Eric Van Reeth Van Reeth

Geetha Soujanya

Michaël Sdika

Frederic Cervenansky

Chueh Loo Poh
email: clpoh@ntu.edu.sg

Eric Van Reeth
email: eric.van-reeth@creatis.insa-lyon.fr

Frédéric Cervenansky

Eric Van Reeth

misoSR: Medical Image Isotropic

Keywords:

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.

Introduction

In magnetic resonance imaging (MRI), a large number of factors such as patient or physiological motion, signal-to-noise ratio (SNR), available static magnetic field B 0 and total scanning time limit the acquired resolution. In order to reduce the acquisition time and increase the SNR, highly elongated voxels are usually acquired by increasing the slice thickness. This inevitably limits the accurate assessment of anatomical structures in the through-plane dimension, increases the partial volume effect (less contrast and more blur), penalizes oblique reconstructions and further processing such as structure segmentation, 3D modelling or registration. Super-resolution (SR) is a post-processing technique that optimally combines several anisotropic acquisitions of the same scene into a single isotropic volume that was not -or could not be -acquired in practice. If each acquisition provides independent information, which is the case when patient motion occurs or when different scanning planes are used, resolution enhancement is possible.

Unlike classic interpolation techniques (linear, cubic, zero-padding-interpolation), SR is able to add significant high-frequency content into the reconstructed volume and efficiently remove some aliasing that might be present in the initial images if the acquisition sampling does not meet the Nyquist criterion. Note that two types of resolution enhancement can theoretically be performed using SR, namely i) in-plane and ii) through-plane resolution enhancement. The feasibility of in-plane improvement in MRI using SR remains a subject of controversy because of i) the inherent lack of high-frequency components acquired in the in-plane dimensions and ii) the fact that a shift in the in-plane dimension corresponds to a simple phase modulation in the acquired k-space [START_REF] Scheffler | Superresolution in mri?[END_REF]. This means that for two spatially shifted volumes with similar field-of-views (FOV), similar k-space points are sampled which does not meet the necessary observation independence criterion for resolution enhancement. Later studies have been performed to clarify this point [START_REF] Tieng | Mri demodulation frequency changes provide different information[END_REF][START_REF] Uecker | Reply to: Mri resolution enhancement: How useful are shifted images obtained by changing the demodulation frequency?[END_REF][START_REF] Tieng | Mri resolution enhancement: How useful are shifted images obtained by changing the demodulation frequency?[END_REF]. On the other hand, through-plane resolution improvement, and in particular isotropic reconstruction, has been extensively and successfully applied to MRI in various contexts such as functional imaging [START_REF] Scherrer | Super-resolution reconstruction of diffusion-weighted images from distortion compensated orthogonal anisotropic acquisitions[END_REF][START_REF] Poot | Super-resolution for multislice diffusion tensor imaging[END_REF], volumetric imaging [START_REF] Woo | Reconstruction of high-resolution tongue volumes from mri[END_REF][START_REF] Van Reeth | Isotropic reconstruction of a 4-d mri thoracic sequence using super-resolution[END_REF], brain MRI [START_REF] Greenspan | Mri inter-slice reconstruction using super-resolution[END_REF][START_REF] Rousseau | A non-local approach for image super-resolution using intermodality priors[END_REF][START_REF] Gholipour | Robust super-resolution volume reconstruction from slice acquisitions: Application to fetal brain mri[END_REF]. Review articles can be found in [START_REF] Greenspan | Super-resolution in medical imaging[END_REF][START_REF] Van Reeth | Super-resolution in magnetic resonance imaging: A review[END_REF]. Various acquisition strategies have been compared in [START_REF] Shilling | Sampling strategies for superresolution in multi-slice mri[END_REF] to optimize the reconstruction. Combining orthogonal acquisitions provides an optimal sampling of the k-space and offers a good trade-off between resolution, signalto-noise ratio, and acquisition time [START_REF] Plenge | Super-resolution methods in mri: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?[END_REF][START_REF] Van Reeth | Isotropic reconstruction of a 4-d mri thoracic sequence using super-resolution[END_REF]. This paper presents a generic implementation of a SR algorithm -misoSR -that reconstruct an isotropic volume from several observations. It can handle various acquisition protocols (number of inputs, volume orientation, different FOVs) since the implementation is based on the header information. The proposed algorithm is written in C++ and based on the Insight Toolkit (ITK) library [START_REF] Ibanez | The ITK Software Guide[END_REF]. It is uploaded on the Virtual Imaging Platform (VIP) [START_REF] Glatard | A virtual imaging platform for multi-modality medical image simulation[END_REF] hosted at Creatis (vip.creatis.insa-lyon.fr), which allows users to run misoSR without system compatibility constraints. This article starts by presenting the theory of SR, then details the implementation step by step and presents each individual parameter. Different working examples including the set of used parameters are given and limitations and perspectives are listed to conclude.

Theory

Image Acquisition Model

SR algorithms are based on an acquisition model given in Eq. 1 that links the observations Y k to the high-resolution image X. Observations are considered displaced, blurred, down-sampled and noisy versions of the ideal and unknown high-resolution image:

Y k = D k B k G k X + V k , k = {1, . . . , N } (1)
In this equation, N is the total number of observations, G k is the geometric transformation operator that encompasses both the transformation due to the plane acquisition orientation and the motion between acquisitions. B k is a space invariant 3-D point-spread function (PSF), D k is the down-sampling operator from the high-resolution space to the observation space, and V k is an additive zero mean Gaussian noise. It is assumed that the PSF (B k ) is separable and has a Gaussian profile in each dimension. The choice of a Gaussian PSF is motivated by the study of Greenspan et al. in which it is shown that such a model is able to produce high-quality reconstructions [START_REF] Greenspan | Mri inter-slice reconstruction using super-resolution[END_REF]. In the through-plane dimension, the variance (σ t ) of the PSF is usually chosen so that the full width at half maximum (FWHM) equals to the slice thickness (t), such as: σ t = t 2.35 . The PSF profile in this dimension is thus defined by:

PSF t (z) = 1 2πσ 2 t exp - z 2 2σ 2 t
The in-plane PSF is constructed as a convolution of two identical Gaussian functions which variance is user defined.

Regularization

The general model given in Eq. 1 is reformulated as a regularized inverse problem because of its ill-posed nature. Let Ŷk = D k B k G k X be the observations estimator, and Γ T T V ( X) the regularization term:

X = arg min X N k=1 Y k -Ŷk 2 2 + λΓ T T V ( X) (2)
where λ balances the influence of the regularization term and the data fidelity term. The chosen regularization term prevents the apparition of noise by penalizing the L 1 norm of the magnitude of the image gradient, which favours the reconstruction of smooth images with sharp transitions [START_REF] Rudin | Nonlinear total variation based noise removal algorithms[END_REF]. Farsiu et al. introduced in [START_REF] Farsiu | Fast and robust multiframe super resolution[END_REF] the bilateral-TV that proved to produce high-quality results and to be computationally efficient, which is an interesting consideration when processing large dataset. In misoSR, the bilateral-TV regularizer is extended to 3-D to match the dimension of the isotropic reconstruction

Xn+1 = Xn + γ × β n-1 N N k=1 G T k B T D T k (Y k -Ŷ n k ) -λ P l=-P m=-P n=0 l+m+n≥0 α |m|+|l|+|n| [Id -S -(lmn) xyz ] sgn( Xn -S lmn xyz Xn ) (3)
problem. It is defined as:

Γ T T V (X) = -P ≤l≤P -P ≤m≤P 0≤n≤P l+m+n≥0 α |m|+|l|+|n| ||X -S lmn xyz X|| 1
In this equation, P is the size of the regularizer kernel, S lmn xyz is the global shift operator and regroups respectively the horizontal (S l x ), vertical (S m y ) and depth (S n z ) circular shift operators. For example, (S l x ) shifts X by l pixels in the x axis and allows the calculation of the gradient in this dimension. The scalar parameter (0 < α < 1) controls the influence of higher scales derivatives in the regularization term. In a 3D problem the choice of P has a significant impact on the computation time. If P is set to 1, the TTV operator can be viewed as the classical TV operator and α does not impact the regularization process.

