{"doi":"10.1016/j.gastha.2023.09.005","title":"Ascertainment of Helicobacter pylori Infection and Eradication Treatment Using a Nationwide Electronic Health Record Database","abstract":"Background and AimsThere are limited contemporary population-based data on Helicobacter pylori epidemiology and outcomes in the United States. Our primary aim was to create a validated cohort of veterans with H pylori testing or treatment using Veterans Health Administration data.MethodsUsing Veterans Health Administration structured and unstructured data, we developed and validated 4 algorithms for H pylori infection (3 algorithms) and treatment status (1 algorithm). During the development phase, we iteratively modified each algorithm based on a manual review of random sets of electronic health records (reference standard). The a priori validation goal was to achieve a one-sided 95% confidence lower bound (LB) for positive predictive value (PPV) and/or negative predictive value (NPV) >90%. We applied the Bonferroni correction when both PPV and NPV were relevant.ResultsFor H pylori infection, we achieved 99.0% PPV (LB = 94.6%) and 100% NPV (LB = 96.4%) for discriminating H pylori positive vs negative status using structured (ie, laboratory tests) and 95% PPV (LB = 90.3%) and 97.9% NPV (LB = 93.9%) using unstructured (ie, histopathology reports) data. Diagnostic codes achieved 98% PPV (LB = 93.0%) for H pylori diagnosis. The treatment algorithm was composed of multiple antimicrobial combinations and overall achieved ≥98% PPV (LB = 93.0%) for H pylori treatment, except for amoxicillin/levofloxacin (PPV<60%). Application of these algorithms yielded nearly 1.2 million veterans with H pylori testing and/or treatment between 1999 and 2018.ConclusionWe assembled a validated national cohort of veterans who were tested or treated for H pylori infection. This cohort can be used for evaluating H pylori epidemiology and treatment patterns, as well as complications of chronic infection. There are limited contemporary population-based data on Helicobacter pylori epidemiology and outcomes in the United States. Our primary aim was to create a validated cohort of veterans with H pylori testing or treatment using Veterans Health Administration data. Using Veterans Health Administration structured and unstructured data, we developed and validated 4 algorithms for H pylori infection (3 algorithms) and treatment status (1 algorithm). During the development phase, we iteratively modified each algorithm based on a manual review of random sets of electronic health records (reference standard). The a priori validation goal was to achieve a one-sided 95% confidence lower bound (LB) for positive predictive value (PPV) and/or negative predictive value (NPV) >90%. We applied the Bonferroni correction when both PPV and NPV were relevant. For H pylori infection, we achieved 99.0% PPV (LB = 94.6%) and 100% NPV (LB = 96.4%) for discriminating H pylori positive vs negative status using structured (ie, laboratory tests) and 95% PPV (LB = 90.3%) and 97.9% NPV (LB = 93.9%) using unstructured (ie, histopathology reports) data. Diagnostic codes achieved 98% PPV (LB = 93.0%) for H pylori diagnosis. The treatment algorithm was composed of multiple antimicrobial combinations and overall achieved ≥98% PPV (LB = 93.0%) for H pylori treatment, except for amoxicillin/levofloxacin (PPV<60%). Application of these algorithms yielded nearly 1.2 million veterans with H pylori testing and/or treatment between 1999 and 2018. We assembled a validated national cohort of veterans who were tested or treated for H pylori infection. This cohort can be used for evaluating H pylori epidemiology and treatment patterns, as well as complications of chronic infection.","journal":"Gastro Hep Advances","year":2023,"id":388811,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":22.178386323199504,"corpus_rank":9377,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.7853,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":456613,"name":"Rohan Gupta","orcid":"0000-0002-7835-4306","position":1,"is_corresponding":false},{"id":242794,"name":"Ranier Bustamante","orcid":"0000-0002-4987-7917","position":2,"is_corresponding":false},{"id":893589,"name":"Mark Lamm","orcid":null,"position":3,"is_corresponding":false},{"id":893588,"name":"Hanin Yassin","orcid":null,"position":4,"is_corresponding":false},{"id":244138,"name":"Ashley Earles","orcid":null,"position":5,"is_corresponding":false},{"id":11400,"name":"Adriana M. Hung","orcid":"0000-0002-3203-1608","position":6,"is_corresponding":false},{"id":404609,"name":"Alese E. Halvorson","orcid":"0000-0001-6909-4891","position":7,"is_corresponding":false},{"id":260165,"name":"Robert A. Greevy","orcid":"0000-0002-1821-3544","position":8,"is_corresponding":false},{"id":242797,"name":"Samir Gupta","orcid":"0000-0003-4192-5002","position":9,"is_corresponding":false},{"id":242792,"name":"Joshua Demb","orcid":"0000-0002-3338-1730","position":10,"is_corresponding":false},{"id":1034348,"name":"Lin Liu","orcid":"0000-0003-2347-5773","position":11,"is_corresponding":false},{"id":371566,"name":"Christianne L. Roumie","orcid":"0000-0002-6555-2055","position":12,"is_corresponding":false},{"id":374375,"name":"Shailja C. Shah","orcid":"0000-0002-2049-9959","position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-19T01:18:18.214733Z","pmid":"39132175","pmcid":"PMC11308087","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}