Evidence map›Paper›PMID 42330439›Full record

ArticleSexually transmitted diseases2026

Validation of diagnostic codes for identifying chlamydial infections from TriNetX electronic health record data in the United States.

Lin Li, Kexin Zhu, Hosn Nabaa, Konstantin Kozmenko, Valerie Bosch Castells, Delphine Saragoussi, Sandra S Chaves

Abstract read
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Article in Sexually transmitted diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Lin Li. Epidemiology and Benefit Risk, Sanofi, Morristown, NJ, USA.
Kexin Zhu. Epidemiology and Benefit Risk, Sanofi, Morristown, NJ, USA.
Hosn Nabaa. Epidemiology and Benefit Risk, Sanofi, Gentilly, France.
Konstantin Kozmenko. Patient Safety and Pharmacovigilance, Sanofi, Lyon, France.
Valerie Bosch Castells. New Products and Innovation, Sanofi, Lyon, France.
Delphine Saragoussi. Epidemiology and Benefit Risk, Sanofi, Gentilly, France.
Sandra S Chaves. New Products and Innovation, Sanofi, Lyon, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe accuracy of diagnostic codes for chlamydial infections in electronic health records (EHR) is understudied. TriNetX, a global federated database, provides real-time access to patient-level EHRs, but its suitability for studying chlamydial infections is unknown. This study aimed to validate ICD-10-CM diagnostic codes for chlamydial infections in TriNetX USA data.

methodsA retrospective cohort study was conducted using the TriNetX USA Network from 2016 to 2024. Patients aged 14-44 years with chlamydia diagnosis codes (including C. trachomatis, C. psittaci, and C. pneumoniae) or known Nucleic Acid Amplification Test (NAAT) results were included. All eligible patients were categorized into four mutually exclusive groups based on the presence of diagnosis code (yes or no) and NAAT results (gold standard, positive or negative): true positive, false positive, false negative, and true negative. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity and their 95% confidence intervals (CI) were calculated for chlamydia diagnosis, including stratified analyses by age, sex, race, ICD codes and calendar year.

resultsAmong 4,501,240 eligible patients (75.9% female), chlamydial infection diagnosis codes showed: PPV: 84.3% (95% CI: 84.2-84.5%); NPV: 95.5% (95% CI: 95.5-95.5%); sensitivity: 42.8% (95% CI: 42.6-42.9%); specificity: 99.3% (95% CI: 99.3-99.4%). Stratified analyses revealed PPV: 80.9-87.1%, NPV: 91.7-97.3%, specificity: 99.1-99.9%, and sensitivity: 10.4-49.7% with lowest sensitivity observed in C. trachomatis code analysis.

conclusionsMost diagnostic codes can accurately identify chlamydial infections in the TriNetX USA Network. However, low sensitivity suggests that many cases could be missed without laboratory test results.

Indexed as

chlamydiaICD-10-CMpositive predictive valueTriNetXvalidation

Identifiers

PMID42330439
PMCPMC13641258

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.