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 showed no significant improvement in model fit compared with dyspnea or quantitative chest CT phenotypes (emphysema, air trapping, airway wall thickness).Interpretation: Race-neutral/free reference equations may improve pulmonary disease diagnoses and treatment in populations highly vulnerable to lung disease relative to race-specific equations. Study 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 showed no significant improvement in model fit compared with dyspnea or quantitative chest CT phenotypes (emphysema, air trapping, airway wall thickness). Interpretation: Race-neutral/free reference equations may improve pulmonary disease diagnoses and treatment in populations highly vulnerable to lung disease relative to race-specific equations. Interpretation of spirometry results has traditionally relied upon reference equations to provide an estimate of “normal” lung function for an individual’s age, gender, height—and controversially—race/ethnicity. These equations are used for clinical, research, and occupational purposes to diagnose pulmonary disease, assess disease progression, and explain radiographic abnormalities, as well as determine disability and evaluate fitness for higher risk jobs, and thus have enormous clinical and financial importance. The inclusion of race in these equations is based on large cross-sectional, population-wide studies that consistently show lower measures of lung function for some racial/ethnic minority groups, specifically up to 10% to 15% lower FEV1 for black individuals.1Hankinson J.L. Odencrantz J.R. Fedan K.B. Spirometric reference values from a sample of the general US population.Am J Respir Crit Care Med. 1999; 159: 179-187Crossref PubMed Scopus (3426) Google Scholar,2Quanjer P.H. Stanojevic S. Cole T.J. et al.Multi-ethnic reference values for spirometry for the 3-95-yr age range: the Global Lung Function 2012 equations.Eur Respir J. 2012; 40: 1324-1343Crossref PubMed Scopus (3593) Google Scholar However, the clinical value of race adjustments has increasingly been questioned.3Anderson M.A. Malhotra A. Non A.L. Could routine race-adjustment of spirometers exacerbate racial disparities in COVID-19 recovery?.Lancet Respir Med. 2021; 9: 124-125Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar, 4Bhakta N.R. Kaminsky D.A. Bime C. et al.Addressing race in pulmonary function testing by aligning intent and evidence with practice and perception.Chest. 2022; 161: 288-297Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar, 5Braun L. Breathing Race into the Machine: The Surprising Career of the Spirometer from Plantation to Genetics. University of Minnesota Press, Minneapolis, MN2014Crossref Google Scholar, 6Beaverson S, Ngo VM, Pahuja M, Dow A, Nana-Sinkam P, Schefft M. Things We Do for No ReasonTM: race adjustments in calculating lung function from spirometry measurements [published online ahead of print October 22, 2022]. J Hosp Med. https://doi.org/10.1002/jhm.12974.Google Scholar Although recent studies found no prognostic benefit of race-specific equations compared with “race-neutral” equations in mortality or respiratory events,7Baugh A.D. Shiboski S. Hansel N.N. et al.Reconsidering the utility of race-specific lung function prediction equations.Am J Respir Crit Care Med. 2022; 205: 819-829Crossref PubMed Scopus (32) Google Scholar, 8Elmaleh-Sachs A. Balte P. Oelsner E.C. et al.Race/ethnicity, spirometry reference equations and prediction of incident clinical events: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study.Am J Respir Crit Care Med. 2022; 205: 700-710Crossref PubMed Google Scholar, 9McCormack M.C. Balasubramanian A. Matsui E.C. Peng R.D. Wise R.A. lung and mortality in the National Health and Nutrition Survey J Respir Crit Care Med. 2022; 205: PubMed Google Scholar, et racial in adults with a data analysis of the Lung Med. 2022; PubMed Scopus Google Scholar, M. Race-specific reference values and lung function and analysis of 2022; PubMed Scopus Google Scholar to the of race in prediction and reference equations for J Respir Crit Care Med. 2022; PubMed Scopus Google M.C. US occupational on race and lung J Respir Crit Care Med. 2022; PubMed Scopus Google Scholar Race-specific equations are by the recent US and S. Kaminsky D.A. et on for routine lung function Respir J. 2022; Scopus Google Scholar and are used in clinical and pulmonary However, race-specific equations may or lung minority C. C. of and respiratory to lung function and from PubMed Scopus Google Scholar, of in with in the Study.Am J PubMed Scopus Google Scholar, of pulmonary J Med. PubMed Scopus Google Scholar and in risk of respiratory S. in a sample of gender, and J Chronic Google Scholar, S. S. S. and respiratory function from to a and Scopus Google Scholar, et and disparities are in of Google Scholar, The of Race on the Interpretation of Function of and on with Lung Google Scholar racial We how the percent predicted FEV1 values from race-neutral, and race-free reference equations pulmonary phenotypes in large of a sample of healthy never-smoking adults from both National Health and Nutrition Survey (2007-2012) and COPDGene generated race-free equations that race from model formation and race-specific prediction equations for FEV1 and these equations were compared with the Global Lung Initiative race-specific P.H. Stanojevic S. Cole T.J. et al.Multi-ethnic reference values for spirometry for the 3-95-yr age range: the Global Lung Function 2012 equations.Eur Respir J. 2012; 40: 1324-1343Crossref PubMed Scopus (3593) Google Scholar the race-specific equations of et J.L. Odencrantz J.R. Fedan K.B. Spirometric reference values from a sample of the general US population.Am J Respir Crit Care Med. 1999; 159: 179-187Crossref