Convergence

To iteratively converge towards the optimal estimation, a gradient descent algorithm is used as explicitly shown in Eq. 3. The step size γ is decreased at each iteration by the parameter β, (0 < β ≤ 1). Note that the step size is also normalized with respect to the number of inputs to facilitate convergence in case a large number of inputs are used. In Eq. 3, T represents the transpose operator, sgn the sign function and Id the identity matrix. A stable solution is considered to be reached when the difference between two successive iterations is below a pre-determined threshold which is detailed in section 3.3.

Implementation

In this section, the successive steps of the algorithm and their associated parameters are detailed.

Registration

Registration is a key step in the SR process and should be optimized with great care. It corresponds to the deformation operator G k of Eq. 1 that estimates the displacement between successive acquisitions. As mentioned earlier, the first input volume specified in the option file is used as the rigid image and all subsequent inputs are registered to it. The output image will thus be reconstructed in the same position as Y 1 . Note that the choice of the rigid input might have an significant impact in some applications. A multi-resolution scheme is used to improve speed, accuracy and robustness. For more details on the multi-resolution scheme, the reader is referred to [START_REF] Ibanez | The ITK Software Guide[END_REF]. The number of multi-resolution levels can be set. Setting it to one is equivalent to a normal registration strategy. To avoid divergence, the maximum step length can be set, together with the maximum number of iterations per resolution level. Convergence is considered reached when the difference between successive iterations is below a minimum step length or when the maximum iteration number is reached. Iteration details are output to monitor the registration convergence with the selected parameters. Having a look at it is strongly advised at least for first uses since it is hard to set default registration parameters robust to any applications. The registration used in misoSR is a 6 degree-of-freedom rigid transformation, allowing 3D translations and 3D rotations since it is the most common transformation found in practice. This might be a limitation for some applications but deformable registration often requires fine tuning which we decided to let the user handle separately.

Image Modelling

This section explains how the different steps of imaging model of Eq. 1 are implemented, and details the parameters associated to each process.

Initialization

A first guess of the output image, X0 , is performed by averaging all the inputs after they have been registered to the rigid image and linearly interpolated to the isotropic sample grid. We have noted that the choice of the initialization does not dramatically influence the reconstructed image but a clever choice might reduce the required iteration number.

Deformation

This step uses the result of the registration step where the deformation operators were calculated. Note that in the registration step, the displacement from position k to position 1 is estimated, which actually corresponds to the operator G T (k): Y k → Y 1 for k = {2, . . . , N }. The current iteration estimation (in position 1), Xn , is successively displaced with the operator G k to match the k-th position. Note that G 1 is the identity operator.

PSF Filtering

The next step corresponds to the PSF filtering of each previously deformed image. The 3D Gaussian PSF function is defined in part 2.1. The PSF variance in all three dimensions can be set. It should be noted that when no in-plane resampling is performed, i.e. when all inputs have similar in-plane resolutions, a low variance (σ IP = 0.75 px) is usually a good choice. The variance of the through-plane PSF is set by default so that the full width at half maximum (FWHM) equals the slice thickness. However, user can

Down-sampling

The last operator of the imaging model consists of down-sampling the current estimation to each of the observation sampling space. This operation is performed using a simple linear interpolation.

Error Computation

Successively applying the previously described operators allows to create the estimated observations: Ŷk = D k B k G k X that lie in the same sampling space as the actual observations Y k . In order to improve the current estimation at next iteration, the error, e k = Y k -Ŷk , is computed and back-projected into the high-resolution space as can be seen in Eq. 3.

Error Back-projection

The error back-projection step consists of applying the transform operators to the estimation error. It reverses the image acquisition process. The transform of the downsampling operator is performed by upsampling e k back to the isotropic sampling grid. Zero values are inserted in between known values as illustrated in Figure 1. For noninteger sampling factor, voxel values are linearly interpolated to match the isotropic grid positions. The transform of the Gaussian blur operator is the blur operator itself. The same filtering that was applied for the error computation is applied to the upsampled error. Note that after upsampling, the number of zero-values present in the upsampled volume increases with the upsampling ratio. For this matter, the intensity values obtained after PSF filtering are normalized with respect to the through-plane to in-plane ratio. Finally the displacement operator is inverted to move each error image back to the reference position (X) by applying:

G T k B T k D T k (e k ).

Stopping criterion

To stop the optimization process, a stopping criterion is set by the user (tolerance). When the difference between two successive iterations is less than the fixed threshold, the optimization process stops. The stopping criterion is normalized with respect to the difference between the first and second iteration. So if set to 0.03, process stops when the difference between the current and previous iterations is less than 3% of the difference between the initialization and the second iteration. In practice, this has proved to be a reliable method for most applications. At the end of each iteration, the value of this criterion is displayed for the user to monitor the convergence process. In parallel, the maximum number of iterations can also be set to avoid excessive running time.

Examples

This sections presents two examples of SR reconstruction based on misoSR. Both examples with inputs and parameter values can be found on VIP.

T2-weighted Rat Brain Data

Data

A first application of misoSR is shown in the context of T2-weighted rat brain images. Three orthogonal images are acquired in each anatomical plane on an horizontal MRI 4.7T Bruker Biospec with a surface quadrature receiving coil adapted to rat brain (Rapid Biomed). They are illustrated in Figure 2. The acquired voxel size is 0.1×0.1×0.5 mm 3 . A 2D multi-slice RARE sequence is used without slice gaps (TE=72.9 ms, TR>4.8 s depending on the number of slices needed to cover the whole brain). The TR is considered long enough for the longitudinal magnetization to recover. Acquisitions are triggered on the respiratory motion, and the animal is anaesthetized with isoflurane during the imaging process.

Reconstruction

An isotropic reconstruction was performed at a voxel size of 0.1 × 0.1 × 0.1 mm 3 and is shown in Figure 2. The coronal image was used as the reference for the registration process. Note that a field inhomogeneity corrector is used as the only pre-processing step [START_REF] Tustison | N4itk: Improved n3 bias correction[END_REF]. The parameters used for the reconstructions are listed in Table 1. Total computation was around 7 minutes on a 3.7Ghz, 8 cores machine. 13 iterations were needed for convergence. The full volume is 256×128×273 pixels and can be downloaded from VIP for accurate evaluation. The second example of misoSR is on TWIST images of a lung cancer patient. 3D TWIST images are obtained in three orthogonal planes on a whole body Siemens Trio 3T MR scanner (Siemens Trio MRI scanner, Siemens Healthcare, Erlangen, Germany). The inplane resolution of images is 2.1 × 2.1 mm 2 and the slice thickness is 7 mm with no slice gaps (TE= 0.8 ms, TR = 2.05 ms, FOV= 269 mm; Acquisition matrix = 128 x 128).

Registration

Reconstruction

An isotropic reconstruction of voxel size 2.1 × 2.1 × 2.1 mm 3 is performed using misoSR.

The coronal plane is taken as reference image for registration. All inputs are considered being at a similar phase of the breathing cycle. Orthogonal inputs and SR reconstruction are shown in Figure 3. The parameters used for registration and super-resolution are listed in Table 2. Total computation time was around 1 minute on 3.7 GHz, 8 cores machine to output a 128 × 97 × 120 pixels volume in 10 iterations.

VIP implementation

Inputs -Outputs

Inputs are required to have correct header information. Standard image formats (Nifti, Dicom) are expected. Each input must be a single volumetric image.

The number of inputs used is up to the user. Using a single input is equivalent to single image interpolation with TV regularization and can be compared to classical interpolation methods (linear, cubic). A large input number will induce longer processing time but generally increases the reconstruction quality. Note that the first input is taken as the reference image for the subsequent registration steps. Depending on the application, this can significantly influence the reconstruction. The result of the reconstruction is exported as a single Nifti volume. It can be read using for example the open-source software Fiji [START_REF] Schindelin | Fiji: an open-source platform for biological-image analysis[END_REF] or ITK-SNAP [START_REF] Yushkevich | User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability[END_REF]. The output file is automatically placed in a folder corresponding to the date and time of the simulation. Finally, note that the orientation of the output file is set to the following matrix : (1 0 0 ; 0 1 0 ; 0 0 1). This usually causes the orientation of the output volume to be different from the orientation of the inputs.

