Related Experiment Video
Updated: Jan 9, 2026
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
Nine-year risk stratification and prediction of Helicobacter pylori infection using Group-Based Trajectory Modeling
Heping Zhao1, Sainan Liu2, Manzhen Wei2
1Department of Gastroenterology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Background:
Helicobacter pylori (H. pylori) infection remains prevalent in regions such as Shanxi, China, contributing to gastrointestinal morbidity. Accurately identifying high-risk individuals is essential for effective screening and early intervention.
Methods:
We conducted a retrospective longitudinal cohort study of 35,206 adults who underwent repeated annual health checkups with H. pylori testing at a single center from 2016 to 2024. Group-Based Trajectory Modeling (GBTM) identified risk subgroups. Multivariable logistic regression identified predictors of high-risk trajectories; alcohol consumption was assessed as an effect modifier. Five machine learning models-including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Logistic regression, etc.-were trained using a 7:3 split. Temporal validation (2016-2020 training/2021-2024 validation) assessed generalizability. SHapley Additive exPlanations (SHAP) improved interpretability. A prediction tool was deployed via R Shiny.
Results:
GBTM identified high-risk (14.63%) and low-risk (85.37%) groups. Protective factors included women (OR = 0.042, 95% CI: 0.039-0.046) and unmarried status (OR = 0.092, 95% CI: 0.085-0.099); risk factors included obesity (OR = 1.138, 95% CI: 1.070-1.210), blue-collar workers (OR = 1.557, 95% CI: 1.454-1.666), and alcohol consumption (OR = 1.277, 95% CI: 1.165-1.401). Alcohol consumption interacted with all significant factors in subgroup analysis (all p < 0.001), with the strongest interaction observed for being married (OR = 8.622, 95% CI: 7.872-9.437). LightGBM achieved AUCs of 0.851 (training), 0.843 (validation), 0.863 (temporal training), and 0.831 (temporal validation). SHAP ranked marital status and sex as top predictors. The tool is available at: https://prediction-model-for-hp.shinyapps.io/hp_shinyapp-/.
Conclusion:
We developed an online, interpretable risk prediction tool with validated accuracy to support precision screening of H. pylori infection.
More Related Videos
04:56Detection of Helicobacter pylori Infection and Antibiotic Resistance via Stool Quantitative Polymerase Chain Reaction Analysis
Published on: May 16, 2025
05:23Gastric Mucosa Quantitative Polymerase Chain Reaction Analysis for Detecting Helicobacter pylori and Antibiotic Resistance
Published on: March 7, 2025
Related Concept Videos
01:16Treating Helicobacter pylori in Peptic Ulcers: Antimicrobial Therapy
01:28Peptic Ulcer Disease III: Clinical Manifestations and Diagnostic Studies
Few clinical manifestations differentiate gastric ulcers from duodenal ulcers. Distinctions in the location, timing, and pain relief are crucial for healthcare providers in differentiating between gastric and duodenal ulcers during clinical assessments.