Analysis of Clinical Phenotypes through Machine Learning of First-Line H. pylori Treatment in Europe during the Period 2013–2022: Data from the European Registry on H. pylori Management (Hp-EuReg)

Citation: Nyssen, O.P.; Pratesi, P.; Spínola, M.A.; Jonaitis, L.; Pérez-Aísa, Á.; Vaira, D.; Saracino, I.M.; Pavoni, M.; Fiorini, G.; Tepes, B.; et al. Analysis of Clinical Phenotypes through Machine Learning of First-Line H. pylori Treatment in Europe during the Period 2013–2022: Data from the European Registry on H. pylori Management (Hp-EuReg). Antibiotics 2023,12, 1427. https:// doi.org/10.3390/antibiotics12091427 Academic Editors: Mehran Monchi and Nicholas Dixon Received: 3 August 2023 Revised: 28 August 2023 Accepted: 5 September 2023 Published: 10 September 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). antibiotics Article Analysis of Clinical Phenotypes through Machine Learning of First-Line H. pylori Treatment in Europe during the Period 2013–2022: Data from the European Registry on H. pylori Management (Hp-EuReg) Olga P. Nyssen 1,2,3,4,†,‡ , Pietro Pratesi 5,†, Miguel A. Spínola 6, Laimas Jonaitis 7,Ángeles Pérez-Aísa 8, Dino Vaira 9,10, Ilaria Maria Saracino 9, Matteo Pavoni 9, Giulia Fiorini 9, Bojan Tepes 11, Dmitry S. Bordin 12,13,14 , Irina Voynovan 12 ,Ángel Lanas 4,15,16 , Samuel J. Martínez-Domínguez 4,15,16 , Enrique Alfaro 15, Luis Bujanda 4,17,18, Manuel Pabón-Carrasco 19, Luis Hernández 20 , Antonio Gasbarrini 21 , Juozas Kupcinskas 7, Frode Lerang 22, Sinead M. Smith 23 , Oleksiy Gridnyev 24 , M¯ arcis Leja 25,26,27 , Theodore Rokkas 28 , Ricardo Marcos-Pinto 29,30,31, Antonio Meštrovi´c 32,33, Wojciech Marlicz 34 , Vladimir Milivojevic 35,36 , Halis Simsek 37, Lumir Kunovsky 38,39,40,41,42 , Veronika Papp 43, Perminder S. Phull 44, Marino Venerito 45 , Lyudmila Boyanova 46, Doron Boltin 47 , Yaron Niv 48, Tamara Matysiak-Budnik 49, Michael Doulberis 50 , Daniela Dobru 51, Vincent Lamy 52 , Lisette G. Capelle 53, Emilija Nikolovska Trpchevska 54,55 , Leticia Moreira 4,56,57 , Anna Cano-Català58,59 , Pablo Parra 1,2,3,4, Francis Mégraud 60, Colm O’Morain 23, Guillermo J. Ortega 6,61,62,* , Javier P. Gisbert 1,2,3,4 and on behalf of the Hp-EuReg Investigators ‡ 1Digestive System Service of the Hospital Universitario de La Princesa, 28006 Madrid, Spain; [email protected] (O.P.N.); [email protected] (P.P.); javier[email protected] (J.P.G.) 2Instituto de Investigación Sanitaria Princesa (IIS-Princesa), 28006 Madrid, Spain 3Departamento de Medicina, Universidad Autónoma de Madrid (UAM), 28049 Madrid, Spain 4Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), 28029 Madrid, Spain; [email protected] (Á.L.); [email protected] (S.J.M.-D.); [email protected] (L.B.); [email protected] (L.M.) 5Dipartimento di Statistica e Metodi Quantitativi (DISMEQ), Universitádegli studi di Milano–Bicocca, 20126 Milano, Italy; [email protected] 6Unidad de Análisis de Datos del Instituto de Investigación Sanitaria Princesa (IIS-Princesa), 28006 Madrid, Spain; [email protected] 7Institute for Digestive Research, Department of Gastroenterology, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania; [email protected] (L.J.); [email protected] (J.K.) 8Hospital Universitario Costa del Sol, 29603 Marbella, Spain; [email protected] 9 Department of Surgical and Medical Sciences, Sant’Orsola-Malpighi University Hospital, 40138 Bologna, Italy; [email protected] (D.V.); [email protected] (I.M.S.); [email protected] (M.P.); [email protected] (G.F.) 10 Cardiovascular Internal Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy 11 Department of Gastroenterology, DC Rogaska, 3250 Rogaska Slatina, Slovenia; [email protected] 12 Department of Pancreatic, Biliary and Upper Digestive Tract Disorders, A. S. Loginov Moscow Clinical Scientific Center, 111123 Moscow, Russia; dbor[email protected] (D.S.B.); [email protected] (I.V.) 13 Department of Outpatient Therapy and Family Medicine, Tver State Medical University, 170100 Tver, Russia 14 Department of Propaedeutic of Internal Diseases and Gastroenterology, A.I. Yevdokimov Moscow State University of Medicine and Dentistry, 127473 Moscow, Russia 15 Servicio de Aparato Digestivo, Hospital Clínico Universitario Lozano Blesa, 50009 Zaragoza, Spain; [email protected] 16 Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50009 Zaragoza, Spain 17 Department of