Osama Mazhar

Sofiane Ramdani

Benjamin Navarro

Robin Passama

Andrea Cherubini

A Framework for Real-Time Physical Human-Robot Interaction using Hand Gestures

A physical Human-Robot Interaction (pHRI) framework is proposed using vision and force sensors for a two-way object hand-over task. Kinect v2 is integrated with the state-of-the-art 2D skeleton extraction library namely Openpose to obtain a 3D skeleton of the human operator. A robust and rotation invariant (in the coronal plane) hand gesture recognition system is developed by exploiting a convolutional neural network. This network is trained such that the gestures can be recognized without the need to pre-process the RGB hand images at run time. This work establishes a firm basis for the robot control using hand-gestures. This will be extended for the development of intelligent human intention detection in pHRI scenarios to efficiently recognize a variety of static as well as dynamic gestures.

I. INTRODUCTION

For a successful and safe pHRI, appropriate understanding of the human user is essential for the robot. Increasingly popular and affordable consumer standard depth cameras like Microsoft Kinect has enabled the computer-vision and robotic researchers to develop robust pHRI systems in dynamic workplaces. It is well known that 93% of the human communication is non-verbal [START_REF] Mehrabian | Nonverbal Communication[END_REF], of which 55% is accounted for elements like facial-expressions, posture, etc. In this perspective, ability to recognize human gestures can be extremely useful for a robotic system in pHRI scenarios [START_REF] Canal | A real-time Human-Robot interaction system based on gestures for assistive scenarios[END_REF].

Similar human-robot interaction (HRI) settings are found in the literature, such as [START_REF] Canal | A real-time Human-Robot interaction system based on gestures for assistive scenarios[END_REF], where the authors have used Kinect for the detection of pointed direction by the user and for navigation of the robot. In [START_REF] Cicirelli | A Kinect-Based Gesture Recognition Approach for a Natural Human Robot Interface[END_REF], the authors have used multiple Kinects in a fixed workspace and have used neural networks to detect dynamic gestures. Kinect based object recognition through 3D gestures is proposed in [START_REF] Raheja | 3d gesture based realtime object selection and recognition[END_REF]. The OpenNI and NITE middleware are used to extract skeleton information of the human user. Authors in [START_REF] Ehlers | A human-robot interaction interface for mobile and stationary robots based on real-time 3d human body and hand-finger pose estimation[END_REF] and [START_REF] Yang | Real-time human-robot interaction in complex environment using kinect v2 image recognition[END_REF] also propose HRI scenarios using Kinect. Mostly the researchers have used OpenNI or Microsoft SDK to extract the human-skeleton, which is a model based skeleton tracker having several discrepancies including the need of initialization pose, not being able to detect gestures on which the model is not trained on, and noisy detections. Moreover, most of the gesture/intention detection work is done in the perspective of Human-Computer Interface (HCI) while pHRI implementations are not discussed.

In this paper, we integrate the state-of-the-art 2D skeleton extraction library namely Openpose [START_REF] Cao | Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields[END_REF] with Microsoft Kinect v2, which is a time-of-flight sensor, to get a realtime 3D skeleton of the human user. We also develop and train a convolutional neural network (CNN) for on-line hand All authors are with LIRMM, Université de Montpellier, CNRS, Montpellier, France. osama.mazhar@lirmm.fr gesture detection to control the robot. Moreover, a robot control framework is developed that combines vision and force sensors to achieve a two-way object handover between a human operator and a robot. The overall proposed pHRI scenario is shown in Fig. 1.

II. METHODOLOGY

The proposed pHRI framework is divided in three main modules namely: Skeleton extraction and hand image acquisition, CNN for pHRI hand gestures recognition and Robot control for pHRI.

A. Skeleton Extraction and Hand Images Acquisition

OpenPose is based on Convolutional Pose Machines (CPMs) [START_REF] Wei | Convolutional Pose Machines[END_REF] which extracts 2D skeleton information from RGB images. We use libfreenect2 library to get RGB image and depth map from Kinect v2, and extract the depth values of each 2D skeletal joint obtained from OpenPose, thus acquire a 3D skeleton of the human user. The angle of forearm is computed by fitting a line between the elbow joint and the wrist joint obtained using OpenPose. This line is then extended to one-third of its length to approximate the hand position. The angle that this line makes with horizontal, and the mean depth value of 36 pixels (6×6 matrix) of the wrist joint is used to fit a rotated bounding box centered at the approximated hand location. This enables our vision system to acquire a square image aligned with the forearm, of the size relative to the human hand, irrespective of the distance of the human operator from the camera within Kinect v2 depth sensing range. We crop, rotate and zoom the pixels lying within this bouding box to a size 244×244 pixels. In the detection phase, the hand cropped images are passed through the trained network without performing any time consuming image processing operation for gesture detection.

B. CNN for Hand Gestures Recognition

We develop a CNN and train it on four gestures namely Handover, Stop, Resume and None. The architecture of our CNN is mainly inspired from LeNet [START_REF] Lecun | Gradient-based learning applied to document recognition[END_REF] and is shown below:

INPUT→CONV(RELU:6×6×64)→MAXPOOL(2×2) →DROPOUT(0.3)→CONV(RELU:3×3×128) →MAXPOOL(2×2)→DROPOUT(0.5)→FC(128) →FC(128)→FC(128)→SOFTMAX [START_REF] Raheja | 3d gesture based realtime object selection and recognition[END_REF] To make our CNN background invariant in an indoor environment, we used the depth map from the Kinect v2 to augment the data by removing the background from the training set of hand images. We also create an inverted binary mask of the hand to add different colors to the background in the training set. Keras data-augmentation is also used to introduce contrast stretching, channel shift and horizontal flip in the training data. The CNN is trained overnight on a set of 1800 RGB images of size 244×244 on an Intel Core i7-6800K CPU at 3.40GHz, 12 cores with no GPU. Validation accuracy, with 600 test images, is 98.8 %. We evaluated our model with 300 more test images extracted from a video recorded in different light conditions, and achieved 95.7 % accuracy on these data. We plan to extend this system by collecting more data from multiple users and to test the accuracy of our network on persons not included in the data.

C. Robot Control for pHRI

The BAZAR robot used for the experiments is composed of two Kuka LWR 4+ arms with two Shadow Dexterous Hands attached at the end-effectors. The arms are attached to a Neobotix MPO700 omnidirectional mobile platform. In our scenario, the mobile base is kept fixed and only the right hand-arm system is used. The control of the arm is done using the FRI library and the control of the hand is based on a ROS interfaces. The external force applied to the arm's endeffector is estimated by FRI based on joint torque sensing and on knowledge of the robot's dynamic model. The control rate is set to 5ms.

III. PHRI EXPERIMENT AND RESULTS

For safe pHRI, the robot must perceive the intention of the operator. Here, 3D human body joint coordinates and hand gesture recognition are the cues used for robot operation. We realize a tool (here, a portable screw-driver) handover experiment, guided by a finite state machine designed for the robot control. The robot waits for the user commands in the form of hand gestures, to take and then place the tool to a predefined location in its workspace. Once the handover gesture is detected, the robot moves to a predefined open hand position in the workspace. The human operator places the tool in the robotic hand and applies a downward force (in X-direction) on the end-effector to trigger tool grasping.

The experiment is demonstrated in a video that can be accessed through this link 1 . The commands are fulfilled by the robot when three successive identical instances of the 1 www.youtube.com/watch?v=Mj5YqTDrdb4 corresponding gesture are detected, and only if the forearm is in the upper two quadrants of the axes centered at the elbow joint. This aids in ignoring all gesture detections when the operator does not intend to interact with the robot and has relaxed his/her arm.

This experiment is performed indoor and all gesture permutations are tested. The operator moves closer and farther from the robot and is allowed to move his hand in the coronal plane depending on his comfort. The robot is able to detect and obey the intended commands given by a single operator within approximately 384 milliseconds after the first instance of command is detected.

IV. CONCLUSION Our current HRI framework detects hand gestures with a frame-rate of approximately 5.2 fps. The use of multiple GPUs for OpenPose library can enhance the temporal performance of our system. We explain our presented work in more detail in [START_REF] Mazhar | Towards Real-time Physical Human-Robot Interaction using Skeleton Information and Hand Gestures[END_REF]. We plan to extend our work by developing a background independent hand gesture detector by substituting backgrounds with rich-textured images. This substitution makes the gesture detection a complex problem, thus we plan to exploit the concept of transfer learning in CNN to train our gesture detector. We also plan to integrate OpenPose asynchronously to the gesture detector to ensure faster execution of the algorithm.

Fig. 1 .

 1 Fig. 1. Overall proposed pHRI scenario with BAZAR -The dual-arm mobile manipulator developed at LIRMM.

ACKNOWLEDGMENTS

This work was supported by the CNRS PICS Project MedClub.


Akinori Murata

Daniel Delahaye
email: daniel@recherche.enac.fr

Keiki Takadama

Merging Flows and Optimizing Aircraft Scheduling in Terminal Maneuvering Area Based on GA

Keywords: Air Traffic Control, Aircraft Merging Problem, Genetic Algorithm

Runway capacity problem have been emerging in current overloaded congestion airports. This paper focuses on improving the runway capacity and resolution of conflicts of aircraft in terminal maneuvering area and proposes its method which can find the feasible solution using detour structure. In detail, our proposed method use alternative routes or sub-route as detour to avoid conflict and to change the position of arrival sequence. Through the intensive simulation on Paris Charles De-Gaulle airport, we revealed that our proposed method can find the efficient arrival sequence without conflict and detours contribute to optimize the landing sequence.

