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PROFESSEURS DES UNIVERSITES-PRATICIENS HOSPITALIERS

Monsieur le Professeur Marc Ruivard, Je vous remercie de me faire l'honneur de présider mon travail de thèse. Durant mon internat, j'ai pu avoir le plaisir de me compter parmi les internes de votre service pendant un semestre. Un semestre qui m'aura appris la rigueur et élargi mon cercle de connaissances, le tout dans une très bonne ambiance de travail qui ne serait pas possible sans vous.

Monsieur le Docteur Vincent Grobost, Merci pour tout. Merci d'avoir dirigé et corrigé mon travail de thèse. Tu as d'ailleurs su me trouver un sujet qui m'a beaucoup plu. Merci d'avoir été si présent, si disponible et bienveillant. Merci pour l'optimisme dont tu as fait preuve pendant tout mon travail, il m'a permis de garder la tête haute malgré les imprévus. Merci pour tes encouragements, tout mon travail n'aurait pas été possible sans toi. Merci aussi pour ces 5 mois passés en médecine interne. Tu m'auras, là aussi, beaucoup apporté.

Madame le Docteur Corinne Bouteloup,

Merci d'avoir accepté de participer à ma thèse qui s'oriente grandement vers votre domaine de prédilection qu'est la maladie coeliaque. Merci d'avoir su m'aiguiller sur les parties relatives à la gastro-entérologie et la nutrition. Je suis vraiment très contente de pouvoir vous compter parmi les membres de mon jury.

Monsieur le Docteur Nil Ghelis,

Je te remercie de faire partie de mon jury. Nous n'avons pas eu l'occasion de travailler ensemble, mais avoir un radiologue dans mon jury me tenait vraiment à coeur, donc je te remercie infiniment de prendre le temps de lire et juger de mon travail. Au plaisir d'avoir à retravailler avec toi un jour.

Monsieur le Docteur Aurélien Simon,

Toi qui as si bien réussi à me vendre la pneumologie à Clermont-Ferrand. De mon co-interne, tu es passé à chef et aujourd'hui te voilà parti. Mais dans tout cela, il y a un statut que tu as depuis maintenant plusieurs années et que tu as toujours malgré ton départ, c'est celui d'ami. Merci d'accepté de faire partie de mon jury de thèse, tu seras le Xanax de ma soutenance.

Madame le Docteur Camille Roland-Debord, Merci infiniment d'avoir accepté de relire ma thèse. Votre expérience en pneumologie fait de votre jugement un élément indispensable pour moi pour la concrétisation de

MAITRES DE CONFERENCES ASSOCIES DES UNIVERSITES

LISTE DES TABLEAUX ET FIGURES

with a diffusion impairment.

Conclusion:

CD is an independent risk factor of developing tracheobronchial damages and nodules independently tobacco, COPD, pneumonitis history and diet. In patients with CD, a clinical screening of pulmonary symptoms could be interesting and patients may need PFT during the follow-up with chest HRCT if there is an anormal PFT and/or pulmonary symptoms.

INTRODUCTION

Celiac disease (CD) is a chronic multi-visceral autoimmune disease that targets the small intestine in genetically predisposed people, caused by the ingestion of gluten [START_REF] Al-Toma | European Society for the Study of Coeliac Disease (ESsCD) guideline for coeliac disease and other gluten-related disorders[END_REF]. It is a rare disease (0.7-1.4%) [START_REF] Therrien | Celiac Disease: Extraintestinal Manifestations and Associated Conditions[END_REF], but the prevalence is probably underestimated and increased significantly over the last 50 years [START_REF] Al-Toma | European Society for the Study of Coeliac Disease (ESsCD) guideline for coeliac disease and other gluten-related disorders[END_REF][START_REF] Lebwohl | Coeliac disease[END_REF]. The main manifestations of CD are intestinal (diarrhoea, abdominal pain, weight loss, constipation), but some extra-intestinal manifestations are possible (dermatological, haematological, rheumatological, etc…) [START_REF] Al-Toma | European Society for the Study of Coeliac Disease (ESsCD) guideline for coeliac disease and other gluten-related disorders[END_REF][START_REF] Therrien | Celiac Disease: Extraintestinal Manifestations and Associated Conditions[END_REF][START_REF] Lebwohl | Coeliac disease[END_REF]. The gluten-free diet is the main and essential treatment, but it is difficult to follow across the life.

Acknowledgment about lung involvement in CD is scarce contrary to inflammatory bowel disease (IBD) where various pulmonary disorders are described (chronic bronchitis, asthma, interstitial lung disease (ILD), bronchiectasis, bronchiolitis, nodules, tracheobronchial stenosis, pleuritis) [START_REF] Massart | Pulmonary Manifestations of Inflammatory Bowel Disease[END_REF]. In CD, only pulmonary hemosiderosis in paediatric populationis well described [START_REF] Saha | Association between idiopathic pulmonary hemosiderosis and celiac disease in pediatric patients: A scoping review of the literature over the past 50 years[END_REF][START_REF] Işikay | Celiac disease with pulmonary haemosiderosis and cardiomyopathy[END_REF][START_REF] Khemiri | Screening for celiac disease in idiopathic pulmonary hemosiderosis[END_REF]. However, a statistically significant association between asthma and CD is reported in children [START_REF] Canova | Coeliac disease and asthma association in children: the role of antibiotic consumption[END_REF][START_REF] Patel | Heterogeneity of asthma and the risk of celiac disease in children[END_REF]. An association with sarcoidosis is suggested [START_REF] Collin | Coeliac disease--associated disorders and survival[END_REF][START_REF] Douglas | Sarcoidosis and coeliac disease: an association?[END_REF]. Ludvigsson and al. demonstrate an increased risk of having COPD in celiac patients [START_REF] Ludvigsson | A nationwide cohort study of the risk of chronic obstructive pulmonary disease in coeliac disease[END_REF], with a significant reduction in airflow (FEV1) [START_REF] Tarlo | Association between celiac disease and lung disease[END_REF]. A few case reports of lung damage specifically linked to CD (bronchial, bronchiolar damage, etc.) are described in the literature [START_REF] Brightling | A case of cough, lymphocytic bronchoalveolitis and coeliac disease with improvement following a gluten free diet[END_REF][START_REF] Mahadeva | Bronchiectasis in association with coeliac disease[END_REF][START_REF] Edwards | Bronchopulmonary disease in coeliac patients[END_REF]. We conducted a monocentric retrospective study on adult patients with CD and undergone chest HRCT during their follow-up.

PATIENTS AND METHODS

Population studied.

Retrospective study included patients with CD followed in the Internal Medicine, Pulmonology and Gastroenterology Departments at Clermont-Ferrand University Hospital (UH), Clermont-Ferrand, France.

Selection method. We searched the institution's medical record index for patients examined between January 1990 and December 2020 using the diagnostic code 'maladie coeliaque'.

abnormal when volumes were <80% or >120% of the predicted values, when FEV1/FVC ratio was <0.7 and/or when DLCO was <70% of the predicted value.

HRCT scan analysis.

RESULTS

Characteristics of the patients

Of the 242 patients initially selected, 37 patients with CD were included in this study.

Thirty-one patients did not fulfilled the required diagnosis of CD criteria, and one hundred and seventy-one patients were excluded (for reasons including no chest HRCT scan, drug induced ILD, hypersensitivity pneumonitis, history of chest irradiation and bronchial or pulmonary infection at time of the HRCT; Figure 1).

HRCT scan analysis

HRCT scan analysis were found with good agreement between the two radiologists (with a good percentage of agreement and a good kappa correlation coefficient) and summarised in Table 2. The reader 1 was used for the analysis.