Gastroenterology, Biodonostia Health Research Institute, 20014 San Sebastián, Spain 18 Department of Medicine, Universidad del País Vasco (UPV/EHU), 20014 San Sebastián, Spain 19 Department of Gastroenterology, Hospital Universitario de Valme, 41014 Seville, Spain; [email protected] 20 Gastroenterology Unit, Hospital Santos Reyes, 09400 Aranda de Duero, Spain; [email protected] 21 Medicina interna e Gastroenterologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy; [email protected] 22 Department of Gastroenterology, Østfold Hospital Trust, 1714 Grålum, Norway; [email protected] 23 School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland; [email protected] (S.M.S.); [email protected] (C.O.) Antibiotics 2023,12, 1427. https://doi.org/10.3390/antibiotics12091427 https://www.mdpi.com/journal/antibiotics Antibiotics 2023,12, 1427 2 of 18 24 Departments the Division for the Study of the Digestive Diseases and Its Comorbidity with Noncommunicable Diseases, Government Institution L.T. Malaya Therapy National Institute of NAMS of Ukraine, 61039 Kharkiv, Ukraine; [email protected] 25 Department of Gastroenterology, Digestive Diseases Centre, LV-1006 Riga, Latvia; [email protected] 26 Institute of Clinical and Preventive Medicine, LV-1079 Riga, Latvia 27 Faculty of Medicine, University of Latvia, LV-1004 Riga, Latvia 28 Gastroenterology Clinic, Henry Dunant Hospital, 115 26 Athens, Greece; [email protected] 29 Gastroenterology Department, Centro Hospitalar do Porto, 4150-001 Porto, Portugal; ricardomar[email protected] 30 Instituto De Ciências Biomédicas de Abel Salazar (ICBAS), Universidade do Porto, 4050-313, Porto, Portugal 31 Center for Research in Health Technologies and Information Systems (CINTESIS), 4200-450 Porto, Portugal 32 Department of Gastroenterology, University Hospital of Split, 21000 Split, Croatia; [email protected] 33 School of Medicine, University of Split, 21000 Split, Croatia 34 Department of Gastroenterology, Pomeranian Medical University, 70-204 Szczecin, Poland; [email protected] 35 Department of Gastroenterology, University Clinical Center of Serbia, 11000 Belgrade, Serbia; [email protected] 36 School of Medicine, University of Belgrade, 11000 Belgrade, Serbia 37 Department of Gastroenterology, HC International Clinic, Hacettepe University, 06690 Ankara, Turkey; [email protected] 38 Department of Internal Medicine—Gastroenterology and Geriatrics, University Hospital Olomouc, 779 00 Olomouc, Czech Republic; kunovsky[email protected] 39 Faculty of Medicine and Dentistry, Palacky University Olomouc, 779 00 Olomouc, Czech Republic 40 Department of Surgery, University Hospital Brno, 625 00 Brno, Czech Republic 41 Faculty of Medicine, Masaryk University, 601 77 Brno, Czech Republic 42 Department of Gastroenterology and Digestive Endoscopy, Masaryk Memorial Cancer Institute, 656 53 Brno, Czech Republic 43 Department of Surgery, Transplantation and Gastroenterology, Semmelweis University, 1085 Budapest, Hungary; [email protected] 44 Department of Digestive Disorders, Aberdeen Royal Infirmary, Aberdeen AB25 2ZN, UK; [email protected] 45 Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital of Magdeburg, 39120 Magdeburg, Germany; [email protected] 46 Department of Medical Microbiology, Medical University of Sofia, 1431 Sofia, Bulgaria; [email protected] 47 Division of Gastroenterology, Rabin Medical Center, Tel Aviv University, Tel Aviv-Yafo 49100, Israel; [email protected] 48 Adelson Faculty of Medicine, Ariel University, Ariel 4070000, Israel; [email protected] 49 Hepato-Gastroenterology & Digestive Oncology Unit, University Hospital of Nantes, 44000 Nantes, France; [email protected] 50 Gastroenterology Department, Kantonsspital Aarau, 5001 Aarau, Switzerland; [email protected] 51 Department of Gastroenterology, University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, 540142 Târgu Mures, Romania; [email protected] 52 Department of Gastroenterology & Hepatology, CHU de Charleroi, 6042 Charleroi, Belgium; dr[email protected] 53 Department of Gastroenterology and Hepatology, Meander Medical Center, 3813 Amersfoort, The Netherlands; [email protected] 54 Department of Gastroenterology, University Clinic for Gastroenterohepatology, 1000 Skopje, North Macedonia; [email protected] 55 Faculty of Medicine, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia 56 Hospital