Parameters summary

I/O:

Reference Image : The complete name (name + extension) of the image that will be used as a reference for the registration step.

Input Images : A single archive file (tar.gz) containing all volumes that will be used for the SR reconstruction. It must at least contain the file previously set as the reference image.

Registration:

Registration

Discussion

This algorithm was originally developed on applications where only rigid registration was needed. For this reason, applications requiring deformable registration can not be directly used in misoSR. More generally, if the registration integrated in misoSR is not able to provide satisfactory results, registration tools like Elastix [START_REF] Klein | Elastix: a toolbox for intensity-based medical image registration[END_REF] can be used to pre-register all volumes. The resulting registered volumes can then be used as inputs in misoSR while setting the registration maximum number of iterations to 0. A second restriction is the use of Gaussian PSF only. In some application like MRI, the slice selection profile might be better approximated using a box or a sinc function for example. Giving to the user the choice on the PSF profile in all 3 dimensions is part of future work.

The algorithm is currently available for external use through the VIP platform hosted at Creatis (vip.creatis.insa-lyon.fr). External users have to create an account and are allowed storage space to upload their data. Note that users are given administrator rights on their account, meaning that they can manage access rights to both their data and results. Unless users specify so, Creatis does not have access to the uploaded data and results. Finally, we would like to remind the users that this algorithm should not be used for clinical nor commercial activities. It was developed and published for research purposes only. This is why the authors would appreciate that researchers cite this present article and VIP [START_REF] Glatard | A virtual imaging platform for multi-modality medical image simulation[END_REF] whenever misoSR is used in their study.

Conclusion

This article describes misoSR, a new tool for isotropic reconstruction of medical images. One or more volumes representing the same scene are combined to produce a single highresolution volume. Inputs are iteratively combined using an image acquisition model that comprises a rigid 6 degrees-of-freedom registration, a Gaussian PSF filtering and a down-sampling step. The overall reconstruction quality mainly depends on various factors :

• the image acquisition model accuracy (registration accuracy, PSF choice). Default parameters are advised in the option file but should be optimized for each application.

• the resampling factor. The reconstruction quality usually decreases when the initial voxels anisotropy increases. A study performed in [START_REF] Van Reeth | Phantom-based evaluation of isotropic reconstruction of 4-d mri volumes using super-resolution[END_REF][START_REF] Van Reeth | Isotropic reconstruction of a 4-d mri thoracic sequence using super-resolution[END_REF] illustrates this point and advises that anisotropy ratios higher than 5 should be avoided, i.e. slice thickness should not be more than 5 times than the in-plane pixel spacing. For high anisotropy ratios, it is usually hard to reduce the partial volume effect present in the initial scans.

• the initial scan orientations. Reconstruction quality increases when the redundancy between initial scans decreases. A denser coverage of the k-space logically leads to an optimal isotropic reconstruction, which explains why many studies combine orthogonal scans.

• the input homogeneity. It is important to keep in mind that the SR model assumes that all inputs provide the same information at similar spatial locations. Contrast in MRI can vary due to acquisition factors or field inhomogeneity. Important variations can thus alter the reconstruction procedure. Users might consider preprocessing such as field inhomogeneity corrections [START_REF] Tustison | N4itk: Improved n3 bias correction[END_REF] before using misoSR.

Figure 1 :

 1 Figure 1: Illustration of the upsampling process used for error back-projection

Axial (0. 1 ×Figure 2 :

 12 Figure 2: SR Reconstruction example on T2-weighted rat brain data. Boxed images show the original acquisitions ; the others are orthogonal reconstructions.

Axial ( 2 . 1 ×Figure 3 :

 213 Figure 3: SR Reconstruction example on lung data. Boxed images show the original acquisitions ; the others are orthogonal reconstructions.

Table 1 :

 1 Parameters used for the rat brain example

	Super-resolution

Table 2 :

 2 Parameters used for the lung example

	Registration		Super-resolution
	Nb of multi-resolution	3	Output voxel size (in mm 3 ) 2.1 × 2.1 × 2.1
	Max step length (in mm)	1	In-plane PSF (in px): σ IP	0.1
	Max iteration nb	300	Through-plane PSF: σ T P	1
			Convergence step: γ	3
			Decreasing rate: β	0.99
			Regularizer weight: λ	0.01
			Regularizer nbhood: P	2
			Tolerance	0.01
			Max iteration nb	15
	4.2 Lung Data
	4.2.1 Data

  multi-res nb : Number of resolution levels used for registration Output voxel size : Voxel size (in mm) in each dimension of the output volume SigmaIP : variance (in voxel) of the in-plane Gaussian PSF (see section 3.2.3)

	SigmaTP : variance of the through-plane Gaussian PSF (section 3.2.3)
	Beta : convergence step decreasing rate (Eq. 3)
	Gamma : convergence step (Eq. 3)
	Lambda : regularization weight (Eq. 3)
	TV Neighborhood : regularizer neighborhood (P) (section 2.1.1)
	Convergence tolerance : threshold under which convergence is considered reached (sec-
	tion 3.3)
	Max SR iteration : Maximum iteration number allowed for SR reconstruction
	Result directory : folder name in which the output will be placed
	Registration max step size : Maximum step size (in mm) allowed for registration
	Registration max iteration nb : Maximum iteration number allowed for registration at
	each resolution level
	SR reconstruction:

Acknowledgements

This work was supported by Singapore National Medical Research Council (NMRC) under grant number NMRC/NIG/1033/2010 ; and by the "Avenir Lyon Saint-Etienne" program from the Université de Lyon in the framework of the "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR) ; and within the framework of the LABEX PRIMES (ANR-11-LABX-0063). The authors would also like to thank the Tan Tock Seng Hospital, Singapore radiographers, Ivan WK Tham (Department of Radiation Oncology, National University Cancer Institute, Singapore), Cher Heng Tan (Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore), Sophie Gaillard (CREATIS, France) and Olivier Beuf (CREATIS, France) for providing the images used in this work.


Vincent Wagner
email: vincent.wagner@univ-nantes.fr

Didier Acier

Jean-Eric Dietlin

Outpatient addiction treatment for problematic alcohol use: What makes patients who dropped out different from those who did not?

Keywords:

Background: A minority of individuals with problematic alcohol use effectively seek help. Moreover, dropouts from care are not uncommon. It remains a major concern for health professionals, as adherence to treatment is significantly associated with better physical and psychological outcomes.

Objectives: The main aim of this research was to assess what factors could distinguish patients with problematic alcohol use who dropped out from those who did not.

Methods: The sample included 150 patients followed-up in an outpatient treatment center in France for a problematic alcohol use. Two measurement times were planned: at the first appointment and after six month of treatment. A large set of individual, environmental and institutional variables were considered to compare both subgroups.

Results: Patients who dropped out mostly differ from patients who did not with a higher level of alcohol-related problems, ambivalence, inclinations to use the substance, number of missed appointments. Significant results were also observed regarding a lower time gap between the first contact with the center and the first appointment, as well as the season of the last appointment.

Conclusions: Tailored motivational interventions could be offered to ambivalent patients, especially during the beginning of the treatment and some significant periods of the year. A particular focus should be brought on patients presenting such profiles in terms of level of alcohol problems, inclinations to drink and motivation to change. Overall, the study provides elements to better understand what may bring one patient to drop out of the treatment, and to improve the continuity of care.