Among the 37 patients included, 24 (64.9%) had nodules without suspect aspect like spicular or metastasis of a cancerous mass (n = 3). Nineteen patients (51.4%) had a tracheobronchial damage; ten (27%) had bronchial thickening and nine (24.3%) had bronchiectasis. Among patients with tracheobronchial damage, only 4 had PFT and 2 had pathological FEV1/FVC ratio (56.8% and 64.32%) and FEV1 (63% and 75.6%) but both had COPD and tobacco using. Two had pathological DLCO (29% and 33%) but both had COPD with emphysema. One had a normal PFT. Ten (27%) had emphysema, including seven (18.9%) smokers. Among patients with emphysema, only 2 patients had an alpha 1 antitrypsin (a1AT) assay and they were not deficient. As for the other 8 patients, 6 had a serum protein electrophoresis and none had an a1 globulin deficiency.

Three patients (8.1%) had ILD, including usual interstitial pneumonia (UIP) (n = 2, 5.4%, both smokers, 74 and 59 years old) and interstitial lung abnormalities (ILA) (n = 1, 2.7%).

Six patients (16.2%) had fibro-retractile remodelling or apical caps. Among these patients, 3 had a known pneumonia history.

Pleural lesions were found in seven (18.9%) patients; including effusion, n = 6 and thickening, n = 1.

In mediastinum, eight (21.6%) had pathological lymph node, found in a context of fortuitous screening (n = 3), chest pain (n = 2), or for other reason (n = 3). Four (10.8%) had pericardial effusion; all were minimal and none were detected in a context of chest pain suggestive of pericarditis or inflammatory syndrome. Among them, 2 were malnourished but not severe denutrition with a body mass index (BMI) >16kg/m 2 (16.2 with serum albumin 38.9g/L and 16.6 with serum albumin 30g/L) and 2 had a BMI between 18 and 25kg/m 2 (24.4 and 21.5, without known albumin assay).

Confounding factors analysis

After exclusion of confounding factors (tobacco exposure, COPD, pneumonitis history) nodules, bronchial thickening and bronchiectasis were independently linked to celiac disease (There is no statistical relation to tobacco exposure: nodules p = 0.696, bronchial thickening p = 0.444, bronchiectasis p = 1.00; COPD: nodules p = 0.618, bronchial thickening p = 0.597, bronchiectasis, p = 0.081; and pneumonitis history: nodules p = 0.539, bronchial thickening p = 0.557, bronchiectasis p = 1.00). Rheumatoid arthritis and sarcoidosis had no influence on the presence or absence of nodules (p = 1.00). Asthma had no influence on the presence or absence of bronchiectasis or bronchial thickening (p = 1.00). Autoimmune diseases and immunodeficiency (IgA deficit, MGUS or autoimmune neutropenia) had no influence on the presence or absence of bronchiectasis (autoimmune diseases, p = 1.00; and immunodeficiency, p = 0.09). However, bronchiectasis are more frequent in elderly patients (p = 0.0384, 74 years [65; 77] vs 59 years [50; 67.5]). On the other hand, emphysema is not associated with CD but secondary to tobacco exposure (p = 0.018) and COPD (p = 0.019). Emphysema is more frequent in men (p = 0.009), but men had significatively more tobacco use (8/10 men vs 5/27 women, p <0.001). Well-conducted gluten free diet had no influence on the presence or absence of these anomalies: nodules (p = 0.185), bronchial thickening (p = 0.425), bronchiectasis (p = 1.00). These analyses are summarised in Table 3. which is a key component of host mucosal defence. The lung and gut may develop similar inflammatory reactions because of these shared anatomical features. A second possibility, the shared antigen theory, notes that gut and lung epithelia are exposed to the same antigens and this shared exposure may induce similar inflammation in both systems. Immune system activation may induce aberrant migration of the lymphocytes which invade the gut and other organs such as the lungs [START_REF] Massart | Pulmonary Manifestations of Inflammatory Bowel Disease[END_REF]. We also have reported six patients (16.2%) with fibro-retractile remodelling or apical caps suggestive of lung damage scar including three with known pneumonia history. One had pneumonitis in his story but without fibro-retractile remodelling or apical caps. Splenic atrophy (with asplenia and presence of Howel-Jolly bodies) and selective IgA deficiency are associated with coeliac disease and could predispose to development of recurrent infections [START_REF] Poddighe | The Role of HLA in the Association between IgA Deficiency and Celiac Disease[END_REF][START_REF] Simons | Celiac Disease and Increased Risk of Pneumococcal Infection: A Systematic Review and Meta-Analysis[END_REF]. Our patients did not have selective IgA deficiency, but we did not know if they had splenic atrophy or asplenia. Impairment of nasal mucociliary clearance could play a role in the development of frequent lung infections in patients with CD. In another way, Comba and colleagues showed an impairment of nasal mucociliary clearance could play a role in the development of frequent lung infections in patients with CD, they showed that nasal mucociliary clearance was significatively prolonged in children with celiac disease (531 ± 155 s vs 448 ± 80 s ; p = 0,006) [START_REF] Comba | Evaluation of nasal mucociliary clearance time in children with celiac disease[END_REF].

On the other hand, emphysema is not significatively associated with CD but associated to COPD and tobacco exposure. Emphysema is more frequent in men, as it is reported in the literature [START_REF] Buendia-Roldan | Risk factors associated with the detection of pulmonary emphysema in older asymptomatic respiratory subjects[END_REF], but it may be because they are significatively more smokers than women (p <0.001).

Pleural lesions were found in seven (18.9%) patients (effusion, n = 6 and thickening, n = 1). It is the first study reporting pleural lesions related to CD, unlike IBD which present pleural manifestations recognized in the literature [START_REF] Massart | Pulmonary Manifestations of Inflammatory Bowel Disease[END_REF]. We found in our population five patients (13.5%) with COPD and four (10.8%) with asthma. These findings are correlated with others found in the literature [START_REF] Canova | Coeliac disease and asthma association in children: the role of antibiotic consumption[END_REF][START_REF] Patel | Heterogeneity of asthma and the risk of celiac disease in children[END_REF][START_REF] Ludvigsson | A nationwide cohort study of the risk of chronic obstructive pulmonary disease in coeliac disease[END_REF][START_REF] Tarlo | Association between celiac disease and lung disease[END_REF]. Contrary to Collin P and al. [START_REF] Collin | Coeliac disease--associated disorders and survival[END_REF] and Douglas J. G. and al. [START_REF] Douglas | Sarcoidosis and coeliac disease: an association?[END_REF], we did not find any sarcoidosis in our population. We found no history of tuberculosis either. These findings need to be contrasted with those of Ludvigsson et al. who found more tuberculosis in patients with celiac disease (p<0.001) [START_REF] Ludvigsson | Coeliac disease and risk of tuberculosis: a population based cohort study[END_REF]. Eight (21.6%) had pathological mediastinum lymph node. These findings could have a link with mesenteric lymph node cavitation which is an exceptional complication of celiac disease occurring exclusively in adults [START_REF] Ruch | Mesenteric lymph node cavitation in celiac disease: Report of four cases and literature review[END_REF]. But additional studies are needed to confirm the hypothesis.

Finally, we reported four (10.8%) pericardial effusion (all minimal and none with cardiac symptoms or severe denutition). It is correlated to findings of Riccabona and al. who reported in a study that 50% of children with celiac disease had asymptomatic and limited pericardial effusion that only detectable with instrument, independently serum protein level [START_REF] Riccabona | Pericardial effusion in celiac disease--an incidental finding?[END_REF]. In our knowledge, it is the first study showing pericardial effusion with a significative prevalence (n = 4, 10.8%) in a cohort of adult CD patients without severe denutrition (BMI >16kg/m 2 . Indeed, severe malnutrition can induce this anomaly [START_REF] Giovinazzo | Anorexia nervosa and heart disease: a systematic review[END_REF].

