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)

Nyssen, Olga. P.Pratesi, PietroSpinola, Miguel. A.Jonaitis, LaimasPerez-Aisa, AngelesVaira, DinoSaracino, Ilaria MariaPavoni, MatteoFiorini, GiuliaTepes, BojanBordin, Dmitry. S.Voynovan, IrinaLanas, AngelMartinez-Dominguez, Samuel. J.Alfaro, EnriqueBujanda, LuisPabon-Carrasco, ManuelHernández, LuisGasbarrini, AntonioKupcinskas, JuozasLerang, FrodeSmith, Sinead. M.Gridnyev, OleksiyLeja, MarcisRokkas, TheodoreMarcos-Pinto, RicardoMestrovic, AntonioMarlicz, WojciechMilivojevic, VladimirSimsek, HalisKunovsky, LumirPapp, VeronikaPhull, Perminder. S.Venerito, MarinoBoyanova, LyudmilaBoltin, DoronNiv, YaronMatysiak-Budnik, TamaraDoulberis, MichaelDobru, DanielaLamy, VincentCapelle, Lisette. G.Trpchevska, Emilijia NikolovskaMoreira, LeticiaCano-Catalia, AnnaParra, PabloMegraud, FrancisO'Morain, ColmOrtega, Guillermo. J.Gisbert, Javier. P.Hp EuReg Investigators2025-01-312025-01-312023-09-10Nyssen OP, Pratesi P, Spínola MA, Jonaitis L, Pérez-Aísa Á, Vaira D, 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 (Basel). 2023 Sep 10;12(9):1427.2079-6382https://hdl.handle.net/10668/28463The 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Helicobacter pyloriclusteringphenotypingmachine learningtreatmenteradicationScience & TechnologyLife Sciences & BiomedicineHelicobacter pyloriEradication TherapyCluster AnalysisAntibiotic Resistance ElectronicHealth RecordsAnalysis 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)research article37760723open accessHelicobacter pyloriTerapia de ErradicaciónAnálisis de ClústerResistencia a los AntibióticosRegistros de Salud10.3390/antibiotics12091427WOS:001074506900001