Liang Zhou

Benoit Geller

Anne Wei

Baoyu Zheng

Jingwu Cui

Joint Routing and Rate Control Scheme for Multi-Stream High-Definition Video Transmission over Wireless Home Networks

Keywords: wireless home networks, routing, rate control, high-definition video

The support for multiple high-definition video streams in wireless home networks requires appropriate routing and rate control measures ascertaining the set of links for transmitting each stream and the encoding rate of the video to be delivered over the chosen links. The routing and rate allocation procedures impact the sustained quality of each video stream measured as the mean squared error distortion at the receiver, and the overall network congestion in terms of queuing delay per link. We study the trade-off between these two competing objectives in a united convex optimization formulation, and propose a distributed solutions for joint routing and rate control for multiple streams. For each stream, the optimal allocated rate strikes a balance between the selfish motive of minimizing video distortion and the global good of minimizing network congestions, while the routes are chosen over the least-congested links in the network. Simulation results are provided which demonstrate the effectiveness of our proposed joint routing and rate control scheme in the context of wireless home networks.

I. INTRODUCTION

As multimedia is expected to be a major traffic source on the next-generation wireless networks, supporting multiple High-Definition (HD) video streaming sessions over a wireless home media network has been one of the main fields of attention in the Smart Home Environment (SHE) community. Obviously, HD video gives rise to many attractive applications, as well as numerous technical challenges in the SHE. The design of such a system needs to address the unpredictable nature of wireless communication channels, while meeting the high data rate and low latency requirements of media streaming.

Inevitably, there are huge and different kinds of data streaming from different HD video streams which may influence each other and thus, reasonable routing and careful rate control are needed to prevent multiple simultaneous stream sessions from congesting the shared wireless networks. More specifically, the utility of the available routing and allocated rate are also different for streams with different contents: the same rate increase may impact a sequence containing fast motion rather differently than a head-and-shoulder news clip. In addition, it is observed that quality of the received video stream is also affected by the self-inflicted network congestion leading to packet drops due to late arrivals. An attempt to enhance the system performance should therefore account for both metrics in a congestion-distortion optimized fashion. When multiple streams are present in a wireless network, the chosen rate and routes for each stream would also affect the performance of others. Therefore, both rate allocation and routing need to be optimized for all streams in the network, preferably in a distributed manner.

In this paper, we study a convex optimization formulation of the joint routing and rate control problem for multiple HD video sessions sharing a common wireless home network. The main contributions or novelties of this paper consist of: (1) developing a united distortion model which captures both the impact of encoder quantization and packet loss due to network congestion on the overall video quality; (2) proposing a distributed joint routing and rate control scheme, where the allocated rate at each video stream depends on both the distortion-rate characteristic of the video and the network congestion increment, which in turn is obtained from a distributed routing procedure. In order to get a distributed solution of joint routing and rate control, we show that the optimal global trade-off between total video distortion of all streams versus overall network congestion can be translated into the local balance between reducing encoded video distortion versus constraining network congestion at each stream. So, for each stream, the optimal allocated rate strikes a balance between the selfish motive of minimizing video distortion and the global good of minimizing network congestions, while the routes are chosen over the least-congested links in the network.

The rest of paper is organized as follows. In the next section, we formulate the problem from the aspects of the video compression and network congestion, respectively. In Section III, we first summarize the congestion-distortion optimized routing, which serves as the basis for the joint routing and rate control scheme in the latter. And then, we provide some selected simulation results for the proposed joint scheme in Section IV. In section V we present related works on routing protocols and rate control methods for multiple streams related to wireless home networks, followed by concluding remarks in Section VI.

II. PROBLEM FORMULATION

In low-latency streaming applications, compressed video is transmitted over a network at a given rate. Typically, it is desirable to achieve end-to-end delays of no more than a few hundred milliseconds. In fact, the reconstructed video quality is affected by both source compression and quality degradation due to packet losses either caused by transmission errors or late arrivals. More specifically, the distortion introduced by quantization is evenly distributed across the encoded frames and is determined by the encoding bit-rate, while packet loss which usually introduces decoding errors lies in the frames containing the missing packets. In this paper, we assume that the two forms of induced distortion are independent and additive. Thus, we can calculate the overall distortion D all in terms of using MSE (Mean Square Error) as

D all = D comp + D loss (1)
where the distortion introduced by quantization at the decoder is denoted by D comp , and the additional distortion caused by packet loss is denoted by D loss . Considering a set of wireless links L, and a set of video streams denoted by S, for each video stream s ∈ S, the distortion caused by source compression can be approximated by:

D comp = θ s R s -R s 0 + D s 0 (2)
where R s is the rate of the video stream s ∈ S, θ s , R s 0 and D s 0 are the parameters of the distortion model which depend on the encoded video sequence as well as on the encoding structure [START_REF] Stuhlmuller | Analysis of video transmission over lossy channels[END_REF].

Using nonlinear regression techniques, these parameters can be estimated from empirical ratedistortion curves obtained by encoding a sequence at different rates. Likewise, the distortion caused by packet loss to decoded video can be modeled by a linear model related to the packet loss rate P loss :

D loss = αP loss ( 3
)
where α depends on parameters related to the compressed video sequence, such as the proportion of intra-coded macro-blocks and the effectiveness of error concealment at the decoder [START_REF] Stuhlmuller | Analysis of video transmission over lossy channels[END_REF]. The packet loss rate P loss reflects the combined rate of random losses and late arrivals of video packets. In a bandwidth-limited network, this combined loss rate can be further modeled based on the M/M/1 queuing model [START_REF] Zhu | Congestion-distortion optimized video transmission over ad hoc networks[END_REF]. In this case, the delay distribution of packets over a single link is exponential. Note that, since the end-to-end delay of packet delivery in multi-hop network is dominated by the queuing delay at the bottleneck link, the empirical delay distribution for realistic traffic patterns can still be modeled by an exponential:

P r{Delay > T } = e -λT ( 4
)
where T is the delay constraint and λ is the arriving rate which is determined by the average delay. Generally, λ needs to be determined empirically from end-to-end delay statics over the network [START_REF] Zhu | Congestion-distortion optimized video transmission over ad hoc networks[END_REF]. In order to present a general solution for online operation, here we construct a model to approximate the average link delay in a general wireless home network.

At first, it is assumed that at any time instance, only one of the competing links is allowed to transmit, which is similar to the CSMA/CA mechanism used in 802.11 networks. We denote link utilization as the fraction of time occupied by a link l ∈ L with traffic rate R l and link capacity C l :

u l = R l /C l (5)
where the value of R l equals to one of the R s , s ∈ S. The effective C l for an stream R l is measured as: The set of links that cannot transmit simultaneously with link l constitutes the interference set of link l, denoted as I l . In this case, the total utilization is constrained to be smaller than unity:

C l = ςC prev l + (1 -ς) ω T d ( 6
u I l = l∈I l u l < 1 (7)
In practice, the total channel time proportion is constrained to be u I l < γ, where γ < 1 is over-provisioning factor.

Congestion over each wireless link is measured as the average delay for all packets traversing that link. Following the classic M/M/1 queuing model, where average packet delay over a single link is inversely proportional to the residual link capacity, we propose to model the average packet delay for multiple links competing within the same network as 1 :

E{Delay} = 1 λ = l∈I l n • ω C l (γ -u I l ) ( 8
)
where n is the hop count which can be obtained from the routing algorithm at the network layer.

Therefore,

P r{Delay > T } = e - 1 l∈I l n•ω C l (γ-u I l ) T ( 9
)
Together with an average packet loss rate P B due to transmission errors, the total packet loss rate is then:

P loss = P B + (1 -P B )P r{Delay > T } = P B + (1 -P B )e - 1 l∈I l n•ω C l (γ-u I l ) T ( 10
)
The total distortion from packet loss can be expressed as

D loss = αP loss = α P B + (1 -P B )e - 1 l∈I l n•ω C l (γ-u I l ) T ( 11
)
Based on the previous discussion, we seek to minimize the sum of the total distortion D all = D comp + D loss as follows:

min R s ,s∈S s∈S θ s R s -R s 0 + D s 0 + α P B + (1 -P B )e - 1 l∈I l n•ω C l (γ-u I l ) T (12)
Intuitively, reconstructed video quality is limited by coarse quantization at lower rates; whereas at high rates, the video stream will cause more network congestion. This, in turn, translates into higher loss rates, hence reduces video quality. For live video steaming in bandwidth-limited environments, we therefore expect to achieve maximum decoded quality for some intermediate rate. 1 In practice, congestion may be a more complicated function of rate as predicted by M/M/1 model. However, this expression can be viewed as an approximation of the average link delay, capturing the non-linear increase of delay with total channel time utilization.

III. JOINT ROUTING AND RATE CONTROL SCHEME

In this section, we address the problem of joint routing and rate control among multi-stream HD video transmission over wireless networks. At first, we show route selection and rate control for each stream can be jointly optimized to trade-off between overall network congestion and total video distortion of all streams.

A. Congestion-Optimized Multi-Path Routing

Consider a simpler special case of the problem formulated in [START_REF] Couto | A High-Throughput Path Metric for Multi-Hop Wireless Routing[END_REF], where only one video stream is involved, and its rate R s is fixed. This then becomes the classical problem of minimizing network congestion via optimal flow assignment. Some centralized routing and traffic partitioning algorithms have been proposed for video over wireless ad-hoc networks, where multiple routes are extracted from the optimal flow assignment result, and the total traffic is dispersed over the multiple paths in a congestion-optimized manner [START_REF] Setton | Cross-layer Design of Ad Hoc Networks for Real-Time Video Streaming[END_REF].

