Respiratory care · 2026
Predictors of Respiratory Oscillometry Measurements in a Healthy Population.
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Holley Aaron B, Watson Nora L, Kuenstler Molly R, Fabyan Kimberly D, Gonzales Michael A, Hayes Jackie A, Morris Michael J
Аннотация
BACKGROUND: The factors that determine variability in impulse oscillometry (IOS) are not well defined.
METHODS: We used IOS data from a well-screened population of active-duty service members (ADSMs) cleared for deployment (STAMPEDE II cohort) to identify variables independently associated with IOS measurements. We constructed our own predictive models and compared them with existing reference equations when applied to postdeployment STAMPEDE II subjects and two additional ADSM IOS datasets.
RESULTS: There were 775 STAMPEDE II subjects without a history of respiratory symptoms, tobacco exposure, or lung disease (32.0 ± 9.0 years old, BMI = 26.8 ± 3.5, 16.4% female, 57.3% white) predeployment. Age, height, weight, sex, self-reported race/ethnicity, and military rank (a surrogate for socioeconomic status) in various combinations were significantly associated with the individual measures (R5, R20, X5, fres, and AX) comprising impedance. Existing equations universally predicted lower impedance when applied to the 775 subjects from STAMPEDE II. External validation with postdeployment STAMPEDE II subjects and non-STAMPEDE II ADSM datasets showed our derived equations over-estimated while existing equations under-estimated IOS measurements. The degree of respective over and under-estimation was similar in magnitude but varied across IOS variables and between external datasets.
CONCLUSIONS: In a well-screened ADSM population, we found OS measurements were higher than predicted by existing equations. Our models suggest differences in predicted values were driven, at least in part, by the demographic characteristics (race and military rank) of the underlying derivation populations.
METHODS: We used IOS data from a well-screened population of active-duty service members (ADSMs) cleared for deployment (STAMPEDE II cohort) to identify variables independently associated with IOS measurements. We constructed our own predictive models and compared them with existing reference equations when applied to postdeployment STAMPEDE II subjects and two additional ADSM IOS datasets.
RESULTS: There were 775 STAMPEDE II subjects without a history of respiratory symptoms, tobacco exposure, or lung disease (32.0 ± 9.0 years old, BMI = 26.8 ± 3.5, 16.4% female, 57.3% white) predeployment. Age, height, weight, sex, self-reported race/ethnicity, and military rank (a surrogate for socioeconomic status) in various combinations were significantly associated with the individual measures (R5, R20, X5, fres, and AX) comprising impedance. Existing equations universally predicted lower impedance when applied to the 775 subjects from STAMPEDE II. External validation with postdeployment STAMPEDE II subjects and non-STAMPEDE II ADSM datasets showed our derived equations over-estimated while existing equations under-estimated IOS measurements. The degree of respective over and under-estimation was similar in magnitude but varied across IOS variables and between external datasets.
CONCLUSIONS: In a well-screened ADSM population, we found OS measurements were higher than predicted by existing equations. Our models suggest differences in predicted values were driven, at least in part, by the demographic characteristics (race and military rank) of the underlying derivation populations.