Clínic de Barcelona, 08036 Barcelona, Spain 57 Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, 08036 Barcelona, Spain 58 Gastrointestinal Oncology, Endoscopy and Surgery (GOES) Research Group, Althaia Xarxa Assistencial Universitària de Manresa, 08243 Barcelona, Spain; [email protected] 59 Institut de Recerca i Innovacióen Ciències de la Vida i de la Salut de la Catalunya Central (IRIS-CC), 08500 Barcelona, Spain 60 INSERM, Institut National de la SantéEt de la Recherche Médicale U1312, Universitéde Bordeaux, 33077 Bordeaux, France; [email protected] 61 Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Ciudad Autónoma de Buenos Aires C1425FQB, Argentina 62 Science and Technology and Department, Universidad Nacional de Quilmes, Bernal B1876, Argentina *Correspondence: agetr[email protected] Antibiotics 2023,12, 1427 3 of 18 †These authors contributed equally to this work. ‡The remaining authors, their affiliations, and contributions are listed in Supplementary File S1. Hp-EuReg Investigators. Abstract: The segmentation of patients into homogeneous groups could help to improve eradication therapy effectiveness. Our aim was to determine the most important treatment strategies used in Europe, to evaluate first-line treatment effectiveness according to year and country. Data collection: All first-line empirical treatments registered at AEGREDCap in the European Registry on Helicobacter pylori management (Hp-EuReg) from June 2013 to November 2022. A Boruta method determined the “most important” variables related to treatment effectiveness. Data clustering was performed through multi-correspondence analysis of the resulting six most important variables for every year in the 2013–2022 period. Based on 35,852 patients, the average overall treatment effectiveness increased from 87% in 2013 to 93% in 2022. The lowest effectiveness (80%) was obtained in 2016 in cluster #3 encompassing Slovenia, Lithuania, Latvia, and Russia, treated with 7-day triple therapy with amoxicillin–clarithromycin (92% of cases). The highest effectiveness (95%) was achieved in 2022, mostly in Spain (81%), with the bismuth–quadruple therapy, including the single-capsule (64%) and the concomitant treatment with clarithromycin–amoxicillin–metronidazole/tinidazole (34%) with 10 (69%) and 14 (32%) days. Cluster analysis allowed for the identification of patients in homogeneous treatment groups assessing the effectiveness of different first-line treatments depending on therapy scheme, adherence, country, and prescription year. Keywords: Helicobacter pylori; clustering; phenotyping; machine learning; treatment; eradication 1. Introduction Helicobacter pylori (H. pylori) infects half of the population worldwide [ 1 ], causing initially a chronic non-atrophic gastritis. In some individuals, chronic gastritis may lead to a loss of mucosal glands (atrophy), the substitution of gastric by intestinal epithelium (intestinal metaplasia), and eventually dysplasia [2,3]. The recently published VI Maastricht Consensus Report recognises H. pylori as an infectious disease, now included in the International Classification of Diseases 11th Revision, leading to the recommendation that all infected adult patients should receive an eradication treatment [4]. In this context, a continuous evaluation of the wide range of clinical scenarios associated with H. pylori infection is required to provide the best standard of care with regards to diagnosis and treatment strategies, in order to ultimately contribute to the prevention of cancer and other health-related complications (such as peptic ulcer disease). However, the clinical management of this infection still remains challenging given the disparity in bacterial antibiotic resistance in the different regions and the paucity of proven efficacious first-line and rescue eradication therapies [5]. The European Registry on H. pylori management (Hp-EuReg) is a prospective, international registry created to collect, systematically, the epidemiology, efficacy, and safety of the great diversity of treatment lines used to eradicate H. pylori, to evaluate the implementation of H. pylori infection consensus and clinical guidelines in different countries, and ultimately to evaluate the accessibility to healthcare technologies and drugs used in the management of this infection. Hp-EuReg’s open inclusion criteria