This centralized scheme, however, requires knowledge of global network information such as capacities and flows along all the links, which restricts the scalability of the network. The computational complexity of the optimization and route extraction from the flow assignment may also exceed the capabilities of any single node in the network. To counter the problem, the total rate of the video stream R s is split into K small increments such that

R s = K k=1 R s k .
Then the optimal allocation of increment R s k can be achieved by finding a path P * k that accomplishes the following:

min P * k l∈L C l C l -R l R s k ( 13
)
where

R l = R l + l ∈I l
R l includes current traffic R l and other competing traffics for link l.

Note that the minimization objective in ( 13) corresponds to end-to-end accumulated sum of

C l /(C l -R l )
, which can be interpreted as "congestion sensitivity" over that link (i.e., amount of increase in congestion per unit increase in rate). Consequently, the optimal path P * k in (13) can be found via the distributed Bellman-Ford algorithm [START_REF] Boyd | Convex Optimization[END_REF], by setting the link cost to congestion sensitivity. Every node maintains a minimum-cost path from itself to the source, exchanges this information with its neighbors, and updates to a lower cost path if it discovers one via a neighbor. When the destination node reports the chosen path for a given rate increment R s k to the sender, it can easily append the corresponding accumulated congestion sensitivity value to the path information, for later use in the rate control process as explained in the next section.

The reader is referred to the original paper for further details of the distributed routing protocol.

B. Distributed Joint Routing and Rate Control

Here, we discuss the framework for a distributed methodology to allocate rate to a video stream s ∈ S. The Karush-Kuhn-Tucker (KKT) necessary and sufficient conditions for the optimal solution to [START_REF] Couto | A High-Throughput Path Metric for Multi-Hop Wireless Routing[END_REF] state that the allocated rate to stream s should either meet the boundary condition exactly, or correspond to zero partial derivative:

dD comp dR s + α dD loss dR s = 0 (14)
where dD comp /dR s is derived from the video distortion model [START_REF] Couto | A High-Throughput Path Metric for Multi-Hop Wireless Routing[END_REF] as

dD comp dR s = - θ s (R s -R s 0 ) 2 (15)
Hence the distortion reduction caused by increasing encoding rate by R s k is

-D k comp ≈ θ s (R s -R s 0 ) 2 R s k ( 16
)
The slope of packet loss distortion increment dD loss /dR s , on the other hand, can be expressed as:

dD loss dR s ∝ (1 -P B ) l∈L 1 γ -u I l ≈ (1 -P B ) l∈L 1 1 - l ∈I l u l ( 17
)
The resulting packet loss distortion increment D k loss can be then be approximated as:

D k loss ≈ (1 -P B ) l∈L C l C l -R l R s k ( 18
)
where cross-traffic R l includes contributions from current traffic R l and other video streams.

Note that ( 18) is almost the same as the optimization criterion in [START_REF] Marina | On Demand Multi-Path Distance Vector Routing in Ad hoc Networks[END_REF], and can be accumulated over the chosen links of path. This information is then collected at the destination node and fed back to the source.

Given the packet loss distortion increment D k loss in [START_REF] Jacobson | Congestion avoidence and control[END_REF] and the video compression distortion reduction -D k comp in ( 16), the source node can make the rate control decision by comparing the two quantities. The allocated rate will be increased by R s k until -D k comp > α D k loss , i.e., when the benefit of distortion reduction is no longer worthwhile the consequential network congestion. Due to the convex nature of both D comp and D loss , the initial distortion reduction is typically significant for small rate increments, whereas increase in network congestion starts out slowly. Therefore, the rate control algorithm can continue until it reaches the optimal rate that strikes a balance between the two trade-off slopes.

In order that the source rates can be adapted at the transport layer according to network states reported from the network layer, the cross-layer information exchange is needed. Fig. 1 illustrates various components in such a system. At the MAC layer, a link state monitor keeps an online estimate of the effective capacity C l . It also records the intended rate allocation R s advertised by each stream, and calculates the link utilization u l accordingly. Periodic broadcast of link state messages are used to collect the values of C l and u l from neighboring links in the same interference set. At the network layer, the routing information obtained from the routing algorithm can be used to calculate the P loss . At the application layer, the video rate controller at the source advertises its intended rate control R s , and calculates the value of D all accordingly.

The link state monitor traversed by the stream then calculates the relevant parameters in [START_REF] Couto | A High-Throughput Path Metric for Multi-Hop Wireless Routing[END_REF] based on its local cache of capacity, utilization information of all the links within its interference set. The destination node extracts such information from the video packet header and reports back to the sender in the acknowledgment packets, so that the video rate controller can re-optimize its intended rate R s based on the proposed joint routing and rate algorithm, with updated link state information.

IV. SIMULATION RESULTS AND DISCUSSIONS

In this section, we conduct simulation experiments to study the performance of the proposed joint routing and rate control scheme in a distributed framework, in which we employ 802.11a network as a wireless home network. At first, we describe the simulation environment. And then, we present the main simulation results where we show the objective results of the performance of the proposed scheme under different scenarios. Finally, we conclude this section by summarizing the conclusions based on the selected simulation results.

A. Simulation Setting

We simulate a small wireless network with 15 nodes randomly placed in a 100m-by-100m square in ns-2, all with transmission range of each other, as illustrated in Fig. 2. Each node follows the IEEE 802.11a protocol, with a rate of 54Mbps for playload and 6Mbps for MAC headers and ACK packets. Here, we use a two-state Markov model (i.e.Gilbert model) to simulate the bursty packet loss behavior. The two states of this model are denoted as G (Good) and B (Bad).

In state G, packets are received correctly and timely, whereas, in state B, packets are assumed to be lost. This model can be described by the transition probabilities P GB from state G to B

B. Performance Evaluation of the Proposed Scheme

At first, we validate the distortion model introduced in Section II. Fig. 3 shows the rate-PSNR (Peak Signal-to-Noise Ratio) tradeoff when HD City and Crew (300 frames) stream over the 2 The state transitional probabilities PGB and PBG are fitted to a 15-second packet delivery trace collected in [START_REF] Van Beek | Delay-constrained rate adaptation for robust video transmission over home networks[END_REF], with MAClevel packet loss ratio of 8.3%, average duration of 0.8ms for the bad state, and 8.8 ms for the good state. We then choose similar state transitional probability values, to simulate channels with MAC-level loss ratios in the range of 3-14% wireless network, respectively. The two sequences are sent following the routing illustrated in Fig. 2. The model is fit to experimental data for two cases: in the first case, the only losses considered are due to late arrivals; in the second, an additional end-to-end random loss rate of 5% is considered. The bell-shape of the curves illustrates that the highest performance is obtained when the streaming rate achieves the optimal tradeoff between compression quality and self-inflicted congestion. The approximate optimal operating rate computed by numerically solving (12) matches closely with experimental data.

We next evaluate the performance of the proposed optimization measures with multiple video streams. Note that if the chosen routes for each stream travel over non-overlapping links, then the network can be decomposed into independent subsets supporting each stream unaffected by others, which reduces to the scenario of single stream. In order to investigate the interactions among multiple streams, we choose the same source and destination nodes for all streams so as to have shared links in the chosen routes. Fig. 4 illustrates the initial and final routes chosen for all two streams during the optimization of the joint routing and rate allocation process. The corresponding rate allocated to each stream is shown in Fig. 5 over iteration steps. It can be observed that the selected routes for two of the streams (City in solid line and Crew in dashed line) have both changed over the iterations, each re-dispensing its own traffic over the network Fig. 5. Allocated rate for each stream over the iterations, corresponding to the same setting in Fig. 4 to avoid already congested links. Changes in the routes also affect the congestion increment information calculated during routing, which in turn leads to changes in the rate allocation decisions.

To demonstrate the effectiveness of our proposed rate control algorithm, we use the representative drop-tail scheme which employs the fixed rate allocation and the Additive-Increase-Multiplicative-Decrease (AIMD)-based rate control method which is used by TCP (Transmission Control Protocol) congestion control [START_REF] Altman | Performance analysis of AIMD mechanisms over a multi-state Markovian path[END_REF] for comparison. In order to get a more clear picture of how the allocated rate reflects the reconstructed quality, we just use one test sequence, city, streaming over network in this simulation. More specifically, the drop-tail scheme employs a fixed source coding rate R f = 8Mbps and when the rate exceeds the current optimal transmission rate available for the selected source-destination pair, it will drop the subsequent encoded packets.

Instead of performing proactive rate controls by optimizing a chosen objective according to observed network and video characteristics, the AIMD-based scheme is reactive in nature, probing the network for available bandwidth and reducing rate control after congestion occurs.

Each stream s initiates its rate at a specified rate R AIM D s corresponding to the minimum acceptable video quality, and increases its allocation by R s every t seconds unless network

(R s -R AIM D s /2).
In Fig. 6, we show a performance comparison between our proposed rate control scheme and the competing methods, drop-tail and AIMD, in the scenario where packet losses are caused only by channel over-pumping 3 . In Fig. 6(a), the average PSNR using the proposed rate control algorithm is 36.08 dB while it is 35.39 using AIMD-based method and 34.46 dB for the case of drop-tail.

