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Lower Limits of Normal for Spirometric Reference Values

  • Robert O. Crapo
  • Reed M. Gardner
  • Michael L. Beus

Abstract

Since spirometry was introduced by Hutchinson in 18461, numerous studies of asymptomatic volunteers have been published to provide equations that predict reference values. Most prediction equations are linear regressions using age and height as the independent variables2–6. Non-linear prediction equations have also been proposed7–8,9. There are presently five studies of lifetime smokers that should provide reasonable estimates of reference values for Caucasian patients 3–6,7. There is less certainty about other ethnic groups. Despite the plethora of studies allowing the prediction of average reference values there is considerable uncertainty about how these values can be used to establish if the measured value in a patient is normal or abnormal. Published reference studies do not consistently address the issue of establishing the “normal” limits of their prediction equations and the clinician is, therefore, frequently left to select a method for defining “abnormal”. Additional uncertainty exists because there is little information available that defines the ranges of spirometric values for healthy subjects and for different pulmonary disease states. Ideally one needs information about the distribution of test values for both reference and abnormal categories to use the test to separate the two groups10,11. For most obstructive lung diseases, there is no currently accepted non-spirometric method of defining the disease, especially in the early stages, making it unlikely that the distribution of spirometric values for abnormal categories will be available in the near future. We are left with predicting reference values, establishing some type of lower limit of normal and calling patients with measured values outside the limits “abnormal”. If limits are set using 95th percentile or 95% confidence limits, we arbitrarily choose to classify 5% of healthy people as abnormal (alpha error)12. In the absence of information about the distribution of spirometric values in disease states, we can say little about the number of abnormal subjects classified as normal (beta error)12.

Keywords

Prediction Equation Obstructive Lung Disease Lifetime Smoker Moderate Obstruction Abnormal Subject 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1983

Authors and Affiliations

  • Robert O. Crapo
    • 1
  • Reed M. Gardner
    • 1
  • Michael L. Beus
    • 1
  1. 1.Departments of Medicine and Medical Biophysics and ComputingLDS Hospital and University of UtahSalt Lake CityUSA

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