Our study has some limitations. We examined only a small number of patients due to monocentric design and few celiac patients have ever had a chest HRCT scan in their story. The prognosis and severity of disease and degree of pulmonary dysfunction in CT period could not be assessed because of the lack of regular PFT monitoring or repeated low dose CT scan in all patients. Furthermore, PFT monitoring in our study were not performed at the same time of the chest HRCT scan. Respiratory symptoms are not always screened in CD patients due to the lack of data concerning respiratory manifestations of CD in the literature. Moreover, we did not consider all risk factors of lung damage. Indeed, many causes of immunodeficiency are missing, such as oral steroids, HIV status or systematically screening for hyposplenia or asplenia. In addition, only two patients had an a1AT assay although a significant number had bronchiectasis and emphysema, which are lesions associated with a1AT deficiency [START_REF] Cazzola | α1-Antitrypsin deficiency and chronic respiratory disorders[END_REF][START_REF] Greenwald | α1-Antitrypsin Deficiency, Emphysema, Cirrhosis, and Intestinal Mucosal Atrophy[END_REF]. Yet, an association between a1AT deficiency and CD has been reported in the literature [START_REF] Walker-Smith | ALPHA-1-ANTITRYPSIN, AUTISM, AND COELIAC DISEASE[END_REF], even if Klasen and al demonstrated the opposite later [START_REF] Klasen | 'exploiterai pas le pouvoir hérité des circonstances pour forcer les consciences. Je donnerai mes soins à l'indigent et à quiconque me les demandera[END_REF]. However, we tried to compensate for this missing data by analysing serum protein electrophoresis, which did not suggest an alpha 1 antitrypsin deficiency in these patients. Finally, we have also many missed data regarding the HLA status and serological and histological status at CT, so it is difficult to define a profile of celiac patients more at risk of developing lung damage. Finally, our study does not have a control group (without CD).

In conclusion, CD is an independent risk factor of developing tracheobronchial damages (especially bronchial thickening and bronchiectasis) and nodules, independently tobacco exposure, COPD or pneumonitis history, and with no relationship with well-conducted gluten free diet. Small mediastinal lymph nodes and small pericardial effusion are note rare in adult CD adult patients. In adult patients with CD, a clinical screening of pulmonary symptoms could be interesting. This study suggests that a medical attention for dyspnoea, cough and pulmonary auscultation is necessary. PFT during the follow-up with spirometry, plethysmography and DLCO measurement may be required with chest HRCT if there is an anormal PFT and/or pulmonary symptoms. But further controlled and more powerful studies are needed.

CONCLUSION

  INTRODUCTION .................................................................................................................. PATIENTS AND METHODS ............................................................................................... RESULTS ................................................................................................................................ Characteristics of the patients ........................................................................................... HRCT scan analysis ............................................................................................................ Confounding factors analysis ............................................................................................. DISCUSSION ......................................................................................................................... CONCLUSION ....................................................................................................................... REFERENCES ....................................................................................................................... Annexe 1. ................................................................................................................................. Annexe 2. ................................................................................................................................. Annexe 3. .................................................................................................................................

Figure 1 .

 1 Figure 1. Flow-chart.

aAutoimmune neutropenia n = 1 c 3

 13 PFT during the follow-up were not realized at the same time of HRCT b Rheumatoid arthritis n = 1; Scleroderma n = 1; primary Sjogren syndrome n = 1; Horton disease n = 1; Graves' disease n = 1; Spondylarthritis n = 1; months before or after HRCT d asymptomatic, discovery on family screening or assessment of another autoimmune disease e from CD diagnosis to chest HRCT scan

COPD:

  Chronic obstructive pulmonary disease a rheumatoid arthritis, spondyloarthritis, primary Sjogren syndrome, sarcoidosis, lupus and IBD (inflammatory bowel disease) b rheumatoid arthritis and sarcoidosis c IgA deficit, MGUS (monoclonal gammopathy of undetermined significance) and autoimmune neutropenia r p-value<0.05

Pr

  En conclusion, la maladie coeliaque est un facteur de risque indépendant de développement de nodules et de lésions trachéobronchiques (en particulier les épaississements bronchiques et les bronchectasies), indépendamment du tabac, de la BPCO ou d'antécédent de pneumopathie. La présence de ces anomalies n'est pas modifiée par le bon suivi du régime sans gluten. Cette étude suggère qu'une attention médicale particulière sur la dyspnée, la toux et l'auscultation pulmonaire est nécessaire. La réalisation d'EFR au cours du suivi avec spirométrie, pléthysmographie et mesure de la DLCO semblerait pertinente chez les patients avec signes cliniques et/ou physiques, complétée d'une TDM thoracique low dose haute résolution en cas d'EFR anormale et/ou de symptômes pulmonaires. D'autres études à plus grande échelle sont cependant nécessaires pour confirmer ces résultats. Clermont-Ferrand, le 29 avril 2022 Clermont-Ferrand, le 26 avril 2022

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		CLASSE NORMALE
	UNIVERSITE CLERMONT AUVERGNE
	N° : TURPIN Dominique UFR DE MÉDECINE ET DES PROFESSIONS PARAMÉDICALES UNIVERSITÉ CLERMONT AUVERGNE THÈSE D'EXERCICE pour le DIPLÔME D'ÉTAT DE DOCTEUR EN MÉDECINE par CHARPENTIER Oriane PRESIDENTS HONORAIRES UNIVERSITE D'AUVERGNE : JOYON Louis UNIVERSITE D'AUVERGNE : DOLY Michel : VEYRE Annie : DULBECCO Philippe : ESCHALIER Alain PRESIDENTS HONORAIRES : CABANES Pierre UNIVERSITE BLAISE PASCAL : FONTAINE Jacques : BOUTIN Christian : MONTEIL Jean-Marc : ODOUARD Albert : LAVIGNOTTE Nadine PRESIDENT DE L'UNIVERSITE et PRESIDENT DU CONSEIL ACADEMIQUE PLENIER :BERNARD Mathias PRESIDENT DU CONSEIL ACADEMIQUE RESTREINT :DEQUIEDT Vianney VICE-PRESIDENTE DU CONSEIL D'ADMINISTRATION : FOGLI Anne VICE-PRESIDENT DE LA COMMISSION DE LA RECHERCHE :HENRARD Pierre VICE PRESIDENTE DE LA COMMISSION DE LA FORMATION ET DE LA VIE UNIVERSITAIRE : PEYRARD Françoise M. CLEMENT Gilles Médecine Générale M. VORILHON Philippe Médecine Générale Mme MALPUECH-BRUGERE Corinne Nutrition Humaine Bactériologie virologie Mle AUBEL Corinne Oncologie Moléculaire Mle GUILLET Christelle Nutrition Humaine M. BIDET Yannick Oncogénétique M DALMASSO Guillaume Bactériologie M. PIZON Frank Santé Publique M. SOLER Cédric Biochimie Biologie Moléculaire M. GIRAUDET Fabrice Biophysique et traitement de l'Image M. LOLIGNIER Stéphane Neurosciences-Neuropharmacologie Mme MARTEIL Gaëlle Biologie de la Reproduction M. PINEL Alexandre Nutrition Humaine M. BERNARD Pierre Médecine Générale Mme ESCHALIER Bénédicte Médecine Générale Mme RICHARD Amélie Médecine Générale M. TESSIERES Frédéric Médecine Générale PROFESSEURS M. BAILLY Jean-Luc Mme ROUGE Laure Médecine Générale
	DIRECTEUR GENERAL DES SERVICES	: PAQUIS François
	Mme BOTTET-MAULOUBIER Anne	Médecine Générale
	M. CAMBON Benoît	Médecine Générale
	M. TANGUY Gilles	Médecine Générale
		UFR DE MEDECINE
	ET DES PROFESSIONS PARAMEDICALES
	DOYENS HONORAIRES	: DETEIX Patrice
		: CHAZAL Jean
	DOYEN	: CLAVELOU Pierre
	RESPONSABLE ADMINISTRATIVE	: ROBERT Gaëlle

TABLE

TABLE 1 .

 1 PATIENT CHARACTERISTICS. ......................................................................................

TABLE 2 .

 2 HRCT SCANNER ANALYSIS. ......................................................................................

TABLE 3 .

 3 CT SCAN ANOMALIES ACCORDING TO PATIENT CHARACTERISTICS. ........................... Oriane Charpentier e , Antoine Perrey b , Thomas Pacheco b , Marc Ruivard a,c , Bruno Pereira f , Camille Rolland-Debord e , Corinne Bouteloup d , Vincent Grobost a a Department of Internal Medicine, CHU Estaing, 1 Place Lucie et Raymond Aubrac -63100 CLERMONT-FERRAND, France.