Thus, using our proposed algorithm can achieve almost 0.69 dB and 1.62 dB performance gains comparing to the AIMD and drop-tail scheme, respectively. From the network profile, illustrated in Table I (the value is averaged over one GOP), we can see that for GOP No.1, No.2, No.4, and No.6, the operation transmission rate is higher than the fixed 8 Mbps. Thus, using our rate control can fully exploit the optimal transmission rate resulting in improved performance compared to using a fixed-rate coding scheme. On the other hand, for GOP No.3, No.5, and

No.7, it is obvious that the fixed source coding rate is higher than the operation transmission rate; therefore, packet losses will occur when the transmission buffer is full resulting in the last couple of frames being lost which cause substantial performance degradation. A lost frame is concealed by just copying the previous frame and if several consecutive frames are lost, the degradation will be even more serious since the concealed frames are then used as correctly received frames to conceal the subsequent lost frames. This results in substantial error propagation which can obviously observe from Fig. 6(b) which presents the each frame PSNR value in GOP No.3. It should also be noted from Fig. 6 that the performance achieved by the proposed rate control method is also super to the traditional AIMD-based method. On one hand, although the AIMDbased method can adapt to the network condition, the network is so dynamic that a congested node mobility and wireless channel variations. This can result in significant protocol overhead, especially under high node mobility and dynamic channel conditions. The on-demand protocols on the other hand involve discovery of the route whenever data need to be transmitted between a source-destination pair. They typically incur less overhead traffic than the table-driven protocols, and can consequently better adapt to dynamically varying topologies. Comparative study of various routing protocols has been an active research area in the wireless ad-hoc networking community.

The aforementioned routing strategies evaluate the best sequence of nodes in accordance with criteria like minimum hop or delay, and forward data along a single path. Couto et al. [START_REF] Couto | A High-Throughput Path Metric for Multi-Hop Wireless Routing[END_REF] pointed out the inadequacy of minimum-hop routing in wireless ad-hoc networks, and proposed alternative link metrics for evaluating a path. Extensions to multi-path routing have also been proposed for multi-path AODV [START_REF] Marina | On Demand Multi-Path Distance Vector Routing in Ad hoc Networks[END_REF] and for ExOR, an opportunistic multi-hop routing strategy that broadcasts data packets to explore multiple paths in the network [START_REF] Biswas | ExOR: Opportunistic Multi-Hop Routing for Wireless Networks[END_REF]. For video streaming, benefits of multi-path routing over ad-hoc networks are demonstrated in terms of robust packet delivery via path diversity and higher sustainable rate and quality due to bandwidth aggregation [START_REF] Li | Multi-source multi-path video streaming over wireless mesh networks[END_REF].

Unlike most previous works that consider routing for generic data traffic over wireless networks, we take into account specific characteristics of video streaming in the evaluation of routes. Network congestion is incorporated explicitly into the route selection metric, to meet the stringent latency requirement for video packet delivery. The rate-distortion characteristic of each stream is also considered in the rate allocation procedure to accommodate multiple streams with various video contents and complexity.

B. Multi-Stream Rate Allocation

The issue of multi-stream rate allocation is still an open problem and has received considerable attention recently. Practical solutions such as TCP congestion control [START_REF] Jacobson | Congestion avoidence and control[END_REF], [START_REF] Altman | Performance analysis of AIMD mechanisms over a multi-state Markovian path[END_REF] and TCP-Friendly Rate Control (TFRC) [START_REF] Floyd | Promoting the use of end-to-end congestion control in the Internet[END_REF] are widely used over the Internet. A mathematical framework of multi-user rate allocation is presented in [START_REF] Kelly | Rate control for communication networks: Shadow prices, proportional fairness and stability[END_REF], where the authors also analyzed two classes of distributed solutions, corresponding to the primal and dual decomposition of the optimization objective. In wireless networks, adaptive transmission techniques are typically used to protect the video stream against the time-varying channel [START_REF] Shen | Error resilient video communications over CDMA networks with a bandwidth constraint[END_REF]. When multiple streams are involved, centralized channel time allocation among multiple wireless stations has been investigated in [START_REF] Van Der Schaar | Cross-layer wireless multimedia transmission: challenges, principles, and new paradigms[END_REF]. Distributed algorithms have also been proposed, using rate-distortion optimized packet scheduling in [START_REF] Chakareski | Rate-distortion optimized distributed packet scheduling of multiple video streams over shared communication resources[END_REF] for rate allocation among streams sharing a bottleneck link, and using the subgradient method in [START_REF] Zhu | Distributed rate allocation for multistream video transmission over ad hoc networks[END_REF] for streams competing over a wireless mesh network.

For a more practical setting, a rate allocation algorithm combined with a packet partitioning algorithm has been proposed to support video streaming from multiple senders to a single receiver over the Internet [START_REF] Nguyen | Multiple sender distributed video streaming[END_REF]. The rates are chosen to match the available network bandwidth for each stream, and the packet partitioning is designed to minimize start up delay. For video streaming over a wireless hop, a rate control scheme has been shown to efficiently utilize the available wireless link capacity using multiple TFRC connections [START_REF] Chen | Rate Contcrol for Streaming Video over Wireless[END_REF].

Our approach targets rate control in conjunction with route selection. The optimization objective function is comprised of both video distortion and network congestion. This differs from most existing works where routing and rate allocation are performed separately, without the notion of limiting overall network congestion.

VI. CONCLUDING REMARKS

In this paper, we use cross-layer design to maximize the perceived wireless video quality by combing routing and rate control techniques. As detailed in the paper, our proposed joint scheme can adaptively to dynamic network condition by adjust the routing and the allocated rate for each video stream. The simulation results demonstrate the effectiveness of our proposed joint routing and rate control scheme for multi-stream high-definition video transmission over wireless home networks.

  the distortion caused by packet loss P loss total packet loss rate which contains the random packet loss and the packet loss caused by late arrival P B the random packet loss rate due to transmission errors

  ) by logging average packet size ω and average delivery time T d at the MAC (Medium Access Control) layer, including the overhead of header and ACK (ACKnowledge) transmissions. In (6), C prev l denotes the pervious instantaneous estimation of C l , which is smoothed over time with the value of ς empirically chosen at 0.95.

Fig. 1 .

 1 Fig. 1. Cross-layer information exchange among the network state monitor at the network layer and the video rate controller at the transport layer.

Fig. 2 .

 2 Fig. 2. Example network with 15 nodes randomly positioned in a 100m-by-100m square area. Both of the video streams are sent from node 1 to node 15.

Fig. 3 .

 3 Fig. 3. Decoded video quality approximate model and experimental data for HD City and Crew sequences at 60 frames per second and GOP length of 16 and a playout deadline of 350 ms. The value of α is 202 for both sequences.

Fig. 4 .

 4 Fig. 4. (a)Initial and (b) final route selections of all two streams: City in solid line and Crew in dashed line. For this instance, the rate increment is 500 kbps.

Fig. 6 .

 6 Fig. 6. Performance comparison between different rate control schemes.

Here, we assume that no transmission errors occurred

ACKNOWLEDGMENTS

This work is partially supported by NSF under Grant 0430886 and the International Project PRA-SI (financed by France and China governments) under Grant No. SI04-03. Moreover, we also thank the anonymous reviewers for insightful comments and suggestions.

node forwarding a few seconds might not be used at all at the point in time when the source reacts to the congestion. On the other hand, the proposed rate control method further take advantage of explicit knowledge of the video distortion-rate characteristics, and can achieve more balanced video quality.

C. Observations

Based on the selected objective simulation results described above, there are several main observations:

• The routing algorithm is a basic facet in implementing the video transmission over wireless networks. The proposed routing algorithm can re-dispensing the traffic to avoid already congested links.

• The rate control plays an important role in the whole video transmission system. If the operating rate is lower than the optimal transmission rate, performance loss is due to the source coding inefficiency resulting from the use of an unnecessarily lower source coding rate; if the operating rate is higher, performance loss is caused by packet losses due to buffer overflow and network congestion.

• The joint routing and rate control scheme has the effectiveness in achieving the optimal congestion-distortion trade-off for the overall system.

V. RELATED WORKS

In this section, we present the related works on the ad-hoc routing protocols and multi-stream rate control algorithm related to the wireless home networks. In addition, we also indicate the difference between our proposed routing and rate control with the previous works.

A. Ad-Hoc Routing Protocols

Several ad-hoc routing protocols that have been proposed over the years include proactive tabledriven protocols like Destination-Sequenced Distance-Vector (DSDV) routing [START_REF] Perkins | Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Moblie Computers[END_REF] and Optimized Link State Routing (OLSR) [START_REF] Clausen | Optimized Link State Routing Protocol (OLSR)[END_REF], as well as on-demand protocols like Dynamic Source Routing (DSR) [START_REF] Johnson | Dynamic Source Routing in Ad hoc Wireless Networks. Mobile Computing[END_REF] and Ad-hoc On-demand Distance Vector (AODV) routing [START_REF] Perkins | Ad-hoc On-Demand Distance Vector (AODV) Routing[END_REF]. The former involve the evaluation and storage of the routing tables pertaining to the topology at each node.

