{"doi":"10.1016/j.chest.2023.07.019","title":"Race-Specific Spirometry Equations Do Not Improve Models of Dyspnea and Quantitative Chest CT Phenotypes","abstract":"BackgroundRace-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 QuestionDoes including self-identified race in spirometry reference equation formation improve the ability of predicted FEV1 values to explain quantitative chest CT abnormalities, dyspnea, or Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification?Study Design and MethodsUsing 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 FEV1. 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.ResultsRace-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.InterpretationRace-specific equations offered no advantage over race-neutral/race-free percent predicted FEV1 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. 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. Does including self-identified race in spirometry reference equation formation improve the ability of predicted FEV1 values to explain quantitative chest CT abnormalities, dyspnea, or Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification? 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 FEV1. 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. 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. Race-specific equations offered no advantage over race-neutral/race-free percent predicted FEV1 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. Take-home PointsStudy Question: Does including self-identified race in the formation of spirometry reference equations improve the ability of predicted FEV1 values to explain quantitative chest CT abnormalities, dyspnea, or GOLD classification?Results: Race-neutral and race-free equations reclassified up to 19% of black smokers to worse GOLD classes in the COPDGene smoking cohort, with the greatest effects seen in individuals with mild smoking-related disease. The generated ppFEV1 values from race-neutral and race-free spirometry equations ","journal":"CHEST Journal","year":2023,"id":338644,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9493,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":946658,"name":"Barbara Bailey","orcid":null,"position":1,"is_corresponding":false},{"id":274766,"name":"Surya P. 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