have brought together heterogeneous data on the real clinical practice of a large number of European countries, evaluating the widest range of therapeutic options (e.g., over 100 first-line therapies) and patient contexts (30 countries), which have been reported in several high-impact publications [6]. The combination and cross-correlation of data from this large and growing dataset continuously provides the latest, up-to-date, evidence-based recommendations for the daily clinical practice of gastroenterologists. A traditional approach to identify factors associated with treatment effects is based on regression models, whereby any potential Antibiotics 2023,12, 1427 4 of 18 relationship between the outcome and the regressors can be explained by using, primarily, the logistic regression. However, when the independent variables—usually categorical ones—interact between them, or when a non-linear relationship exists between the outcome and the regressors, other approaches may be more appropriate. One of these is Random Forest, a well-known machine learning algorithm that additionally employs a statistical approach based on ensemble learning to obtain more robust results. Once the number of variables to be analysed has been reduced, the next step is trying to find some structure within the data. To achieve this objective, a clustering procedure is usually performed to identify potential hidden patterns; this is particularly relevant when the dataset to be analysed contains incomplete or fragmented information, as in many multicentre databases. As an unsupervised technique, clustering allows for the grouping of patients without considering the actual outcome—that is, the treatment eradication rate—thereby providing a first look at the patients’ characteristics. Thus, the aim of the current study was to conduct an exploratory analysis of the Hp-EuReg first-line treatment data through two well-known machine learning techniques, a supervised one (the Random Forest) and an unsupervised one (by clustering on the multicorrespondence components). Both techniques allowed for a description of the potential associations between the characteristics of different types of treatments and adherence and diversity through the countries, as well as an evaluation of the eradication treatment success. 2. Results 2.1. Variable Importance In this study, a total of 35,852 H. pylori-infected adults (17 < age < 90) and treatmentnaïve patients from 30 countries were analysed in groups of patients corresponding to each of the different years of the study period (Figure 1): 3239 (2013), 4292 (2014), 3693 (2015), 4350 (2016), 3665 (2017), 3668 (2018), 3695 (2019), 3092 (2020), 4018 (2021), and 2140 (2022). Antibiotics 2023,12, 1427 5 of 18 Antibiotics 2023, 12, x FOR PEER REVIEW 5 of 18 Figure 1. Study flowchart. The variable importance obtained by means of the Boruta algorithm is depicted in the plot of Figure 2. This variable ordering according to importance placed treatment adherence and eradication treatment in the first two positions; however, the country and the year of the prescribed therapy were likewise ranked in high positions (third and fifth, respectively), which suggested that a further analysis involving these two variables should be conducted. Thus, a subsequent analysis was performed, in a year-by-year fashion, with the first six variables: adherence, treatment, country, treatment duration, PPI dose, and other non-frequent gastrointestinal symptoms, showing that this ranking remained approximately unchanged (only treatment and country swapped positions in some of the years) during the entire study time span. A representative example for the year 2022 is shown in Figure 3. The ranking of the same variables for the remaining years is reported in Supplementary Figures S1–S9. Figure 1. Study flowchart. The variable importance obtained by means of the Boruta algorithm is depicted in the plot of Figure 2. This variable ordering according to importance placed treatment adherence and eradication treatment in the first two positions; however, the country and the year of the prescribed therapy were likewise ranked in high positions (third and fifth, respectively), which suggested that a further analysis involving these two variables should be conducted. Thus, a subsequent analysis was performed, in a year-by-year fashion, with