The routing tables are periodically updated to counter the topological changes associated with


Liang Zhou

Benoit Geller

Anne Wei

Baoyu Zheng

Jingwu Cui

Shan Xu

Cross-Layer Rate Allocation for Multimedia Applications in Pervasive Computing Environment

An important issue for supporting multimedia applications in multiple heterogeneous networks, a typical pervasive computing environment, is how to optimize the rate allocation by intelligently utilizing the available network resources while, at the same time, to meet each application's QoS (Quality of Service) requirement. In this work, we develop and evaluate a rate allocation scheme in terms of audio and video applications based on a cross-layer design framework. At first, we construct a general distortion model according to the observed parameters in each network, as well as each application's rate-distortion characteristic. Then, the rate allocation is formulated as a convex optimization problem that minimizes the sum of the expected distortion of all applications. Furthermore, the realization of the distributed rate allocation algorithm for achieving an optimal or close-to-optimal end-to-end QoS under the overall limited resource budget is the highlight of this paper. Simulation results are provided which demonstrate the effectiveness of our proposed distributed rate allocation scheme.

I. INTRODUCTION

Pervasive computing environment provides seamless access to multiple heterogeneous networks at any time from any location for any user [START_REF] Ren | A Novel Privacy Preserving Authentication and Access Control Scheme in Pervasive Computing Environments[END_REF]. In this environment, the service providers have interests in operating kinds of multimedia applications, and integrating the characteristics of heterogeneous networks and multimedia applications to provide pervasive service.

Inevitably, there are huge and different kinds of application data streaming from different users which may influence each other and thus, it is essential to enforce a distributed rate allocation policy designed for suitable application metrics and efficient network utilization. Indeed, the problem of rate allocation over heterogeneous networks is, compared to traditional network, further complicated by the heterogeneity in both the application contents and the network conditions. This poses a serious challenge for providing, in this context, dependable and different applications that meet the users' requirements in terms of QoS, reliability and availability [START_REF] Zahran | A Generic Framework for Mobility Modeling and Performance Analysis in Next-Generation Heterogeneous Wireless Networks[END_REF].

The issue of rate allocation among multiple flows over limited network resources is still an open problem and has received considerable attention recently. For media applications over UDP, TCP-friendly rate control is widely used [START_REF] Handley | TCP Friendly Rate Control (TFRC): Protocol Specification[END_REF], and several modifications have been proposed to improve its media adequation [START_REF] Yan | Media-and TCP-friendly congestion control for scalable video streams[END_REF], [START_REF] Chen | Rate control for streaming video over wireless[END_REF]. The problem of efficient utilization of multiple networks via a suitable allocation of media flows has also been explored from different aspects. [START_REF] Alpcan | A utility-based congestion control scheme for Internet-style networks with delay[END_REF] addresses a game-theoretic framework which allocates bandwidth for elastic services with fixed capacities. In addition, [START_REF] Shakkottai | The case for non-cooperative multihoming of users to access points in IEEE 802.11 WLANs[END_REF] proposes a cost price mechanism, which enables a mobile device to split its traffic among several 802.11 access points based on the throughput obtained and the price charged. However, [START_REF] Shakkottai | The case for non-cooperative multihoming of users to access points in IEEE 802.11 WLANs[END_REF] does not take into account the existence of heterogeneous networks with different traffic characteristics, nor does it specify an operational method to split the traffic. In [START_REF] Jurca | Media-specific rate allocation in heterogeneous wireless networks[END_REF], a specific rate allocation in heterogeneous networks is proposed, by taking into account the impact of both packet loss rates and available bandwidth over each link. To the best of our knowledge, the current literatures consider the multimedia application and the heterogeneous networks characteristics separately and independently. In order to provide a satisfying QoS for any application in the context of heterogeneous networks, the above two factors are jointly considered in this paper.

The rest of paper is organized as follows. In the next section, we present the system distortion model of the application and network, respectively. In Section III, we propose a distributed rate allocation scheme for multimedia application streaming sessions in heterogeneous networks. Then, we provide some selected simulation results for the proposed rate allocation scheme in Section IV and make some concluding remarks in Section V.

II. SYSTEM DISTORTION MODEL

A. Multimedia Applications

Assume multiple users S = {1, ...s..., S} that simultaneously access any of two different multimedia applications via a server, namely real-time video streaming (A 1 ), and audio conversation (A 2 ). Let user s ∈ S access one of the available applications A i (i = 1, 2) at one transmission period. The server decides the average allocated rate to user s that has chosen application A i . We assume that the server can scalably adapt the transmission process to the channel conditions for user s. To this end, for each application A i , the server can choose the right transmission parameters, from a predefined set of available parameters R Ai . We assume that N A1 encoded video layers and N A2 audio transcoders are available at the server. Each video layer l ≤ N A1 is characterized by its average encoding rate ρ l and each transcoder v ≤ N A2 is characterized by its encoding rate ρ v . We define

R A1 = {ρ l : 1 ≤ l ≤ N A1 }, R A2 = {ρ v : 1 ≤ v ≤ N A2 }
as the available parameter sets for the video and audio applications, respectively.

In general, all the video and audio sources should be compressed conveniently for transmission and storage; however, this leads to information loss. According to [START_REF] Zhu | Congestion-distortion optimized video transmission over ad hoc networks[END_REF], the distortion caused by source compression can be approximated by:

D comp = θ R -R 0 + D 0 ( 1
)
where R is the rate of the video/audio stream, which is equal to R Ai (i=1,2) as described above; θ, R 0 and D 0 are the parameters of the distortion model which depend on the encoded video/audio sequence as well as on the encoding structure.

Using nonlinear regression techniques, these parameters can be estimated from empirical rate-distortion curves obtained by encoding a sequence at different rates.

B. Multiple Heterogeneous Networks

Similarly to D comp , the distortion caused by packet loss in heterogeneous networks can be modeled by a linear model related to the packet loss rate P loss :

D loss = κP loss (2)
where κ depends on parameters related to the compressed sequence, such as the proportion of intra-coded macro-blocks and the effectiveness of error concealment at the decoder. The packet loss rate P loss combines the rate of random losses and late arrivals of packets. In a bandwidth-limited network, this combined loss rate can be further modeled based on the M/M/1 queuing model. In this case, the delay distribution of packets over a single link is exponential [START_REF] Zhu | Congestion-distortion optimized video transmission over ad hoc networks[END_REF]. Note that, since the end-to-end delay of packet delivery in wireless network is dominated by the queuing delay at the bottleneck link, the empirical delay distribution for realistic traffic patterns can still be modeled by an exponential formulation function:

P r{Delay > T } = e -ωT (3)
where T reflects the delay constraint, and ω is the arriving rate which is determined by the average delay:

ω = 1 E{Delay} (4)
Generally, ω needs to be determined empirically from endto-end delay statistics over the network. In order to present a general solution for online operation, here we employ a model to approximate the average packet delay. Consider multiple networks N = {1, 2, ..., N } simultaneously available to multiple users S = {1, 2, ..., S}. Each network n ∈ N is characterized by its Available Bit Rate ABR n and Round Trip Time RT T n , which are measured and updated periodically. It should be noted that as channel conditions in wireless environments change on very short time scales (e.g., up to a few tens of ms), we assume that ABR n and RT T n represent average values computed on larger time scale (e.g., one to a few seconds), and represent the average channel conditions for user s ∈ S on the given period.

Therefore, we can use a matrix R = {R n s } S×N to denote the rate allocation, where each element R n s corresponds to the allocated rate of user s ∈ S over the network n ∈ N. Therefore, the total allocated rate over network n is R n = s∈S R n s , and the total allocated rate for user

s is R s = n∈N R n s .
We express the Residual Bandwidth (RB) over network n as:

RB n = ABR n - s∈S R n s (5)
For user s in network n, the observed available bandwidth ABR n s is :

ABR n s = ABR n - s =s,s ∈S R n s (6)
In fact, the average packet delay typically increases due to the network congestion. Here, we employ a simple fractional function to approximate the non-linear increase of the packet delay in n ∈ N based on M/M/1 model [START_REF] Draper | A universal approach to queuing with distortion control[END_REF]:

E{Delay} = β n RB n = β n ABR n - s∈S R n s (7)
Assuming equal delay on both directions, the value of β n can be estimated from the last past observations of RT T n and RB n :

β n = RB n • RT T n 2 (8)
More specifically, if the current residual bandwidth is equal to the past observation value for network n ∈ N (RB n = RB n ), the average current delay is RT T n /2. Therefore, for each network n ∈ N:

P r{Delay > T } = e -ωT = e - 2(ABR n - P s∈S R n s ) RB n RT T n T ( 9
)
Taking into account P n B , the random packet loss rate in network n ∈ N due to transmission errors, the total packet loss rate in network n ∈ N is then:

P n loss = P n B + (1 -P n B )P r{Delay > T } = P n B + (1 -P n B )e - 2(ABR n - P s∈S R n s ) RB n RT T n T ( 10
)
The overall distortion from packet loss in network n ∈ N can be expressed as:

D n loss = κP n loss = κ P n B + (1 -P n B )e - 2(ABR n - P s∈S R n s ) RB n RT T n T (11)

III. DISTRIBUTED RATE ALLOCATION SCHEME

In general, the reconstructed video/audio quality over network is affected by both the source compression from the encoder and the packet loss during the transmission. Here, we assume that the two forms of the induced distortion are independent and additive. Thus, we can calculate the overall distortion D all as D all = D comp + D loss [START_REF] Dantzig | Linear Programming and Extensions[END_REF] Based on the previous discussion, we seek to minimize the sum of the total distortion D all as follows:

min s∈S,n∈N D all (R n s ) = s∈S θs P n∈N R n s -R 0 s + D 0 s + n∈N κ P n B + (1 -P n B )e - 2(ABR n - P s∈S R n s ) RB n RT T n T ( 13
)
subject to R n s = ABR n s P n∈N ABR n s R s , ∀n ∈ N R n s ≤ ABR n s , ∀n ∈ N where θ s , R 0
s and D 0 s are the corresponding parameters for user s ∈ S. Intuitively, the reconstructed quality is limited by coarse quantization at low rates, whereas at high rates, the application stream will cause more network congestion. This, in turn, leads to higher loss rates and reduces the reconstructed quality. For multiple applications in bandwidthlimited environments, we therefore expect to achieve optimal QoS for some intermediate rate.