	LISTE DES ABRÉVIATIONS
	CD: celiac disease
	ILD: interstitial lung disease
	IBD: inflammatory bowel disease
	PFT: pulmonary function test
	COPD: chronic obstructive pulmonary disease
	FEV1: forced expiratory volume in 1 second
	HRCT: high-resolution computed tomography
	UH: university hospital
	CPP: Comité de Protection des Personnes
	TLC: total lung capacity
	FVC: forced vital capacity
	DLCO: diffusing capacity for carbon monoxide
	MPR: multiplanar reformations
	MIP: maximum intensity projection
	MinIP: minimum intensity projection
	ATS: American Thoracic Society
	ERS: European Respiratory Society
	MCA: multiple correspondence analysis
	MGUS: monoclonal gammopathy of undetermined significance
	HLA: human leucocyte antigen
	UIP: usual interstitial pneumonia
	ILA: interstitial lung abnormalities
	PA: pulmonary arterial
	ADM: adenomely
	a1AT: alpha 1 antitrypsin
	BMI: body mass index
	BPCO: bronchopneumopathie chronique obstructive
	EFR: explorations fonctionnelles respiratoires
	TDM: tomodensitométrie
	TVO: trouble ventilatoire obstructif

FIGURES

FIGURE 1. FLOW-CHART. .......................................................................................................... TVR : trouble ventilatoire restrictif CELIAC DISEASE and LUNG: a monocentric retrospective study

Tel: +33 4 73 75 00 85 Fax: +33 4 73 75 03 61 E-mail: vgrobost@chu-clermontferrand.fr ABSTRACT Background

	: Lung involvement specifically linked to celiac disease (CD), such as bronchial
	and bronchiolar (asthma, COPD) or sarcoidosis, has been suggested in the literature but without
	any well-conducted descriptive study to measure the association except for pulmonary
	hemosiderosis in paediatric population. No study analysing lung involvement on HRCT
	scanning has been conducted in a population of adult patients with CD.
	Objective: To determine if there is a link between CD and specific lung involvement found on
	chest HRCT.
	Methods: Monocentric retrospective study conducted in patients followed for CD and
	underwent chest HRCT at the time of diagnosis or during follow-up, analysed by two
	independent and double-blinded radiologists.
	Results: Two hundred and forty-two patients were screened. n = Thirty-seven patients fulfilled
	inclusion criteria and had chest HRCT without drug induced interstitial lung disease (ILD),
	hypersensitivity pneumonitis, history of thoracic radiotherapy or infectious lung disease. The
	three most common involvements found were nodules (n = 24, 64.9%), then emphysema (n =
	10, 27%; including n = 7, 18.9% smokers), bronchial thickening (n = 10, 27%), and
	bronchiectasis (n = 9, 24.3%). Nodules, bronchial thickening and bronchiectasis were
	independently linked to celiac disease, contrary to emphysema which was associated with
	COPD p = 0.019, tobacco exposure p = 0.018 and men, p = 0.009. None had relationship with
	well-follow gluten free diet. Clinical findings during CT scan and symptoms were cough (n =
	12, 32.4%), dyspnoea (n = 8, 21.6%), inspiratory crackles (n = 5, 13.5%), snorted (n = 3, 8.1%)
	and wheezing (n = 2, 5.4%). Pulmonary function tests (PFT) were analysed in 8 (21.6%) cases,
	including 5 with obstructive ventilatory disorder, 2 with restrictive ventilatory disorder and 4

  The assumption of normality (Gaussian) distribution was assessed using the Shapiro-Wilk test. Agreement between observers was assessed by agreement rate and concordance Cohen's kappa coefficient. The results concerning kappa coefficient were interpreted in relation to the recommendations reported in the literature

	by Altman[20]: <0.4: no agreement, 0.4-7: poor agreement, >0.7: moderate to strong
	agreement. The relationships between categorical data were analyzed using Chi-squared or
	Fisher's exact tests. For continuous data, the comparisons between independent groups were
	performed with Student's t-test or the Mann-Whitney test if the assumptions of the t-test were
	always present or always absent were not considered and only individuals without missing data
	were included in this analysis. Statistical analyses were performed using Stata software (version
	15, StataCorp, College Station, Texas, US) and R 3.3.3 (http://cran.r-project.org/). All the tests
	were two-sided, with a Type I error set at 0.05.

All chest CT-scanners were analyzed on a dedicated software by the Maincare PACS of the UH of Clermont Ferrand (Change Healthcare Radiology solutions, Version 12.4.1), in an independent and double-blinded process by a resident radiologist (7th semester) (P.T.) and a (senior) radiologist (P.A.) specialized in thoracic imaging. For each analyzed CT-scanner, the two radiologists completed a standardized grid on the RedCap software, consisting of 57 items of imagery data, and 6 items of general data regarding the indication of the exam and its precise technique (annexes 2 and 3). A systematical analyze of the images was realized, consisting of an analyze in the 3 anatomical dimensions of reference in axial, coronal and sagittal multiplanar reformations (MPR); with a « hard » filter for the lung window, with a MPR « soft » filter for the mediastinal window, with a « soft » filter for the lung window in the axial plan with MIP (Maximum Intensity Projection) reconstructions of 5-10 mm, and finally with a « soft » filter for the lung window in the coronal plan with variable MinIP (Minimum Intensity Projection) reconstructions (5-7mm).

Definition of interstitial lung disease.

According to the American Thoracic Society (ATS)/European Respiratory Society (ERS) international multidisciplinary consensus classification of idiopathic interstitial pneumonia

[START_REF] Shih | Practical application and validation of the 2018 ATS/ERS/JRS/ALAT and Fleischner Society guidelines for the diagnosis of idiopathic pulmonary fibrosis[END_REF]

, ILD is characterised by the continuous presence of bilateral parenchymal lung infiltrates on chest HRCT.

Exclusion criteria. Patients who did not respond to the CD's classification criteria or without chest HRCT scan were excluded. Patients with drug induced ILD, hypersensitivity pneumonitis, who had undergone thoracic radiotherapy, or bronchial or pulmonary infectious disease at time of the chest HRCT were also excluded. Data analysis. Categorical parameters were expressed as frequencies and associated percentages, and continuous data as mean and standard deviation or as median and [25th; 75th percentiles], according to statistical distribution. not met. The equality of variances was analyzed by the Fisher-Snedecor test. Due to sample size, non-parametric tests were often preferred. Finally, a multiple correspondence analysis (MCA) was proposed to: (i) study the relations between the modalities of the variables, and (ii) determine groups of patients sharing very similar characteristics. MCA can be used to perform cross tabulations and can be considered as a useful tool for revealing the relationships among categorical variables. The significance of association as tested by Chi-squared or Fisher's exact tests provides no information regarding significant individual associations between row-column pairs. On the contrary, factorial analyses such as MCA show how the variables are related, and not just if a relationship exists. This approach enables handling a large amount of data in order to unfold hidden patterns. For these analyses, the variables were chosen according to aforementioned univariate results, clinical relevance and statistical distribution. Parameters

  The symptoms or clinical exam experienced by patients during the HRCT period were: cough (n = 12, 32.4%), dyspnoea (n = 8, 21.6%), inspiratory crackles (n = 5, 13.5%), snorted (n = 3, 8.1%) and wheezing (n = 2, 5.4%). PFT were performed only in 8 (21.6%) patients and not at the same

	time of the chest HRCT; 5 had an anormal FEV1/FVC ratio including 4 with a FEV1 less than
	80%, 4 had a DLCO <70%, 2 had a TLC<80% and none with TLC>120%. Among patients
	with pathological FEV1/FVC, 2 had COPD and 2 had asthma. Among patients with
	DLCO<70%, 2 had COPD with emphysema and 1 had asthma.
	In our population, no patient had sarcoidosis, lupus, psoriasis or IBD. The two
	predominant dysimmune diseases are Hashimoto thyroiditis (n = 3, 8.1%) and monoclonal
	gammopathy of undetermined significance (MGUS) (n = 3, 8.1%, 2 patients with IgG kappa

Thirteen patients (35.1%) were smokers (weaned or not), five (13.5%) had chronic obstructive pulmonary disease (COPD) and four (10.8%) had asthma. Four (10.8%) had pneumonitis history but none with tuberculosis. including 1 that progressed to myeloma and 1 patient with IgA kappa fraction). Serological status was missing for 10 patients (27%) at diagnosis and 18 patients (48.6%) at the time of chest HRCT scan. Any was seronegative or IgA deficiency. Histology was missing for 9 patients (24.3%) at diagnosis and 22 patients (59.5%) at the time of CT scan. HLA data were available for only 4 patients (10.8%).

