The role of reference values in interpreting lung function tests.

The paper by GARCIA-RIO et al. [1] in this issue of the European Respiratory Journal provides good reference values for an under-represented population and an opportunity to review the role of reference values in interpreting lung function tests. The interpretation of all observed data involves comparisons with one or more types of reference data. In clinical medicine, the comparisons usually involve reference data from those with and without relevant diseases [2–4]. They may also be based on studies relating a clinical measurement to risk of disease. An elevated cholesterol level might simultaneously indicate an increased risk of cardio-vascular disease and fall within the range of values obtained on individuals who met health criteria at the time of a study. Observed data in medicine can come from many sources: clinical information from a patient interview, physical examination and laboratory data (including pulmonary function tests). Reference comparisons usually start with a comparison to data from individuals without relevant diseases ("normal" or healthy subject values). If a patient9s data fall outside an appropriate reference range (are not "normal"), the next comparisons are to data from individuals with relevant diseases (disease patterns). The comparisons can be made in a variety of ways: intuitive (based on clinical experience), knowledge or uncertainty (e.g. with validated evidence-based criteria), formal algorithms, or reasoning based on knowledge of anatomy and physiology [3]. Whatever the question and whatever the data, the analysis of observed data always involves relating it to reference data [2–4]. A simple example would be a patient who presents to a physician with symptoms that lead the physician to order pulmonary function tests. In that process, the observed data are the patient symptoms and the physical findings which, compared to the physician9s knowledge of and experience with healthy subjects, suggest the patient may not be healthy and, moreover, that the pattern suggests a lung problem. Those original steps involve several reference comparisons, leading to a beginning differential diagnosis. Should the pulmonary function test results not fit into the distribution or reference range for healthy subjects, an additional series of disease pattern comparisons will further characterise the patient. For pulmonary diseases these patterns include asthma, central airway obstruction, chronic obstructive pulmonary disease and interstitial lung disease. However, this process is rarely a formal one. The starting point for the interpretation of lung function tests is comparing the measured values with average values from a representative sample of healthy …