Identification of epicardial connections can improve the success rate of first-pass right pulmonary vein isolation.

Background: Epicardial connections between the right pulmonary vein (PV) and the right atrium have been reported. Objective: The purpose of this study was to evaluate the usefulness of our new pulmonary vein isolation (PVI) strategy with identification of these epicardial connections. Methods: Overall, 235 patients with atrial fibrillation were included. High-density mapping of the left atrium was performed to identify the earliest activation sites (EASs) before PVI in all patients. With our new strategy, if EASs around the right PV carina were identified, we ablated these sites and performed usual first-pass circumferential PVI. The patients were divided into 2 groups according to the ablation strategy. One hundred fifteen patients underwent first-pass PVI without information on EASs (nonanalyzed group), and 78 patients underwent ablation at EASs around the right PV carina in addition to PVI (analyzed group). After first-pass ablation around the PV antrum, remapping was performed. Results: High-density mapping before PVI showed that the prevalence of EASs around the right PV carina was 10.9% in all patients (9.6% in the nonanalyzed group, 12.8% in the analyzed group; P = .74. The first-pass right PVI success rate was higher in the analyzed group than in the nonanalyzed group (93.6% vs 82.6%; P = .04). The radiofrequency application time for PVI was significantly shorter in the analyzed group than in the nonanalyzed group (45.6 ± 1.0 minutes vs 51.2 ± 0.9 minutes; P <.05). Conclusion: Identification of epicardial connections before ablation could improve the success rate of first-pass right PVI.

Impact of Lipoprotein (a) on Long-Term Outcomes in Patients With Acute Myocardial Infarction.

BACKGROUND: Lipoprotein (a) (Lp(a)) is a complex circulating lipoprotein, and there is increasing evidence it is a risk factor for atherosclerotic cardiovascular disease (ASCVD). This study aimed to investigate the influence of Lp(a) serum levels on long-term outcomes after acute myocardial infarction (AMI).Methods and Results: Between January 2015 and January 2018, we enrolled 262 patients with AMI who underwent coronary angiography within 24 h of the onset of chest pain and had available Lp(a) data enabling subdivision into 2 groups: high Lp(a) (≥32 mg/dL: n=76) and low Lp(a) (<32 mg/dL: n=186). The primary endpoint was major adverse cardiac events (MACE), which was defined as a composite of cardiac death, nonfatal MI, and readmission for heart failure. Multivariate Cox regression analysis was performed to identify the predictors of MACE. The incidence of MACE was significantly higher in the high Lp(a) group than in the low Lp(a) group (32.8% vs. 19.6%, P=0.004). Multivariate analysis showed that Lp(a) ≥32 mg/dL was an independent predictor of MACE (hazard ratio 2.84, 95% confidence interval 1.25-6.60, P=0.013). CONCLUSIONS: High Lp(a) levels were associated with worse long-term outcomes after AMI, so Lp(a) may be useful for risk assessment.

An Intelligent Approach for Cloud-Fog-Edge Computing SDN-VANETs Based on Fuzzy Logic: Effect of Different Parameters on Coordination and Management of Resources.

The integration of cloud-fog-edge computing in Software-Defined Vehicular Ad hoc Networks (SDN-VANETs) brings a new paradigm that provides the needed resources for supporting a myriad of emerging applications. While an abundance of resources may offer many benefits, it also causes management problems. In this work, we propose an intelligent approach to flexibly and efficiently manage resources in these networks. The proposed approach makes use of an integrated fuzzy logic system that determines the most appropriate resources that vehicles should use when set under various circumstances. These circumstances cover the quality of the network created between the vehicles, its size and longevity, the number of available resources, and the requirements of applications. We evaluated the proposed approach by computer simulations. The results demonstrate the feasibility of the proposed approach in coordinating and managing the available SDN-VANETs resources.

Performance Evaluation of an Integrated Fuzzy-Based Driving-Support System for Real-Time Risk Management in VANETs.