Table 1 .

 1 Patient characteristics.

	Total n = 37

CD: celiac disease; CT: computed tomography; PFT: pulmonary function test; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; DLCO: diffusion capacity for carbon monoxide; TLC: total lung capacity; IBD: inflammatory bowel disease; MGUS: monoclonal gammopathy of undetermined significance; HLA: human leucocyte antigen

Table 2 .

 2 HRCT scanner analysis.

	Reader 1	Reader 2	% agreement
			(kappa)

CT: computed tomography; UIP: usual interstitial pneumonia; ILA: interstitial lung abnormalities; PA: pulmonary arterial; ADM: adenomegaly

Table 3 .

 3 CT scan anomalies according to patient characteristics.

			Bronchiectasis	Bronchial thickening	Emphysema	Nodules
			Yes	No	Yes	No	Yes	No	Yes	No
	Median age at CT		74	59	61	65	59	63	61	61
			[65; 77] r	[50; 67] r	[58; 79]	[47; 71]	[58; 75]	[47.5; 71]	[58; 74]	[50; 74]
	Sex ratio (M/F)		3/6	7/21	3/7	7/20	6/4 r	3/21 r	20/4	5/5
	Tobacco	Yes	3	10	5	8	7	5	8	4
			(8.1%)	(27%)	(13.5%)	(21.6%)	(18.9%) r	(13.5%) r	(21.6%)	(10.8%)
		No	6	16	5	17	3	17	15	5
			(16.2%)	(43.2%)	(13.5)	(45.9%)	(8.1%) r	(45.9%) r	(40.5%)	(13.5%)
	COPD	Yes	3	2	2	3	4	1	3	2
			(8.1%)	(5.4%)	(5.4%)	(8.1%)	(10.8%) r	(2.7%) r	(8.1%)	(5.4%)
		No	6	26	8	24	6	23	21	8
			(16.2%)	(70.3%)	(21.6%)	(64.9%)	(16.2%) r	(62.1%) r	(56.8%)	(21.6%)
	Asthma	Yes	1	3	1	3
			(2.7%)	(8.1%)	(2.7%)	(8.1%)
		No	7	26	9	24
			(18.9%)	(70.3%)	(24.3%)	(64.9%)
	Pneumonitis	Yes	1	3	0	4			3	0
			(2.7%)	(8.1%)		(10.8%)			(8.1%)
		No	8	25	10	23			21	10
			(21.6%)	(67.6%)	(27%)	(62.1%)			(56.8%)	(27%)
	Autoimmune	Yes	0	2
	disease a			(5.4%)
		No	9	26
			(24.3%)	(70.3%)
	Autoimmune	Yes							1	0
	disease b								(2.7%)
		No							23	10
									(62.1%)	(27%)
	Immunodeficiency c Yes	5	6
			(13.5%)	(16.2%)
		No	4	22
			(10.5%)	(59.5%)
	Well-conducted	Yes	5	18	5	18	5	17	18	4
	gluten free diet		(13.5%)	(48.6%)	(13.5%)	(48.6%)	(13.5%)	(45.9%)	(48.6%)	(10.8%)
		No	2	9	4	7	3	6	5	4
			(5.4%)	(24.3%)	(10.8%)	(18.9%)	(8.1%)	(16.2%)	(13.5%)	(10.8%)

  the pulmonary arterial phase With injection in the aortic arterial phase With Injection in the mixed arterial phase With portal phase injection Without and with injection in the pulmonary arterial phase without and with injection in the aortic arterial phase Without and with injection in the mixed arterial phase Without and with injection in the portal phase

	Reason	Acute respiratory distress
		Chronic respiratory insufficiency
		Thoracic pain
		Screening
		Deterioration of general condition
		Neoplasia extension assessment
		Biological inflammatory syndrome
		Pre-interventional cardia procedure
	Technical	Without injection
	With injection in Technical 2 Decubitus
		Procubitus
		Inhale
		Exhale
	Kilovoltage	70KV
		80KV
		90KV
		100KV
		110KV
		120KV
		multiKV (GSI)
	Cutting thickness (mm)

SERMENT D'HIPPOCRATE

En présence des Maîtres de cette FACULTE et de mes chers CONDISCIPLES, je promets et je jure d'être fidèle aux lois de l'Honneur et de la Probité dans l'exercice de la Médecine.

Je donnerai mes soins gratuits à l'indigent et je n'exigerai jamais un salaire au-dessus de mon travail. Admis dans l'intérieur des maisons, mes yeux ne verront pas ce qui s'y passe, ma langue taira les secrets qui me seront confiés et mon état ne servira pas à corrompre les moeurs ni à favoriser le crime.

Respectueux et reconnaissant envers mes MAÎTRES, je rendrai à leurs enfants l'instruction que j'ai reçue de leurs pères.

Que les HOMMES m'accordent leur estime si je suis fidèle à mes promesses. Que je sois couvert d'OPPROBRE et méprisé de mes confrères si j'y manque

Ethics approval. This study was conducted in compliance with the Declaration of Helsinki principles and is awaiting approval of an Ethic Committee from France; CPP Sud Est n°….

Inclusion criteria. Patients with:

-CD, confirmed by duodenal biopsies, which is the gold standard, interpreted through its progressive phases using the Marsh classification [START_REF] Ensari | Diagnosing celiac disease: A critical overview[END_REF][START_REF] Marsh | Mucosal histopathology in celiac disease: a rebuttal of Oberhuber's sub-division of Marsh III[END_REF]. The Modified Marsh Classification (Oberhuber) broadly itemises those stages through which a normal mucosa (Marsh 0) evolves in becoming 'flat' according to increased intraepithelial lymphocytes, crypt hyperplasia and villous atrophy (Marsh of study participants (benign in more than 95% of cases) [START_REF] Bach | Benefits and Harms of CT Screening for Lung Cancer: A Systematic Review[END_REF][START_REF] Walter | Pulmonary Nodules[END_REF]. This percentage remains below our results (64.9%) and studies about CT scan in the screening of lung cancer include smokers.

In literature, nodules are associated with tobacco, COPD, history of pneumonitis and some autoimmune disease [START_REF] Bach | Benefits and Harms of CT Screening for Lung Cancer: A Systematic Review[END_REF][START_REF] Walter | Pulmonary Nodules[END_REF]. Emphysema is associated with men, tobacco use, a1AT

deficiency and COPD [START_REF] Buendia-Roldan | Risk factors associated with the detection of pulmonary emphysema in older asymptomatic respiratory subjects[END_REF][START_REF] Bagdonas | Novel aspects of pathogenesis and regeneration mechanisms in COPD[END_REF]. Bronchial thickening and non-cystic fibrosis bronchiectasis are associated with tobacco, a1AT deficiency, COPD and asthma, history of pneumonitis, immunodeficiency (notably humoral deficiency) and autoimmune diseases (rheumatoid arthritis, primary Sjogren syndrome, sarcoidosis, IBD, etc…) [START_REF] Bagdonas | Novel aspects of pathogenesis and regeneration mechanisms in COPD[END_REF][START_REF] Wang | A morphologic study of the airway structure abnormalities in patients with asthma by high-resolution computed tomography[END_REF][START_REF] Martinez-Garcia | Bronchiectasis in COPD patients: more than a comorbidity?[END_REF][START_REF] Macfarlane | Diagnosis and management of non-cystic fibrosis bronchiectasis[END_REF][START_REF] Mcshane | Non-cystic fibrosis bronchiectasis[END_REF][START_REF] King | The pathophysiology of bronchiectasis[END_REF].