A. Piecewise Approximate Theorem

In order to get an optimal or close-to-optimal result with a fast convergence adapting to the online operation, here we propose a Continuous Piecewise-Linear (CPL) approach for solving the rate allocation optimization based on the utility framework introduced in [START_REF] Kelly | Stability of End-to-End Algorithm for Joint Routing and Rate Control[END_REF], which iteratively takes a locally approximate optimal decision on each user in each network. The foundation of our proposed piecewise approximate method follows the fact that, the CPL function used to approximate the original goal function is convex in the convex union of many small hypercubes, and that an approximately globally optimal solution of the original problem confined in this union can be found in the set of locally solutions. In many cases, the number of such unions may be much less than that of all smaller hypercubes partitioned. Hence, the CPL approach can decrease the computational effort greatly.

Since D all is the sum of the univariate functions D comp for each user s and D loss for each network n, CPL approximation can be obtained based on each function by a univariate CPL function. This can be achieved by partitioning the interested region of each univariate function into sufficiently many nonoverlapping small intervals. Let g be an arbitrary univariate function whose interested region is [α, α ] ⊂ R. Let m breakpoints α < α 1 < α 2 < . . . < α m < α be suitably chosen so that g can be approximated by the linear function

lk = A k x + B k in each small interval I k = [α k-1 , α k ] for any 1 ≤ k ≤ m + 1
, where α 0 = α, α m+1 = α ; A k and B k are determined by the linear equations g(α k-1 ) = lk (α k-1 ), g(α k ) = lk (α k ). An approximation function ϕ of g on [α, α ] can be obtained by connecting these segments.

For any 1 ≤ k ≤ m, we call α k an inflection point if A k > A k+1 . Denote by α τ (1) < α τ (2) < . . . < α τ (q) all inflection points among the breakpoints α k (q ≤ m). Let τ (0) = 0, τ (q + 1) = m + 1, and define Ît = [α τ (t-1) , α τ (t) ] for any 0 ≤ t ≤ q+1. It can be seen that Ît is the union of intervals I k , τ (t-1) < k < τ(t). Based on the above partition we can get a piecewise-convex expression of the function ϕ, which is very useful for the global optimization of separable programming problems. This is given by the following theorem:

Theorem: For any 1 ≤ t ≤ q + 1,

ϕ(λ) = max k∈ŝt lk (λ), ∀λ ∈ Ît (14)
where ŝt = k, τ(t -1) < k ≤ τ (t). Proof: For any

1 ≤ t ≤ q + 1, since A τ (t-1)+1 < A τ (t-1)+2 < • • • < A τ (t)
, according to [START_REF] Dantzig | Linear Programming and Extensions[END_REF], function ϕ( λ) is convex on Ît , where λ is arbitrarily chosen in Ît . There should be an integer k ∈ ŝt such that λ ∈ I k and ϕ( λ) = lk ( λ). Then, the following relation must be satisfied,

lk ( λ) ≤ lk ( λ), ∀k ∈ ŝt -{k} (15)
Otherwise, there is a k ∈ ŝt -{k} for which lk ( λ) > lk ( λ), and we can choose a λ ∈ Îk ⊂ Ît and a sufficiently small positive number

ε such that λ = ε λ+(1-ε) λ ∈ I k . Because ϕ is convex on Ît , we have ϕ(λ ) ≤ εϕ( λ) + (1 -ε)ϕ( λ) = ε lk ( λ) + (1 -ε) lk ( λ) < ε lk ( λ) + (1 -ε) lk ( λ) = lk (λ ) (16)
which contradicts the known relation ϕ(λ ) = lk (λ ). As the above λ is arbitrarily chosen, the theorem is proved. According to the above theorem, in order to get an approximate optimal value for the rate allocation problem (( 13) is a convex function), we can partition the original interested region into a number of smaller non-overlapping hypercubes and approximate the goal function on every hypercube by a convex CPL function. In fact, the goal function D all corresponds to the arbitrary univariate function g and any potential rate allocation is the breakpoint in the above theorem. Therefore, finding the appropriate breakpoints and judging whether they are inflection points or not are the key issues for implementing the piecewise approximate theorem to our rate allocation problem. In order to get around this difficulty, we employ a utility-based function described in the following subsection.

B. Rate Allocation Algorithm

We define R n s → R n s as the transition of the next allocation rate for user s ∈ S in network n ∈ N, and

R n s is selected in the set of R Ai (i=1,2). R n s = R n s + R n s where R n s
is the rate improvement varied at each iteration 1 . The utility of this transition can be calculated as:

U n s = ϕ(R n s ) -ϕ(R n s ) R n s -R n s ( 17
)
where ϕ is the approximate linear function for D all in the interval of [R n s , R n s ]. The total utility matrix is U = {U n s } S×N . At each iteration, the proposed algorithm finds R = {R n s } S×N that brings the highest utility U * = {U n s } S×N to the overall system by its transition:

U * = arg max R U ( 18
)
1 In theory, the initial R n s can be chosen at random as long as it is less than ABR n s , here the initial value R n s is set to ABR n s /2.

One starts to allocate resources to user s in network n. Once the resources of the network n are depleted, the algorithm will find a different user that can free the required resources for user s in the other network, by allocating part of its rate. This operation is performed as long as the overall utility of the system is still improved, and as long as free network resources still exist in the overall system. The algorithm stops when there are no more free resources in the network system, or when no other possible user transition can bring any improvement in the overall system utility. The proposed Distributed Rate Allocation (DRA) algorithm (see Table .I) represents a sketch of the proposed algorithm. In this algorithm, the IntraNet procedure always attempts to increase the system's utility by allocating the resource in the network n ∈ N to the best user. If the free resources are not sufficient, the InterNet procedure tries to find a new user that can provide enough resources by allocating parts of its allocated rate through another network n = n ∈ N. As long as the network resources allow it, the whole procedure is operated until no extra utility improvement can be brought to the overall system.

U n s = ϕ(R n s )-ϕ(R n s ) R n s -R n s ; 12: R n s = R n s /U n s ; 13: R n s = R n s +
In order to adapt source rates at the transport layer according to network states reported from the network layer, some crosslayer information exchange is needed. At the network layer, the distributed allocation scheme would require track the

IV. SIMULATION RESULTS AND DISCUSSIONS

In this simulation, we allow each user to stream video or audio applications via three access networks (Ethernet, 802.11b and 802.11g) with a maximum allowable total delay T = 350 milliseconds. Each network is simulated as a link with varying available bandwidth and delay, according to the traces collected from the actual access networks using the ABR and RTT measurements2 . Table II summarizes the statistics of the collected ABR ad RTT of each network tracing over 200 GOP (Group Of Pictures) periods. For audio application, we use 4 audio transcoders, namely G.723.1B, iLBC, SPEEX and G.711 with average encoding rates of 6.4, 15.2, 24.6 and 64kbps, respectively. For video application, we use a HD (High-Definition) sequence, which has a spatial resolution of 1280×720 pixels, and a frame rate of F = 60fps. Each video stream is encoded using a fast implementation of H.264/AVC codec at various quantization step sizes, with GOP length of 30 and IBBP... structure similar to that often used in MPEG-2 bitstreams.

To demonstrate the effectiveness of our proposed DRA algorithm, we use the Additive-Increase-Multiplicative-Decrease (AIMD)-based rate allocation method which is used by TCP congestion control [START_REF] Altman | Performance analysis of AIMD mechanisms over a multi-state Markovian path[END_REF] for comparison. AIMD-based scheme probes the network for available bandwidth and reduces the rate allocation after congestion occurs. In the simulations, each network rate allocation is increased proportion to each average ABR. In addition, congestion over network n is indicated upon detection of some lost packets, or when the observed RT T exceeds a specified threshold, based on the playout deadline of the audio/video stream. We test the proposed rate allocation method in a dynamic environment where users can join or leave the networks. To give a reasonable evaluation for both audio and video applications, we exemplify a concrete quality metric based on MOS (Mean Opinion Score) value. MOS reflects the average user satisfaction on a scale from 1 to 4.5. The minimum value reflects an unacceptable application quality, and the maximum value refers to an excellent QoS. We start with 4 users (two video applications and two audio applications). At time t = 2s, we add 4 users (two video applications and two audio applications), and at time t = 8s, we randomly remove 2 users (one video application and one audio application). Fig. 1(a) and Fig. 1(b) present the average MOS for the original 4 users obtained by the AIMD method and the proposed DRA method, respectively. It is observed that proposed DRA method outperforms AIMD method on the aspect of constant performance. That is because our proposed DRA method manages to keep a rather constant application quality for all active users by redistributing parts of the network resources to the new users. In addition, the proposed DRA method further takes advantage of explicit knowledge of the application distortion-rate characteristics, and can achieve more better performance than the AIMD method. It should also be noted from Fig. 1 that the network influence on audio application is much less than that on video application. This is because the video application usually requires more network resources than audio application.

