RT Journal Article
T1 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)
A1 Nyssen, Olga. P.
A1 Pratesi, Pietro
A1 Spinola, Miguel. A.
A1 Jonaitis, Laimas
A1 Perez-Aisa, Angeles
A1 Vaira, Dino
A1 Saracino, Ilaria Maria
A1 Pavoni, Matteo
A1 Fiorini, Giulia
A1 Tepes, Bojan
A1 Bordin, Dmitry. S.
A1 Voynovan, Irina
A1 Lanas, Angel
A1 Martinez-Dominguez, Samuel. J.
A1 Alfaro, Enrique
A1 Bujanda, Luis
A1 Pabon-Carrasco, Manuel
A1 Hernández, Luis
A1 Gasbarrini, Antonio
A1 Kupcinskas, Juozas
A1 Lerang, Frode
A1 Smith, Sinead. M.
A1 Gridnyev, Oleksiy
A1 Leja, Marcis
A1 Rokkas, Theodore
A1 Marcos-Pinto, Ricardo
A1 Mestrovic, Antonio
A1 Marlicz, Wojciech
A1 Milivojevic, Vladimir
A1 Simsek, Halis
A1 Kunovsky, Lumir
A1 Papp, Veronika
A1 Phull, Perminder. S.
A1 Venerito, Marino
A1 Boyanova, Lyudmila
A1 Boltin, Doron
A1 Niv, Yaron
A1 Matysiak-Budnik, Tamara
A1 Doulberis, Michael
A1 Dobru, Daniela
A1 Lamy, Vincent
A1 Capelle, Lisette. G.
A1 Trpchevska, Emilijia Nikolovska
A1 Moreira, Leticia
A1 Cano-Catalia, Anna
A1 Parra, Pablo
A1 Megraud, Francis
A1 O'Morain, Colm
A1 Ortega, Guillermo. J.
A1 Gisbert, Javier. P.
A1 Hp EuReg Investigators, 
K1 Helicobacter pylori
K1 clustering
K1 phenotyping
K1 machine learning
K1 treatment
K1 eradication
K1 Science & Technology
K1 Life Sciences & Biomedicine
AB 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.
PB MDPI
SN 2079-6382
YR 2023
FD 2023-09-10
LK https://hdl.handle.net/10668/28463
UL https://hdl.handle.net/10668/28463
LA en
NO Nyssen 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.
NO 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 Hp- EuReg 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.
DS RISalud
RD Feb 23, 2025