficients), fundamental frequency and spectral centroid] which combines an artificial neural network (ANN) trained on acoustic attributes and a long short-term memory (LSTM) model trained on MFCC attributes. Subsequently, the probabilities generated by both the ANN and LSTM models are stacked and used as input for XGBoost, which detects whether a voice is disordered or not, resulting in more accurate voice disorder detection. This approach achieved promising results, with an accuracy of 95.67%, sensitivity of 95.36%, specificity of 96.49% and f1 score of 96.9%, outperforming existing techniques.

Nonacog Alfa for Prophylaxis and Treatment of Bleeding Episodes in Previously Treated Patients with Moderately Severe or Severe Hemophilia B in India.

Purpose: Hemophilia B is an X-linked congenital bleeding disorder caused by a deficiency of coagulation factor IX (FIX) clotting activity. This study evaluated safety and efficacy of nonacog alfa, a recombinant human blood coagulation FIX replacement product, in males aged 12-65 years with hemophilia B (FIX activity ≤ 2%) with or without inhibitors in India. Methods: In this multicenter, open-label, post-approval phase 4 study, participants were treated for up to 8 weeks, with up to a 4-week screening period and a subsequent post-treatment 28-day safety observation period. Intravenous nonacog alfa 40 IU/kg (range 13-78 IU/kg) was administered at intervals of 3-4 days, in accordance with the approved local product document. Results: A total of 25 participants were enrolled and completed the study. No participants developed FIX inhibitors during the study, experienced treatment-related adverse events (AEs) or serious AEs, or developed a thrombotic event and/or hypersensitivity reaction. No participants experienced bleeding events requiring on-demand treatment with nonacog alfa. Seventeen bleeding episodes (16 spontaneous and 1 traumatic) were reported in 10 participants; all occurred post treatment, with the exception of a minor gum-bleeding event, and were managed without treatment. The mean (SD) annualized total factor consumption (TFC) per patient was 224,582 (75,527) IU; the mean (SD) annualized TFC by weight per patient was 3639 (573) IU/kg. Conclusion: Nonacog alfa was safe and effective for the prevention of hemorrhagic episodes in Indian males with congenital, severe hemophilia B. No participants developed FIX inhibitors, and no new safety signals were reported.

Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach.

An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.

Fitness dependent optimizer with neural networks for COVID-19 patients.

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://github.com/Tarik4Rashid4/covid19models.

A statistical analysis of tweets on covid-19 vaccine hesitancy utilizing opinion mining: an Indian perspective.

The world witnessed the emergence of a deadly virus in December 2019, later named COVID-19. The virus was found to be highly contagious, and so people across the world were highly prone to be affected by the virus. Being a virus-borne disease, developing a vaccine was one of the most promising remedies. Thus, research organizations across the globe started working on developing the vaccine. However, it was later found by many researchers that a large number of people were hesitant to receive the vaccine. This paper aims to study the acceptance and hesitancy levels of people in India and compares them with the acceptance and hesitancy levels of people from the UK, the USA, and the rest of the world by analyzing their tweets on Twitter. For this study, 2,98,452 tweets were fetched from January 2020 to March 2022 from Twitter, and 1,84,720 tweets from 1,22,960 unique users were selected based on their country of origin. Machine learning based Sentiment analysis is then used to evaluate and analyze the tweets. The paper also proposes an NLP-based algorithm to perform opinion mining on Twitter data. The study found the public sentiment of the Indian population to be 63% positive, 28% neutral, and 9% negative. While the worldwide sentiment distribution is 45% positive, 34% neutral, and 21% negative, the USA has 42% positive, 34% neutral, and 23% negative and the UK has 50% positive, 29% neutral, and 21% negative. Also, sentiment analysis for individual vaccines in Indian context resulted in "Covaxin" with the highest positive sentiment at 43% followed by "Covishield" at 36%. The outcome of this work yields an insight into the public perception of the COVID-19 vaccine and thus can be used to formulate policies for existing and future vaccine campaigns. This study becomes more relevant as it is the consolidated opinion of Indian people, which is versatile in nature.

Moroctocog Alfa (AF-CC) for Prophylaxis and Treatment of Bleeding Episodes in Previously Treated Patients with Hemophilia A in India.

Purpose: Hemophilia A is an X-linked congenital disorder, characterized by factor VIII (FVIII) deficiency. Globally, India has the highest population of patients with hemophilia, and there is a clear unmet need for appropriate and effective treatment for this patient population. This multicenter, open-label, post-approval study evaluated the safety and efficacy of moroctocog alfa in patients with moderate or severe congenital hemophilia A in India. Methods: Intravenous moroctocog alfa was administered 30 ± 5 IU/kg 3 times weekly for bleeding prophylaxis, according to the local product document. Participants were treated for up to 8 weeks, with an up to 4-week screening period and a subsequent post-treatment, 28-day safety observation period. Patients continued in the study until at least 24 exposure days or a period of up to 8 weeks on moroctocog alfa. Results: A total of 50 participants were enrolled, and 48 (85.7%) completed the study. No participants developed FVIII inhibitors during the study. The mean (SD) annualized bleeding rate during moroctocog alfa prophylaxis was 0.79 (2.0) with a median (range) of 0.00 (0.0, 6.8). The mean (SD) annualized total factor consumption (TFC) per participant was 287,432 (93,866) IU; the mean (SD) annualized TFC by weight per participant was 4176 (858) IU/kg. Moroctocog alfa was well tolerated with no reported treatment-emergent adverse event-related dose reductions, discontinuations, or serious adverse events. Conclusion: Moroctocog alfa was safe, effective, and well tolerated in Indian participants with congenital moderate to severe hemophilia A. No participant developed FVIII inhibitors during the study.

Kurdish Handwritten character recognition using deep learning techniques.

Handwriting recognition is regarded as a dynamic and inspiring topic in the exploration of pattern recognition and image processing. It has many applications including a blind reading aid, computerized reading, and processing for paper documents, making any handwritten document searchable and converting it into structural text form. High accuracy rates have been achieved by this technology when recognizing handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. However, there is not such a system for recognizing Kurdish handwriting. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic/Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive database has been created for handwritten Kurdish characters which contain more than 40 thousand images. The created database has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system the experimental results show an acceptable recognition level. The testing results reported an 83% accuracy rate, and training accuracy reported a 96% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and comparable to the similar to other languages handwriting recognition systems.

Harmony search: Current studies and uses on healthcare systems.

One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science. The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions. In this review paper, the current studies and uses of harmony search are studied in four main domains. (i) The variants of HS, including its modifications and hybridization. (ii) Summary of the previous review works. (iii) Applications of HS in healthcare systems. (iv) And finally, an operational framework is proposed for the applications of HS in healthcare systems. The main contribution of this review is intended to provide a thorough examination of HS in healthcare systems while also serving as a valuable resource for prospective scholars who want to investigate or implement this method.

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem.

Economic load dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to economic load dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness-dependent optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness-dependent optimizer (FDO) examines the search spaces based on the searching approach of particle swarm optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have used a fitness-dependent optimizer to solve the economic load dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of the fitness-dependent optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for the fitness-dependent optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multidimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. The empirical results obtained using the enhanced variant of the fitness-dependent optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional fitness-dependent optimizer. The experimental study obtained 7.94E-12, the lowest transmission loss using the enhanced fitness-dependent optimizer. Correspondingly, various standard estimations are used to prove the stability of the fitness-dependent optimizer in phases of exploitation and exploration.

Feature Selection by Hybrid Brain Storm Optimization Algorithm for COVID-19 Classification.

A large number of features lead to very high-dimensional data. The feature selection method reduces the dimension of data, increases the performance of prediction, and reduces the computation time. Feature selection is the process of selecting the optimal set of input features from a given data set in order to reduce the noise in data and keep the relevant features. The optimal feature subset contains all useful and relevant features and excludes any irrelevant feature that allows machine learning models to understand better and differentiate efficiently the patterns in data sets. In this article, we propose a binary hybrid metaheuristic-based algorithm for selecting the optimal feature subset. Concretely, the brain storm optimization algorithm is hybridized by the firefly algorithm and adopted as a wrapper method for feature selection problems on classification data sets. The proposed algorithm is evaluated on 21 data sets and compared with 11 metaheuristic algorithms. In addition, the proposed method is adopted for the coronavirus disease data set. The obtained experimental results substantiate the robustness of the proposed hybrid algorithm. It efficiently reduces and selects the feature subset and at the same time results in higher classification accuracy than other methods in the literature.