This document is the preprint version of the published article in Substance Use & Misuse. Final publication is available from Taylor & Francis, publishers: https://doi.org/ 10.1080/10826084.2018.1441310 Wagner, V., Acier, D., & Dietlin, J.-E. (2018). Outpatient addiction treatment for problematic alcohol use: What makes patients who dropped out different from those who did not? Substance Use & Misuse. 10.1080/10826084.2018.1441310

Introduction

Problematic alcohol use

A report from the French Monitoring Centre for Drugs and Drug Addiction (Observatoire Français des Drogues et des Toxicomanies, OFDT) stated that in 2014, 10% of people from 18 to 75 years old were using alcohol daily in France (Observatoire Français des Drogues et des Toxicomanies, 2015). Moreover, around 3.4 million of individuals were engaged in problematic alcohol drinking (i.e., abusers or dependent drinkers). This data is concerning especially when considering adverse effects of such consumption. Recent research noted that on the 535'000 premature deaths occurred in France in 2009, 49'000 could be related to direct and indirect effects of alcohol use [START_REF] Guérin | Alcohol-attributable mortality in France[END_REF].

It represents almost 13% of premature deaths among men, 5% among women, and 22% among individuals from 15 to 24. This prevalence is higher in France than in any other European countries [START_REF] Britton | A comparison of the alcohol-attributable mortality in four European countries[END_REF][START_REF] Corrao | Alcohol-attributable and alcohol-preventable mortality in Italy: A balance in 1983 and 1996[END_REF][START_REF] Rehm | Alcohol consumption and alcoholattributable burden of disease in Switzerland, 2002[END_REF].

However, the literature highlights as well that only a minority of individuals with alcohol problematic use are effectively seeking help [START_REF] Alonso | Use of mental health services in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project[END_REF][START_REF] Probst | Alcohol use disorder severity and reported reasons not to seek treatment: a cross-sectional study in European primary care practices. Substance Abuse Treatment[END_REF]. According to the OFDT, in France, approximately 133'000 problematic drinkers were treated in addiction-specialized treatment centers in 2014.

Therefore, there is a significant gap between the estimated number of problematic drinkers and those finally received in care. Moreover, even if they complete their treatment, previous data showed that up to 83% of patients in France were likely to relapse within a 18-month period after treatment [START_REF] Malet | Outcomes from primary care management of alcohol dependence in France[END_REF].

During the treatment itself, it is also not uncommon to observe dropouts from care (i.e., a patient leaving care before its commonly expected end). Previous results indicated that around 50% of patients in outpatient or inpatient addiction-treatment settings dropped out of care [START_REF] Deane | Predicting dropout in the first 3 months of 12-step residential drug and alcohol treatment in an Australian sample[END_REF][START_REF] Mchugh | Predictors of Dropout from Psychosocial Treatment in Opioid-Dependent Outpatients: Predictors of Treatment Dropout[END_REF]. It is a major concern for health professionals as adherence to treatment is significantly associated with an improvement in general health and well-being [START_REF] Gossop | Factors associated with abstinence, lapse or relapse to heroin use after residential treatment: protective effect of coping responses[END_REF]Perkins, Tharp, Ramsey, & Patterson Adelv Unegv Waya), 2016). Some research tried to highlight important factors underlying this dropout process [START_REF] Brorson | Drop-out from addiction treatment: A systematic review of risk factors[END_REF].

Factors predicting dropout of addiction-specialized treatment

One of the dimension most often associated with dropout likelihood is a younger age [START_REF] Brorson | Drop-out from addiction treatment: A systematic review of risk factors[END_REF][START_REF] Perkins | Mapping the Evidence to Improve Retention Rates in Addiction Services[END_REF][START_REF] Stevens | Disadvantageous Decision-Making as a Predictor of Drop-Out among Cocaine-Dependent Individuals in Long-Term Residential Treatment[END_REF][START_REF] Vafaeinasab | Investigation of Affecting Factors on Persistence in the Treatment of Patients under Methadone Maintenance Therapy in Addiction Therapy Centers, Yazd-Iran[END_REF].

Nonetheless, the role of many other sociodemographic factors (i.e., gender, education, marital status) remain under discussion [START_REF] Brorson | Drop-out from addiction treatment: A systematic review of risk factors[END_REF][START_REF] López-Goñi | Addiction Treatment Dropout: Exploring Patients' Characteristics: Treatment Dropout in Addictions[END_REF][START_REF] Smith | Treatment outcomes and predictors of drop out for problem gamblers in South Australia: a cohort study[END_REF][START_REF] Vafaeinasab | Investigation of Affecting Factors on Persistence in the Treatment of Patients under Methadone Maintenance Therapy in Addiction Therapy Centers, Yazd-Iran[END_REF]. On the contrary, in their review of 122 studies, [START_REF] Brorson | Drop-out from addiction treatment: A systematic review of risk factors[END_REF] highlighted the rather consistent negative influence of cognitive deficits on retention rates. These impairments are frequent in psychoactive substance users and can affect a large set of cognitive functions: problem resolution, learning, memory, etc. Previous research stated that cognitive impairments could act on care efficacy, treatment outcomes or substance use during treatment [START_REF] Aharonovich | Cognitive impairment, retention and abstinence among cocaine abusers in cognitive-behavioral treatment[END_REF][START_REF] Likhitsathian | Cognitive changes in topiramate-treated patients with alcoholism: A 12-week prospective study in patients recently detoxified: Cognition, topiramate and alcohol[END_REF][START_REF] Marceau | The Montreal Cognitive Assessment (MoCA) is Sensitive to Head Injury and Cognitive Impairment in a Residential Alcohol and Other Drug Therapeutic Community[END_REF].

However, the impact of many other substance-related or personal factors remain inconsistent across study settings, samples or methods, such as the use of a specific substance, co-occurring mental disorders (with the exception of anti-social and histrionic personality disorders) [START_REF] Brorson | Drop-out from addiction treatment: A systematic review of risk factors[END_REF][START_REF] López-Goñi | Addiction Treatment Dropout: Exploring Patients' Characteristics: Treatment Dropout in Addictions[END_REF][START_REF] Meier | Mental health problems: Are they or are they not a risk factor for dropout from drug treatment? A systematic review of the evidence[END_REF]. In particular, the influence of psychiatric comorbidities on retention in treatment appears to be particularly controversial. Overall, these disorders are often associated with worse treatment outcomes and less time in care [START_REF] Darke | Patterns of major depression and drug-related problems amongst heroin users across 36 months[END_REF][START_REF] González-Saiz | Impact of Psychiatric Comorbidity on the In-Treatment Outcomes of Cocaine-Dependent Patients in Therapeutic Communities[END_REF]. However, there is some evidence that dual diagnosis is associated with better outcomes following treatment as well as long-term retention in treatment [START_REF] Maremmani | The long-term outcomes of heroin dependent-treatmentresistant patients with bipolar 1 comorbidity after admission to enhanced methadone maintenance[END_REF]. Other studies also stated that this condition may have no significant effect on dropouts (see for instance, [START_REF] Astals | Impact of Co-Occurring Psychiatric Disorders on Retention in a Methadone Maintenance Program: An 18-Month Follow-Up Study[END_REF]. It is also possible that some psychopathological traits or symptomatology could lead to a very specific set of outcomes [START_REF] Maremmani | The impact of psychopathological subtypes on retention rate of patients with substance use disorder entering residential therapeutic community treatment[END_REF]. Dual diagnosis remains relevant to investigate, as it is a frequent situation in substance users and can represent a significant factor in the development and maintenance of alcohol misuse [START_REF] Roncero | Psychiatric Comorbidity in Treatment-Seeking Alcohol Dependence Patients With and Without ADHD[END_REF][START_REF] Van Emmerik-Van Oortmerssen | Psychiatric comorbidity in treatment-seeking substance use disorder patients with and without attention deficit hyperactivity disorder: results of the IASP study: Comorbidity in SUD patients with ADHD[END_REF].