I. INTRODUCTION

The air traffic demand is increasing yearly. In particular, an airport is a one of the high-density air traffic, which the air traffic controller considers their speed and angle of heading to avoid their conflicts. In the heavy traffic, congestion of aircraft makes the air traffic controller high cognitive load. In an airport, they decide to landing sequence of aircraft arriving to the destination airport and manage to their aircraft. To navigate aircraft, they consider a lot of aircrafts to avoid conflicts between any aircraft and determine the landing sequence based on first come first serve (FCFS). In aircraft landing, to determine both the landing routes of aircrafts and their landing order to minimize an occupation time of the airport as the air transport service are important issue because of directly affecting the occupancy time. This problem is called as the aircraft landing problem [START_REF] Bennell | Airport runway scheduling[END_REF]. Some projects of future concept have launched such as SESAR in Europe, NextGEN in US and CARATS in Japan. To increase the runway capacity and resolve the conflict and navigate efficiently for aircraft, it is the key factors for air traffic management system in TMA to minimize the potential conflicts and increase the runway capacity. To support air traffic controller, our approach focuses on the route structure without holding pattern. This paper addressed new merging method without holding pattern the optimization of the landing sequence near a little far from terminal maneuvering area and proposed without holding pattern which improve the runway capacity and deal with the congestion in the terminal maneuver area. The proposed method based on Genetic algorithm to optimize landing sequence and to resolve the conflict between aircraft.

II. RELATED WORK

In this field, some heuristic methods can successfully obtain good result and save the calculation time for optimization such as GA [START_REF] Goldberg | Genetic Algorithms in Search, Optimization and Machine Learning[END_REF], simulated annealing(SA), and ant colony algorithm and so on. Hu et al. proposed efficient search algorithm for arrival sequence based on GA. This method focused on computational time and employed the concept of receding horizontal control(RHC) [START_REF] Hu | Genetic algorithm based on receding horizon control for arrival sequencing and scheduling[END_REF]. RHC is the decomposition-based technique and divide the problem into several small problems. This method including RHC can obtain better performance than that of pure GA. As another method, Man et al. proposed an autonomous system with point merge (PM) method which contents the effective for applying the real situation [START_REF] Liang | Integrated sequencing and merging aircraft to parallel runways with automated conflict resolution and advanced avionics capabilities[END_REF]. PM is one of the new future technology of the air traffic management system proposed by [START_REF] Favennec | Point merge integration of arrival flows enabling extensive rnav application and cda-operational services and environment definition[END_REF] and [START_REF] Boursier | Merging arrival flows without heading instructions[END_REF]. This technique which is composed of predefined the route called sequential leg enables controllers to easily understand and provide with absorbing the required delay and changing the order of arrival sequence. In this paper, they conducted on the real environment in the Beijing airport which is the one of congestion airport in the world. Their method could optimize one all day traffic data even in the crowded condition thorough the SA. This concept is suitable for controller due to considering cognitive load of controllers in the objective function. However, PM is limited by the optimization performance. Since this optimization are is terminal maneuvering with holding pattern, it is required to extend the TMA. To absorb the time consumption, it is required for their model to extend the environment., Tuniga et al proposed the optimization method of arrival sequence based on GA [START_REF] Zuniga | Integrating and sequencing flows in terminal maneuvering area by evolutionary algorithms[END_REF]. In detail, they add the detours to landing in order to adjust the separation of the space between the aircraft. This paper contributed to find the feasible solution for arrival sequence without holding pattern. These are useful to apply into future tactical air traffic control Our purpose in this paper is that we show the potential of the optimization of the arrival sequence in the extend terminal maneuvering area (TMA). In detail, our proposed method applied the detours which is useful to change the arrival order.

III. MODELING

A. Given data

As the data set, we use a set of flights F . This flight data is represented as following: t i : time of flight f i at initial point v: speed at enter point w: wake turbulence category Aircraft speed do not change drastically in near the TMA. Such a speed change indicates as the following equation.

v ′ k = v k * (1 + ∆v), ∆v ∈ ψ (1)
Where, v k , ∆v and v ′ k are initial aircraft speed, speed change rate and new speed of aircraft i respecticely. we define the percenage of change as ψ ∈ [-20%, 5%] As following this equation, the aircraft is restricted of the speed and route and altitude. this is because that near the airport, there is very crowded condition and risk of the conflicts aircraft. Our simulation model set the speed only one time.

B. Detour design

During congestion situation, there are a lot of conflicts between aircrafts in near the airport. The controllers take two types of actions to manage aircrafts. One is speed change of aircraft and the other is heading change of aircraft. We employed route structure which enable aircraft to change route. Our model develops STandard Arrival Route (STAR) with the detour. To control the detours, our model uses the parameter of deter (dr) which can change the total distance of the detour. Such parameter is described as following equation 0 ≤ dr ≤ 1 This parameter has a range from 0 to 1 (e.g.,when the dr takes 1, the total distance to the destination is maximum.) It is the key factor how to detect the conflict between any two aircraft. Our route design is that each route does not cross another route, however, there has a possibility to occur the conflict. The regulation of the air traffic control say that any two aircraft keep the appropriate space to avoid the conflict. Controllers pay attention to the speed and altitude and separation of all aircraft in every sector. Normally, we have to only consider node and link conflict using graph model. Node conflict is that aircraft need to the separation between this aircraft and any aircraft through the same node. Link conflict is that aircraft need to separation according to wake turbulence of leading aircraft when aircraft navigate through the same link. However, our detour design is not enough to detect conflict using these two methods. To detect the conflict, we use hash map which record the position of the aircraft every Δ t minutes. In detail, hash-map has discrete value of time and aircraft information. Fig. 1 indicates the example of the hash map. The vertical axis indicates the time schedule in near the destination. Each time has the list of aircraft. The aircraft flying at that time is added to the aircraft list. Into the list, aircraft size, speed, and position are on the aircraft list. According to this information, this algorithm calculates the distance of each space of distance at the same time. Such a distance is checked whether the constraint condition is met or not. If the value of the distance smaller than the threshold, this model considers the conflict. Our prosed method repeats this process until the end of the time schedule and count the total number of conflicts. Generally, the aircraft are categorized into three types on the basis of the size of aircraft. The size of aircraft significant impact on the landing space interval. The different size of aircraft makes the difference of ditstance. In this figure, left side and top of table indicate the trailing and leading aircraft respectively. The number show the minimum space distance between the leading trailing aircraft. 6NM for the requirement of the minimum distance is needed between the light trailing aircraft and the heavy leading aircraft while 3NM is needed between the light trailing aircraft and the light leading aircraft (see Table I).

C. Conflicts detection

D. Objective function

Our goal is to increase the runway through put which means that aircraft land on the destination airport as much as possible. What important thing is that aircraft land on a destination airport on time for passengers. For this purpose, our proposed method considers the delay of the arrival. The objective function is defined as below:

F itness = N ∑ k=1 (t k -ET A k ) (2)
In Eq. 2, t k and ET A k indicate the i-th actual landing time and estimated time of arrival respectively. ET A subtracted from t means the delay of the schedule. Note that the delay become small when fitness is small. For safe aircraft landing, no conflicts in the TMA is indispensable. Since our fitness function does not consider the conflict, the individual with conflict is identified as an infeasible solution. In detail, firstly Fig. 2. Modeling method our algorithm ranks individuals according to the total number of conflict, then these individuals are ranked according to the fitness. These order aims at not only resolving the conflict preferentially but also keep the landing schedule.

IV. PROPOSED ALGORITHM

A. overview Fig. 2 shows the structure of our approach. The optimized solution. The optimization unit plays a role of finding a feasible and optimal solution. In this paper, we proposed method based on GA proceeding parallelly. The simulation unit using above the model is that aircraft flying with the parameter which the optimization unit generates the candidate of the solution. Note that the simulation takes much time to detect the conflict and evaluate the fitness of the solution. After calculating fitness and counting the total conflicts, the simulation unit gives the feedback to the individuals. These processes continue repeatedly until the termination condition. Optimization solution is generated.

B. Genetic algorithm

To optimize the landing sequence of the destination airport. This paper applied genetic algorithm to this problem. In this section, we describe the mechanism of our proposed GA. Our GA is composed selection, crossover, mutation and evaluation besides local search. The procedure of the GA is as followings; Firstly, population is initialized, our algorithm selects two individuals as the parents the based on the tournament selection and applies these to crossover operation. Such the individual is regard as the child and child s gene changes the value with certain probability. To calculate the fitness value and count the total number of conflicts of all aircraft, our algorithm simulates the management of the air traffic using the value of this evaluated individual. After all individuals estimate the value of the fitness and conflicts, our algorithm sorts by the fitness and conflict and progress next generation. To search more efficiently, we explain mutation operator and crossover operation.