V. CONCLUDING REMARKS

In this paper, we develop and evaluate a framework for rate allocation in pervasive computing environment, which contains multiple heterogeneous networks, based on observed each access network's parameters, as well as each application's characteristics. The simulation results demonstrate the effectiveness of our proposed rate allocation scheme for multimedia applications in heterogeneous networks.

Fig. 1 .

 1 Fig. 1. Average performance per user in case users join/leave the network.

  ABR n and RT T n over all available access networks. It also records the intended rate allocation R n s advertised by each user, and calculates the values of D all and U n s accordingly. At the transport layer, the rate controller at the source advertises its neighbors of its intended rate allocation R n s . The network state monitor traversed by the stream then calculates the relevant parameters based on its local cache of ABR n , RT T n and RB n within the same access network. The destination node extracts such information from the packet header and reports back to the sender with acknowledgment packets, so that the rate controller can re-optimize its intended rate R n s based on the proposed DRA algorithm, with updated network state information.

		TABLE II
	STATISTICS OF MEASURED ABR AND RTT
	Network	Parameter	ABR (Mbps)	RTT (ms)
	Ethernet	Avg. Std. Dev.	30.3 1.9	202.0 4.9
	802.11b	Avg. Std. Dev.	4.4 1.3	224.0 8.7
	802.11g	Avg. Std. Dev.	15.8 4.9	297.0 12.8
	observations of

Forward and backward trip delays are both simulated as half of the measured RTT

ACKNOWLEDGMENTS

This work is supported by the International Project PRA-SI (financed by France and China governments) under Grant No. SI04-03, the Key Project of Nature Science Foundation of Jiangsu (China) under Grant BK2007729 and the Climbing Plan in NJUPT under Grant NY207061.


MD Raoul Bacquelin
email: raoul.bacquelin@gmail.com

Emmanuel Oger

Emmanuelle Filippi

Jean-Philippe Hacot

Vincent Auffret

Marielle Le Guellec

Isabelle Coudert

Philippe Castellant

Benoît Moquet

Benoît MOQUET Philippe Druelles

Jean- Philippe Hacot

Antoine Rialan

Gilles Rouault

Bertrand Boulanger

Josiane Treuil

Guillaume Leurent

Marc Bedossa

Dominique Boulmier

Bertrand Avez

Martine Gilard

Hervé Le Breton

Safety of prasugrel in real-world patients with ST-segment elevation myocardial infarction: 1-year results from a prospective observational study (Bleeding and Myocardial Infarction Study

Keywords: Prasugrel, Bleeding, ST-segment myocardial infarction, Real-world patient Pragugrel, Saignements, Syndrome coronarien aigu avec sus-décalage du segment

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

RESUME

Résultats : 1083 patients ont été recrutés. Les patients traités par Aspirine + Clopidogrel avaient moins de saignements BARC 3 ou 5 que les patients traités par Aspirine + Prasugrel (9 patients, 1,8% versus 2 patients, 0,4%, p = 0,04) mais ils avaient plus de saignements BARC 2 (20 patients, 4,0% versus 45 patients, 9,3%, p<0.001). Les caractéristiques des patients traités par Prasugrel étaient différentes des patients traités par Clopidogrel car la sélection était attentive : patients plus jeunes, indice de masse corporelle plus élevé, moins d'antécédent d'accident vasculaire cérébral. Cette sélection a permis un meilleur contrôle du risque hémorragique. Les pourcentages de saignement intra-hospitalier majeur et de saignement extra-hospitalier majeur étaient respectivement de 2,6% (n = 28) et 1,3% (n = 13).

Conclusion :

Les saignements majeurs, en particulier extra-hospitaliers, sont peu fréquents chez les patients traités par Prasugrel dans l'année qui suit un SCA ST+. Une sélection attentive des patients pouvant bénéficier du Prasugrel permet une diminution de ce risque.

ABBREVATIONS LIST

ACS

INTRODUCTION

Cardiovascular diseases, including acute myocardial infarction, are currently one of the leading causes of death in Western countries. Medical therapies focus on anticoagulation and platelet inhibition to reduce the risk of thrombotic events and improve ischemic outcomes of patients with ST-segment elevation myocardial infarction (STEMI). These, coupled with early use of cardiac catheterization and revascularization, reduce the rate of death and re-infarction but carry a bleeding risk. Indeed, bleeding is the most common non-cardiac complication of antithrombotic therapy in acute coronary syndromes (ACS) and has been associated with adverse outcomes, including myocardial infarction, stroke and death [START_REF] Rao | Impact of bleeding severity on clinical outcomes among patients with acute coronary syndromes[END_REF][START_REF] Eikelboom | Adverse impact of bleeding on prognosis in patients with acute coronary syndromes[END_REF]. Thus, clinicians must balance antithrombotic treatment efficacy in preventing ischemic events with the need to minimize the risk of serious bleeding complications. Bleeding is even more frequent when percutaneous coronary intervention (PCI) is performed in the context of acute STEMI, when compared to bleeding complicating an elective procedure [START_REF] Steg | ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation[END_REF]. Several new antiplatelet therapies, including prasugrel, have been evaluated in randomized trials. They reduce the ischemic risk but are associated with an increased risk of major bleeding. In patients with STEMI undergoing PCI, prasugrel is more effective than clopidogrel for prevention of ischemic events, without excess in bleeding [START_REF] Montalescot | TRITON-TIMI 38 Investigators. Prasugrel compared with clopidogrel in patients undergoing percutaneous coronary intervention for ST-elevation myocardial infarction (TRITON-TIMI 38): double blind, randomized controlled trial[END_REF]. However, there are few data about the safety of new antiplatelet agents and bleeding risk within one year after a STEMI in real-world patients [START_REF] Parodi | Residual platelet reactivity, bleedings, and adherence to treatment in patients having coronary stent implantation treated with prasugrel[END_REF][START_REF] Bonello | Relationship between post-treatment platelet reactivity and ischemic and bleeding events at 1year follow-up in patients receiving prasugrel[END_REF][START_REF] Cayla | Prasugrel monitoring and bleeding in real world patients[END_REF]. The Bleeding and Myocardial Infarction Study (Bleed-MI study) sought to evaluate the safety of prasugrel in real-world patients with STEMI, from hospital discharge to the anniversary date of the STEMI. Safety was assessed by analyzing the out-of-hospital bleeding.

METHODS

Study patients

Patients were included, from April 1, 2011 to March 31, 2012, if they had a STEMI and were hospitalized in any of the 9 primary percutaneous intervention centers in Brittany, France, within 24 hours after the onset of symptoms. There was no exclusion criterion. Data were recorded in the ORBI-registry. This registry was approved by the Commission Nationale Informatique et Liberté. All patients were informed of the aims of the study and could request to be excluded. The follow-up interviews were conducted by research nurses at three months and at one year by telephone. The follow-up questionnaire permitted key information about antiplatelet and anticoagulation therapies, medical treatment, and occurrence of bleeding or ischemic events. In case of bleeding or ischemic events, the hospitalization reports and all medical examination results (e.g., blood test, medical imaging, etc) were recovered so all data were centralized. Patients in whom follow-up data were lacking were considered lost to follow-up.

Definitions

The Bleeding Academic Research Consortium (BARC) [START_REF] Mehran | Standardized bleeding definitions for cardiovascular clinical trials: a consensus report from the Bleeding Academic Research Consortium[END_REF] was used to define bleeding events occurring within one year after a STEMI. BARC 3 or 5 bleeding were considered major bleedings; BARC 2 bleeding were considered minor bleedings. BARC 1 bleedings were not collected because they do not require treatment by a healthcare professional. Thus, there collection would not have been exhaustive. Coronary artery bypass graft (CABG)related bleeding (BARC 4 bleedings) were not analyzed. Bleeding rates were also defined according to Thrombolysis In Myocardial Infarction (TIMI) definitions [START_REF] Rao | Thrombolysis in Myocardial Infarction (TIMI) Trial-phase I: hemorrhagic manifestations and changes in plasma fibrinogen and the fibrinolytic system in patients treated with recombinant tissue plasminogen activator and streptokinase[END_REF].

Cardiovascular death was any death due to a new ACS, severe cardiac arrhythmia, refractory congestive heart failure, cardiogenic shock, stroke, sudden death or unwitnessed death. Acute myocardial infarction was defined in accordance with the universal definition of myocardial infarction proposed in 2012 [START_REF] Thygesen | Third universal definition of myocardial infarction[END_REF]. Evaluation for stent thrombosis was performed according to the Academic Research Consortium criteria [START_REF] Cutlip | Academic Research Consortium. Clinical end points in coronary stent trials: a case for standardized definitions[END_REF].

Study outcomes

The primary outcome was the occurrence of any minor or major bleeding between discharge and the end of the follow-up at one year. A -bleeding events‖ committee validated, classified, and located the site of each bleeding event, thanks to the phone interviews, the hospitalization reports and the medical examination results. Pre-specified secondary outcomes included in-hospital major bleeding and rates of premature stops (transitory or not) of any antithrombotic treatment after a bleeding complication. The rates of major adverse cardiac events (MACE), including cardiovascular death, nonfatal STEMI, nonfatal stroke, and stent thrombosis were calculated.

Statistical methods

Data description started with displaying the number of patients having hemorrhage or who were censored with no event at the date of point (12 months after myocardial infarction).