PubMed Scopus (3426) Google Scholar and the race-neutral a and the racial in the reference P.H. Stanojevic S. Cole T.J. et al.Multi-ethnic reference values for spirometry for the 3-95-yr age range: the Global Lung Function 2012 equations.Eur Respir J. 2012; 40: 1324-1343Crossref PubMed Scopus (3593) Google Scholar applied these spirometry prediction equations and how they the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification in both the and COPDGene smoking and model quantitative chest CT phenotypes and dyspnea in the COPDGene study intent to how the reference equations model pulmonary on formation and of and smoking are in to and of the and COPDGene by of reference equations. The equations for individuals were used to estimate ppFEV1 for the and groups, studies The FEV1 values for the used the equation used to race-neutral of ppFEV1 for racial/ethnic of are as Global Lung National Health and Nutrition airway wall estimate based on of wall of a et wall with in and of Respir J. PubMed Scopus Google ppFEV1 percent predicted quantitative chest significant relative to to the for of black and racial/ethnic in and analysis of with for racial/ethnic relative to participants in the of reference equations. The equations for individuals were used to estimate ppFEV1 for the and groups, studies The FEV1 values for the used the The equation used to race-neutral of ppFEV1 for racial/ethnic in a of are as Global Lung National Health and Nutrition airway wall estimate based on of wall of a et wall with in and of Respir J. PubMed Scopus Google ppFEV1 percent predicted quantitative chest significant relative to to the for of black and racial/ethnic in and analysis of with for racial/ethnic relative to participants in the The predicted FEV1 and lower of values for that from the equations were the predicted by et J.L. Odencrantz J.R. Fedan K.B. Spirometric reference values from a sample of the general US population.Am J Respir Crit Care Med. 1999; 159: 179-187Crossref PubMed Scopus (3426) Google Scholar and values were by equations. both never-smoking data data of healthy individuals and the COPDGene data of healthy multivariable used to predicted and models and values The value is a of of the data in and is used to of fit and for 1999; Scopus Google Scholar in the race-specific equations age and self-identified in race-free equations age, and used to models for predicted and values for generated race-specific and race-neutral/race-free models for the predicted and from populations and of the the predicted race-neutral, and race-free models were used to the of race in the of and for and COPDGene in in equation used to model the predicted is as predicted lower of FEV1 both cohorts, race is as black relative to as the reference data from the data of healthy race is with as the reference and racial/ethnic were as and by the race both cohorts, for is and for The of FEV1 values were used for data and for COPDGene in used the compared with FEV1 models including age, gender, and models including age, and National Health and Nutrition in a of and for FEV1 FEV1 by M.A. Malhotra A. Non A.L. Could routine race-adjustment of spirometers exacerbate racial disparities in COVID-19 recovery?.Lancet Respir Med. 2021; 9: 124-125Abstract Full Text Full Text PDF PubMed Scopus (18) Google P.H. Stanojevic S. Cole T.J. et al.Multi-ethnic reference values for spirometry for the 3-95-yr age range: the Global Lung Function 2012 equations.Eur Respir J. 2012; 40: 1324-1343Crossref PubMed Scopus (3593) Google healthy by and adjustments the adjustments for the groups, over and M.A. Malhotra A. Non A.L. Could routine race-adjustment of spirometers exacerbate racial disparities in COVID-19 recovery?.Lancet Respir Med. 2021; 9: 124-125Abstract Full Text Full Text PDF PubMed Scopus (18) Google an applied for of the racial in the data D.A. S. A.L. in and lung function in a PubMed Scopus Google healthy healthy models and populations used to of the race-neutral, and race-free models used in and equations are race-neutral, but are race-free, as they racial/ethnic age, gender, and age, gender, and COPDGene data of healthy for and Global Lung National Health and Nutrition Survey data of healthy National Health and Nutrition quantitative by and adjustments the adjustments for the groups, over and an applied for of the racial in the data in a The equation used to model the predicted is as predicted lower of FEV1 both cohorts, race is as black relative to as the reference data from the data of healthy race is with as the reference and racial/ethnic were as and by the race both cohorts, for is and for The of FEV1 values were used for data and for COPDGene in used the compared with FEV1 models including age, gender, and models including age, and National Health and Nutrition models and populations used to of the race-neutral, and race-free models used in and equations are race-neutral, but are race-free, as they racial/ethnic age, gender, and age, gender, and COPDGene data of healthy for and Global Lung National Health and Nutrition Survey data of healthy National Health and Nutrition quantitative in both smoking a GOLD spirometry the spirometry A. et GOLD to J Respir Crit Care Med. 2021; PubMed Scopus Google Scholar or GOLD and ppFEV1 the race-specific and race-neutral The of individuals GOLD from the in the data and racial for both former smokers and COPDGene smoking FEV1 values were as or the to assess the and of ppFEV1 reference equation to model chest CT phenotypes in COPDGene CT phenotypes were as the percent the percent air or the airway wall estimate based on of wall of a A. et the of pulmonary PubMed Scopus Google Scholar The and the under the were for model in the and the ability of the of model to a dyspnea and and and the ppFEV1 