1) gene structure: As shown Fig. 3, this figure indicates the structure gene. Each gene corresponds to one aircraft. Aircraft need to decide the velocity and route. The velocity indicates the integer value and parameter value is real value. For example, in case total aircraft is N, each individual has to decide 2 N decision values. 2) crossover operation: The crossover operation is the important factor to find the feasible solution efficiently. We use the bias crossover operation which promotes to generates better child individual than the other child.Two parents selected according to the selection method are operated crossover, then two individuals take over the characters of gene. One of the new individual takes over the better chromosome from the parents, while the other individual takes over the worse chromosome. Here, better chromosome means that less total number of conflicts of gene. Our proposed method applies the simulated binary crossover (SBX) to detour parameter 3) mutation operation: Our algorithm operates mutation to the new individuals with some extent probability. Generally speaking, some values in chromosome are changed randomly. In this problem, speed of aircraft and parameter of detour are changed by mutation operator. Note that the value of the probability changes each gene according to the number of conflicts. This is because the aircraft which has a large amount of conflict disturbs another aircraft. The mutation operator promotes to change the values in such the bad effective aircraft. Our algorithm makes less total conflict to give a higher probability value.

V. EXPERIMENT AND RESULT

To investigate effectiveness of our proposed method, we conduct the simulated experiment in the Charles de Gaulle Airport as shown in Fig. 4 where there 8 routes, five merging point and five detours. At first each route is merged into one route. These four merged routes are connected to the final approach point. There are four runways in this airport and these two long runways and two short runways parallelly setting. Although Actually this airport operates aircraft departing and arriving with both long runways, our experiment use the only one runway as fundamental experiment.

We define 5 detours added on the routes which meet the requirements of the rule for detour route reguration. In this experiment, the total detours are 5. The aircraft in the data fly along with the waypoints. We use the real traffic data in the Charles de Gaulle Airport . The total number of aircraft is 29 per one hour. Table II indicates the traffic ratio of each route. The air traffic from south is much congested during this time. We used the following parameter setting (Table. II). All process is run on a 2.71GHz and 12 GB RAM Windows10 operating system operating PC. As the evaluation criterior, we used the total confilicts of any aircraft and fitness value which indicates the runway through put. Our approach can find the feasible solution in all trials and use the detour to minimize the delay of the schedule (See Fig. 5). These values in Table III are the average of all 10 runs. In Fig. 5 , the left and right vertical and horizontal axis indicate fitness value, total conflicts between aircraft and generation. From 200 generation, our prposed method can obtain feasible solution without conflict. Fitness is fluctuated until 200 generation because it is possibily to select the best fitness including conflicts in every generation. If all individuals in population are infeasible solution (including conflict), The lower value of fitness is selected. After this generation, this value gradually decrease to find better solution in terms of runway through put. In table III, the number of aircraft which did not select detour is 14.7. This mean that sub-route sutructure contributed to resolve the confilicts and improve the optimization performance. This paper addressed new merging method without holding pattern and proposed new merging and optimizing techniqe which improve the runway capacity and deal with the congestion in terminal maneuver area. In detail, the proposed method based on GA optimizes landing sequence and resolves the conflict between aircraft using hash map. The following implications to be found; our proposed method can find the feasible solution in the real environment where we conducted the experiment on the Charles de Gaulle Airport. Our proposed method developed the detour which adapt estimate arrival time and contributes to decrease the runway occupation time. Note that our result shows the only one hour traffic data and our optimized schedule did not consider the departure aircraft. And our proposed method takes much calculation time to simulate the experiment. Now we are tackling on improvement finding solution quickly. What should be noticed that our proposed method only used the detours to extend the estimated arrival times flexibly without holding pattern. Our environment is more a little far from the TMA, which enable arrival scheduling easy to find the arrival sequence.

Fig. 1 .

 1 Fig. 1. Hash map

Fig. 3 .

 3 Fig. 3. Gene structure

Fig. 5 .

 5 Fig. 5. Fitness and total conflict

TABLE I AIRCRAFT

 I MINIMUM SEPARATION


REMERCIEMENTS

Au Professeur Jean-Yves Le Reste, merci de m'avoir guidé durant tout le long de ce travail de thèse. Merci pour votre dévouement au sein du Département de Médecine Générale pour le rendre dynamique en termes de Recherche Clinique. Merci enfin d'honorer le statut de Président du jury.

Au Professeur Bernard Le Floch, merci de me faire l'honneur de faire partie de ce jury. Merci de votre investissement pour les étudiants brestois et de la recherche en Médecine Générale. Soyez assuré de ma profonde reconnaissance.

Au Docteur Benoit Chiron, merci à vous de faire partie de ce jury de thèse. Merci pour votre implication et votre accueil auprès des étudiants de Médecine Générale de Brest.

Au Docteur Nolwenn Gac, merci de me faire l'honneur de faire partie de ce jury. Soyez assurée de mon profond respect.

A Florence Gatineau, je vous remercie pour ce long travail statistique et votre envie de nous le faire comprendre malgré sa complexité et notre manque de connaissance à ce sujet.

Aux membres du groupe de thèse pour tous ces jeudis passés ensemble à bûcher sur la multi-morbidité.

A mes parents, merci pour votre amour et votre soutient sans faille depuis le début sans quoi je ne serai pas devenu la personne que je suis aujourd'hui. je vous serai éternellement reconnaissant.

A toute ma famille, merci de m'avoir soutenu en toutes circonstances et de former ensemble une famille unie.

A ma Grand mère, et mes Grand-parents qui ne sont plus là, que j'admire énormément.

A Maeva, pour ton soutien pendant la rédaction de cette thèse et cette belle année passée ensemble et toutes celles à venir.

A tous mes amis , d'enfance , du handball ou rencontrés par ailleurs. Pour toutes les personnes que l'on ne perd pas à travers les années et malgré la distance. J'espère que nos chemins se croiseront toujours à Brest ou ailleurs.

A Marielle, Gaelle, Camille, Jacques et Maxime, pour avoir affronté et réussi ensemble la P1. Merci pour tout ces moments passés ensemble.

A philippe et Christine, pour leur soutient et les rigolades lorsqu'après mes journées d'externe j'allais travailler dans leur pizzeria.

Au Handball, à la musique et à la gastronomie , parce qu'il n'y a pas que la Médecine et que la vie est belle ! SERMENT D'HIPPOCRATE « Au moment d'être admis à exercer la médecine, je promets et je jure d'être fidèle aux lois de l'honneur et de la probité.

Mon premier souci sera de rétablir, de préserver ou de promouvoir la santé dans tous ses éléments, physiques et mentaux, individuels et sociaux.

Je respecterai toutes les personnes, leur autonomie et leur volonté, sans aucune discrimination selon leur état ou leurs convictions. J'interviendrai pour les protéger si elles sont affaiblies, vulnérables ou menacées dans leur intégrité ou leur dignité.

Même sous la contrainte, je ne ferai pas usage de mes connaissances contre les lois de l'humanité. J'informerai les patients des décisions envisagées, de leurs raisons et de leurs conséquences. Je ne tromperai jamais leur confiance et n'exploiterai pas le pouvoir hérité des circonstances pour forcer les consciences.

Je donnerai mes soins à l'indigent et à quiconque me le demandera. Je ne me laisserai pas influencer par la soif du gain ou la recherche de la gloire.

Admis dans l'intimité des personnes, je tairai les secrets qui me seront confiés. Reçu à l'intérieur des maisons, je respecterai les secrets des foyers et ma conduite ne servira pas à corrompre les moeurs.

Je ferai tout pour soulager les souffrances. Je ne prolongerai pas abusivement les agonies. Je ne provoquerai jamais la mort délibérément.

Je préserverai l'indépendance nécessaire à l'accomplissement de ma mission. Je n'entreprendrai rien qui dépasse mes compétences. Je les entretiendrai et les perfectionnerai pour assurer au mieux les services qui me seront demandés. J'apporterai mon aide à mes confrères ainsi qu'à leurs familles dans l'adversité.

Que les hommes et mes confrères m'accordent leur estime si je suis fidèle à mes promesses ; que je sois déshonoré et méprisé si j'y manque. »

CONCLUSION:

This study showed that resident in NH decompensate more (hospitalization over 7 days or death) if their entourage is absent. These results are in accordance with a previous study with fewer patients but larger studies with less biases need to be performed to confirm the significance of these risk factors or to find others.

INTRODUCTION

On 1 January 2018, 67.20 million of inhabitants live in France [START_REF]Population par sexe et groupe d'age 2018[END_REF]. Life expectancy keeps rising, to reach 85,3 years old for Women and 79,5 years old for Men. France's Population is aging and people over 65 years old represent 19,6% of the population.

One in three people will aging over 60 years in 2050 [START_REF]Projections de population pour la France métropolitaine à l'horizon 2050[END_REF]. With this population evolution, more and more people suffering from one or more chronic condition at once rises. Terms of comorbidity and multimorbidity thus emerged.

Comorbidity's concept was created in the 1970s (3). It defines « any disease or risk factors that could interact with one main disease with the effect of making it worse » (4)(5) [START_REF] Boyd | A pilot test of the effect of guided care on the quality of primary care experiences for multimorbid older adults[END_REF]. Then, the concept of multimorbidity appeared in Germany in 1976 [START_REF] Brandlmeier | Multimorbidity among elderly patients in an urban general practice[END_REF]. It is a global and a centered-person approach, internationally recognized in the 1990s as connected with chronic health conditions. Family medical practice is a medical specialty, described by the WONCA ( World Organization of National Colleges, Academies and Academic Associations of General Practitioners / Family Physicians ) as « an academic and scientific disciplines, with its own educational content, research , evidence base ans clinical activity, and a clinical specialty oriented to primary care » [START_REF] Allen | European Academy of Teachers in General Practice (Network within Wonca Europe). The European definition of general practice/family medicine[END_REF].