Descriptive statistics included mean and standard deviation for normally distributed continuous variables, median and interquartile range otherwise and number and percentage for categorical variables. Comparisons according to dual antiplatelet regimen on discharge used chi-square or Fisher exact test for categorical variables and Student's t test or Wilcoxon test for continuous variables. For the main outcome, a survival analysis was used. Only one event per subject could occur in the analysis.

First, univariate (marginal) Wald test statistics using the Cox model were performed for each covariate. Then, multivariate modeling was completed with a Cox proportional hazards model that contained all covariates significantly associated in the univariate analyses at the P = 0.15 level. We used a hand-made stepwise backward elimination process: following the fit of the multivariable model, we used the P values from the Wald tests of the individual coefficients to identify covariates that might be deleted from the model. The linearity of the relationship between continuous covariates was not rejected except for hematocrit value when considering death; a cutoff at 37.4% was identified from martingale residuals analysis. In addition, the graphical and numerical methods derived from cumulative sums of martingale residuals over follow-up times or covariate values of Lin, Wei, and Ying [START_REF] Lin | Checking the Cox model with cumulative sums of martingalebased residuals[END_REF] were used for checking the proportional hazard assumption. For all statistical analyses, SAS version 9.3 (SAS Institute, Cary, North Carolina) was used. ) were lost to follow-up at the end of the follow-up period (Figure 1).

RESULTS

Study patients and in-hospital

Bleeding

An out-of-hospital major bleeding according to BARC 3 or 5 definitions occurred in 13 patients out of 1006 (1.3%) (Table 2). An out-of-hospital major bleeding according to TIMI definition occurred in 6 patients out of 1006 (0.6%). Total BARC 3 or 5 bleeding, including in-hospital bleeding, was observed in 41 patients (3.8%) and occurred during the first trimester in 33 patients (80.5% of major bleedings) (Figure 2). The most frequent out-ofhospital BARC 3 or 5 bleeding sites were gastrointestinal-(n = 6) and cerebral sites (n = 4).

The most frequent out-of-hospital BARC 2 bleeding sites were ENT-(n = 26), cutaneous-(n = 14) and gastrointestinal sites (n = 12) (Table 3).

Patients treated with aspirin + prasugrel compared to patients treated with aspirin + clopidogrel had statistically significantly less BARC 3 or 5 bleeding (0.4% vs. 1.8%, respectively, p=0.04) but more BARC 2 bleeding (9.3% vs. 4.0%, respectively, p<0.001).

Among the 988 patients treated with dual antiplatelet therapy, baseline characteristics of patients treated with prasugrel were different compared to those of patients treated with clopidogrel (Table 1). Patients treated with aspirin + prasugrel were more often male, younger, with a higher body max index, and with less history of arterial hypertension, stroke

Antithrombotic agents

Among the 79 patients with out-of-hospital BARC 2, 3 or 5 bleeding, 25 patients were treated with aspirin + clopidogrel (5.2% of patients with this association), 45 with aspirin + prasugrel (9.5% of patients with this association), 2 with aspirin + VKA (14.3% of patients with this association), 4 with aspirin + clopidogrel + VKA (16% of patients with this association), 2 with aspirin + prasugrel + VKA (28.6% of patients with this association) and 1 with aspirin alone (5.3% of patients with aspirin alone). Thereby, 7.3% of patients (70/956) treated with 2 antiplatelet agents underwent out-of-hospital bleeding, 18.7% of patients (6/32) treated with 3 antithrombotic agents underwent out-of-hospital bleeding. Moreover, 0.9% of patients (9/956) treated with dual antiplatelet therapy alone underwent a BARC 3 or 5 out-ofhospital bleed and 7.7% (4/52) of patients treated with a VKA underwent BARC 3 or 5 outof-hospital bleeding. Anticoagulant agents therefore had an important role in the occurrence of major bleeding events.

Discontinuation of medication

Bleeding (BARC 2, 3 or 5) had led to temporary or definitive discontinuation of ≥ 1 antithrombotic agent in 27 out of 79 patients who had bleeding. Among these patients, prasugrel was replaced by clopidogrel for 5 patients. The discontinuation was definitive for 20 patients (74% of patients with discontinuation of treatment): aspirin in 2 patients, clopidogrel in 8, prasugrel in 8, VKA in 1, and prasugrel + VKA in 1 patient. The discontinuation was transient for 7 patients : aspirin in 3 patients, clopidogrel in 1, aspirin + clopidogrel in 1, and VKA in 2 patients. The mean delay of antithrombotic agent discontinuation was 6.5 months (± 4.5). Antithrombotic agents were interrupted before month 6 in 13 patients (48.1%), after month 6 in 14 patients (51.9%), and beyond month 9 in 11 patients (40.7%).

The most frequent bleeding sites leading to treatment discontinuation were gastrointestinal (25.9%), ENT (25.9%) and cutaneous (25.9%). Interestingly, bleeding that led to discontinuation of medications was not major in most of cases. In fact, 74.1% of those bleeds were BARC 2, 14.8% BARC 3a, 3.7% BARC 3b, and 7.4% were BARC 3c. No ischemic event was observed after discontinuation of antithrombotic medications. There was no relation found between bleeding occurrence and increased risk of ischemic event.

Predictive factors of death

Forty-four patients (4.4%) died from any cause during follow-up. From multivariable analysis, the factors independently associated with death included age (HR, 2.2; 95% CI, 1.6-3.0; P < 0.001), signs of congestive heart failure on presentation (HR, 4.1; 95% CI, 2.1-8.1; P < 0.001) and baseline hematocrit ≤ 37.4% (HR, 0.3; 95% CI, 0.2-0.6; P < 0.001). Although any BARC 3 or 5 bleedings predicted death in univariate analysis, they did not remain independent predictors of death after adjustment with other covariates. It was the same for sex, weight, body mass index, prior hypertension, prior coronary heart disease, prior chronic renal disease, baseline creatinine, and hospitalization duration.

Out-of hospital ischemic events

The rate of out-of-hospital MACE among patients with BARC 2, 3 or 5 bleeding was 7.6% (6 patients). There was no statistically significant difference compared to the entire population. Among all patients, the rate of out-of-hospital MACE was 6.1% (61 patients):

death from cardiovascular causes occurred in 24 patients (2.4%), nonfatal MI in 10 (1.0%), nonfatal non-STEMI in 7 (0.7%), nonfatal stroke in 5 (0.5%), fatal stent thrombosis in 8 (0.8%), and nonfatal stent thrombosis in 7 patients (0.7%). The rate of stent thrombosis was 1.5%: 5 patients had a definite-, 5 patients had a probable-, and 5 patients had a possible stent thrombosis.

DISCUSSION

The Bleed-MI study allowed the evaluation of safety of prasugrel in non-selected STEMI patients in a real-world setting, according to the bleeding risk. This knowledge is crucial for the clinician who should choose the best antithrombotic treatment and its optimal duration according to the ischemic and bleeding risks. The final decision concerning an individual patient must be made by the responsible physician taking into account the balance benefit/risk as recalled in the last European Society of Cardiology Guidelines for the management of STEMI [START_REF] Steg | ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation[END_REF].

In the Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition with Prasugrel-TIMI (TRITON-TIMI 38) [START_REF] Wiviott | TRITON-TIMI 38 Investigators. Prasugrel versus Clopidogrel in Patients with Acute Coronary Syndromes[END_REF], patients aged ≥75 years showed no net benefit from prasugrel and there were no statistically significant higher rates of non-CABG-related TIMI major bleeding in the prasugrel group versus the clopidogrel group (4.3% vs. 3.3%, p=0.10). In the Bleed-MI study, patients treated with prasugrel compared to patients treated with clopidogrel had more BARC 2 bleeding but less BARC 3 or 5 bleeding.

This low risk of major bleeding in prasugrel-treated patients in this real-life registry, despite frequent use, is reassuring and can be explained by the selection of patient candidates for prasugrel (lower bleeding risk patients). It means that the contraindications and the precautions for use of prasugrel are relevant. Moreover, it means that physicians are using the drug appropriately in appropriate candidates. Nevertheless, 33 patients (6.81% of patients treated with prasugrel) with contraindications or precautions for use of prasugrel received prasugrel in the study: 5 patients with history of stroke or TIA, 8 patients aged ≥75 years and 13 patients weighing < 60 kg, 7 patients with concomitant use of VKA.

Unlike many studies, we included non-selected patients, which make our results useful in real-world practice. Most studies include selected patients (patients undergoing PCI for STEMI for example). In the Bleed-MI study, coronary angiography was not performed for 22 patients (2%) and no angioplasty was performed for 109 patients (10.1%). These patients are rarely studied in randomized trials. Moreover, we focused on out-of-hospital events: the inhospital bleeding, often related to procedural bleeding complications (PCI or CABG) should be differentiated from out-of-hospital bleeding. The rate of out-of-hospital major bleeding within 1 year after a STEMI was low at 1.3% according to the BARC definition and 0.6% according to the TIMI definition. The rate of major out-of-hospital bleeding is particularly low in patients treated with prasugrel due to accurate selection of patients. The chronology of major bleeding is important due to its impact on the duration of antithrombotic treatment association in high bleeding risk patients. Bleeding can lead to a transitory or permanent discontinuation of one or more antithrombotic treatments, with the risk of stent thrombosis in stented patients and the risk of ischemic complications in all patients. In total, 68.3% of major bleedings occurred during the initial hospitalization. In a study published by Collet [START_REF] Collet | Impact of Prior Use or Recent Withdrawal of Oral Antiplatelet Agents on Acute Coronary Syndromes[END_REF] in 2004, 5.4% patients admitted for a suspected ACS had recently withdrawn antiplatelet agents, which was found to be an independent predictor of mortality and bleeding at 30 days. In the Bleed-MI study, out-of-hospital bleeding (BARC 2, 3 or 5) led to early discontinuation of any thrombotic agent (before 6 months) in only 13 patients (16.5% of out-of-hospital bleedings).

