Race-Specific Spirometry Equations Do Not Improve Models of Dyspnea and Quantitative Chest CT Phenotypes

Background Race-specific spirometry reference equations are used globally to interpret lung function for clinical, research, and social purposes, but inclusion of race is under scrutiny. Research Question Does including self-identified race in spirometry reference equation formation improve the ability of predicted FEV 1 values to explain quantitative chest CT abnormalities, dyspnea, or Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification? Study Design and Methods Using data from healthy never-smoking adults in both the National Health and Nutrition Survey (2007-2012) and COPDGene study cohorts, race-neutral, race-free, and race-specific prediction equations were generated for FEV 1. Using sensitivity/specificity, multivariable logistic regression, and random forest models, these equations were applied in a cross-sectional analysis to populations of smokers and former smokers to determine how they affected GOLD classification and the fit of models predicting quantitative chest CT phenotypes or dyspnea. Results Race-specific equations showed no advantage relative to race-neutral or race-free equations in models of quantitative chest CT phenotypes or dyspnea. Race-neutral reference equations reclassified up to 19% of black participants into more severe GOLD classes, and race-neutral/race-free equations may improve model fit for dyspnea symptoms relative to race-specific equations. Interpretation Race-specific equations offered no advantage over race-neutral/race-free percent predicted FEV 1 values in three distinct explanatory models of dyspnea and chest CT scan abnormalities. Race-neutral/race-free reference equations may improve pulmonary disease diagnoses and treatment in populations highly vulnerable to lung disease.