quantitative chest CT scan J. et of the and in healthy 2012; PubMed Scopus Google et in the percent of lung in a The Lung PubMed Scopus Google Scholar multivariable logistic models were generated of chest CT phenotypes these and the ppFEV1 values from of the race-neutral and race-specific equations were compared by and used to model dyspnea and of smoking age, lung from the CT J. et of the and in healthy 2012; PubMed Scopus Google Scholar and the of FEV1 and ppFEV1 values from the race-neutral and race-specific equations The random forest used to models of the chest CT phenotypes and dyspnea the as the logistic models The classification were compared to assess model the race-specific and race-neutral ppFEV1 used in the for and The never-smoking healthy of and 10% racial or to black participants but a higher lower and racial/ethnic a lower FEV1 and and a higher compared with and individuals higher race-specific ppFEV1 values the The COPDGene healthy of black and to black participants were lower FEV1 and and higher with J.L. Odencrantz J.R. Fedan K.B. Spirometric reference values from a sample of the general US population.Am J Respir Crit Care Med. 1999; 159: 179-187Crossref PubMed Scopus (3426) Google A. Balte P. Oelsner E.C. et al.Race/ethnicity, spirometry reference equations and prediction of incident clinical events: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study.Am J Respir Crit Care Med. 2022; 205: 700-710Crossref PubMed Google Scholar participants higher airway wall estimate based on the of wall of a and lower percent relative to participants with the never-smoking cohort, the COPDGene healthy participants were and but they were in and spirometry with the COPDGene smoking cohort, the with higher and higher FEV1 and severe smoking-related disease a predicted values generated race-specific equations for FEV1 and for of the the FEV1 values generated the race-specific equations show in the predicted FEV1 values black and populations These show the of the generated healthy data and the race-neutral equations generated higher predicted FEV1 and values race-specific equations for the black participants but or to lower values in the participants of both never-smoking models generated higher predicted FEV1 and values of were no racial in the race-neutral equations. race in the FEV1 prediction equations model fit as by higher values in both healthy but in FEV1 and black individuals in both healthy populations a seen and race populations in the Using measurements of improve model fit or racial The ppFEV1 value generated from the race-specific equations were of the generated race-specific equations for the COPDGene smoking the race-specific and race-neutral/race-free ppFEV1 values in both smoking with the black to a lower ppFEV1 by an of to the in the in higher ppFEV1 to values seen in the healthy never-smoking cohort, the race-free equations generated lower ppFEV1 values race-neutral equations in COPDGene smokers the effects of the ppFEV1 equations on GOLD the of individuals were reclassified the race-specific equations compared with the race-specific equations. the smoking cohorts, the GOLD on to the ppFEV1 values race-specific reference equations were from by race-specific reference equations to race-neutral and race-free an of of black participants in the smoking were reclassified to worse GOLD the more COPDGene of the black participants were reclassified to worse GOLD classes race-neutral/race-free equations. of these GOLD were from GOLD ppFEV1 and to the spirometry distinct models were used to the utility of the ppFEV1 and values from race-specific and race-neutral equations to model chest CT phenotypes and dyspnea The and of ppFEV1 reference equation to model chest CT scan were the equations generated and thus analysis showed no advantage of the race-specific equations over race-neutral equations to model chest CT scan The multivariable logistic models of chest CT phenotypes showed and values the race-specific and race-neutral equations in the COPDGene as well as in the black and a random forest used to model the of chest CT in the COPDGene equations in the and and they of the quantitative chest CT were no in the race-specific equations offered significant in classification over the race-neutral or race-free equations from models dyspnea to the models of quantitative chest CT the sensitivity/specificity, and values were race-specific and race-neutral/race-free equations and the and values of the ppFEV1 models were higher in relative to black COPDGene of race-specific or race-neutral/race-free ppFEV1 models were used The values from the multivariable logistic models of dyspnea were lower the race-neutral or race-free race-specific ppFEV1 values in the COPDGene model but the clinical of is classification in the random forest models were of race-neutral/race-free race-specific no advantage in predicting dyspnea race-specific equations in the COPDGene or in to race The in model fit race-free equations is the predicted FEV1 in and of healthy populations study the of race-neutral, and race-free ppFEV1 reference equations in disease and pulmonary Using three show that race-specific equations no advantage relative to race-neutral or race-free equations in quantitative chest CT phenotypes or dyspnea in smoking found compared with race-specific race-neutral/race-free equations reclassified black participants into more severe GOLD classes up to and they may improve models of dyspnea. of race-neutral/race-free equations may in pulmonary disease as well as more treatment in populations highly vulnerable to lung disease. are in with recent studies that found no prognostic benefit of race-specific over race-neutral spirometry reference A.D. 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