TY - JOUR T1 - Respiratory Impedance Measured using Impulse Oscillometry in a Healthy Urban Population JF - ERJ Open Research JO - erjor DO - 10.1183/23120541.00560-2020 SP - 00560-2020 AU - Kenneth I. Berger AU - Margaret Wohlleber AU - Roberta M. Goldring AU - Joan Reibman AU - Mark R. Farfel AU - Stephen M. Friedman AU - Beno W. Oppenheimer AU - Steven D. Stellman AU - James E. Cone AU - Yongzhao Shao Y1 - 2021/01/01 UR - http://openres.ersjournals.com/content/early/2020/11/26/23120541.00560-2020.abstract N2 - This study derives normative prediction equations for respiratory impedance in a healthy asymptomatic urban population using an impulse oscillation system (IOS). In addition, this study uses body mass index (BMI) in the equations to describe the effect of obesity on respiratory impedance.Data from an urban population comprising 472 healthy asymptomatic subjects that resided or worked in lower Manhattan, New York City were retrospectively analysed. This population was the control group from a previously completed case-control study of the health effects of exposure to World Trade Center dust. Since all subjects underwent spirometry and oscillometry, these previously collected data allowed a unique opportunity to derive normative prediction equations for oscillometry in an urban, lifetime non-smoking, asymptomatic population without underlying respiratory disease.Normative prediction equations for men and women were successfully developed for a broad range of respiratory oscillometry variables with narrow confidence bands. Models that used BMI as an independent predictor of oscillometry variables (in addition to age and height) demonstrated equivalent or better fit when compared with models that used weight. With increasing BMI, resistance and reactance increased compatible with lung and airway compression from mass loading.This study represents the largest cohort of healthy urban subjects assessed with an IOS device. Normative prediction equations were derived that should facilitate application of IOS in the clinical setting. In addition, the data suggest that modelling of lung function may be best performed using height and BMI as independent variables rather than the traditional approach of using height and weight.FootnotesThis manuscript has recently been accepted for publication in the ERJ Open Research. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJOR online. Please open or download the PDF to view this article.Conflict of interest: Dr. Berger reports grants from CDC/NIOSH, during the conduct of the study.Conflict of interest: Dr. Wohlleber has nothing to disclose.Conflict of interest: Dr. Goldring has nothing to disclose.Conflict of interest: JR has received funding as a consultant for AstraZeneca, Genentech and Novartis. She has also been a recipient of grant and contract funding from the Centers of Disease Control, NIOSHConflict of interest: Dr. Farfel has nothing to disclose.Conflict of interest: Dr. Friedman reports grants from CDC/NIOSH, during the conduct of the study.Conflict of interest: Dr. Oppenheimer has nothing to disclose.Conflict of interest: Dr. Stellman has nothing to disclose.Conflict of interest: Dr. Cone reports grants from NIOSH CDC, during the conduct of the study.Conflict of interest: Dr. Shao reports grants from National Institute of Occupational Safety and Health, grants from National Institute of Environmental Health Science, during the conduct of the study. ER -