Novel Improved Salp Swarm Algorithm: An Application for Feature Selection.

We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets.

Real-world outcomes associated with standard half-life and extended half-life factor replacement products for treatment of haemophilia A and B.

: Standard-of-care treatment for haemophilia A or B is to maintain adequate coagulation factor levels through clotting factor administration. The current study aimed to evaluate annualised bleeding rates (ABR) and treatment adherence for haemophilia A or B patients receiving standard half-life (SHL) vs. extended half-life (EHL) factor replacement products. We analysed data from the Adelphi Disease-Specific Programmes, a health record-based survey of United States and European haematologists. Analysis included 651 males with moderate-to-severe haemophilia A or B (the United States, n = 132; Europe, n = 519). The haemophilia A analysis included 501 patients (SHL, n = 435; EHL, n = 66). In the combined United States/European population, mean (SD) ABR was 1.7 (1.69) for the SHL group and 1.8 (2.00) for the EHL group. A total of 72% of patients receiving SHL factor VIII and 75% of patients receiving EHL factor VIII in the combined population were fully adherent (no doses missed of the last 10 doses), as reported by physicians. The haemophilia B analysis included 150 patients (SHL, n = 114; EHL, n = 36). The mean (SD) ABR in the combined population was 2.1 (2.16) for patients receiving SHL factor IX (FIX) and 1.4 (1.48) for patients receiving EHL FIX. The percentage of fully adherent patients (physician-reported) was similar in both treatment groups (SHL FIX, 68%; EHL FIX, 73%). In this preliminary real-world survey in a relatively small sample of patients, measures of ABR and adherence between SHL and EHL products were evaluated. Additional real-world research on prescribing patterns, SHL vs. EHL effectiveness, and adherence is warranted.

Comparison of Different Remission and Low Disease Definitions in Psoriatic Arthritis and Evaluation of Their Prognostic Value.

OBJECTIVE: There is no agreement on the optimal definitions for assessing disease state in patients with psoriatic arthritis (PsA), and some of the commonly used definitions do not include assessment of skin lesions. We investigated the performance of various definitions in patients with PsA and psoriasis. METHODS: This was a posthoc analysis of data from the PRESTA study. The remission definitions analyzed were very low disease activity (VLDA) index, defined as 7/7 of the minimal disease activity (MDA) cutoffs; Disease Activity Index for PsA (DAPSA); and clinical (c-) DAPSA. The low disease activity (LDA) definitions analyzed were as follows: MDA defined as 5/7 cutoffs; MDA joint with both the tender joint count (TJC) and swollen joint count (SJC) cutoffs mandated; MDA skin where skin cutoff was mandated; MDA joint + skin where TJC, SJC, and skin cutoffs were mandated; DAPSA LDA; and cDAPSA LDA. RESULTS: At Week 24, the proportions of patients achieving VLDA, DAPSA, and cDAPSA remission were 10%, 35%, and 37%, respectively. Of the patients achieving DAPSA and cDAPSA remission, 55% and 56%, respectively, had Psoriasis Area and Severity Index > 1. The proportions of patients achieving MDA 5/7, MDA skin, MDA joint, and MDA joint + skin were 44%, 19%, 36%, and 14%, respectively, versus 70% achieving DAPSA and cDAPSA LDA. Notable residual levels of psoriasis were observed in patients achieving the definitions that did not require skin disease control. CONCLUSION: VLDA and MDA definitions are more stringent than DAPSA and cDAPSA definitions for the assessment of PsA. The relevance of residual disease to patients, however, remains to be determined. [Clinical Trial registration: ClinicalTrials.gov NCT00245960].