To our knowledge, it is the first study excluding these confounding factors (except a1AT deficiency), showing specific lung involvement related to CD (nodules, bronchial thickening and bronchiectasis). However, we notify a marginal but non-significant difference in bronchiectasis according to COPD (p = 0.081) and immunodeficiency (p = 0.076).

Tracheobronchial damage and symptoms experienced by patients (cough, crackles, etc…) are also described only in case reports in the literature [START_REF] Brightling | A case of cough, lymphocytic bronchoalveolitis and coeliac disease with improvement following a gluten free diet[END_REF][START_REF] Mahadeva | Bronchiectasis in association with coeliac disease[END_REF][START_REF] Edwards | Bronchopulmonary disease in coeliac patients[END_REF]. No previous study analysed nodules specifically linked to CD. The range of pulmonary conditions reported in association with CD is analogous to IBD in which steroid sensitive bronchial inflammation, bronchiectasis and nodules are recognised complications [START_REF] Massart | Pulmonary Manifestations of Inflammatory Bowel Disease[END_REF]. Lung and bowel are derived from the same embryonic cell line and share a common epithelium with submucosal lymphoid tissue Annexe 1.

Mots-clés :

-Maladie coeliaque -Poumon -Pneumopathie interstitielle diffuse -Bronchectasie -Syndrome bronchique -Nodule


Claudia Aristizábal

Fernando Lopez-Caballero

Comparison Between Two Surrogate Models for Embankment Earthquake-Liquefaction-Induced Settlements Prediction

Keywords: surrogate models, artificial neural networks, Gaussian process, fragility function, damage levels

, performed multiple finite element (FE) numerical simulations to assess the effect of the liquefaction-induced settlements of the soil foundation of an embankment due to real earthquakes. However, due to computational limitations of the FE analysis, they proposed a Gaussian Process (GP) emulator to represent the output of the more expensive FE model. Furthermore, they derived the analytical fragility curves constructed on the basis of the output of the nonlinear dynamic surrogate model. The aim of this work is to confront two different kinds of meta-models techniques, based on the same earthquake-liquefaction-induced settlements data. Hence, we proposed a comparison between two different surrogate models: (1) the GP model proposed by the authors and (2) an Artificial Neural Network (ANN) model proposed under the scope of this work. A comparison between the resultant fragility curves of the levee using both surrogate models is discussed, together with the impact of both meta-models in terms of fragility curves and its corresponding uncertainty. Finally, the main advantages and drawbacks of the surrogate models are highlighted.

INTRODUCTION

The quick development of highly sophisticated computer codes to recreate the behavior of complex physical phenomenon is currently possible thanks to the availability of accessible, affordable and faster computational resources. Nowadays, stateof-the-art simulators are parameterize in a more elaborated way, with large number of input parameters describing the complexity of the model such as: material properties, initial conditions, boundary conditions, constitutive laws, among other. Nevertheless, and despite the fact that computational capacity of computers has increased exponentially in the past decades [START_REF] Hilbert | The world's technological capacity to store, communicate, and compute information[END_REF]], there still exist computational limitations for plenty of applications. Where performing uncertainty quantification (UQ) tasks with Monte Carlo (MC) methods is almost always infeasible because of the need to perform hundreds of thousands or even millions of forward model evaluations in order to obtain convergent statistics [START_REF] Tripathy | Deep uq: Learning deep neural network surrogate models for high dimensional uncertainty quantification[END_REF]]. Hence, the development of quick, easy-to-handle, surrogate models of complex simulations is a key issue to derive a rapid approximation of more expensive models. For instance, [START_REF] Lopez-Caballero | Vulnerability assessment for earthquake liquefaction-induced settlements of an embankment using gaussian processes[END_REF], performed multiple finite ele-13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26-30, 2019 ment (FE) numerical simulations to assess the effect of the liquefaction-induced settlements of the soil foundation of an embankment due to real earthquakes. However, due to computational limitations of the FE analysis to properly perform UQ analysis, a Gaussian Process (GP) emulator to represent the output of the more expensive FE model was proposed by the authors. Furthermore, the authors derived the fragility model of the embankment constructed on the basis of the output of the nonlinear dynamic surrogate model proving its use for uncertainty quantification. The aim of this work is to confront two different kinds of meta-models techniques, based on the same earthquake-liquefactioninduced settlements data. Hence, a comparison between two different surrogate models is discussed:

(1) the GP model proposed by the authors and (2) an Artificial Neural Network (ANN) model proposed under the scope of this work. A comparison between the resultant fragility curves of the levee using both surrogate models is provided, together with the discussion on the impact of both metamodels in terms of fragility curves and its corresponding uncertainty. Finally, the main advantages and drawbacks of the surrogate models are highlighted .

METHODOLOGY

The methodology followed in this article is shown in Figure 1, and described in the following steps: (1) Select a robust earthquake database; (2) Propagate the different time histories (TH) through the soil media using the FEM model; (3) Train both surrogate models (GP and ANN) using the learning database (LDB) and the validation database (VDB). ( 4

Surrogate Models

López-Caballero and Khalil 2018

Surrogate Models

A metamodel, an emulator or a surrogate model is an analytical function used to provide rapid approximations of a more expensive model (e.g. analitical model, finite-element model) [START_REF] Lopez-Caballero | Vulnerability assessment for earthquake liquefaction-induced settlements of an embankment using gaussian processes[END_REF]] while being computationally cheaper to evaluate . Even though there are multiple surrogate models that could be used for this purpose, for this case study a Gaussian Process Metamodel (GP) and an Artificial Neural Network (ANN) were used to predict the crest settlement of the levee based on predefined database containing the input an output parameters of the costly FEM model of the embankment.

Gaussian Process (GP)

A Gaussian Processes is an stochastic process (a collection of random variables indexed by time or space) that offer a mathematically funded and versatile framework to building statistical models. The main assumptions are: the phenomenon output is Gaussian and is a functional choice of covariance function (kernel). The statistical model needs physical knowledge: through data and expertise guiding the choice of kernel (which may come from the physical model). The statistical model is in essence complementary to the physical model and typically useful for decision making(e.g. optimization, uncertainty propagation, distributions shape, among others). Detailed Gaussian process references include [START_REF] Cressie | Statistics for spatial data: Wiley series in probability and mathematical statistics[END_REF], [START_REF] Williams | Gaussian processes for regression[END_REF], [START_REF] Stein | Interpolation of spatial data: Some theory for kriging springer-verlag new york google scholar[END_REF], [START_REF] Rasmussen | Gaussian processes for machine learning[END_REF].

Artificial Neural Network (ANN)

The Artificial Neural Network (ANN) approach was inspired by investigations into the structure of the human brain, which consists of interconnected neurons [START_REF] Mcculloch | A logical calculus of the ideas immanent in nervous activity[END_REF]. It consist of a mathematical process that uses different layers to deal with the information its fed with. ANN's learn from examples and capture subtle functional relationships among the data even if the underlying relationships are unknown or hard to describe with more physical models. Thus ANNs are well suited for problems whose solutions require knowledge that is difficult to specify but for which there are enough data or observations [START_REF] Zhang | Forecasting with artificial neural networks:: The state of the art[END_REF]]. The simplest ANN is composed of an input, a hidden and an output layers, but they can become as complex of any order by adding additional hidden layers, which will turn the problem into what its better know as deep learning [START_REF] Schmidhuber | Deep learning in neural networks: An overview[END_REF]]. Currently, there exist countless of excellent ANN references and [START_REF] Zhang | Forecasting with artificial neural networks:: The state of the art[END_REF] provides an excellent overview of the ANN technique. On the other hand, [START_REF] Kong | Machine learning in seismology: Turning data into insights[END_REF] provides an overview of current applications of machine learning (ML) in seismology, while [START_REF] Paolucci | Broadband ground motions from 3d physics-based numerical simulations using artificial neural networks[END_REF] presents an example of application for earthquake engineering purposes.