the first six variables: adherence, treatment, country, treatment duration, PPI dose, and other non-frequent gastrointestinal symptoms, showing that this ranking remained approximately unchanged (only treatment and country swapped positions in some of the years) during the entire study time span. A representative example for the year 2022 is shown in Figure 3. The ranking of the same variables for the remaining years is reported in Supplementary Figures S1–S9. Antibiotics 2023,12, 1427 6 of 18 Antibiotics 2023, 12, x FOR PEER REVIEW 6 of 18 Figure 2. Random Forest variable importance based on the mean decrease in accuracy for the preselected 15 variables associated with the modified intention-to-treat effectiveness, during 2013–2022. Adverse_events, defined as the incidence of at least one adverse event, as yes/no; Age, categorised in six levels: from 18 to 30, 30 to 42, 42 to 54, 54 to 66, 66 to 78, and 78 to 90, disregarding those patients with ages under 18 and over 90; Compliance, defined as yes: > 90% drug intake; no: < 90% drug intake; Dose_of_PPI, defined as low-dose PPI: 4.5 to 27 mg OE b.i.d; standard dose PPI: 32 to 40 mg OE b.i.d; high-dose PPI: 54 to 128 mg OE b.i.d; Duration, as a duration of treatment of 7, 10, or 14 days; Indication, as ulcer vs. dyspepsia; None_Sympt, defined as the absence of any gastrointestinal symptoms; Heartburn, as yes/no; dyspepsia as yes/no; Other_Sympt, defined as other nonfrequent gastrointestinal symptoms; Sex, as female/male. Figure 2. Random Forest variable importance based on the mean decrease in accuracy for the preselected 15 variables associated with the modified intention-to-treat effectiveness, during 2013–2022 . Adverse_events, defined as the incidence of at least one adverse event, as yes/no; Age, categorised in six levels: from 18 to 30, 30 to 42, 42 to 54, 54 to 66, 66 to 78, and 78 to 90, disregarding those patients with ages under 18 and over 90; Compliance, defined as yes: >90% drug intake; no: <90% drug intake; Dose_of_PPI, defined as low-dose PPI: 4.5 to 27 mg OE b.i.d; standard dose PPI: 32 to 40 mg OE b.i.d; high-dose PPI: 54 to 128 mg OE b.i.d; Duration, as a duration of treatment of 7, 10, or 14 days; Indication, as ulcer vs. dyspepsia; None_Sympt, defined as the absence of any gastrointestinal symptoms; Heartburn, as yes/no; dyspepsia as yes/no; Other_Sympt, defined as other non-frequent gastrointestinal symptoms; Sex, as female/male. Antibiotics 2023,12, 1427 7 of 18 Antibiotics 2023, 12, x FOR PEER REVIEW 6 of 18 Figure 2. Random Forest variable importance based on the mean decrease in accuracy for the preselected 15 variables associated with the modified intention-to-treat effectiveness, during 2013–2022. Adverse_events, defined as the incidence of at least one adverse event, as yes/no; Age, categorised in six levels: from 18 to 30, 30 to 42, 42 to 54, 54 to 66, 66 to 78, and 78 to 90, disregarding those patients with ages under 18 and over 90; Compliance, defined as yes: > 90% drug intake; no: < 90% drug intake; Dose_of_PPI, defined as low-dose PPI: 4.5 to 27 mg OE b.i.d; standard dose PPI: 32 to 40 mg OE b.i.d; high-dose PPI: 54 to 128 mg OE b.i.d; Duration, as a duration of treatment of 7, 10, or 14 days; Indication, as ulcer vs. dyspepsia; None_Sympt, defined as the absence of any gastrointestinal symptoms; Heartburn, as yes/no; dyspepsia as yes/no; Other_Sympt, defined as other nonfrequent gastrointestinal symptoms; Sex, as female/male. Figure 3. Random Forest variable importance based on the mean decrease in accuracy for the first six variables (year 2022). Compliance, defined as 1: yes with >90% drug intake or 0: no with <90% drug intake; Dose of_PPI, defined as low-dose PPI: 4.5 to 27 mg OE b.i.d; standard dose PPI: 32 to 40 mg OE b.i.d; high-dose PPI: 54 to 128 mg OE b.i.d; Treatment, defined as a duration of treatment of 7, 10, or 14 days; Other_Sympt, defined as other non-frequent gastrointestinal symptoms (0: absence, 1: presence). 