In 2005, the World Health Organization ( WHO) involved General Practitioners ( GPs) to focus on prevention and management of chronic diseases, as a new priority [START_REF] Abegunde | Preventing CHRONIC DISEASES a vital investment[END_REF].

The WONCA outlines Competencies of GPs like an evaluation of « each patient as a whole, and a core skill » [START_REF] Fuhrman | Surveillance épidémiologique de la multimorbidité[END_REF].

But in 2008, the WHO defined people being affected by two or more chronic health conditions as multimorbid. The problem was that the term « condition » still unclear [START_REF] Van Lerberghe | The World Health Report 2008. Primary Health Cared Now more than ever[END_REF]. So, in 2012 , using a systematic literature review technique, a research team, including nine national groups involved within the European General Practitioners Research Network ( EGPRN ) has simplified the multimorbidity concept and defined it as :

« Any combination of chronic disease with at least another disease (acute or chronic) or a bio psychosocial factor (associated or not) or somatic risk factor. Bio psychosocial factor, risk factor, social network, burden disease, health care consumption and patient's coping strategies may function as modifier ( of the effects of multimorbidity).

Multimorbidity may modify the health outcomes and lead to an increased disability or a decreased quality of life or frailty » [START_REF] Reste | The European General Practice Research Network presents a comprehensive definition of multimorbidity in family medicine and long term care, following a systematic review of relevant literature[END_REF].

It gave an overview on all factors that led to frailty, disability, and decreased quality of life.

Thirteen themes of the EGPRN Multimorbidity definition were translated in ten European languages for further medical researches and long-term care. Its aim is to help GPs in prevention, health care, and prevent decompensation for elderly people (13)(14)(15)(16)(17) [START_REF] Lometti | Quels sont les critères de multimorbidité reconnus par les médecins généralistes ? Diffèrent-ils de ceux de la revue de la littérature[END_REF] [START_REF] Derriennic | Quels sont les critères de multi-morbidité reconnus par les médecins généralistes ? Diffèrent-ils de la littérature[END_REF] [START_REF] Daraban | La multimorbidité -les différences entre la théorie et le vécu des médecins généralistes[END_REF] [START_REF] Bodin | Critères de définition de la multi morbidité en médecine générale : Etude qualitative par entretiens individuels semi-dirigés avec les maitres de stage des universités brestois[END_REF].

The concept of multimorbidity is interesting for patients' conditions investigation and their impact on patients' health to improve General Practitioners training.

In 2014, a feasibility pilot cohort study was performed in Lanmeur Nursing Home (NH) to highlight decompensation risk factors among multimorbidity's themes [START_REF] Colleter | Etude pilote de faisabilité d'une étude de cohorte en soins primaires en établissement d'hébergement pour personnes âgées dépendantes (EHPAD) à la recherche de facteurs de risque de fragilité parmi les différents thèmes de la définition de la multi-morbidité[END_REF].

This study follows the first cohort and also a second period of inclusion ( 2015), at 12 months [START_REF] Allard-Latour | Search for decompensation risk factors within the EGPRN multimorbidity's definition themes. Cohort pilot study followed up at twelve months in nursing home[END_REF].

Research hypothesis

The study hypothesis was that most NH's patients met the multimorbidity definition, and if decompensation factors were detected precociously , GPs can avoid and prevent patients from hospitalization or death.

Using it as a tool, patients at risk could be identified, and family medical practice could evaluate the investment required for each.

Research objective

The first objective was to highlight which criteria or group of criteria could help to identify patients at risk of decompensation.

The second objective was to identify difficulties encountered in this study, in order to carry out a large scale study in France and in Europe to detect the most discriminant multimorbidity's factor which can lead to decompensation.

METHOD Study design

It was an analytical, epidemiological and a prospective cohort study. A Feasibility pilot study was initially led with 64 patients resident in Lanmeur nursing home in 2014 [START_REF] Colleter | Etude pilote de faisabilité d'une étude de cohorte en soins primaires en établissement d'hébergement pour personnes âgées dépendantes (EHPAD) à la recherche de facteurs de risque de fragilité parmi les différents thèmes de la définition de la multi-morbidité[END_REF]. But because of a lack of power in comparability between groups, a second period of inclusion was planned in 2015. A global of 88 patients were included on 127 required for an expected difference of 20% on variables, based on alpha level of 5% and beta level of 20%, using a non symmetrical sample ( 75%-25).

The two periods of inclusion lasted six months each and the monitoring of patients was carried out three months of their inclusion. This thesis dealt with the all-cohort pilot study followed up at twelve months of their inclusion.

Study population

The study population was the feasibility pilot study cohort completed by 24 patients of a second period of inclusion to obtain 88 patients. It included all the multimorbid patients (according to the EGPRN definition of multimorbidity) whose GPs was practicing in the university center of Lanmeur and residing in Lanmeur nursing home.

Enrollment criteria were all patients corresponding to the definition of multimorbidity, meaning « any combination of chronic disease with at least another disease (acute or chronic) or a bio psychosocial factor (associated or not) or somatic risk factor ».

For the team, consisting of doctors, residential students and researchers in family practice on multimorbidity, a bio psychosocial factor included psychological risk factors, psychosocial risk factors, lifestyle, demographics (age, gender), psychological distress, social-demographic characteristics, aging, beliefs and expectations of patients, physiology, and pathophysiology [START_REF] Reste | The European General Practice Research Network presents a comprehensive definition of multimorbidity in family medicine and long term care, following a systematic review of relevant literature[END_REF].

Patients were excluded when they did not met the multimorbid definition, unable to be followed up for the duration of the study, patients under legal protection, and patients for whom a survival of less than three months was estimated.

To take part in the study, patients must have signed the information consent paper.

Informed consent of the family for patients without legal protection was no longer required after protocol was revised by team consensus due to inoffensive nature of the observational study and to simplify protocol.

Data Collection

Recruiters doctors were GPs of the Lanmeur University Center, having agreed to participate in the study after mail solicitation. To select patients, the team checked the presence of inclusion criteria. Then patients were seen individually to explain the study process and receive all informations they guessed. Consent was signed on paper and collected by the team.

Potential risk factors for decompensation were collected by completing a questionnaire pre-filled by the team and then completed by GPs.

Risk factors for decompensation had to be analyzed among themes and sub-themes of multimorbidity . ( table 1 )

THEMES SUBTHEMES

For an evaluation of the concept of somatic risk factor, the team relied on the coding book and retained variables « cardiovascular risk factor », « fall risk factor », « assessment of hygiene », « assessment of feeding » and « assessment of physical activity ». Team used the CETAF score to calculate the fall risk.

Some not relevant variables were suppressed because of the impossibility to evaluate or redundancy : « chronic condition », « cost of care », « disability », « quality of life », « health outcomes », « frailty », « physiology », « disease and assessment », « demography and aging ».

The questionnaire was validated in peer group and data were saved using microsoft excel.

In order to have anonymous results, each patient had a corresponding number of inclusion. After twelve months of the inclusion date, team checked patient's status to determine their categories , « Decompensation » (D) or « noting to report » ( NTR).

Team defined, by consensus in peer groups formed by researchers GPs and medical interns, decompensation as a hospitalization of more than seven days or death.

Then, sub themes of multimorbidity were analyzed in the two groups to find risk factors of decompensation.

Study endpoint

The principal criterion was the decompensation risk factor during the twelve months of follow up, as defined by consensus in peer groups consisting of doctors, researchers in general practice and residential students. It was the occurence of hospitalization for more than seven days or death .

The secondary criterion was the collection of difficulties faced during the study process.

Data cleaning

Patient's status at 12 months was notified by « NTR » ( nothing to report = no decompensation ) versus « decompensation » on the table. Data were saved using Microsoft Excel.

In previous studies patients were notified « frail » or « not frail », but due to confusion with « frailty » it was change. Frailty definition is nowadays no consensual with at least two different concepts ( « the rockwood's frailty concept » and « the fried's frailty concept ).

In order to harmonize data, a cleaning was undertaken before statistical analysis. Some disease were grouped into one entity and some were removed ( inguinal hernia, appendicectomy). Abbreviations were annotated, and all changes were notified in a dictionary ( appendix 4 ).

Statistical analysis

Population description

Like previous studies about multimorbidity lead in Brest, the team based statistical analysis on an alpha risk of 5% and a beta risk of 20% to expect a significant differ-ence on the variables of 20%. 127 patients were required. A first period of inclusion lasted from July to December 2014 but decompensated patients were not enough for a significant difference. Thus, another period of inclusion was planned from July to December 2015 using the same method as described above.

A bi-dimensional analysis was realized for each variable. The aim was to compare the two patient's groups « decompensation » and « noting to report », and to verify data quality.

A fisher's exact test, chi2 with or without Yates correction were used for qualitative data , with an alpha level set at 5%.

For quantitative data, a Wilcoxon/ Mann-Whitney test was performed to compare median in a non-parametric test.

