Probabilistic Computational Analysis of Transcatheter Aortic Valve Leaflet Design

2013 ◽  
Author(s):  
Kewei Li ◽  
Wei Sun
Keyword(s):  
Aortic Valve ◽  
Computational Analysis ◽  
Computational Simulation ◽  
Medium Term ◽  
Short Term ◽  
Geometry Design ◽  
Transcatheter Aortic Valve ◽  
Aortic Valve Leaflet ◽  
Improved Performance

Aortic stenosis (AS) is abnormal narrowing of the aortic valve which partially obstructs outflow of blood from the left ventricle to aorta. Symptomatic AS is associated with a high mortality rate, approximately 50% in the first 2 years, if left untreated [1, 2]. Transcatheter aortic valve (TAV) implantation has been recently developed as an effective endovascular treatment for high-risk AS patients, in which a stented bioprosthetic valve is deployed through a catheter within the diseased aortic valve. Since the first procedure in 2002 [3], there has been an explosive growth in TAV implantation. By the end of 2011, there were 10 TAV companies that had first-in-man implantation data [4]. More than 50,000 TAV implantations have been performed worldwide since 2007. Short-term and medium-term outcomes after TAV implantation are encouraging with significant reduction in rates of death. However, adverse events associated with TAV implantation were reported [5, 6]. Furthermore, long-term durability and safety of these devices are largely unknown and needed to be evaluated and studied carefully [7, 8]. It is widely accepted that valve designs that reduce leaflet stresses are likely to give improved performance in long-term applications. The objective of this study was to quantify the effect of 2D TAV leaflet geometry design on 3D valve stress distribution using probabilistic computational simulation.

scholarly journals Impact of the timing of coronary revascularization relative to the transcatheter aortic valve implantation procedure: insights from a propensity score analysis based on a nationwide analysis

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
P Lantelme ◽  
A Bisson ◽  
T Lacour ◽  
J Herbert ◽  
F Ivanes ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Aortic Valve ◽  
Propensity Score ◽  
Short Term ◽  
Transcatheter Aortic Valve ◽  
All Cause Mortality ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Abstract Background The significance and the management of coronary artery disease (CAD) are disputed in patients treated by transcatheter aortic valve implantation (TAVI). In the presence of a significant CAD eligible for percutaneous coronary intervention (PCI), the issue of the timing of PCI relative to TAVI is unsettled. To answer this question, the present study aimed at comparing the short-term and long-term outcome in patients treated by staged PCI within a 90-day time interval before or after TAVI. Methods Based on the French administrative hospital-discharge database, the study collected information for all consecutive patients treated with TAVI between 2014 and 2018. Patients treated with PCI in the preceding 90 days before the TAVI procedure (pre-TAVI PCI) or subsequent 90 days after the TAVI procedure (post-TAVI PCI) were included. All-cause mortality, cardiovascular mortality, stroke, myocardial infarction and a combined cardiovascular endpoint were assessed at 30 days after the last procedure (short-term) and during the whole follow-up (long-term). Propensity score matching was used for the analysis of outcomes. Results 8613 patients met the inclusion criteria with a vast majority of pre-TAVI PCI patients (N=8324) as opposed to post-TAVI PCI (N=229). After propensity score matching, 2 groups of 227 patients with comparable characteristics were obtained. At 30 days, no significant difference was observed for any of the outcome tested with the exception of myocardial infarction more frequent in post-TAVI PCI (OR 2.43 [1.17–5.07]). After a mean [SD] follow-up of 459 [569] days, all outcomes were identical between subgroups. The figure below illustrates the Kaplan Meier curve for all-cause mortality. Conclusions Our study based on a French nationwide database shows that PCI is performed pre-TAVI in a majority of cases, with no significant impact on outcome. Deferring PCI after TAVI seems safe and may provide an opportunity to make the decision on more objective parameters while the stenosis has been removed (such as FFR or IFR). In any case, the timing of PCI relative to TAVI does not seem to represent a concern and should be decided on an individual basis. Figure 1 Funding Acknowledgement Type of funding source: None

P5579Pre-existing depression significantly improves after transcatheter aortic valve implantation (TAVI): analysis of long-term effects and screening for novel biomarkers

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
G Dannberg ◽  
L Baez ◽  
M Wiesel ◽  
S Moebius-Winkler ◽  
A Berndt ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Serum Levels ◽  
Depression And Anxiety ◽  
Short Term ◽  
Transcatheter Aortic Valve ◽  
Long Term Follow Up ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Abstract Background and aims Depression negatively affects symptom tolerance as well as clinical endpoints in cardiovascular diseases. For aortic stenosis (AS) patients undergoing Transcatheter Aortic Valve Implantation (TAVI), a reduction of pre-existing depression and anxiety in short term follow-up could be recently shown by our group. The current study was aimed to evaluate these effects in long-term follow-up and to screen for promising biomarkers, e.g., 5-Hydroxytryptamin (5-HT), Endothelin-1 (ET-1), neutrophil gelatinase associated lipocalin (NGAL) and Tenascin-C (Tn-C) variants. These molecules might reflect a pathophysiological link between reverse cardiac remodelling and mental state. Methods The study included 182 out of 226 patients who underwent TAVI at the University Hospital Jena since August 2016. Besides clinical parameters, the EuroQol questionnaire (EQ-5D), the Visual Analog Scale (VAS), the Clinical Frailty Scale (CFS) and, to specifically detect depression and anxiety, the Hospital Anxiety and Depression Scale (HADS-D) were assessed directly before TAVI, at 6-weeks, 6-month as well as 12- months follow-up. Blood samples were withdrawn before TAVI and during 6-weeks and 6-month follow-up. Results Study patients represented a typical moderate- to high-risk TAVI collective (n=182, mean age 78,1±7.9 years, 46,9% male, mean STS-Score 4.6±2,8). Before TAVI, analysis of HADS revealed ≥8 points, defined as pathologic, for depression and/or anxiety in 71 patients (39%) and for depression only in 46 patients (25.3%). In the depressive subgroup, there was a significant improvement after 6 weeks for depression (p<0.001) and anxiety (p=0.006). BNP serum levels were significantly reduced (p=0.007) and 6-minutes' walk distance was significantly increased from a low level (p=0.008), VAS, CFS and 2 out of 5 parameters of the EQ-5D were significantly improved (p<0.05). All observed short-term effects continued at stable levels over time. A pre-existing depression state was not associated with an increased long-term mortality rate, which was 14.8%. Circulating biomarker levels in depressive patients before and 6 weeks after TAVI revealed no significant differences. At the 6 months follow-up, only for C+ Tn-C there was a significant increase compared to both, the timepoint before TAVI (p=0.046) and the 6 weeks follow-up (p=0.033). Conclusions Already in short-term follow-up after successful TAVI, a remarkable decrease in depression could be detected using HADS. Especially in the depressive subgroup, the patients showed benefit also with respect to other surrogate parameters of mental health and functional performance. Interestingly, these effects were completely maintained not only in mid-term but also in long-term follow-up. We could show that the improvement of depression after TAVI is reflected by a delayed decrease of C+ Tn-C serum levels. C+ Tn-C can be suggested as promising biomarker possibly linked to reactive depression in somatic diseases.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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