Real-World Analysis of Dispensed International Units of Coagulation Factor VIII and Resultant Expenditures for Hemophilia A Patients: A Comparison Between Standard Half-Life and Extended Half-Life Products.

PURPOSE: To identify international units (IUs) dispensed and consequent expenditures for standard half-life (SHL) versus extended half-life (EHL) recombinant factor VIII (rFVIII) replacement products in hemophilia A patients in a real-world setting. DESIGN: Two U.S. claims databases were analyzed. METHODOLOGY: Number of IUs dispensed and quarterly expenditures for rFVIII products were collected from the Optum Clinformatics Data Mart and Truven Health MarketScan Databases. Truven claims were also analyzed for factor IUs dispensed and expenditures for patients with data for ≥3 months before and after switching to an EHL product. RESULTS: The Optum and Truven databases, respectively, included 276 (SHL, n=243; EHL, n=33) and 500 (SHL, n=409; EHL, n=91) hemophilia A patients. Median quarterly factor IUs dispensed in Optum were 10% higher with EHL versus SHL products over nine quarters, and 45% higher with EHL versus SHL products in Truven over 10 quarters. Median quarterly expenditures in the EHL cohort were 51% (individual quarterly medians range, 1%-101%) higher than in the SHL cohort in Optum and 122% higher (individual quarterly medians range, 1%-189%) in Truven. Twenty-nine Truven patients switched to an EHL product; median factor IUs dispensed varied quarterly. The lowest SHL and highest EHL values occurred in the quarter immediately before switching and the first quarter post-switch, respectively. Overall median quarterly expenditures were higher post-switch; this was consistent over seven quarters. CONCLUSION: We found higher expenditures over two years for hemophilia A patients using EHL versus SHL products. Switching to an EHL rFVIII product was associated with variable factor IUs dispensed and consistently higher expenditures.

Real-World Analysis of Dispensed IUs of Coagulation Factor IX and Resultant Expenditures in Hemophilia B Patients Receiving Standard Half-Life Versus Extended Half-Life Products and Those Switching from Standard Half-Life to Extended Half-Life Products.

BACKGROUND: Hemophilia B requires replacement therapy with factor IX (FIX) coagulation products to treat and prevent bleeding episodes. A recently introduced extended half-life (EHL) recombinant FIX replacement product provided the opportunity to compare the amount of dispensed factor and expenditures for EHL treatment compared with a standard half-life (SHL) product. OBJECTIVE: To determine factor international units (IUs) dispensed and expenditures associated with switching from nonacog alfa, the most commonly used SHL replacement product, to eftrenonacog alfa, an EHL FIX replacement product. METHODS: Two U.S. claims databases were analyzed. A large national specialty pharmacy dispensation claims database was used to identify the number of IUs dispensed and monthly charges for all patients with hemophilia B from April 2015 to June 2016. Truven Health MarketScan Research Databases (January 2010-July 2016) were used to identify IUs and expenditures for patients with claims data for at least 3 months before and after switching from the SHL to the EHL product. Medians for IUs and expenditures are presented to accommodate for skewness of data distribution. RESULTS: The national specialty pharmacy database analysis included 296 patients with moderate or severe hemophilia B (233 on SHL; 94 on EHL). Median monthly factor dispensed was 11% lower (2,142 IU) in the EHL versus SHL cohort over the study period, while individual monthly reductions ranged from 32% to 47% (9,838 IU to 16,514 IU). Using the wholesale acquisition cost, the median per-patient monthly factor expenditures over the 15-month study period were 94% higher ($23,005) for the EHL than for the SHL product. Individual median monthly expenditure differences ranged from 15% ($6,562) to 49% ($19,624). In the Truven database, 14 patients switched from the SHL to the EHL product. The amount of factor dispensed was variable; in the 1-year period before and after the switch from the SHL to the EHL product, mean IUs dispensed decreased by 3,005 IU, while median IUs dispensed increased by 4,775 IU. Factor replacement expenditures were higher after switching from the SHL to the EHL product in each of the 3-month periods examined before versus after the switch. CONCLUSIONS: This analysis of real-world data showed that switching from the SHL to the EHL product was associated with higher expenditures. Increased expenditures noted in the first 3 months after switching may be related to initial stocking up of the EHL product, but expenditures were sustained throughout the 1-year period of data analysis. Further analysis of these findings with larger numbers of patients should be explored. DISCLOSURES: This study was sponsored by Pfizer. Pfizer employees were involved in the study design; the collection, analysis, and interpretation of data; the review of the manuscript; and the decision to submit for publication. All authors are employees of Pfizer. No author received an honorarium or other form of payment related to the development of this manuscript. All authors participated in the study design, data interpretation, and manuscript review and revisions and granted approval for the submission of the manuscript. Alvir, McDonald, and Tortella also participated in data analysis. Data from this paper were presented in part at the European Association for Haemophilia and Allied Disorders Annual Meeting, February 1-3, 2017, Paris, France; at the International Society for Pharmacoeconomics and Outcomes Research Annual Meeting, May 20-24, 2017, Boston, MA; and at the International Society on Thrombosis and Haemostasis Congress, July 8-13, 2017, Berlin, Germany.

