Presenter Status
Resident/Psychology Intern
Abstract Type
Research
Primary Mentor
Bridgette Jones, MD
Start Date
13-5-2025 12:30 PM
End Date
13-5-2025 12:45 PM
Presentation Type
Oral Presentation
Description
BACKGROUND: Spirometry is a widely used pulmonary function test that assesses lung function by examining airflow and capacity. Spirometry measurements assist in the diagnosis, severity classification, and management of asthma, and can affect one’s eligibility into various events (e.g., clinical trials, high-risk jobs, sports). Reference equations are used to establish predicted values to account for biological variables such as age, sex, and height; however past clinical guidelines recommended utilizing race-correction. The incorporation of race may negatively impact people of color due to misrepresentation of one's lung function, consequently impacting future medical management. OBJECTIVES: To characterize differences in spirometry values across prediction equations and to illustrate the impact of race-corrected values by a case scenario of an asthma clinical trial.
METHOD: We utilized spirometry data (FEV1, FVC, FEV/FVC) collected from 38 participants enrolled in a real asthma clinical trial and comparisons were made across equations using t-test for comparison of mean values and Chi-square test for comparison of the percent of participants eligible. Spirometry predicted values were calculated using race-corrected reference equations (POLGAR, GLI Race-Adjusted [GRA]) and a race-independent reference equation (GLI Race-Neutral [GRN]). Asthma inclusion criteria in the case scenario for the asthma clinical trial is defined as participants with a predicted FEV1% < 80%.
RESULTS: Among the 38 participants, 26% met inclusion criteria for the clinical trial based off the predicted POLGAR and GRA, however 37% met inclusion criteria with the predicted GRN. The average FEV1% was not significantly different between POLGAR compared to GRA or GRN independently (p=0.353 and p=0.416, respectively), however a greater distinction was noted specifically between GRA and GRN (26% vs. 37%, p=0.089). Black/African American participants were 50% more likely to meet inclusion criteria when using the predicted GRN compared to the predicted GRA (32% vs. 21%, p=0.028), as the GRN FEV1% values were significantly decreased compared to the GRA values (81.0 FEV1 % vs. 91.4 FEV1 %, p=0.028). There was no significant difference between the eligibility of White participants when utilizing GRN compared to GRA predicted values (5% vs. 5%, p=0.749).
CONCLUSION: Identifying a significant decrease in FEV1% predicted values for African American participants when utilizing the GLI Race-Neutral compared to Race-Adjusted algorithms demonstrates the falsely increased predicted values when incorporating race-correction into reference equations. This highlights the potential negative impacts of race correction. More recent guidelines should be followed for removal of race correction in spirometry measurement.
Included in
Higher Education and Teaching Commons, Medical Education Commons, Pediatrics Commons, Science and Mathematics Education Commons
The Impact of Race-Corrected Spirometry Results on Eligibility into an Asthma Clinical Trial
BACKGROUND: Spirometry is a widely used pulmonary function test that assesses lung function by examining airflow and capacity. Spirometry measurements assist in the diagnosis, severity classification, and management of asthma, and can affect one’s eligibility into various events (e.g., clinical trials, high-risk jobs, sports). Reference equations are used to establish predicted values to account for biological variables such as age, sex, and height; however past clinical guidelines recommended utilizing race-correction. The incorporation of race may negatively impact people of color due to misrepresentation of one's lung function, consequently impacting future medical management. OBJECTIVES: To characterize differences in spirometry values across prediction equations and to illustrate the impact of race-corrected values by a case scenario of an asthma clinical trial.
METHOD: We utilized spirometry data (FEV1, FVC, FEV/FVC) collected from 38 participants enrolled in a real asthma clinical trial and comparisons were made across equations using t-test for comparison of mean values and Chi-square test for comparison of the percent of participants eligible. Spirometry predicted values were calculated using race-corrected reference equations (POLGAR, GLI Race-Adjusted [GRA]) and a race-independent reference equation (GLI Race-Neutral [GRN]). Asthma inclusion criteria in the case scenario for the asthma clinical trial is defined as participants with a predicted FEV1% < 80%.
RESULTS: Among the 38 participants, 26% met inclusion criteria for the clinical trial based off the predicted POLGAR and GRA, however 37% met inclusion criteria with the predicted GRN. The average FEV1% was not significantly different between POLGAR compared to GRA or GRN independently (p=0.353 and p=0.416, respectively), however a greater distinction was noted specifically between GRA and GRN (26% vs. 37%, p=0.089). Black/African American participants were 50% more likely to meet inclusion criteria when using the predicted GRN compared to the predicted GRA (32% vs. 21%, p=0.028), as the GRN FEV1% values were significantly decreased compared to the GRA values (81.0 FEV1 % vs. 91.4 FEV1 %, p=0.028). There was no significant difference between the eligibility of White participants when utilizing GRN compared to GRA predicted values (5% vs. 5%, p=0.749).
CONCLUSION: Identifying a significant decrease in FEV1% predicted values for African American participants when utilizing the GLI Race-Neutral compared to Race-Adjusted algorithms demonstrates the falsely increased predicted values when incorporating race-correction into reference equations. This highlights the potential negative impacts of race correction. More recent guidelines should be followed for removal of race correction in spirometry measurement.
