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 race-neutral equations resulted in false discovery rates (FDR) of 14% for Hispanics and 45% for Blacks and false negative rates (FNR) of 21% for Hispanics and 27% for Blacks in diagnosing airway obstruction. These findings indicate that removing race as a covariate in spirometry equations increases FDR and FNR, leading to higher misclassification rates. The 4%–13% discordance in interpreting airway obstruction and restrictive patterns has significant clinical implications, underscoring the need for careful consideration in developing spirometry reference equations.