Along with the examination of the substance used, another useful dimension is the severity of problems associated with this consumption. Indeed, there is evidence that a higher level of use and misuse, was associated with a reduced retention and attendance in treatment [START_REF] Skule | Levels of alcohol use and depression severity as predictors of missed therapy sessions in cognitive behavioural psycho-educational group treatment for depression[END_REF]. Other authors found that it is a lower severity of alcohol dependence which was related to dropout likelihood [START_REF] Ray | Psychosocial Predictors of Treatment Outcome, Dropout, and Change Processes in a Pharmacological Clinical Trial for Alcohol Dependence[END_REF][START_REF] Vuoristo-Myllys | Predictors of Dropout in an Outpatient Treatment for Problem Drinkers Including Cognitive-Behavioral Therapy and the Opioid Antagonist Naltrexone[END_REF]. However, rather than solely considering the intensity of the substance use disorder, one could also evaluate to what extent the quality of the individual's physical, psychological and social life is adversely affected when entering in treatment and how it can hamper treatment retention [START_REF] Daeppen | Quality of Life Depends on the Drinking Pattern in Alcohol-Dependent Patients[END_REF][START_REF] Picci | Quality of life, alcohol detoxification and relapse: Is quality of life a predictor of relapse or only a secondary outcome measure?[END_REF]. In that sense, health-related quality of life is often used as an indicator of substance use consequences and treatment-induced changes [START_REF] Daeppen | Quality of Life Depends on the Drinking Pattern in Alcohol-Dependent Patients[END_REF][START_REF] Johnson | Improvement of Physical Health and Quality of Life of Alcohol-Dependent Individuals With Topiramate Treatment<subtitle>US Multisite Randomized Controlled Trial</subtitle&gt[END_REF][START_REF] Luquiens | Quality of life among alcoholdependent patients: How satisfactory are the available instruments? A systematic review[END_REF].

Additionally, craving, as a conscious expression of desire directed toward the use a substance [START_REF] Tiffany | The clinical significance of drug craving: Tiffany & Wray[END_REF], is a key factor when studying patterns of substance use and trajectory in care [START_REF] Morgenstern | Relationship between early symptoms of alcohol craving and binge drinking 2.5 years later[END_REF][START_REF] Witkiewitz | Predictors of heavy drinking during and following treatment[END_REF]. Alcohol urges are known to take a variety of form and intensity, and are particularly unpredictable for the user [START_REF] Kvamme | Drinking resumption: problematic alcohol use relapse after rehabilitation. A phenomenological hermeneutical perspective[END_REF]. The most intense experiences of craving can overwhelm any intention to change [START_REF] Bradshaw | Addiction and the Mediation of Hope on Craving, Readiness, and Coping[END_REF][START_REF] Browne | Alcohol Consumption, Craving, and Craving Control Efforts Assessed Daily in the Context of Readiness to Change Among Individuals with Alcohol Dependence and PTSD[END_REF]. Once again, the role of this factor on treatment dropouts remains unclear. In some studies, urges to drink can significantly predict early dropouts [START_REF] Field | Attentional biases in abstinent alcoholics and their association with craving[END_REF][START_REF] Ray | Psychosocial Predictors of Treatment Outcome, Dropout, and Change Processes in a Pharmacological Clinical Trial for Alcohol Dependence[END_REF], but not in others [START_REF] Deane | Predicting dropout in the first 3 months of 12-step residential drug and alcohol treatment in an Australian sample[END_REF][START_REF] Filho | Psychosocial and Clinical Predictors of Retention in Outpatient Alcoholism Treatment[END_REF].

Motivation to change is also an interesting personal-related dimension related to the trajectory in care of problematic drinkers [START_REF] Collins | Motivation to change and treatment attendance as predictors of alcohol-use outcomes among project-based Housing First residents[END_REF][START_REF] Rice | Longitudinal Trajectories of Readiness to Change: Alcohol Use and Help-Seeking Behavior[END_REF]. It refers to a multidimensional dynamic process describing the individual disposition for behavioral and psychological change [START_REF] Arkowitz | Motivational Interviewing in the Treatment of Psychological Problems (1st Ed)[END_REF][START_REF] Prochaska | In search of how people change: Applications to addictive behaviors[END_REF]. However, the literature is rather inconsistent with studies suggesting that either lower or higher motivation to change could be related to higher drop-out risks, while for other research, there is no significant association [START_REF] Brorson | Drop-out from addiction treatment: A systematic review of risk factors[END_REF][START_REF] Callaghan | Does stage-of-change predict dropout in a culturally diverse sample of adolescents admitted to inpatient substance-abuse treatment? A test of the Transtheoretical Model[END_REF][START_REF] Kelly | Ready, willing, and (not) able » to change: young adults' response to residential treatment[END_REF]. Some authors also noted that treatment-related variables and processes (e.g., treatment duration, setting, satisfaction, alliance or therapist characteristics) could be among the most significant predictors of treatment completion, but strong evidence of this association is still lacking [START_REF] Brorson | Drop-out from addiction treatment: A systematic review of risk factors[END_REF][START_REF] Curran | Individual and Program Predictors of Attrition from VA Substance Use Treatment[END_REF]. They could indeed act as treatment-related barriers (i.e., external elements which hinder the care process; [START_REF] Allen | Development of an instrument to identify barriers to treatment for addicted women, from their perspective[END_REF] or facilitators of retention in treatment [START_REF] Saunders | Person-related and treatmentrelated barriers to alcohol treatment[END_REF]. Barriers and facilitators can affect the mere initiation of the care process, as well as the later retention and adherence in treatment [START_REF] Saunders | Person-related and treatmentrelated barriers to alcohol treatment[END_REF]. Simultaneously, the actual relevance of the place where patients are referred, for instance an outpatient or an inpatient settings, when they come from other health-, social-, or judicial facilities, cannot be ignored [START_REF] Mulder | Lack of motivation for treatment in emergency psychiatry patients[END_REF][START_REF] Roncero | Treatment adherence to treatment in substance users referred from Psychiatric Emergency service to outpatient treatment[END_REF].

Finally, a growing number of studies highlights the relationship between time perspective and substance use [START_REF] Beenstock | The association between time perspective and alcohol consumption in university students: cross-sectional study[END_REF][START_REF] Cole | The relationship between temporal profiles and alcohol-related problems in University undergraduates: Results from the United Kingdom[END_REF]. This dimension refers to a relatively stable disposition which guides how an individual considers and acts regarding his past, present and future [START_REF] Adamson | Patient predictors of alcohol treatment outcome: A systematic review[END_REF][START_REF] Zimbardo | Putting time in perspective: A valid, reliable individualdifferences metric[END_REF]. This particular disposition impacts the person's goals and actions [START_REF] Henson | Associations Among Health Behaviors and Time Perspective in Young Adults: Model Testing with Boot-Strapping Replication[END_REF]. For instance, a future orientation implies goal planning and consideration of future consequences of current behaviors. As individuals with future-related perspective would be more likely to engage themselves in healthy behaviors [START_REF] Beenstock | The association between time perspective and alcohol consumption in university students: cross-sectional study[END_REF], we could then assume that the patient time perspectives are able to influence his own engagement and adherence in treatment.

Aims of this research

Even though researchers tried to better understand dropout from addiction treatment, one must admit that these data are often contradictory. Moreover, clinicians sometimes have their own explanations on these dropouts, but not always supported by empirical data.

Therefore, the main aim of this exploratory research is to further contribute to the current knowledge on dropout from addiction-specialized treatment. Based on the previous literature and on discussion with local stakeholders, we chose to include a comprehensive set of both well and sparsely studied indicators to see if they were able to distinguish patients who dropped out of an outpatient addiction treatment center from those who did not. These

Methods

Setting

The present research took place in an outpatient treatment center in France. The care mostly consists in individual consultations with one or several stakeholders in parallel (i.e., addictologist physician, psychiatrist, psychologist, or social worker), with various frequencies of appointments: from once a month to once a week. Depending on the clinical situation, familial and marital interventions, as well as recreational workshops, can be offered. Several inclusion criteria were used: (i) adult (i.e., aged over 18), (ii) problematic alcohol use as main issue, (iii) first visit to the center, (iv) good knowledge of spoken and written French, (v) has given his or her written informed consent. Exclusion criteria were: (i) problematic drug use as main issue, (ii) adult under court-established guardianship or tutorship, (iii) higher cognitive functions disorder, (iv) major co-occurring mental disorder (e.g., massive depressive disorder, psychotic disorders). At their first appointment, patients were informed about the study and were proposed to participate. If they gave their written consent, they were given a set of questionnaires to fill, helped if needed by a member of the clinical staff. After six months of follow-up in the center, participants filled the same set of questionnaires again. The Protection to Persons Committee OUEST IV -Nantes, a local independent research committee, reviewed and approved the research protocol (OR/BB CPP N°883/2014; Project DECA -n°2014-A00717-40 -"Assessment of change at six-month follow-up of patients in an addiction outpatient treatment facility"). This study was also declared to the French Data Protection Authority (CNIL, declaration receipt n°1775023).