Multidimensional analysis : clustering and classification

Patients were also described, whatever their study's status, by common characteristics. First of all , irrelevant and no discriminating variables were removed. A dendrogram was made, using a multiple correspondence analysis (MCA) discriminating variables in each group, and the technique of hierarchical clustering on principal components (HCPC).

survival analysis

To compare survival time of patient's groups, estimating its distribution and how does variables impact it, team used a multivariate model called the Cox Model. Its interest was the adaptation of the follow up at each patient.

First of all, a non-parametric estimator called Kaplan Meier was used to compare for each variable overall survival.

Then to obtain unrefined hazard ratios (HR), using Cox's model a multivariate analysis was performed. At last, to compare the association between the variables ( others being equal ) a multivariate analysis was used (the Cox's model) to calculate the adjusted HR.

Ethics

Ethics committee of the « Université de Bretagne Occidentale » Faculty of medicine approved the study.

RESULTS

Study population

A total of 88 patients from the Lanmeur Nursing Home were enrolled, 64 from July to December 2014 and 24 from July to December 2015. None were excluded and any lost of follow were declared.

In previous studies, the 64 patients cohort was already been analyzed but results

were not enough significant. In fact , 127 patients were required for an expected difference of 20% on variables, based on alpha level of 5% and beta level of 20%, using a non symmetrical sample ( 75%-25).

At twelve months 88 paients were thus analyzed in this study.

Cleaning and recoding data

Many variables were non-discriminating or unnecessary for the analysis. Some answers were more or less unanimous :

-Not discriminating : family history of cardio vascular disease, suicide risk, stress at work, humain help available to the patient, pharmacological treatment, institutional life, vaccination , screening.

-Useless : identification number, inclusion date, date of birth was switched with age.

-Irrelevant for the objective : all variables referring to GPs feelings , lack of time or remuneration , good communication, knowledge about health system, long time relationship, intuition, overview of disease, because the interest was to describe multimorbid population.

Clustering and classification

Using a Ward aggregation criterion, an ascendant hierarchical classification was performed. Considered individually, all patients were grouped according their similitaries.

Finally, a classification tree was obtained called a Cluster Dendrogram. The inertia gain was maximum when 3 groups were studied. Knowing the number of groups, it was easier to analyze variables defining groups.

CLUSTER 1

The population of the cluster 1 was defined by patients who were all without a healthy diet, without any psychologic risk factor and any addiction. Mostly of them were single women with hypertension and osteo-articular pathologies and complication of their acute disease.

In an other hand , patients had more medical treatment. The population of the cluster 2 was defined by patients who were all without psycho social and financial precarity. Mostly of them were not overweight women, without chronic disease and treatment at risk , who did not have complication of their acute disease. In another hand they were younger and had less chronic disease.

Multivariate analysis

Using the Cox model, Hazard Ratio ( HR) of significant variable from univariate analysis were used to calculate the association between a decompensation risk factor and the variable, taking time into account.

The unrefined HR of significant variable and their 95% IC were represented on a table.

However, « Psychosomatic disease » variable was not significant at alpha=0,05 and was thus put off the selection by a step by step protocol. a Chi-2 test was finally used to ensure the hypothesis proportionality of risk ( validated condition of a cox model ) p-value =0,957 table 6 : cox method : unrefined HR Encountered difficulties -First difficulty was the unexpected number of patients recruited ( 88 patients in two different periods on the 127 expected ). It can be explained by the too short duration of inclusion, the long questionnaire to complete, needing to recall GPs, and by the inclusion criterion which restrained patients from Lanmeur NH.

-The questionnaire was quite long because of a large number of variables , and leading to statistical analysis difficulties.

-As the questionnaire was the same for nursing home and ambulatory cohort, some questions were not relevant for this study.

-Difficulties to classify disease intro chronic or acute disease were revealed.

-The subjectivity of some issues made the questionnaire difficult to analyze. Only a few studies analyzed the link between the patient's socio-professional category and its global health status. It was common and well-known that rich patients had a better access to health care than poors [START_REF] Doumont | En quoi la promotion de la santé peut-elle être un outil de réduction des inégalités de santé ?[END_REF].

Nevertheless, any study analyzed the link between health decompensation and type of work before institutionalization.

-Main outcome

The final model showed a link between decompensation and the absence of entourage (all variables being equal ). In a previous study, coping strategy of the entourage was associated with a weak statistical presumption with a lower risk of decompensation [START_REF] Allard-Latour | Search for decompensation risk factors within the EGPRN multimorbidity's definition themes. Cohort pilot study followed up at twelve months in nursing home[END_REF]. Family in medical care of elder people is important as 49% of NH's patients had cognitive trouble [START_REF] Anesm | Qualité de vie en Ehpad (volet 4) L'accompagnement personnalisé de la santé du résident[END_REF]. But never a study highlighted the risk of decompensation and the place of the entourage.

Scandinavian study analyzed coping strategies of relatives, the most helpful one, and also family psychological profile (25) [START_REF] Kuuppelomäki | Coping strategies of family carers for older relatives in Finland[END_REF]. It showed the importance to concentrate on one's priority, to manage stress of elder resident, and to look for positive things. The result of the final model comfort the idea that entourage presence is a protecting factor against decompensation of multimorbid patients.

We could hypothesize that having an extern view by the entourage who come to visit the resident sometimes, help to prevent decompensation (hospitalization more than seven days or death ) by being more aware than nurses and medical staff.

It can also be notice than only a couple of variables were statistically significant between the « decompensation group » and « NTR group » in Lanmeur nursing home. It has already been studied and showed that an admission into institution was the biggest risk factor of decompensation. « 549 residents placed in NH had 2,74 times more risk of dying than the 4602 people who stayed in the community » ( 27) [START_REF] Hjaltadóttir | Predicting mortality of residents at admission to nursing home: a longitudinal cohort study[END_REF].

Encountered difficulties 1) Number of subjects required not reached

Many factors influenced the number of participants included. It was in fact partly related to the fact that the questionnaire was too long ( 52 questions) and needed time to fill it.

It has already been studied that several factors influenced the implication of GPs or reduce it. Among those who influenced the participation of GPs, a visit from a member of the research team and its motivation (28)(29) , a financial incentives [START_REF] Wit | Participation and successful patient recruitment in primary care[END_REF], a multi-centric study, a simple and short data collection [START_REF] Ross | Barriers to participation in randomised controlled trials: a systematic review[END_REF], and combined involvement with practice nurses increase the number of participants included [START_REF] Good | The prevalence of uncontrolled pain in longterm care: a pilot study examining outcomes of pain management processes[END_REF].

On another side several factors badly influenced the participation of GPs because they perceived them as a barrier. It was mainly proved that time pressure and the heaviness of the protocol decreased GPs implication (33) [START_REF] Ross | Barriers to participation in randomised controlled trials: a systematic review[END_REF] .

It was also noticed that the lack of personnal especially in institution or hospital, all about patient's relationship, and the loss of professional autonomy contributed to impact studies.

Finally, about patients, they also felt barriers for inclusion such as additional demands of the trial , patient preferences , worries about uncertainty and concern of information and consent [START_REF] Ross | Barriers to participation in randomised controlled trials: a systematic review[END_REF].

2) The choice and the count of chronic diseases Chronic diseases were listed for each patient -according to the study feature -by the team, without classifying them into ICPC2 (international classification of primary care, second edition ) or ICD10 (international of diseases ,tenth revision ) (35) [START_REF] Diederichs | The measurement of multiple chronic diseases--a systematic review on existing multimorbidity indices[END_REF].

When GPs listed all chronic and acute diseases, team decided to take it all. Only redundant one and incorrect conditions were modified. The variable was just the number of chronic diseases in order to have a quantitative variable for the statistical analysis.

3) Subjectivity of the question « expertise of the GP ».

To compose the EGPRN's multimorbidity's definition, researchers (residents in general practice and GPs specialized in multimorbidity ) discovered eleven themes and sub-themes. But a few time later , another study found two more themes as modulating factor to complete the definition, in detecting and managing multimorbidity : « expertise of the GP in detection and follow up of the patient » and « the dynamics of a doctor-patient relationship ». [START_REF] Huiban | What is the definition of multimorbidity for the Europeans Family Physicians? Tertiary analysis: qualitative study of two groups of European countries[END_REF] Naturally 8 last questions were added to the previous questionnaire and complicated the analysis and assessment of GPs own practice. The other difficulty was that it was rather difficult to transform answers into quantitative variables. It was then proposed to replace dichotomous answers by nuanced one ( yes completely, yes overall, yes a little , not at all ).

Study limitations

1.selection bias

In a previous study concerning multimorbidity's cohort, characteristics of the Lanmeur NH population differed from French NH population. This was because of a Lanmeur centered study only. [START_REF] Colleter | Etude pilote de faisabilité d'une étude de cohorte en soins primaires en établissement d'hébergement pour personnes âgées dépendantes (EHPAD) à la recherche de facteurs de risque de fragilité parmi les différents thèmes de la définition de la multi-morbidité[END_REF] Team had also to include patients with estimated survival time over three months justified by a prevention model of this study. Using their feeling or medical file , GPs had to use some subjectivity in the population inclusion. Necessary to avoid almost dying patients who could have change statistical results [START_REF] Colleter | Etude pilote de faisabilité d'une étude de cohorte en soins primaires en[END_REF].