Draft Genome Sequence of Elizabethkingia meningoseptica, Isolated from a Postoperative Endophthalmitis Patient.

We present the draft genome assembly of an Elizabethkingia meningoseptica strain isolated from a 67-year-old postoperative endophthalmitis patient who suffered loss of vision in the right eye. The draft genome assembly has 167 contigs with a total size of 4,019,665 bp encoding multiple drug-resistant genes.

Incidence of new cardiovascular events in patients with and without peripheral arterial disease seen in a vascular surgery clinic.

BACKGROUND: To investigate the incidence of death and of new cardiovascular events at long-term follow-up of patients with and without PAD seen in a vascular surgery clinic. MATERIAL/METHODS: We investigated the incidence of death, new stroke/transient ischemic attack, new myocardial infarction, new coronary revascularization, new carotid endarterectomy, new peripheral arterial disease (PAD) revascularization, or at least one of the above outcomes at long-term follow-up of patients with and without PAD followed in a vascular surgery clinic. RESULTS: At least one of the above outcomes occurred in 259 of 414 patients (63%) with PAD at 33-month follow-up and in 21 of 89 patients (24%) without PAD at 48-month follow-up (p<0.0001). Death occurred in 112 of 414 patients (27%) with PAD and in 10 of 89 patients (11%) without PAD (p=0.002). Stepwise Cox regression analysis for the time to at least one of the 6 outcomes showed that significant independent risk factors were men (hazard ratio =1.394; 95% CI, 1.072-1.813; p=0.013), estimated glomerular filtration rate (hazard ratio =0.992; 95% CI, 0.987-0.997; p=0.003), and PAD (hazard ratio =3.520; 95% CI, 2.196-5.641; p<0.0001). Stepwise Cox regression analysis for the time to death showed that significant independent risk factors were age (hazard ratio =1.024; 95% CI, 1.000-1.049; p=0.048), estimated glomerular filtration rate (hazard ratio =0.985; 95% CI, 0.974-0.996; p=0.007), and PAD (hazard ratio =2.157; 95% CI, 1.118-4.160; p=0.022). CONCLUSIONS: Patients with PAD have a significantly higher incidence of cardiovascular outcomes, especially death, new PAD revascularization, and new carotid endarterectomy, than patients without PAD followed in a vascular surgery clinic.

Giant myxoma causing heart failure symptoms.

BACKGROUND: Myxomas arising from the eustachian valve are exceedingly rare. CASE REPORT: A 72-year-old Jamaican-Chinese woman was evaluated for worsening dyspnea. The 2-dimensional and real time 3-dimensional transesophageal echocardiogram showed a 75 mm length × 44 mm width, multilobulated, mobile mass arising from the eustachian valve occupying the entire right atrial and right ventricular cavities extending into the coronary sinus, right ventricular outflow tract, and proximal inferior vena cava. The patient underwent successful resection of the mass and replacement of the tricuspid valve. Histopathologic examination confirmed the diagnosis of atrial myxoma. CONCLUSIONS: This is the largest myxoma found on a Eustachian valve.