Participants

Between November 2014 and January 2016, 299 new patients contacted the center for a problematic substance use. Among them, 256 demands were eligible for the Project DECA (i.e., 85.6% of total demands). Main reason for ineligibility was associated with a demand unrelated to alcohol misuse (e.g., cannabis, gambling, tobacco or codeine misuse).

152 patients accepted to take part in the study. Other patients (n = 104) mainly refused or were absent for their first appointment. There were no significant differences regarding their sociodemographic characteristics between the final sample and patients who did not want to participate. Two participants were removed at this stage because of a large number of missing answers. After six months of follow-up, 69 patients (46 % of the initial sample) were considered as dropouts. The decision to categorize patients as dropouts is traditionally made by the clinical team when a patient does not come to a scheduled appointment and that he does not return to resume the follow-up.

Measures

We used participants' answers to (i) a clinical summary sheet gathering several sociodemographic, substance-and treatment-related data, (ii) the Alcohol Use Disorders Identification Test [START_REF] Babor | AUDIT. The Alcohol Use Disorders Identification Test. Guidelines for use in primary health care[END_REF][START_REF] Gache | The Alcohol Use Disorders Identification Test (AUDIT) as a Screening Tool for Excessive Drinking in Primary Care: Reliability and Validity of a French Version[END_REF]

(iii)
a French version of the Stages of Change Readiness and Treatment Eagerness Scale-8A (SOCRATES) [START_REF] Miller | Assessing drinkers' motivation for change: The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES)[END_REF] Treatment Inventory (BTI) [START_REF] Rapp | Treatment barriers identified by substance abusers assessed at a centralized intake unit[END_REF] and (viii) the Zimbardo Time Perspective Inventory (ZTPI) [START_REF] Loose | La temporalité. Un facteur déterminant des conduites addictives[END_REF][START_REF] Zimbardo | Putting time in perspective: A valid, reliable individualdifferences metric[END_REF].

Prior to that, three members of the research team performed the translation of questionnaires unavailable in French (i.e., SOCRATES, AAAQ and BTI). Each draft was discussed as long as there was not an agreement between team members. Respect of the original meaning of labels was an important rule. Common versions were then revised by an external bilingual person and were back-translated in English.

Statistical analysis

Statistical analyses sought to compare patients who dropped out and those who did not. For the purpose of this study, only baseline data were used. According to the study objectives, a large set of variables was considered: socio-demographic characteristics (age, gender, education, professional status, socio-professional category, financial resources, housing), treatment-(source of the demand for care, obligation from the court, interventions and substance-related variables (e.g., frequency of use, polydrug use, previous experiences of care), level of alcohol use problems (i.e., AUDIT), inclinations to use or not alcohol (AAAQ), motivation to change (SOCRATES), cognitive impairment (MOCA), health-related quality of life (SF-36), barriers to treatment (BTI) and time perspectives (ZTPI). Comparisons on our unequal subsamples were conducted using, where applicable, χ 2 test or the Tukey-Kramer method, which aims to control for the family-wise error rate [START_REF] Montgomery | Design and Analysis of Experiments[END_REF].

Results

Patients' demographics

At the first measurement time, the mean age of the sample was 45.01 years (SD = 11.21). Among the 150 participants, 74.0% were men (n = 111), 66.7% were employees or factory workers, and almost the same number of participants received at least 12 years of education (51.3%) than those who did not (48.7%). At their entry in treatment, 66.7% of the sample stated that they had been using alcohol four times a week or more. The mean score at the Alcohol Use Disorders Identification Test (AUDIT) was 21.47 (SD = 8.08), well above the cut-off score of 7 indicating a problematic use of alcohol [START_REF] Gache | The Alcohol Use Disorders Identification Test (AUDIT) as a Screening Tool for Excessive Drinking in Primary Care: Reliability and Validity of a French Version[END_REF]. Table 1 provides full characteristics of the sample.

(Insert Table 1 here)

Overview of dropouts

Overall, 745 interventions were carried out within the follow-up period (M = 4.97; SD = 2.79). Unsurprisingly, patients who dropped out had a lower length of treatment (M = 54.74 days; SD = 51.82 versus M = 194.83 days; SD = 30.09, Tukey-Kramer's q = 29.1, p < .001). A fortiori, they less often met health professionals: M = 3.06 appointments; SD = 1.95; Min = 1; Max = 9 versus M = 6.59 appointments; SD = 2.33, Min = 1; Max = 9, q = 14.1, p < .001. Patients who dropped out received significantly fewer individual consultations (M = 2.97; SD = 1.97) than those who did not (M = 6.26; SD = 1.82, q = 15, p < .001). No differences could be found regarding other interventions.

Among dropouts, 48 patients (69.6%) left the care without notifying stakeholders.

Eleven patients openly said that they wanted to interrupt the treatment, for various reasons (e.g., the care did not seem useful any more for them). Other specific reasons appeared as well: death (n = 2; 2.9%), reorientation or relocation (n = 7; 10.1%) and imprisonment (n = 1; 1.4%).

Socio-demographic and substance-related variables

None of the socio-demographic variables could distinguish patients who dropped out from those who remained in care (see Table 2 for full details). On the contrary, the level of alcohol problems, measured via the global AUDIT score, could highlight a difference. Patients who dropped out present a slightly higher score (M = 23.14; SD = 6.99) than patients who stayed in treatment (M = 20.04; SD = 8.69; q = 3.36, p < .05). However, other substance-related variables do not distinguish the two groups: alcohol use frequency, previous experience of care for the same disorder, poly-drug use, etc.

Treatment-related variables, inclinations to use alcohol and ambivalence

Analyses revealed differences between dropouts and patients remaining in care regarding some treatment-related variables. First, those who dropped out missed significantly more appointments during their follow-up (M = 1.96 missed appointments; SD = 1.47 versus M = 1.02; SD = 1.45, q = 5.56, p < .001). Moreover, this result is not found regarding cancelled appointments (e.g., the patient calls the center to warn that he will not be able to attend the next appointment). Another surprising fact is that the difference in days between the first contact with the center and the first appointment is lower in dropouts than in other patients (M = 27.30 days; SD = 13.64 versus M = 35.60 days; SD = 21.96, q = 3.85, p < .01). We noticed as well that there were significantly more dropouts during fall (i.e., September to November) and even more during winter (i.e., December to February) seasons (χ 2 (3) = 10.97, p < .05).

Specifically, among the 69 patients who dropped out, 19 (27.5%) and 26 (37.7%) left the follow-up during these seasons. No other treatment-related variables had a significant role.

The Table 4 summarizes these data.

(Insert Table 4) At last, among all questionnaires administrated to patients, only three subscales showed significant effects. Precisely, it involves two of the AAAQ subscales (i.e., Obsessed/Compelled and Inclined/Indulgent) and the Ambivalence subscale of the SOCRATES.

Dropouts patients had a higher score on the Inclined/Indulgent (M = 5.04; SD = 2.19 versus M = 3.96; SD = 2.42, q = 3.96, p < .01), Obsessed/Compelled (M = 4.44; SD = 2.28 versus M = 3.68; SD = 2.38; q = 2.81, p < .05) and Ambivalence subscales (M = 16.94; SD = 2.68 versus M = 15.27; SD = 3.69; q = 4.42; p < .01). The last table, Table 5, provides full details on these results.