2.2. Clinical Phenotyping As a second, unsupervised analysis, clustering was conducted based on the multiple correspondence components obtained from the six more important variables, in each year. A predefined number of clusters between 2 and 3 was initially set so the algorithm, a hierarchical clustering based on the principal components using the Ward criterion, selected the final number of clusters that best represented the data classification. Three clusters were always obtained. Once a cluster partition was obtained in each year, the effectiveness of the patients’ treatments was calculated for every cluster, as shown in Table 1, where the number of patients in each cluster was also included. The first observation in the evolution of mITT effectiveness showed an increase during the study period, going from 86% in 2013 to 93.5% in 2022. Table 2and Figure 4show the distribution of patients (percentage) among the different variable levels in the three clusters for the year 2022. For instance, cluster #1, was composed of 138 patients uniquely from Italy (100%) with an overall mITT effectiveness of 93.5%. Most of the cases received seq-CAT/M (68%) and BsQuad-MTcB (14%) therapies. Cluster #2, with 909 patients with an overall eradication rate of 95%, was mostly composed of cases from Spain (81%), where two treatments were most frequently employed: BsQuadMTcB (64% of the cluster cases) and conco-CAT/M (33%). Finally, cluster #3, composed of 1093 patients with an overall mITT effectiveness of 92%, was composed of patients from Russia (40%), Slovenia (15%), Serbia (12%), and Turkey (7%), and triple-CA/M (33%) and quad-CAB (15%) were prescribed in nearly half of the cases. The remaining cases encompassed other non-frequently prescribed first-line therapies (50%). Antibiotics 2023,12, 1427 8 of 18 Table 1. Overall modified intention-to-treat (mITT) effectiveness of the first-line empirical treatment in each cluster by year, in Europe. Year Cluster #1, % mITT (Number of Patients) Cluster #2, % mITT (Number of Patients) Cluster #3, % mITT (Number of Patients) 2013 86.4 (2011) 88.4 (224) 84.5 (1004) 2014 88.5 (435) 86.5 (2644) 84.3 (1213) 2015 89.0 (346) 87.1 (2656) 83.5 (691) 2016 91.6 (2523) 85.5 (1152) 80.3 (675) * 2017 85.4 (323) 91.6 (1969) 82.4 (1373) 2018 90.1 (1974) 90.9 (1122) 90.6 (352) 2019 90.1 (1225) 86.7 (721) 89.6 (1749) 2020 86.1 (1783) 87.4 (585) 92.3 (724) 2021 87.4 (310) 91.1 (1882) 90.4 (1826) 2022 93.5 (138) 95.2 (909) 91.9 (1093) * Cluster #3 in 2016 showed the lowest effectiveness and was composed mostly of 7-day triple-clarithromycinamoxicillin therapy (92.4% cases) mainly from Slovenia and Lithuania (49%), Latvia, and Russia (25%) Table 2. Summary description of patients and variables in each cluster, corresponding to the year 2022. 1 (n= 138) 2 (n= 909) 3 (n= 1093) Overall p-Value N Gastrointestinal symptoms <0.001 2140 Absence (none) 137 (99.3%) 756 (83.2%) 1019(93.2%) Other symptoms1 1 (0.72%) 153 (16.8%) 74 (6.77%) Compliance 0.022 2140 No (<90% drug intake) 3 (2.17%) 9 (0.99%) 28 (2.56%) Yes (>90% drug intake) 135 (97.8%) 900 (99.0%) 1065 (97.4%) Duration (days) 2140 7 1 (0.72%) 1 (0.11%) 55 (5.03%) 10 126 (91.3%) 633 (69.6%) 168 (15.4%) 14 11 (7.97%) 275 (30.3%) 870 (79.6%) Dose of PPI (mg OE)2 <0.001 2140 Low 30 (21.7%) 308 (33.9%) 265 (24.2%) Standard 1 (0.72%) 253 (27.8%) 534 (48.9%) High 107 (77.5%) 348 (38.3%) 294 (26.9%) Country 2140 Austria 0 (0.00%) 5 (0.55%) 0 (0.00%) Bulgaria 0 (0.00%) 0 (0.00%) 15 (1.37%) Croatia 0 (0.00%) 19 (2.09%) 26 (2.38%) Czech Republic 0 (0.00%) 0 (0.00%) 16 (1.46%) Germany 0 (0.00%) 25 (2.75%) 22 (2.01%) Greece 0 (0.00%) 39 (4.29%) 0 (0.00%) Hungary 0 (0.00%) 0 (0.00%) 21 (1.92%) Ireland 0 (0.00%) 0 (0.00%) 21 (1.92%) Israel 0 (0.00%) 2 (0.22%) 0 (0.00%) Antibiotics 2023,12, 1427 9 of 18 Table 2. Cont. 1 (n= 138) 2 (n= 909) 3 (n= 1093) Overall p-Value N Italy 138 (100%) 1 (0.11%) 3 (0.27%) Latvia 0 (0.00%) 0 (0.00%) 13 (1.19%) Lithuania 0 (0.00%) 0 (0.00%) 26 (2.38%) North Macedonia 0 (0.00%) 0 (0.00%) 31 (2.84%) Poland 0 (0.00%) 55 (6.05%) 11 (1.01%) Portugal 0 (0.00%) 20 (2.20%) 0 (0.00%) Russia 0 (0.00%) 0 (0.00%) 437 (40.0%) Serbia 0 (0.00%) 1 (0.11%) 133 (12.2%) Slovenia 0 (0.00%) 0 (0.00%) 163 (14.9%) Spain 0 (0.00%) 739 (81.3%) 46 (4.21%) Switzerland 0 (0.00%) 3 (0.33%) 11 (1.01%) Turkey 0 (0.00%) 0 (0.00%) 78 (7.14%) United Kingdom 0 (0.00%) 0 (0.00%) 20 (1.83%) Most frequent 1st line treatments 2140 Triple-CA/M 3 (2.17%) 9 (0.99%) 365 (33.4%) Seq-CAT-CAM 94 (68.1%) 0 (0.00%) 0 (0.00%) Conco-CAT CAM 9 (6.52%) 305 (33.6%) 2 (0.18%) BsQuad-MTcB 20 (14.5%) 579 (63.7%) 9 (0.82%) Quadruple-CAB 0 (0.00%) 2 (0.22%) 169 (15.5%) Other3 12 (8.70%) 14 (1.54%) 548 (50.1%) A: amoxicillin; B: bismuth salts; C: clarithromycin; Conco: concomitant; M: metronidazole; N: total number of cases in year evaluated; n: number of cases in each cluster; OE: omeprazole equivalent; PPI: proton pump inhibitor; Seq, sequential; T: tinidazole; Tc: tetracycline hydrochloride; MTcB was prescribed either in the classical form or as a three-in-one single capsule, marketed as Pylera ® . 