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Area under the expiratory flow–volume curve (AEX): actual versus approximated values
  1. Octavian C Ioachimescu1,
  2. James K Stoller2
  1. 1 Medicine – Pulmonary, Allergy, Critical Care and Sleep Medicine, Atlanta VAMC, Emory University, School of Medicine, Decatur, Georgia, USA
  2. 2 Respiratory Institute, Cleveland Clinic Foundation, Cleveland, Ohio, USA
  1. Correspondence to Dr Octavian C Ioachimescu, Medicine- Pulmonary, Allergy, Critical Care and Sleep Medicine, Atlanta VAMC, Emory University, School of Medicine, Decatur, GA 30030, USA; oioac{at}


Previous work has shown that area under the expiratory flow–volume curve (AEX) performs well in diagnosing and stratifying respiratory physiologic impairment, thereby lessening the need to measure lung volumes. Extending this prior work, the current study assesses the accuracy and utility of several geometric approximations of AEX based on standard instantaneous flows. These approximations can be used in spirometry interpretation when actual AEX measurements are not available. We analysed 15 308 spirometry tests performed on subjects who underwent same-day lung volume assessments in the Pulmonary Function Laboratory. Diagnostic performance of four AEX approximations (AEX1–4) was compared with that of actual AEX. All four computations included forced vital capacity (FVC) and various instantaneous flows: AEX1 was derived from peak expiratoryflow (PEF); AEX2 from PEF and forced expiratoryflow at 50% FVC (FEF50); AEX3 from FVC, PEF, FEF at 25% FVC (FEF25) and at 75% FVC (FEF75), while AEX4 was computed from all four flows, PEF, FEF25, FEF50 and FEF75. Mean AEX, AEX1, AEX2, AEX3 and AEX4 were 6.6, 8.3, 6.7, 6.3 and 6.1 L2/s, respectively. All four approximations had strong correlations with AEX, that is, 0.95–0.99. Differences were the smallest for AEX–AEX4, with a mean of 0.52 (95% CI 0.51 to 0.54) and a SD of 0.75 (95% CI 0.74 to 0.76) L2/s. In the absence of AEX and in addition to the usual spirometric variables used for assessing functional impairments, parameters such as AEX4 can provide reasonable approximations of AEX and become useful new tools in future interpretative strategies.

  • lung function
  • spirometry
  • lung volumes
  • area under flow-volume curve
  • decision trees
  • forest bootstrap models
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  • Contributors Both authors contributed to the writing of this article; OCI contributed also with the statistical analysis.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Ethics approval Institutional research oversight approval was obtained to conduct the study (Cleveland Clinic IRB EX#0504).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Patient consent for publication Not required.

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