The highly competitive and rapidly advancing autonomous vehicle race has been on for several years now, and it has made the driver-assistance systems a shadow of their former self. Nevertheless, automated vehicles have many obstacles on the way, and until we have them on the roads, promising solutions that can be achievable in the near future should be sought-after. Driving-support technologies have proven themselves to be effective in the battle against car crashes, and with Vehicular Ad hoc Networks (VANETs) supporting them, their efficiency is expected to rise steeply. In this work, we propose and implement a driving-support system which, on the one hand, could immensely benefit from major advancement of VANETs, but on the other hand can effectively be implemented as a stand-alone system. The proposed system consists of a non-intrusive integrated fuzzy-based system able to detect a risky situation in real time and alert the driver about the danger. It makes use of the information acquired from various in-car sensors as well as from communications with other vehicles and infrastructure to evaluate the condition of the considered parameters. The parameters include factors that affect the driver's ability to drive, such as his/her current health condition and the inside environment in which he/she is driving, the vehicle speed, and factors related to the outside environment such as the weather and road condition. We show the effect of these parameters on the determination of the driving risk level through simulations and experiments and explain how these risk levels are translated into actions that can help the driver to manage certain risky situations, thus improving the driving safety.

Application of Fuzzy Logic for Selection of Actor Nodes in WSANs -Implementation of Two Fuzzy-Based Systems and a Testbed.

The development of sensor networks and the importance of smart devices in the physical world has brought attention to Wireless Sensor and Actor Networks (WSANs). They consist of a large number of static sensors and also a few other smart devices, such as different types of robots. Sensor nodes have responsibility for sensing and sending information towards an actor node any time there is an event that needs immediate intervention such as natural disasters or malicious attacks in the network. The actor node is responsible for processing and taking prompt action accordingly. But in order to select an appropriate actor to do one task, we need to consider different parameters, which make the problem NP-hard. For this reason, we consider Fuzzy Logic and propose two Fuzzy Based Simulation Systems (FBSS). FBSS1 has three input parameters such as Number of Sensors per Actor (NSA), Remaining Energy (RE) and Distance to Event (DE). On the other hand, FBSS2 has one new parameter-Transmission Range (TR)-and for this reason it is more complex. We will explain in detail the differences between these two systems. We also implement a testbed and compare simulation results with experimental results.

Estimated glomerular filtration rate as a predictor of secondary outcomes in Japanese patients with coronary artery disease.

BACKGROUND: Coronary artery disease (CAD) is highly prevalent and strongly associated with adverse outcomes in patients with chronic kidney disease (CKD). Recent data demonstrate that estimated glomerular filtration rate (eGFR) is more useful than serum creatinine as a predictor of outcomes. METHODS AND RESULTS: We investigated the clinical significance of eGFR-defined CKD in Japanese patients with CAD. In 702 consecutive patients with suspected CAD who underwent coronary angiography, CKD (eGFR <60 ml/min/1.73 m(2)) was present in 345 patients (49%). The eGFR value was lower in patients with multi-vessel coronary artery disease compared to patients with no significant stenosis (59+/-24 ml/min/1.73 m(2) vs 66+/-21 ml/min/1.73 m(2), p<0.01). During a follow-up period of 36 months, secondary events that included all-cause death and cardiovascular events requiring hospitalization occurred in 114 (16%) patients. Multivariate analysis using a Cox proportional hazards model showed that CKD [relative risk (RR) 1.707, 95%CI, 1.170-2.489; p=0.004] along with diabetes (RR, 1.684, 95%CI, 1.262-2.386; p=0.008) were independent predictors of secondary events. CONCLUSIONS: eGFR-defined CKD is an important predictor of secondary outcomes in Japanese patients with CAD. Anti-atherosclerotic therapies under eGFR monitoring to consider renoprotection would be an important strategy to improve long-term prognosis in Japanese CAD patients.