CASE STUDY

The selected case study consists of an embankment of 9 m height, composed of dry dense sand, founded over a 10 m layers of loose and dense sand overlaying bedrock, as shown in Figure 2. The first top 4 m of foundation consists of liquefiable looseto-medium sand on top of 6 m of saturated dense sand. The underlying bedrock at the bottom of the dense sand has a mass density (ρ bd ) of 2,000 kg/m 3 and a shear wave velocity (Vs) of 1,000 m/s. The water table is located at 1 m below the foot of the embankment and it was kept dry. A 1:3 (vert:horiz) slope characterize the embankment inclination, and the geometry used on the FEM model is based on the one proposed by [START_REF] Rapti | Liquefaction analysis and damage evaluation of embankment-type structures[END_REF]. All specific details related to the FEM analysis and the elastoplastic multimechanism of the soil used to represent the soil behavior in Figure 2 are extensively described in Lopez-Caballero and Khalil (2018) and will no further be discussed in this work.

EARTHQUAKE INPUT MOTIONS

Under the scope of this work the same case study used by Lopez-Caballero and Khalil ( 2018) is considered, hence, the same input ground motions used to build the surrogate model are also considered. A total of 540 unscaled records were selected by the authors from the PEER database [START_REF] Ancheta | Peer nga-west2 database[END_REF]], the Center for Engineering Strong Motion Data, and the Kiban Kyoshin strong-motion network (KIK-NET) [START_REF] Aoi | New japanese uphole-downhole strong-motion observation network: Kik-net[END_REF]]. The events range between 5.2 and 7.6 in moment magnitude (Mw). The recordings have site-to-source distances from 15 to 50 km and concern dense-to-firm soil conditions (i.e., 360 m/s < V S30 < 800 m/s). All input signals have a baseline correction and a sampling time (∆t) equal to 0.005 s. The signals were filtered using a noncausal fourth-order Butterworth bandpass filter (i.e., zerophase digital filtering), between 0.1 and 25.0 Hz. The correlation plots between the embankment crest settlement, ln(δ u /H[%]), and the three input variables, X i , are shown in Figure 3 and its corresponding correlations coefficients are shown in Table 1.

LEARNING, VALIDATION AND TEST

DATABASES. The earthquake database was split as follows: 95 signals concern the learning database (LDB), 50 ground motions are used for the validation set (VDB), and the test database (TDB) is composed of 395 unscaled records. The ranges of the input earthquake features obtained for each database (LDB, VDB and TDB) are summarized in Table 2. The earthquake features used to derive the surrogate models are: maximal outcropping acceleration (PGA), Arias Intensity (Iarias) and period of 3. The VDB was in such a way that it covers a large range of the LDB and avoids any extrapolation from it. Finally, the TDB was also selected in a way that no extrapolation was allowed and with a considerable amount of events (395 events) to perform uncertainty quantification.

SURROGATE MODELS TRAINING

Once the LDB, VDB and TDB had been properly selected. The next step consist on training the two different surrogate models by using the LDB to calibrate the models, and the VDB to validate the results. The validation phase consist on comparing the predicted crest settlements of the surrogate models with those of the FEM model, once an acceptable accuracy is obtained after multiple sensibility analysis and optimization proce-dures, it is possible to proceed with the predictive phase. On the predictive phase, the TDB is used to predict quick approximation of the FEM (δ u F E M /H[%]) by using the surrogate models instead (δ u GP,AN N /H[%]).

Gaussian Process (GP)

As mentioned previously, the aim of this work is to confront two different kinds of meta-models techniques, based on the same input and output data. Nevertheless, the metamodel using a GP for this case study has been widely discussed in Lopez-Caballero and Khalil (2018),. Hence, the methodology concerning the GP will not longer be discussed, only the results provided by the authors will be used for comparison purposes.

Artificial Neural Network (ANN)

The most significant contribution of this work is the use of an artificial neural network (ANN) to predict the crest settlements of an embankment in order to to replace the most costly FEM model.

The ANN used in the present study is the feedforward fully conected neural network as proposed by [START_REF] Hwang | Handbook of neural network signal processing[END_REF].The input layer contains the external information or input variables, which for this case study are: maximal outcropping acceleration (PGA), Arias Intensity (Iarias) and period of equivalent harmonic wave (T V /A ). The number of hidden layers and neurons was finally set after an optimization process. Figure 5 shows the simplest architecture found under the scope of this work: which consists of an input layer, a single hidden layer of 7 neurons and an output layer.

The best architecture was selected trough a sensitivity analysis where the different parameters of the ANN (e.g. learning rate, activation function, number of hidden layers, number of hidden neurons) were modified in order to maximizes the squared correlation coefficient (Q 2 )[ Eq. 1] and minimizes the root mean square error (RMSE)[ Eq. 2] simultaneously.

Q 2 (y i , y pred i ) = 1 - ∑ N n=1 (y pred i -y i ) 2 ∑ N n=1 (y i -µ y ) 2
(1)

RMSE(y i , y pred i ) = √ ∑ N n=1 (y pred i -y i ) 2 N (2)
Once the ANN architecture is defined, then the intrinsically random variability of the regression process of the ANN is studied by training 300 ANN with the exact same architecture and input and output learning and validation databases (LDB and VDB). This uncertainty is related to the convergence process of the ANN due to the feed-forward propagation process that it uses to correct the network until the ANN outputs approach the FEM outputs, nevertheless multiple solutions will converge, but with different levels of accuracy. This sensibility analysis was performed based on the resemblance of the fragility curves of the surrogate models with respect to those of the FEM model. The fragility curves represent the probability of failure of a system, associated with a specified criterion, for a given intensity measure (IM) of the earthquake motion. Failure herein represents the exceedence of a certain limit of the crest settlement (δ uFEM /H), and the IM in this case is the peak ground acceleration (PGA). Thus, the fragility function can be expressed as follows [Eq. 3][Mai et al. (2017)]:

Frag(IM; δ o ) = P[∆ ≥ δ o |IM] (3) Frag(IM; δ o ) = Φ( lnIM-lnα β ) (4)
The classical approach for establishing fragility curves consists in assuming a log-normal shape for the curves described in Eq. 3. This expression can be written in more general form by using the maximum likelihood approach Eq. 4. Where Φ(•) denotes the standard Gaussian cumulative distribution function (CDF), α is the "median" and β is the "log-standard deviation" of the log-normal curve. Now, following this definition, Figure 6 displays the 300 fragility curves built using the results of each one of the 300 trained ANN sets together with its median (µ) and its corresponding standard deviation (σ ). Considering the uncertainty of the regression procedure is important, since it is possible that if only a single train of the architecture is made, even tough the solution converges, the solution could fall on the lower or upper bounds of the 300 fragility curves, affecting considerably the accuracy of the ANN surrogate model. Finally, the closest ANN to the median (µ) of the 300 models is selected as the final surrogate model: in this particular case model 234. Once defined the median (µ) fragility curve and its corresponding ANN model (model 234), it is important to verify if the the number of ANN training sets selected to evaluate the uncertainty: here arbitrarily selected as 300; is sufficient to obtain robust statistics. Figure 7 a,b show the median (µ α ,β ) and standard deviation (σ α ,β ) of the α and β parameters, respectively, when considering 1 to 300 models. As it is possible to observe in both in both figures, above 100 ANN model, neither the median (µ α ,β ) nor standard deviation (σ α ,β ) vary as a func-tion of the number of considered fragility curves. Hence in this particular case, the 300 considered models were adequately to predict robust statistics, nevertheless this conclusion is structure dependent, hence, this validation needs to be performed for each structure and for the different considered damage level.

SURROGATE MODELS PREDICTIONS

The next step consist on predicting the crest settlements (δ u /H[%]) by using the test database (TDB) composed of 395 unscaled records and the validation database (VDB) composed of 50 records. Figure 8 shows the predicted (δ u /H[%]) by the ANN and the GP with respect PGA for the combined databases.