Finally, patients who could not consent, for many reasons, but not under legal protection were included in the study only with family permission. But this had led to the non-inclusion of 4 patients because of family choice. ( 28 families contacted, 24 agreed, 4 disagreed ). These patients would have been included because only legal protection was an exclusion factor.

Information bias

As it has already been described in previous studies, some results had appeared inconsistent :

Due to an incomplete medical record or by uninteresting information for GPs , cardiovascular family antecedent were missing. Probably it was poorly interesting in managing elderly resident in NH.

Two patients were notified to have accepted a screen test but it has never been proposed to them. Questions could be changed about that , like « Does the patient received a personal or organized screen test ? »

Because of a too difficult assessment in NH of annual paramedics ressources, it was arbitrarily chosen to three a day in peer group.

Number of chronic disease was recalculated among data filed by GPs. Some chronic disease were either grouped , removed , transformed in acute disease. Finally only 102 chronic diseases were selected , decided in peer group.

Pain was the variable presenting the greatest difference compared to the French NH population ( 32% in Lanmeur , versus 71,50% in France ) [START_REF] Colleter | Etude pilote de faisabilité d'une étude de cohorte en soins primaires en[END_REF]. In fact, a standardized pain scale was not used by GP to collect the information and they answered to a dichotomous question (yes / no) according their own memory of the patient's condition. Pain killer were not taken into account and GPs probably referred to serious pain filling the questionnaire.

confounding bias

Statistical analysis was difficult because of a large number of variable and a small number of patients. In order to analyze variables taking time into account , a Cox model was used. Some variables were removed -according to expert knowledge and in peer group -If they were statistically not relevant. Results might have been different according to different choices.

In order to propose a French questionnaire to fill in by GPs , themes and sub-themes were translated from english to French but some translations may be inaccurate.

Future prospects

To reduce information bias, and improve study protocol, a questionnaire was proposed by previous study and tested for the second period of inclusion. Some questions were removed or modified to simplify or reduce fill in time.

All modifications were notified in the table 7 This study was feasible and valid but needed to be more powerful due to a small cohort. 127 patients were expected in the pilot study, but only 88 patients were included and analyzed at twelve months.

The main result emerging was that the absence of entourage was a risk factor of decompensation of multimorbid patients. From a smaller cohort of 64 patients at 12 months , coping strategies of the entourage had significant presumption to be a protecting factor.

However, one hypothesis existing was that there is no model which can be realized in nursing home for the reason that institutionalization is the biggest risk factor of decompensation.

Further studies are needed, at 24 months with the all-cohort ( 118 patients included ), to increase the power of the study and to confirm these results. Perhaps new risk factors will be suspected.

CONCLUSION

This pilot cohort study highlighted that the absence of entourage is a risk factor of decompensation of multimorbid patients in Lanmeur nursing home at 12 months.

4) Existence de caractéristiques complexes des pathologies chroniques (Ex : diabète déséquilibré, risque élevé de décompensation cardiaque, alcoolisme, dépression ...)

• Oui • Non 5) Lister les pathologies aiguës du patient selon la CIM 10 (anémie, hématome, idées suicidaires, bronchite…) si aucune noter RAS 6) Réactions à un stress sévère (anxiété, panique, dépression...)

• Oui • Non 7) Existence de complication(s) des pathologies aigues (exemple : OAP secondaire à un IDM, détresse respiratoire sur bronchite aigue...)

• Oui • Non 8) Quels sont les facteurs de risque somatiques du patient ? • Antécédents familiaux cardio-vasculaires • Surpoids • Immunodépression (cancer, alcoolisme, traitement immunosuppresseur) • Aucune de ces réponses 9) Instabilité posturale (Est-ce que le patient est capable de tenir sur une jambe sans bouger les bras pendant les 5 premières secondes // A ne tester que chez les patients marchant, l'expertise du médecin sera considérée comme valable pour les patients alités)

• Oui • Non 10) Nombre de chutes au cours des douze derniers mois 18) Le patient met-il en place au moins une stratégie adaptative parmi les suivantes ? (en entendant par stratégies adaptatives les moyens que le patient met en oeuvre pour essayer d'aller mieux : mise en place de règles hygiéno-diététiques, planification, soutien instrumental, soutien émotionnel, expression des sentiments, réinterprétation positive, acceptation, déni, blâme, humour, religion, distraction, utilisation de substances)

• 0 • 1 • 2 • 3 et
• Oui • Non 19)
Le patient présente-il des complications de ses pathologies ? (en entendant par complication une décompensation aiguë de ses pathologies chroniques, une situation de fin de vie ou de la iatrogénie (aléa thérapeutique et donc non imputable au médecin))

• Oui • Non 20) Quel est le nombre annuel de consultations ou visites du patient au généraliste ? 21) Quel est le nombre annuel de consultations de patient aux spécialistes ? • existence de l'entourage (cocher si oui)

• entourage soutenant (cocher si oui)

• dépendance de l'entourage (dépendance = maintien à domicile impossible en absence de l'entourage) (cocher si oui) • Mise en place de stratégies adaptatives • Absence d'entourage 45) Pensez-vous que bien connaître le système de santé vous a aidé à mieux suivre ou détecter la multimorbidité de votre patient ?

• Oui • Non 46) Pensez-vous que l'habitude à résoudre des problèmes complexes (combinant maladies et facteurs psycho-sociaux) vous a aidé à mieux suivre ou détecter la multimorbidité de votre patient ?

• Oui • Non 47) Pensez-vous qu'avoir une vision globale de toutes les maladies de votre patient vous a aidé à mieux suivre ou à détecter sa multimorbidité?

ABSTRACTOBJECTIVES:

  The European general practice research network (EGPRN) defined multimorbidity. Multimorbid's nursing home patients are frailty and at risk to decompensate. This study's objective was to search for decompensation risk factors among EGPRN multimorbidity definition in Lanmeur nursing home, at 12 months. METHODS: 88 multimorbid patients from Lanmeur nursing home were included from July 2014 to December 2015. Their status « D » decompensation or « NTR » nothing to report were collected at 12 months. Using a survival analysis by a Cox model, multimorbid variables were analyzed to identify association with decompensation considering time. RESULTS: At 12 months of follow up, on the 88 patients, 35 patients were either been hospitalized more than seven days or dead, representing 39,77% of the cohort. Absence of the entourage was significantly associated with decompensation (HR :15.59 , IC95% {1.97-123.45} , p= 0.009).

RESUMEOBJECTIFS:

  Le Réseau Européen de Recherche en Médecine Générale ( EGPRN ) a défini la multimorbidité. Les résidents d'établissement d'hébergement pour personnes âgées dépendantes (EHPAD) sont fragiles et à risque de décompenser. L'objectif de cette étude était de rechercher les facteurs de risque de décompensation parmi la définition de l'EGPRN de multimorbidité à l'EHPAD de Lanmeur à 12 mois de leur inclusion. MATERIEL ET METHODE : 88 résidents multimorbides de l'EHPAD de Lanmeur ont été inclus de Juillet 2014 à Décembre 2015. Leur statut « D » décompensé ou « RAS » rien à signaler ont été recueillis à 12 mois. En utilisant une analyse de survie d'après le modèle de Cox, les variables de multimorbidité ont été analysées pour identifier leur association avec la décompensation en fonction du temps. RESULTATS : A 12 mois de suivi, sur les 88 patients, 35 ont été soit hospitalisés plus de 7 jours soit sont décédés, représentant 39.77% de la cohorte. L'absence d'entourage a été significativement associé à une décompensation (HR :15.59 , IC95% { 1,97-123.45} , p=0.009). CONCLUSION : Cette étude a montré que les résidents d'EHPAD décompensent plus (hospitalisation plus de 7 jours ou décès ) si leur entourage est absent. Ces résultats sont en accord avec une étude antérieure d'une partie de cette même cohorte mais de plus grandes études comportant moins de biais sont nécessaires pour confirmer la validité de ce facteur de risque ou pour en trouver d'autres.

Figure 1 :

 1 Figure 1 : Flow chart :

Figure 2 :

 2 Figure 2 : inertia gain with 3 clusters

figure 4 :

 4 figure 4 : Factor Map

22 )

 22 Quel est le nombre de recours annuel du patient aux paramédicaux (IDE à domicile, kinésithérapeute, podologue...) 23) Le patient bénéficie-t-il de traitements pharmacologiques ?• Oui • Non 24) Bénéficie-t-il de traitement à risque ? (AVK, chimiothérapie, insuline, traitement chirurgical lourd…)• Oui• Non 25) Utilisation quotidienne de traitements psychotropes (aide au calcul du score de chute)• Oui • Non 26) Mettez-vous en oeuvre des procédures de coordinations de soins pour ce patient? (réseaux, interaction avec des correspondants multiples : spécialistes, psychologues...)• Oui • Non 27) A combien d'EXAMENS BIOLOGIQUES votre patient a t-il recourt annuellement? (En entendant examens réalisés EN DEHORS de la surveillance thérapeutique habituelle (HbA1c, INR, préchimiothérapie?) et EN DEHORS du suivi de ses pathologies chroniques (cirrhose, insuffisance rénale, diabète) exemple de diagnostic de pathologie aigue : sérologie IST…) 28) A combien d'EXAMENS D'IMAGERIE votre patient a t-il recourt annuellement? (en entendant les examens réalisés EN DEHORS de la surveillance thérapeutique habituelle et EN DEHORS du suivi de ses pathologies chroniques (cirrhose et échographie abdominale) exemple de pathologie aigue : Fracture) 29) Pensez-vous qu'en ce qui concerne votre patient, la communication avec les autres intervenants de santé est bonne ?• Oui • Non 30) Pensez-vous avoir déjà été négligent envers la santé de votre patient ?