Moreover, there was no ischemic event among the 27 patients who discontinued their treatment. Bleeding (including minor bleeding) must not be underestimated and should be systematically sought during the medical follow-up of patients after ACS since it would promote treatment compliance.

The reported rates of major bleedings at 9-12 months' follow-up after ACS vary from 2 to 6% [START_REF] Steg | Ticagrelor versus Clopidogrel in Patients With ST-Elevation Acute Coronary Syndromes Intended for Reperfusion With Primary Percutaneous Coronary Intervention: A Platelet Inhibition and Patient Outcomes (PLATO) Trial Subgroup Analysis[END_REF][START_REF] Mehran | Bivalirudin in patients undergoing primary angioplasty for acute myocardial infarction (HORIZONS-AMI): 1-year results of a randomised controlled trial[END_REF][START_REF] Parodi | Residual Platelet Reactivity, Bleedings, and Adherence to Treatment in Patients Having Coronary Stent Implantation Treated With Prasugrel[END_REF][START_REF] Mega | ATLAS ACS 2-TIMI 51 Investigators. Rivaroxaban in Patients with a Recent Acute Coronary Syndrome[END_REF]. In the Bleed-MI study, in-hospital and out-of-hospital major bleeding occurred in 3.8% according to BARC definition and 1.5% according to TIMI definition. In TRITON-TIMI 38 [START_REF] Wiviott | TRITON-TIMI 38 Investigators. Prasugrel versus Clopidogrel in Patients with Acute Coronary Syndromes[END_REF], the rate of non-CABG-related TIMI major bleeding at 15 months in the entire population was 2%. Considering only patients undergoing PCI for STEMI [START_REF] Montalescot | TRITON-TIMI 38 Investigators. Prasugrel compared with clopidogrel in patients undergoing percutaneous coronary intervention for ST-elevation myocardial infarction (TRITON-TIMI 38): double blind, randomized controlled trial[END_REF], the risk of non-CABG major bleeding was lower (1.1%), which is close to our rate of 1.5%. In the PLATO study [START_REF] Steg | Ticagrelor versus Clopidogrel in Patients With ST-Elevation Acute Coronary Syndromes Intended for Reperfusion With Primary Percutaneous Coronary Intervention: A Platelet Inhibition and Patient Outcomes (PLATO) Trial Subgroup Analysis[END_REF], the rate of non-CABG-related TIMI major bleedings at 12 months among patients with a STEMI was 2.2%. Considering the localization of bleedings in our study, the most frequent out-of-hospital major bleeding sites are gastrointestinal, but the most frequent out-of-hospital minor bleeding sites are ENT and cutaneous sites and the most severe out-ofhospital bleeding sites are cerebral. In the Global Registry of Acute Coronary Events (GRACE) study [START_REF] Moscucci | Predictors of major bleeding in acute coronary syndromes: the Global Registry of Acute Coronary Events (GRACE)[END_REF], the most common life-threatening in-hospital bleeding complications were also gastrointestinal.

LIMITATIONS

As in any registry, this observational study has some limitations. For instance, 26 patients (2.5%) were lost to follow-up. The follow-up was made by telephone contact at 3 months and at 1 year but data collection was done by qualified hospital staff with directed questionnaires, which limits the loss of data. Moreover, BARC 1 bleedings were not collected because there collection would not have been exhaustive, due to the declaratory nature of data collection. Premature treatment cessation due to BARC 1 bleedings could then not have been analyzed.

No patient was treated with Ticagrelor because this treatment was not yet available in Brittany during the study.

According to an analysis from the TRITON-TIMI 38 study [START_REF] Hochholzer | Predictors of Bleeding and Time Dependence of Association of Bleeding With Mortality: Insights From the Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition With Prasugrel -Thrombolysis in Myocardial Infarction 38 (TRITON-TIMI 38)[END_REF], independent predictors of non-CABG-related serious bleeding throughout 15 months were female sex, use of glycoprotein IIb/IIIa inhibitor, duration of intervention, age, assignment to prasugrel versus clopidogrel, STEMI, femoral access, creatinine clearance, hypercholesterolemia, and hypertension. In the Bleed-MI study, the low rate of major out-of-hospital bleedings reduced the statistical power for detecting clinical and biological predictors of bleeding events. Any usual predictors of bleeding events could not be found and no statistical association between the occurrence of major bleeding and mortality was demonstrated.

CONCLUSION

Major bleeding rate, in patients treated with prasugrel after STEMI within one year after a STEMI is low when patients are carefully selected. The question remains of extending the prescription of new antiplatelet agents to a broader population of patients but with the risk of modifying the benefit-risk balance that we have documented in real world selected patients.

TABLES AND FIGURES LEGENDS

Introduction:

  Les traitements antiplaquettaires, dont le Prasugrel, sont un élément majeur dans le traitement du syndrome coronarien aigu avec sus-décalage du segment ST (SCA ST+) mais entraînent un sur-risque hémorragique. Ce risque a été évalué dans des essais cliniques randomisés mais il existe peu de données dans la « vraie vie ». Méthodes : Les patients avec SCA ST+ ont été recrutés pendant un an et le suivi a été effectué, par téléphone, 3 mois et 1 an après le SCA ST+ afin d'évaluer la sécurité du Prasugrel, en particulier pendant la période post-hospitalière. Le critère d'évènement principal était la survenue d'un évènement hémorragique majeur défini selon la classification Bleeding Academic Research Consortium (BARC) 3 ou 5 ou d'un évènement hémorragique mineur défini selon la classification BARC 2.

  or transient ischemic attack. Patients treated with prasugrel were carefully selected: eight patients (1.7%) were aged ≥75 years among patients treated with prasugrel, while 193 patients (37.6%) were aged ≥75 years in the clopidogrel group. Thirteen patients (2.7%) weighed <60 kg among patients treated with prasugrel, whereas it concerned 107 patients (20.8%) in the clopidogrel group. Five patients (1.0%) had a history of stroke or transient ischemic attack among patients treated with prasugrel, whereas it concerned 19 patients (3.7%) in the clopidogrel group. In the Bleed-MI study, the rate of major out-of-hospital bleeding was low so it reduced the statistical power for detecting any clinical and biological predictors of bleeding events.

FIGURESFigure 1 .

 1 FIGURES

Figure 2 .

 2 Figure 2. Patients with BARC 3 or 5 Bleeding during the Follow-up Period. BARC indicates

Table 1

 1

	. Coronary angiography was performed in 1060 patients (97.9%), with
	radial access in 564 patients (53.2%) and angioplasty was performed in 974 patients (89.9%);
	91.6% (n = 892) of stented patients received ≥1 stent, and 22.5% (n = 219) of stented patients
	had ≥1 drug-eluting stent. 2b3a inhibitors were used for 489 patients (45.2%).

events 1083 consecutive patients with STEMI from nine centers in Brittany were recruited from April 2011 through March 2012. No patient requested to be excluded. Follow-up occurred from July 1, 2011 to March 31, 2013. The baseline characteristics of the patients are summarized in (5.0%) were treated with a vitamin K antagonist (VKA). Overall, 956 patients (92.6%) were treated with dual antiplatelet therapy alone and 32 patients (3.1%) were treated with a triple antithrombotic therapy (aspirin + clopidogrel + VKA or aspirin + prasugrel + VKA). Twentythree patients (2.2%) were treated with only one antiplatelet agent (aspirin: 19 patients; clopidogrel: 3 patients; prasugrel: 1 patient). Data were missing for 23 patients (2.2%). A total of 26 patients (2.5%

Table 1 .

 1 Baseline Characteristics of the Patients

Table 2 .

 2 Chronology of Bleeding: In-hospital and Out-of-hospital Bleeding

Table 3 .

 3 Localization of out-of-hospital bleeding and vascular access

	Figure 1. Flow-Chart
	Figure 2. Patients with BARC 3 or 5 Bleeding during the Follow-up Period

Table 2 .

 2 Chronology of Bleeding: In-hospital and Out-of-hospital Bleeding

	In-hospital	Out-of-hospital
	bleeding	bleeding
	(n=1083)	(n=1006)

Table 3 .

 3 Localization of out-of-hospital bleeding and vascular access

			BARC-n		Radial access-
					n (%)
		BARC 2 BARC 3 BARC 5
	Cutaneous	14	1	0	5 (33)
	Gastrointestinal	12	6	0	13 (72)
	Cerebral bleeding	0	2	2	0 (0)
	ENT	26	0	0	15 (57)
	Respiratory tract bleeding	5	0	0	2 (40)
	Urinary tract bleeding	5	0	0	4 (80)
	Others	4	2	0	3 (50)
	Total	66	11	2	42 (53)

Acknowledgement

We thank all the research technicians involved in this study: M. Le Guellec, E. Babin, A. Piel, P. Hery (Rennes) ; F. Langlais (Saint Brieuc) ; P. Dias, C. Kergoulay (Brest) ; P. Rameau, M. Paquin (Quimper); all the cardiologists and all the emergency physicians involved in the data collection. This work was supported by Daiichi Sankyo and Eli Lilly. The study was investigator initiated. The sponsors of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.