Examining discordance in spirometry reference equations: A retrospective study.

ABSTRACT: This study aimed to evaluate discordance, binary classification, and model fit between race-predicted and race-neutral spirometry prediction equations. Spirometry data from 9506 patients (18-95 years old) self-identifying as White, Black, or Hispanic were analyzed, focusing on the lower limit of normal (LLN). Best-fit prediction equations were developed from 3771 patients with normal spirometry, using Bayesian Information Criterion (BIC) to compare models with and without race as a covariate. Results showed that including race as a covariate improved model fit, reducing BIC by at least ten units compared to Race-Neutral equations. Discordance between race-specific and race-neutral equations for detecting airway obstruction and restrictive spirometry patterns ranged from 4% to 13%. Using ra

SUBMITTER: Zavorsky GS 

PROVIDER: S-EPMC11865334 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature