Research tool for classifying Gulf War illness using survey responses: Lessons for writing replicable algorithms for symptom-based conditions.

Journal Article (Journal Article)

AIMS: Gulf War illness (GWI), a chronic symptom-based disorder, affects up to 30% of Veterans who served in the 1990-1991 Gulf War1. Because no diagnostic test or code for GWI exists, researchers typically determine case status using self-reported symptoms and conditions according to Kansas2 and CDC3 criteria. No validated algorithm has been published and case definitions have varied slightly by study. This paper aims to standardize the application of the original CDC and Kansas case definitions by defining a framework for writing reliable code for complex case definitions, implementing this framework on a sample of 1343 Gulf War Veterans (GWVs), and validating the framework by applying the code to a sample of 41,077 GWVs. MAIN METHODS: Methods were drawn from software engineering: write pseudocode, write test cases, and write code; then test code. Code was examined for accuracy, flexibility, replicability, and reusability. KEY FINDINGS: The pseudocode promoted understanding of the planned algorithm, encouraging discussion and leading to agreement on the case definition algorithms among all team members. The completed SAS code was written for and tested in the Gulf War Era Cohort and Biorepository (GWECB)4. This code was adapted and tested in the Million Veteran Program (MVP)5. The code was documented for reproducibility and reusability. SIGNIFICANCE: Ease of reuse suggests that this method could be used to standardize the application of other case definitions, reducing time and resources spent by each study team. Documentation, code, and test cases are available through the Department of Veterans Affairs (VA) Phenomics catalog6.

Full Text

Duke Authors

Cited Authors

  • Vahey, J; Hauser, ER; Sims, KJ; Helmer, DA; Provenzale, D; Gifford, EJ

Published Date

  • October 1, 2021

Published In

Volume / Issue

  • 282 /

Start / End Page

  • 119808 -

PubMed ID

  • 34242657

Electronic International Standard Serial Number (EISSN)

  • 1879-0631

Digital Object Identifier (DOI)

  • 10.1016/j.lfs.2021.119808

Language

  • eng

Conference Location

  • Netherlands