ABSTRACT
BACKGROUND: Race-specific spirometry reference equations are used globally to interpret lung function for clinical, research, and occupational 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 FEV1 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 adults who have never smoked 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 individuals who currently smoke and individuals who formerly smoked 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, while 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 equations 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.
Subject(s)
Lung Diseases , Pulmonary Disease, Chronic Obstructive , Adult , Humans , Cross-Sectional Studies , Dyspnea/diagnosis , Forced Expiratory Volume , Lung/diagnostic imaging , Pulmonary Disease, Chronic Obstructive/diagnosis , Reference Values , Spirometry , Tomography, X-Ray Computed , Vital Capacity , SmokingABSTRACT
A robust algorithm is presented for labeling rows and columns in an irregular array. The algorithm is based on hierarchical pattern matching to a local lattice, which is used as a template. Starting from the best local match, the pattern is expanded hierarchically to encompass the entire array. An application to labeling digitized images of an array of tissue sections mounted on a microscope slide is discussed.