12 - 16 -▪

 1216 Nombre annuel de recours (si aucun : notez « 0 », si ne sait pas : notez « ? ») : ❏ Au généraliste : … ❏ Aux spécialistes : … 13-Procédures de coordinations de soins mises en place pour ce patient ? (Réseaux, interaction avec des correspondants multiples : spécialistes, psychologues...) ❏ Oui ❏ Non ❏ Je ne sais pas 14-EN DEHORS DE LA SURVEILLANCE THERAPEUTIQUE ET DU SUIVI HABITUEL DES MALADIES CHRONIQUES, Nombre d'examens : ▪ Biologiques par an : ❏ <1 ❏ 1 à 3 ❏ 4 à 6 ❏ 7 à 12 ❏ >12 ▪ Radiologiques par an : ❏ <1 ❏ 1 à 3 ❏ >3 15-Le patient est-il douloureux ? ❏ Oui ❏ Non Si oui, quel(s) traitement(s) prend-t-il ? … Traitements médicamenteux (si aucun : notez « 0 », si ne sait pas : notez « ? ») ❏ Nombre de traitements en cours : … ❏ Nombre de traitements à risque (AVK, insuline, chimio…) : … ❏ Utilisation quotidienne de psychotrope : ❏ oui ❏ non ❏ je ne sais 17-Nombre de chutes ces 12 derniers mois : ❏ 0 ❏ 1 ❏ 2 ❏ 3 ou plus Instabilité posturale (=incapable de tenir en appui monopodal plus de 5 secondes sans bouger les bras) : ❏ oui ❏ non 18-Le patient présente (cochez la ou les propositions) : ❏ Des conduites à risque ❏ Un risque suicidaire ❏ Une addiction ❏ Aucune des propositions 19-Le patient : ▪ Réalise les dépistages organisés : ❏ oui ❏ non ❏ pas d'indication au dépistage ❏ je ne sais pas ▪ Est à jour de ses vaccins : ❏ oui ❏ non ❏ je ne sais pas ▪ A bénéficié d'une éducation thérapeutique (AVK/diabète…) : ❏ oui ❏ non ❏ je ne sais pas 20-Quels sont ses facteurs de risque psychosociaux ? (cochez la ou les propositions justes) : ❏ Conjugopathie ❏ Divorce ❏ Problèmes familiaux ❏ Décès d'un ou plusieurs proches ❏ Précarité financière et sociale 21-Catégorie socio professionnelle (selon INSEE), cocher la profession prédominante exercée avant le passage à la retraite (cochez la proposition) : ❏ Agriculteur / exploitant agricole ❏ Artisan, commerçant, chef d'entreprise ❏ Cadre et professions intellectuelles supérieures ❏ Professions intermédiaires ❏ Employés ❏ Ouvriers ❏ Sans activité (ex : femme au foyer) 22-Caractéristiques de l'entourage : ❏ Entourage aidant et soutenant (stratégie adaptative : aide le patient dans ses démarches administratives, peuvent alerter quand ça ne va…) ❏ Entourage existant mais non soutenant ❏ Absence d'entourage 23-Ces compétences vous aident-elles à appréhender la multimorbidité de votre patient ? La connaissance du système de santé ❏ oui complètement ❏ oui dans l'ensemble ❏ oui un peu ❏ non ▪ L'habitude à résoudre des problèmes complexes ❏ oui complètement ❏ oui dans l'ensemble ❏ oui un peu ❏ non ▪ La vision globale des maladies de votre patient ❏ oui complètement ❏ oui dans l'ensemble ❏ oui un peu ❏ non ▪ Les soins centrés sur les attentes et besoins du patient ❏ oui complètement ❏ oui dans l'ensemble ❏ oui un peu ❏ non ▪ L'attention portée aux facteurs bio-psycho-sociaux ❏ oui complètement ❏ oui dans l'ensemble ❏ oui un peu ❏ non ▪ L'intuition ❏ oui complètement ❏ oui dans l'ensemble ❏ oui un peu ❏ non ▪ Une communication de bonne qualité ❏oui complètement ❏ oui dans l'ensemble ❏ oui un peu ❏ non GRALL (Vincent ) -Research for decompensation risk factors within the EGPR multimorbidity's definition themes. Overall Cohort pilot study followed up at 12 months in Nursing Home. Th. : Méd. : Brest 2018

TABLE OF CONTENTS

 OF Abstract .…………………………………………………………………….………………..11

	Introduction ……………………………………………………………………..………….13
	Method …………………………………………………………………………..…………….16
	study design ……………………………………………………………………..……….…16
	Study population ………………………………………………………………..…….…16
	Study endpoint ………………………………………………………………………….…21
	Data cleaning ……………………………………………………………………………….21
	Statistical analysis ………………………………………………………………………22
	Ethics …………………………………………………………………………….…………..23
	Results ………………………………………………………………………………………..24
	Study population ……………………………………………………………………..…24
	Status at twelve months …………………………………………………………….25
	Survival analysis : research for decompensation risk factors ……34
	Univariate analysis …………………………………………………………………..…34
	Multivariate analysis ……………………………………………………………………36
	Encountered difficulties …………………………………………………………..…37
	Discussion ……………………………………………………………………………………38
	Results analysis ……………………………………………………………………………38
	Encountered difficulties ……………………………………………………..………40
	Study limitation ……………………………………………………………………….…42
	Future prospects ……………………………………………………………………..…45
	Conclusion ………………………………………………………………………………..…47
	Bibliographie …………………………………………………………………………….…48
	Appendices ……………………………………………………………………………….…51

table 1

 1

	: themes and sub-themes of multimorbidity
	THEMES	SUBTHEMES
	CHRONIC DISEASE	chronic condition
		chronic diseases
		complexity characteristics of chronic
		disease
		psychosomatic disease
	ACUTE DISEASE	acute condition
		acute disease
		complexity characteristics of acute
		disease
		reaction to severe stress and acute
		disorders
	BIOPSYCHOSOCIAL FACTORS AND	demographic risk factor
	SOMATIC RISK FACTORS	lifestyle
		patients beliefs / expectations
		physiopathology
		psychological risk factors
		psychosocial risk factors
		sociodemographic characteristics
		somatic risk factors

table 2 :

 2 The following table represents characteristics for each variable , for both groups

	Variables		all cohort	D group	NTR group	P value
			N=88	N=35	N=53 (61%)
				(39%)
	men		33 (38%)	13 (37%)	20 (38%)
	women		55 (63%)	22 (63%)	33 (62%)
	average age		86,0	86,0	86,0	0,415
	number of chronic condition		8,0	8,0	8,0	0,695
	osteoarticular disease		61 (69%)	25 (71%)	36 (68%)	0,910
	hypertension		65 (74%)	27 (77%)	38 (72%)	0,748
	hypercholesterolemia		19 (22%)	5 (14%)	14 (26%)	0,276
	diabetes		11 (13%)	3 (9%)	8 (15%)	0,564
	psychosomatic disease		31 (35%)	17 (49%)	14 (26%)	0,057
	complexity of chronic disease		61 (69%)	25 (71%)	36 (68%)	0,910
	reaction to severe stress		55 (63%)	25 (71%)	30 (57%)	0,238
	complication of acute disease		34 (39%)	17(49%)	17 (32%)	0,183
	cardio-vascular family's		1 (1%)	0	1 (2%)	1,000
	pathology
	overweight		29 (33%)	13 (37%)	16 (30%)	0,654
	immunodepression		16 (18%)	7 (20%)	9 (17%)	0,939
	postural instability		67 (76%)	27 (77%)	40 (75%)	1,00
	average number of falls in a	0	49 (56%)	18 (51%)	31 (58%)	0,855
	year
		1	12 (14%)	6 (17%)	6 (11%)
		2	14 (16%)	6 (17%)	8 (15%)

Only one qualitative variable among all qualitative and quantitative variables was significant ( p<0,05). Patients who decompensated were significantly ordered from different socio-professional categories.

-Farmer : 17% in group D, 23% in group NTR -Artisan / trader : 3% in group D, 19% in group NTR intermediate profession : 3% in group D, 8% in group NTR One other qualitative variable was borderline of the significance. Patients in « noting to report » group were less psychosomatic ( 74% versus 26%, p-value 0,057 ).

table 3

 3

	: description of cluster 1
		CLUSTER 1 (%) GLOBAL (%)
	no Healthy diet	100	61,36
	no psychological risk	100	82,95
	factor
	no addiction	100	87,5

Table 5

 5

	: description of cluster 3
		CLUSTER 3 (%) GLOBAL (%)
	men	100	37,50
	diabete	100	87,50
	any fall	86,96	55,68
	low CETAF score	69,57	18,18
	any children	52,17	23,86
	osteoarticular disease	52,17	30,68
	no hypertension	47,83	26,14
	postural instability	47,83	23,86
	immunodepression	34,78	18,18
	psychosocio and	30,43	12,50
	financial precarity
	risk behaviour	13,04	3,41

figure 5 :survival curve figure 6 : global survival, employees figure 7 : global survival , absence of entourage figure 8 : global survival , psychosomatic disease

table 7 :

 7 difficulties encountered and propositions for further studies.