1 Other gastrointestinal symptoms (excluding the most frequent ones, such as dyspepsia or heartburn) included nausea, diarrhoea, and weight loss. 2 Low-dose PPI: 4.5–27 mg omeprazole equivalents, two times per day (i.e., 20 mg omeprazole equivalents, two times per day); standard-dose PPI: 32–40 mg omeprazole equivalents, two times per day (i.e., 40 mg omeprazole equivalents, two times per day); high-dose PPI: 54–128 mg omeprazole equivalents, two times per day (i.e., 80 mg omeprazole equivalents, two times per day). 3 Other treatments encompassed fewer than 10% of the remaining prescribed regimens. Statistical significance was set at the p-value < 0.05 statistical level. Additionally, Figure 4represents the mITT ranges among clusters #1, #2, and #3, reporting optimal (>90%) [ 4 ] overall first-line effectiveness in all three clusters (93%, 95%, and 92%, respectively). The remaining previous years are described in Supplementary Tables S1–S9 and Supplementary Figures S10–S19. The most relevant information is detailed below. During the period 2013 to 2021, the multi-correspondence analysis data clustering identified, in each year, one cluster reporting the highest effectiveness. Optimal (i.e., >90%) first-line mITT effectiveness was obtained in the year 2016, with 92% in cluster #1; in the year 2017, with 92% in cluster #2; in the year 2018, with 91% in all three clusters; in the year 2019, with 90% in cluster #1; in the year 2020, with 92% in cluster #3; and in the year 2021, with 91% in cluster #2. Those variables with the highest content in each of the clusters of each year were the treatment duration (7, 10 or 14 days), PPI prescribed dose (low, standard or high-dose), country of prescription, and ultimately, the specific treatment scheme used. Antibiotics 2023,12, 1427 16 of 18 than two categorical variables, which in turn, allows for the grouping of patients in a similar fashion (i.e., similar phenotypes), as Principal Component Analysis does with numerical variables [ 23 – 25 ]. In fact, a decomposition in the principal component is also performed in the multi-correspondence analysis. A hierarchical clustering, with a predefined number of clusters in between #1 and #3, was performed on the data points belonging to the principal components. This analysis was also conducted year-by-year. Finally, a last analysis of the evolution of treatment effectiveness was performed. Clustering brings, at a glance, a picture of the treatment’s effectiveness according to the analysed variables, although in some cases, it mixes important information, for example, which treatments are most effective in some particular countries. In order to disclose this information, each treatment effectiveness was calculated by country. Tracking the evolution of treatments’ effectiveness during the study period 2013–2022 in each and every country was difficult since some of the studied treatments were barely used (fewer than 30 patients/year) in some countries. Thus, we performed a post hoc selection of four countries: Spain, Slovenia, Italy, and Russia, in which at least one treatment was employed in at least 30 patients per year, in order to assess the evolution of the treatment’s success. The Cochran–Armitage test was used to assess a potential trend (either an increase or a decrease) in the treatment effectiveness. All statistical analyses were performed using our own codes and base functions in R, version 4.1.2 (http://www.R-project.org; the R Foundation for Statistical Computing, Vienna, Austria) [26]. 5. Conclusions In conclusion, in the current study, we could observe an increase in the first-line treatment effectiveness in Europe during the years 2013 to 2022, ranging from 83% to 95%. Substantial heterogeneity was observed among clusters and years, mainly due to the changes in prescriptions and discontinuous participation in some of the countries. The Random Forest analysis reported that both the selection of the eradication treatment and the treatment adherence were the most important variables for the successful eradication (>90% mITT effectiveness). In addition, regarding the importance of geographic factors, it also showed that effectiveness was strongly dependent on the country in which the treatment was prescribed (probably reflecting the different bacterial antibiotic resistances in each geographic area). Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/antibiotics12091427/s1, Hp-EuReg investigators; Table S1–S9. Summary description of patients and variables in each cluster, corresponding to the year 2013–2022.; Figure S1–S9. Random Forest variable importance based on mean decrease accuracy for the first six variables (years 2013–2022); Figure S10–S18. Clusters composition in terms of variables’ levels in years 2013–2022. Author Contributions: O.P.N., Hp-EuReg Scientific Director, performed the data extraction, the monitoring and the quality check, performed the data synthesis and interpretation, wrote the first draft, and approved the final submitted manuscript. G.J.O., P.P. (Pietro Pratesi) and M.A.S. analysed the data, wrote the first draft and approved the final submitted manuscript. L.J., Á.P.-A., D.V., I.M.S., M.P., G.F., B.T., D.S.B., I.V., Á.L., S.J.M.-D., E.A., L.B. (Luis Bujanda), M.P.-C., L.H., A.G., J.K., F.L., S.M.S., O.G., M.L., T.R., R.M.-P., A.M., W.M., V.M., H.S., L.K., V.P., P.S.P., M.V., L.B. (Lyudmila Boyanova), D.B., Y.N., T.M.-B., M.D., D.D., V.L., L.G.C. and E.N.T.: collected data, critically reviewed the manuscript drafts, and approved the submitted manuscript. C.O., F.M., A.C.-C., P.P. (Pablo Parra), L.M., O.P.N. and J.P.G. are Members of the Hp-EuReg Scientific Committee; they assisted with data interpretation, critically reviewed the manuscript drafts, and approved the final submitted manuscript. J.P.G., Principal investigator, directed the project, obtained funding, designed the protocol and planned the study, recruited patients, analysed and interpreted the data, critically reviewed the manuscript drafts, and approved the final submitted manuscript. All authors have read and agreed to the published version of the manuscript. Antibiotics 2023,12, 1427 17 of 18 Funding: This project was promoted and funded by the European Helicobacter and Microbiota Study Group (EHMSG), the Spanish Association of Gastroenterology (AEG), and the Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd). The HpEuReg was co-funded by the European Union programme HORIZON (grant agreement number 101095359) and supported by the UK Research and Innovation (grant agreement number 10058099). The Hp-EuReg was co-funded by the European Union programme EU4Health (grant agreement number 101101252). This study was funded by Richen; however, clinical data were not accessible, and the company was not involved in any stage of the Hp-EuReg study (design, data collection, statistical analysis, or manuscript writing). We want to thank Richen for their support. Institutional Review Board Statement: The Hp-EuReg Protocol 26 was approved by the Ethics Committee of La Princesa University Hospital, Madrid, Spain, and registered at ClinicalTrials.gov under the code NCT02328131. Informed Consent Statement: All personal data were anonymised, and participants gave informed consent to participate in the study before taking part. Data Availability Statement: Raw data were generated at AEG-REDCap. Derived data supporting the findings of this study are available from the Hp-EuReg Scientific Director and the PI of the project (OPN and JPG) upon request. The data supporting the findings of this study are not publicly available given that the information they contain could compromise the privacy of research participants. However, previous published data on the Hp-EuReg study, or de-identified raw data referring to the current study, as well as further information on the methods used to explore the data could be shared, with no particular time constraint. Individual participant data will not be shared. Acknowledgments: We thank the Spanish Association of Gastroenterology (AEG) for providing the e-CRF service free of charge. Conflicts of Interest: Javier P. Gisbert has served as a speaker, consultant, and advisory member for or has received research funding from Mayoly, Allergan, Diasorin, Gebro Pharma, and Richen. Olga P. 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