Impact of different fixed flow sampling protocols on flow‐independent exhaled nitric oxide parameter estimates using the Bayesian dynamic two‐compartment model. Issue 1 (21st January 2020)

• Record Type: Journal Article
• Authors: Muchmore, Patrick; Xu, Shujing; Marjoram, Paul; Rappaport, Edward B.; Weng, Jingying; Molshatzki, Noa; Eckel, Sandrah P.
• Is Part Of: Physiological reports. Volume 8:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-21
• Subjects: Bayesian inference -- exhaled breath -- FeNO -- sampling protocol; Physiology -- Periodicals; 571
• DOI: 10.14814/phy2.14336
• Languages: English
• ISSNs: 2051-817X

Abstract

Abstract: Exhaled nitric oxide (FeNO) is an established respiratory biomarker with clinical applications in the diagnosis and management of asthma. Because FeNO depends strongly on the flow (exhalation) rate, early protocols specified that measurements should be taken when subjects exhaled at a fixed rate of 50 ml/s. Subsequently, multiple flow (or "extended") protocols were introduced which measure FeNO across a range of fixed flow rates, allowing estimation of parameters including Caw NO and CA NO which partition the physiological sources of NO into proximal airway wall tissue and distal alveolar regions (respectively). A recently developed dynamic model of FeNO uses flow‐concentration data from the entire exhalation maneuver rather than plateau means, permitting estimation of Caw NO and CA NO from a wide variety of protocols. In this paper, we use a simulation study to compare Caw NO and CA NO estimation from a variety of fixed flow protocols, including: single maneuvers (30, 50, 100, or 300 ml/s) and three established multiple maneuver protocols. We quantify the improved precision with multiple maneuvers and the importance of low flow maneuvers in estimating Caw NO. We conclude by applying the dynamic model to FeNO data from 100 participants of the Southern California Children's Health Study, establishing the feasibility of using the dynamic method to reanalyze archived online FeNO data and extract new information on Caw NO and CA NO in situations where these estimates would have been impossible to obtain using traditional steady‐state two compartment model estimation methods. Abstract : A recently developed Bayesian dynamic two‐compartment model of FeNO uses flow‐concentration data from the entire exhalation, which permits estimation of CawNO and CANO from a wide variety of protocols. We compare estimation under single and multiple fixed expiratory flow protocols to demonstrate the gains in precision of multiple flow protocols and the feasibility of estimation with multiple 50 ml/s maneuvers informing, respectively, future study designs and the re‐analysis of archived online FeNO data.