(Insert Table 5 here)

Discussion

Results interpretation

This study aimed to better understand addiction treatment dropouts. Here, 69 patients (46% of the total sample) dropped out during the six-month follow-up period. Regarding the recent literature on similar settings, this proportion of dropout is relatively normal [START_REF] Roncero | Treatment adherence to treatment in substance users referred from Psychiatric Emergency service to outpatient treatment[END_REF][START_REF] Vuoristo-Myllys | Predictors of Dropout in an Outpatient Treatment for Problem Drinkers Including Cognitive-Behavioral Therapy and the Opioid Antagonist Naltrexone[END_REF]. Analyses carried out in this research included a large set of individual, social, treatment or substance-related variables. In the end, only a few of these variables were effectively able to distinguish patients who dropped out and those who did not. Nonetheless, even if they are surprising in light of other research, non-significant results remain important for clinicians and researchers who witness such treatment trajectories. Indeed, several studies highlighted that clinicians were mainly explaining dropouts with client-related variables, whereas patients emphasized issues regarding social support and staff relationships [START_REF] Palmer | Substance User Treatment Dropout from Client and Clinician Perspectives: A Pilot Study[END_REF].

However, some non-significant results need to be further discussed. For instance, past literature identified that age, ethnicity, gender or employment were sometimes able to predict retention or dropout of patients [START_REF] Braitman | Initiation and retention in couples outpatient treatment for parents with drug and alcohol use disorders[END_REF][START_REF] Graff | Engagement and Retention in Outpatient Alcoholism Treatment for Women[END_REF][START_REF] Mccaul | Predictors of outpatient treatment retention: patient versus substance use characteristics[END_REF]) whereas these findings were not replicated here. It has to be noted that previous studies mostly used regression models to predict treatment retention, instead of comparing patients who did drop out and those who stayed in care. Some samples were also more heterogeneous than ours regarding substance use: alcohol only versus various substance [START_REF] Braitman | Initiation and retention in couples outpatient treatment for parents with drug and alcohol use disorders[END_REF][START_REF] Mccaul | Predictors of outpatient treatment retention: patient versus substance use characteristics[END_REF].

Moreover, given the chosen inclusion and exclusion criteria, some additional indicators could not distinguish dropouts and patients remaining in treatment. For example, MOCA scores only have a short range, as presenting higher cognitive dysfunctions was an exclusion criterion. Consequently, influence of cognitive deficits may be poorly judged here, whereas the literature suggests that this dimension is one the most consistent risk factor for addiction treatment dropout [START_REF] Aharonovich | Cognitive impairment, retention and abstinence among cocaine abusers in cognitive-behavioral treatment[END_REF][START_REF] Brorson | Drop-out from addiction treatment: A systematic review of risk factors[END_REF]. The same consideration could be brought to co-occurring mental health problems, even if past research is more contradictory regarding its impact on dropout [START_REF] Meier | Mental health problems: Are they or are they not a risk factor for dropout from drug treatment? A systematic review of the evidence[END_REF].

Nonetheless, patients who dropped out present a higher level of severity of their alcohol-related problems, as measured via the AUDIT. They appear as well more ambivalent regarding their intention to change, as suggested by the score of the Ambivalence subscale of the SOCRATES. Moreover, these patients had a higher score on the overall approach inclination to use alcohol from the AAAQ. It has to be noted that this scale distinguish, respectively, a strong craving-like urge to drink (the Obsessed/Compelled subscale) and a subtler desire to drink generally conditioned by social and context incentives to use alcohol (the Inclined/Indulgent subscale). Therefore, patients who dropped out may feel a more intense craving and are particularly sensitive to social and contextual incentives for alcohol consumption. The articulation between these dimensions, often retrieved in the literature, is interesting to better grasp underlying processes at stake in patients.

There is evidence in the literature that avoidance inclination (i.e., inclination not to use alcohol, covering a large set of behaviors and intentions, as measured by the Resolved/Regulated subscale of the AAAQ) may moderate effects of approach inclination on drinking behavior, especially during initiation of treatment [START_REF] Schlauch | Alcohol craving in patients diagnosed with a severe mental illness and alcohol use disorder: Bidirectional relationships between approach and avoidance inclinations and drinking[END_REF]. Moreover, conflict between high levels of approach and avoidance inclinations may lead to ambivalence, an unstable state regarding motivation to use or not to use alcohol. Thus, it is not so surprising that an ambivalent patient regarding his substance consumption will miss more appointments, before eventually even leaving the care. Moreover, it is also relevant to consider that this outpatient setting may not be relevant, at first, for all patients [START_REF] Roncero | Treatment adherence to treatment in substance users referred from Psychiatric Emergency service to outpatient treatment[END_REF]. Especially, extrinsic motivation for care can be quite common for patients who are referred from other facilities, or are under the pressure of their significant others [START_REF] Wagner | Initiation of Addiction Treatment and Access to Services: Young Adults' Accounts of Their Help-Seeking Experiences[END_REF]. A specific attention is required regarding specific needs, resources, including the individual motivation, and difficulties of the patient before and during the actual referral process.

Then, patients who dropped out had waited less time between the date they first contacted the center and the date they had their first appointment. This fact remains somewhat unsettling. One may assume that those who wait longer and still turn up for their appointment have higher motivation and ongoing needs. However, we may also suggest that some sort of latency period may be needed to better define and incorporate the need for change, especially for the more ambivalent patients. However, this process could be quite uncertain and some patients may not even come for the beginning of the follow-up. Indeed, according to substance users, being on a waiting list seems to be a significant barrier to enter treatment [START_REF] Appel | Barriers to enrollment in drug abuse treatment and suggestions for reducing them : Opinions of drug injecting street outreach clients and other system stakeholders[END_REF][START_REF] Redko | Waiting time as a barrier to treatment entry: Perceptions of substance users[END_REF]. Many patients become less interested to be involved in treatment after being forced to wait for a relatively long period [START_REF] Brown | The Functioning of Individuals on a Drug Abuse Treatment Waiting List[END_REF]. Moreover, it is now long known that waiting time preceding the actual treatment entry does not involve noticeable change regarding substance use pattern [START_REF] Best | The relative impact of waiting time and treatment entry on drug and alcohol use[END_REF]. However, we could consider implementing brief interventions to invest the time the patient will need to wait before the actual first appointment, to improve the later treatment linkage and retention. These interventions could focus on self-awareness and self-management, while informing patients about upcoming treatment and the overall change process. An interesting way to reach patients during this period could be to use mobile-, Internet-applications, or emails.

Finally, we found a rather original finding, related to the season of the last appointment before the dropout. Precisely, the majority of dropouts happened in fall and even more in winter, around the end of the year. This later period is characterized by holiday season, which involves lots of solicitations to drink alcohol, especially in France. One may assume that these events, for instance Christmas and New Year Eve parties, would also be significant in other European, or even all Western, countries, given their shared culture and history. However, the situation might be quite different in other distant countries. Indeed, if the Western cultural influence is now broadly disseminated around the world, different cultures still coexist, with their own calendar of major social and/or religious events. However, in the current situation, it is also true that many patients initiate their care in fall (i.e., 40.7% of those remaining in care and 53.6% of those who dropped out). As those who left prematurely their follow-up spent approximately 55 days in treatment, it may explain a greater number of dropouts in winter.

Nonetheless, the end of the year remains a critical moment for patients in treatment. What is here important to highlight is the relevance of these events on the alcohol use patterns and the treatment course (Alcohol and Public Policy Group, 2010).