The three damage level thresholds are superposed in the same plot and they correspond to δ u /H[%]=0.02, 0.2, and 1.0%. The damage state limits or the performance levels of the levee are those proposed by [START_REF] Swaisgood | Embankment dam deformations caused by earthquakes[END_REF] same surface of the plot and with similar density distributions of sample points. Nevertheless in the next section a quantitative comparison between models is presented.

SURROGATE MODELS COMPARISON

The fragility curves of the FEM, GP and ANN crest settlements (δ uGP /H[%]) for the severe damage level limits state (Prob[δ u /H ≥ 1.0%]) are shown in Figure 9. The goodness of the final surrogate models with respect to the FEM model has been quantitatively estimated in three different ways: (1) Comparison of (δ u /H[%]) values obtained with FEM and with the surrogate models, Figure 10a,b; (2) Ratio between the surrogate models fragility curves and the FEM model, Figure 10c. (3) Comparison of the damage limit states confusion matrix obtained considering FEM with respect to the surrogate models, GP and ANN, respectively Figure 10d,e. Firstly, the regression plot of the FEM Vs. GP/ANN Figure 10a,b allows to calculate the squared correlation coefficient (Q 2 ) and the root mean square error (RMSE)of the final surrogate models, where a Q 2 = 1.0 and an RMS = 0 will represent the perfect model. Secondly, the ration between fragility curves, Figure 10c, allows to estimate if the surrogate models are over estimating or underestimating the real model: for this particular case, an overestimation of the fragility curve is preferable since is on the safety side. For this case example, for a given probability of exceedance (Prob[δ u /H ≥ X]), both surrogate models overestimates the IM (PGA) up to a value of 20% with respect to the FEM model. Nevertheless, the ANN tends to predict better results at larger probabilities, while the GP does it at small probabilities, being particularly preferable the ANN, since for risk purposes it is more important to properly predict the larger return periods. Despite this particular finding, that could motivate future users to prefer the ANN over the GP models, no general conclusion can be drawn from this particular exercise. Finally, the confusion matrices are a very powerful tool to evaluate the accuracy of surrogate models, Figure 10d,e. For instance, the ANN performs slightly better in terms of accuracy than the GP: 85% accuracy for the former, against 79% for the later. Also, even though both models generally tend to overestimate damage levels III and IV, the ANN overestimates less than the GP model, hence, with a better resemblance to the FEM model than the GP model. But again, no general conclusion about which model is better than the other can be drawn based on this particular case example.

CONCLUSIONS

Two surrogate models (GP and ANN) were used as alternative to predict the liquefaction-induced 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26-30, 2019 settlements of a levee initially modeled with a more expensive FEM. The comparative results shown here have been evaluated in terms of the fragility functions, damage levels, and crest settlement predictions. The main conclusions drawn from this study are: (1) The ANN slightly predicts better results than the GP. Nevertheless, both the GP and the ANN surrogate models have shown to be good tools to predict accurately enough the nonlinear FEM response. Both surrogate models represent an economy in CPU time with respect to the FEM models proving their value for UQ purposes. Finally, it is remarkable, that with only 3 input proxies of complex waveforms it was possible to retrieve a very close equivalent model with almost zero computation cost once the ANN or GP models are trained.

Figure 1 :

 1 Figure 1: Methodology followed in this article.

Figure 2 :

 2 Figure 2: Embankment geometry and soil behavior used in the numerical FEM model (after Lopez-Caballero and Khalil (2018)).

Figure 3 :

 3 Figure 3: Correlation plots for the three different databases, LDB (green), VDB (red), TDB (blue), between the input variables (IArias, PGA and TV/A) and the output variable (δ u /H[%]) used to derived the GP and ANN surrogate models.

  Figure 4 displays the FEM crest settlement (δ u F E M /H[%]) versus the input features (PGA, Iarias and T V /A ) plots for the learning (LDB) and validation (VDB) databases, showing similar correlation and variability as the one discussed in Figure

Figure 4 :

 4 Figure 4: FEM crest settlement (δ u /H[%]) versus input features (X i ) plots of the learning (LDB in blue) and validation (VDB in red) database: (a) X i = PGA; (b) X i = Iarias; (c) X i = TV /A;

Figure 5 :

 5 Figure 5: Final Artificial Neural Network architecture after the optimization process used as surrogate model to predict the crest settlements (δ uFEM /H) of the embankment.

Figure 6 :

 6 Figure 6: Fragility curves of the embankment for severe damage level (Prob[δ u /H ≥ 1.0%]), for the 300 ANN models and its µ ± σ (blue).

Figure 7 :

 7 Figure 7: Fragility curve regression parameters derived using the maximum likelihood approach for the severe damage limit state (Prob[δ u /H ≥ 1.0%]): (a) α (median) and (b) β (standard deviation).

Figure 8 :

 8 Figure 8: Crest settlement predicted using the VDB and the TDB databases for the GP model, δ uGP /H[%], (magenta) and the ANN model, δ uAN N /H[%], (blue).

Figure 9 :

 9 Figure 9: Fragility curves: FEM (green); ANN (µ ± σ ) (blue); GP (magenta).

  ) Predict the crest settlements (δ u /H[

	~540 EQ Time Histories	Proxies Iarias PGA	FEM GP	𝛿u FEM /H[%] 𝛿u GP /H[%]	FC FEM FC GP
		TV/A	ANN	𝛿u ANN /H[%]	FC ANN

Table 1 :

 1 Correlation matrix of the input and output variables for the 95 accelerograms used as the learning database (LDB).

	CORR IArias PGA TV/A δ u /H[%]
	IArias	1.00	0.83 0.11	0.85
	PGA	0.83	1.00 -0.01	0.76
	TV/A	0.11 -0.01 1.00	0.27
	δ u /H[%] 0.85	0.76 0.27	1.00

Table 2 :

 2 Statistics of the different input parameters for each database (LDB, VDB and TDB).

	Index Variable Definition	Units	LDB (95)	VDB (50)	TDB (395)
	X 1	IArias Arias intensity.	m/s 0.001 -20.64 0.04 -4.13 0.0036 -20.64
	X 2	PGA	Peak ground acceleration.	g	0.01 -1.93	0.03 -1.16	0.03 -1.93
	X 3	TV/A Period of equivalent harmonic wave.	s	0.09 -1.91 0.13 -1.42	0.13 -1.43

  . From visual inspection, both models covered approximately the 13th International Conference on Applications ofStatistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26-30, 2019

	(a)					(b)						(c)
				Accuracy: 79.42%					Accuracy: 85.01%
		DL I	90.9% 10	7.9% 14	0.0% 0	0.0% 0		DL I	72.7% 8	5.1% 9	0.0% 0	0.0% 0
	GPM Prediction	DL II DL III	9.1% 1 0.0% 0	82.5% 146 9.6% 17	16.3% 24 75.5% 111	0.0% 0 21.4% 24	ANN Prediction	DL II DL III	27.3% 3 0.0% 0	87.6% 155 7.3% 13	9.5% 14 85.7% 126	0.0% 0 18.8% 21
		DL IV	0.0% 0	0.0% 0	8.2% 12	78.6% 88		DL IV	0.0% 0	0.0% 0	4.8% 7	81.2% 91
			DL I	DL II	DL III	DL IV			DL I	DL II	DL III	DL IV
				FEM Response					FEM Response
				(d)						(e)
	Figure 10: (a) FEM Vs. GP crest settlements (δ

u /H[%]); (b) FEM Vs. ANN crest settlements (δ u /H[%])(blue); (c) Surrogate models and the FEM model fragility curves ratio (GP or ANN/FEM); Confusion matrices between the FEM and : (d) GP emulator; (e) ANN emulator.

13th International Conference on Applications ofStatistics and Probability in Civil Engineering, ICASP13 Seoul, South Korea, May 26-30, 2019

ACKNOWLEDGMENTS

This work has been funded by the French SINAPS@ project (Earthquake and Nuclear Facilities : Ensuring Safety and Sustaining).