	Difficulties	Propositions
	same answers for each	questions were removed
	patient for question 13,	in the new questionnaire
	22, 23, 32, 33, 41	( appendix 3)
	important number of
	variables
	No answer for	item removed ( Q8)
	cardiovascular history
	question
	difficulty to count the	dictionary elaborated to
	number of chronic	help the count of chronic
	diseases	diseases ( appendix 2 )
	misunderstanding with	replace Q37 and 38 by
	the screen test question	« does the patient
		received a personal or
		organized screen test ? »
	small cohort	larger inclusion period
		visit from members of
		the research team
		financial reward
		recruitment of FPs
		involve nurses
	subjectivity of question	intermediate answers
		using indirect questions
	inadequate answers	possibility to answer « I
		don't know »

  Pensez-vous que pratiquer des soins centrés sur les attentes et les besoins de votre patient vous a aidé à mieux suivre ou détecter sa multimorbidité? Le motif de consultation du jour/ dernier motif de consultation est (cochez et précisez le motif): ❏ Pathologie aigue simple (ex : cystite) : ………. ❏ Complication d'une pathologie chronique (ex : OEdème pulmonaire aigu sur insuffisance cardiaque chronique) : ………. ❏ Complication d'une pathologie aigue (ex : détresse respiratoire sur pneumopa-

	• Oui Score CETAF Non admis dans ❏ Non • Non 48) • Oui • Non 49) Pensez-vous qu'avoir une relation de long terme attentive sur l'ensemble des Score sur 12 : risque faible (0-3) modéré (4-6) élevé « Hystérectomie » (7-12) Femme (2) Seul (1) Arthrose (1) 1 chute/an (2) ou 2 chutes/an (4) ou 3 chutes/an (6) Psychotrope (1) Test appui monopodal échec (1) pathologie chronique « Appendicectomie » « hernie inguinale » « Cholécystectomie » « Primo infection tuberculeuse » « Insuffisance rénale aigue sur rhabdomyolyse » « CIVD » « Encéphalopathie de Gayet Wernicke » Pathologies proches ou conséquences l'une de l'autre comptant pour une pathologie Nouvelle terminologie Ancienne terminologie Troubles cognitifs = « Alzheimer » + « leucoaraïose » 8-thie) : facteurs bio-psycho-sociaux vous a aidé à mieux suivre ou détecter la multimorbidi-té de votre patient ? • Oui Abréviation AOMI : artériopathie oblitérante des membres inferieurs BPCO : broncho pneumopathie chronique obstructive Admis dans pathologie DMLA : dégénérescence maculaire liée a l'âge Pathologie limite avec facteur de risque « Hypercholestérolémie » « Hypertension artérielle » Troubles cognitifs ………… = « troubles cognitifs » + « leucoaraïose » ❏ Autre (renouvellement d'ordonnance / administratif / suivi) : ………….
	• Non chronique 9-Le patient (cochez la ou les propositions) : AIT : accident ischémique transitoire Cécité oeil = « rétinopathie diabétique » + « cécité oeil
	AVC : accident vasculaire cérébral SAS : syndrome d'apnée du sommeil PTH : prothèse totale de hanche Nouvelle terminologie gauche gauche » ❏ Présente ou a présenté une pathologie psychosomatique Homogénéisation des terminologies Ancienne terminologie Insuffisance respiratoire ❏ Est multi plaintif = « Insuffisance respiratoire chronique » + ❏ Aucune des propositions « BPCO » et/ou « dilatation des 50) Est-ce que le fait d'avoir l'intuition que quelque chose n'allait pas vous a aidé à le détecter? • Oui • Non 51) Une communication de bonne qualité et le fait que le patient puisse s'ouvrir à vous, vous semble t-il important pour détecter sa multi morbidité? • Oui PATHOLOGIES CHRONIQUES Pathologies ostéoarticulaires Cardiopathie ischémique « Angor », « syndrome coronarien aigu » chronique bronches » et/ou « fibrose » et/ou 10-Le patient présente un trouble de l'adaptation à un stress (au moindre « asthme » stress / nouvelle Englobe « fracture », « ostéoporose », « prothèse articulaire », « tassement », « arthrose », « arthrotomie », « ostéotomie », « luxation », « arthroplastie », « Troubles cognitifs « Démence a corps de lewy », « Alzheimer », « démence parkinsonienne », « troubles mnésiques », Plaies chroniques pathologie / mauvaise nouvelle) de type : anxiété majeure, dépression réaction-« Escarres » +/-« pied du diabétique » +/-nelle, idées « ulcères veineux/artériels » suicidaire : algodystrophie ». « démence mixte », « démence Syndrome de « Syndrome de korsakoff » + « exogénose ❏ Oui Si une de ces réponse à la question « pathologie chronique » vasculaire » korsakoff chronique » ❏ Non et « non » répondu à la question « pathologie ostéoarticulaire » : modification par « oui » à la question Trouble du « Fibrillation auriculaire », « extrasystoles AOMI ❏ Je ne sais pas « Endartériectomie » • Non 52) Pensez-vous que la multi morbidité de votre patient influe sur la qualité de votre suivi? • Oui • Non « pathologie ostéoarticulaire » et mise entre parenthèse du nom de ces pathologies. Nombre de pathologies chroniques Addition des pathologies de la question 1 (pathologies ostéoarticulaires, hypertension artérielle, hypercholestérolémie et diabète) et du nombre de pathologies de la question 2 (autres pathologies chroniques) Absence de pathologie ostéoarticulaire = 0 Présence de une ou plusieurs pathologies ostéoarticulaires = rythme et/ou de la conduction auriculaire », « pace maker », « bloc auriculo-ventriculaire » Maladie veineuse Syndrome de 11-Le patient est capable de mettre en place une stratégie adaptative (règles « DMLA évoluée » + « Hallucinations » hygiénodiététiques, planification, soutien émotionnel, expression des sentiments, Charles Bell réinterprétation positive, acceptation, déni, blâme, humour, religion, distraction, « Thrombose veineuse profonde », Pathologies associées Nouvelle Ancienne terminologie utilisation de substance) : « embolie pulmonaire » comptant pour deux terminologie ❏ Oui thromboemboli pathologies ❏ Non que Insuffisance veineuse hémiplégie « varices » AVC « AVC avec hémiplégie séquellaire » ❏ Je ne sais pas
	Non admis dans	1 «Tabagisme» Epilepsie aphasie AVC	« Comitialité », « état de mal épileptique » « AVC avec aphasie séquellaire »
	pathologie chronique car qualifié de facteur de risque plutôt que de	«Hyperuricémie» « ostéopénie » « Accident ischémique transitoire » Pathologie du système urinaire « Vessie neurologique », « infections urinaire », « sonde à demeure » hallucinations urinaires à répétition », « incontinence Démence et «Troubles cognitifs avec hallucinations»
	maladie chronique) PATHOLOGIES AIGUES Hypothyroïdie « thyroïdectomie »
	Non admis dans pathologie aigue car déjà dans pathologie chronique	« Tumeur du tragus » Polype colique « adénome tubuleux de bas grade » « Cancer pulmonaire » « diverticulose » « Anémie ferriprive »
	AUTRE	Exogénose	« OH »
		Tumeurs	carcinome baso-cellulaire
		cutanées

ou plus 13 (15%) 5 (14%) 8 (15%) risk behaviour 3 (3%) 2 (6%) 1 (2%) 0,560 suicid risk 2 (2%) 2 (6%) 0 0;155

DISCUSSION

Results analysis -Univariate analysis

First of all, the description of the cohort showed only one significant different variable between the decompensation group and the nothing to report group. Socio-professional categories were differently distributed.

It is important to notice that another qualitative feature was borderline. In fact, psychosomatic disease was less represented in the NTR group. Socio-professional category was a factor that appeared in the description of the population but also in the final model. There was a significant difference between the two groups. (p-value=0,026 ). Employees were more likely to be represented in Entourage takes a major place helping medical staff to prevent decompensation. Supporting patients, it will be interesting to analyze how does entourage prevent decompensation by their presence and could help GPs in their pragmatic approach.

In fact, a clear identification of decompensation risk factors remains the aim in prevention medicine. This study seems to show the importance to GPs to take into account the entourage in their daily practice. New perspectives could be programmed family staff to prevent from decompensation.

But It cannot be ignored that according some studies the biggest risk factor of decompensation is the entrance in institution.

In knowing risk factors for each patient, GPs could in case of decompensation evaluate limits of the global support before providing healthcare.

Many biases have been identified, nevertheless this result is in agreement with a previous study (shorter cohort at 12 months). New studies should continue on a larger scale, to confirm these results or to find new decompensation risk factors, with a more powerful cohort.

APPENDICES Appendix 1: questionnaire used during the study

Nom prénom patient :

Nom du Médecin :

1) Lister les pathologies chroniques du patient selon la CIM 10 (CIM 10 en lien : http://taurus.unine.ch/icd10 )