According to our findings, we suggest that tailored motivational interventions may be helpful for ambivalent patients in order to reduce their approach inclination of alcohol rather than solely enhance avoidance inclination, especially at the beginning of treatment [START_REF] Spruyt | On the predictive validity of automatically activated approach/avoidance tendencies in abstaining alcohol-dependent patients[END_REF]. The literature stated that being focused on persistent restrictive and/or avoiding strategies of alcohol use would be detrimental for the patients, as they may not be able, at early stages of change, to manage underlying emotional conflicts associated with alcohol use [START_REF] Schlauch | Individual Differences in Approach and Avoidance Inclinations Moderate the Effect of Self-Control Depletion on Ad-Lib Drinking[END_REF][START_REF] Spruyt | On the predictive validity of automatically activated approach/avoidance tendencies in abstaining alcohol-dependent patients[END_REF]. These interventions could be useful as well to manage critical periods of the year. As defined in the literature, brief motivational interventions, early provided in the follow-up, is a significant tool to improve the patient's motivation to change. Furthermore, several studies highlighted how receiving a motivational program could lead to a higher likelihood of long-term retention in treatment [START_REF] Bachiller | Motivational interviewing group at inpatient detoxification, its influence in maintaining abstinence and treatment retention after discharge[END_REF][START_REF] Ana | Efficacy of group motivational interviewing (GMI) for psychiatric inpatients with chemical dependence[END_REF]. We also assume that there is a true need to work with the patient on his cognitions and his expectations regarding retention in treatment and changes in the substance use.

Overall, a specific focus should be brought on patients presenting particular profiles in terms of severity of alcohol problems, inclinations to drink and motivation to change. In any case, evaluation of the two latest dimensions should systematically be conducted [START_REF] Csillik | L'évaluation des stades de changement dans les addictions[END_REF]. Last, given the brevity of the follow-up of patients who dropped out, one may wonder whether the treatment should be focused on the improvement of the adherence to treatment (i.e., identifying patient expectations, needs and difficulties), and/or on a short but efficient management of the substance use disorder.

Limits

This study has limits. First, answers from patients can be influenced by their initial motivation to be in care. Similarly, these 150 participants took part in the research protocol while they were simultaneously in treatment. Therefore, it may be possible that their characteristics are somewhat different from those of the 104 patients who explicitly did not want to participate, were absent or were excluded. Furthermore, this study was conducted on a single site. Therefore, it may limit the generalization of results to very different addiction treatment centers or programs. Third, the study used several questionnaires translated in French but not currently validated in this language. Fourth, no standardized alcohol treatment was given. Indeed, frequency of visits and interventions were vastly depending on individual needs. Although this is clinically sound, it can make group comparisons quite difficult. Moreover, we did not have a reliable description of psychopharmacological treatments received by each patient, whereas it could represent a significant factor of retention or dropout. Last, age of onset and length of alcohol misuse were not consistently assessed in this sample, therefore not included in the analyses.

  indicators include: socio-demographic characteristics (gender, age, education, professional status, socio-professional category, financial resources, housing), alcohol-(frequency of use, polydrug use, level of severity of alcohol-related problems, inclinations to use or not to use alcohol), treatment-related (source of the initial demand, obligation from the court, interventions, motivation to change, barriers to treatment) variables, health-related quality of life, time perspectives and cognitive impairments.

  (iv) a French version of the Approach and Avoidance of Alcohol Questionnaire (AAAQ) (McEvoy, Stritzke, French, Lang, & Ketterman, 2004) (v) the MOntreal Cognitive Assessment (MOCA) (Nasreddine et al., 2005) (vi) the Medical Outcome Study Short Form-36 items health survey (MOS-SF-36) (Leplège, Ecosse, Coste, Pouchot, & Perneger, 2001; Ware & Sherbourne, 1992) (vii) a French adaptation of the Barriers to

Table 3

 3

	provides all results regarding these

Table 1

 1 Sociodemographic characteristics of the sample at the entry in treatment

	Variables

Table 2

 2 Comparisons between dropouts and patients remaining in care according to sociodemographic variables = 150. Group comparisons with χ 2 test or Tukey-Kramer method. *** p < .001. ** p < .01. * p < .05. ns for p > .05.

		Follow-up status
	Remaining in care (N1 = 81)	Dropout (N2 = 69)
	n	%	n	%	χ 2

Table 3

 3 Comparisons between dropouts and patients remaining incare according to substance-related variables = 150. Group comparisons with χ 2 test or Tukey-Kramer method. a During the last 12 months. b Any other substance or behavioral trouble, excepting tobacco. *** p < .001. ** p < .01. * p < .05. ns for p > .05.

	Follow-up status
	Remaining in	Dropout
	care		(N2 = 69)
	(N1 = 81)
	n	%	n %	χ 2

Table 4

 4 Comparisons between dropouts and patients remaining incare according to treatment-related variables Note. N = 150. Group comparisons with χ 2 test or Tukey-Kramer method. a Spring season includes March, April, May, Summer season, June, July, August, Fall season, September, October, November and Winter, December, January, February. b Duration in days. c Includes family and marital consultations, recreational workshops and support or relaxation groups. *** p < .001. ** p < .01. * p < .05. ns for p > .05.

						Follow-up status
				Remaining in care			Dropout
					(N1 = 81)			(N2 = 69)
				n		%		n		%	χ 2
	Source of the demand for treatment							ns
	The patient		25		30.9		24		34.8
	Significant others		3		3.7		7		10.1
	Family doctor, medical	24		29.6		21		30.4
	specialist
	Specialized	care	6		7.4		7		10.1
	setting, hospital
	Justice		19		23.5		7		10.1
	Other			4		4.9		3		4.3
	Season of initial contact a							ns
	Spring			22		27.2		12		17.4
	Summer		14		17.3		10		14.5
	Fall			33		40.7		37		53.6
	Winter		12		14.8		10		14.5
	Season of last appointment a							*
	Spring			29		35.8		13		18.8
	Summer		21		25.9		11		15.9
	Fall			14		17.3		19		27.5
	Winter		17		21.0		26		37.7
	Obligation of treatment from the court							ns
	No			62		76.5		61		88.4
	Yes			19		23.5		8		11.6
				M	SD Min Max	M	SD Min Max	Tukey-Kramer
	Duration of follow-up b		194.83 30.09 92 284 54.74 51.82	0	201	***
	Length	between	first	35.60 21.96	6	176 27.30 13.64	4	98
	contact	and	first							**
	appointment b
	Total appointments		6.59	2.33	3	17	3.06 1.95	1	9	***
	Individual consultations	6.26	1.82	3	11	2.97 1.97	0	9	***
	Other interventions c		0.33	1.28	0	8	0.09 0.33	0	2	ns
	Cancelled appointments	1.38	1.07	0	5	1.10 1.11	0	4	ns
	Missed appointments		1.02	1.45	0	7	1.96 1.47	0	8	***

Table 5

 5 Comparisons between dropouts and patients remaining in care regarding other psychometric variables = 150. Group comparisons with Tukey-Kramer method. *** p < .001. ** p < .01. * p < .05. ns for p > .05.

	Follow-up status
	Remaining in	Dropout
	care		(N2 = 69)
	(N1 = 81)
	M	SD	M	SD	Tukey-
					Kramer

Acknowledgements

We would like to thank the staff of the Beauséjour Addiction Care, Support and Prevention Center (ACSPC) for their time, information and assistance during this research and its parent study (Project DECA). We would like to thank as well outpatients who agreed to participate.

main research protocol and got its approval by the ethical research committee. Jean-Eric Dietlin co-designed the research protocol and coordinated the study in Beauséjour Addiction Care, Support and Prevention Center. All authors contributed to and have approved the final manuscript.

Funding:

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author contributions

Conclusions

Initially, this study searched to answer a real clinical inquiry: Why do patients with problematic alcohol use drop out? Current results highlight the initiation and adherence of alcohol-specialized outpatient treatment in France. Overall, the study provides elements which can provide a better understanding of the dropout phenomenon. Indeed, patients who left the care differ significantly from other patients by a higher severity of their alcohol-related problems, ambivalence, inclinations to use alcohol, and their number of missed appointments.

They also waited less time before receiving their first appointment. Last, most dropouts occurred in fall and especially in winter. By extension, these data could be relevant for practitioners to improve the continuity of care. By taking into account the specific needs and difficulties of this population of patients, one may help them to better define their own expectations regarding the process of care and to be more involved in the treatment. Research on this topic must be continued, for instance, by contacting patients who dropped out to assess what made them leave their follow-up.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Environmental

Research and Public Health,6(11), 2822-2832.