Interpretation of spirometry is based upon a comparison to the “normal range” which is based on a large sample with similar characteristics to the population being measured. Consequently, reference equations have been derived for many populations. Each set of equations has had its own strengths and limitations. Below we summarise some of the key equations that have been available and a brief summary of how reference equations have evolved over the decades.
European Coal and Steel Community Equations
The sets of reference values issued by the ECSC [3] were based on males working in coal mines and steel works. This was not a representative reference population, and in practice the predicted values were deemed to be too high. Even though no women had been tested, the ECSC issued reference values for females: they were 80% of the values for males. In 1983 the ECSC declined allocating funds for a population study to derive reference values obtained with methods that complied with the latest standards. With a view to combining technical recommendations with appropriate prediction equations, and because no material was available that had been obtained with appropriate techniques, for lack of better alternatives the standardisation committee decided to adopt the technique previously applied by Polgar [4] when deriving reference equations for children. This entailed the generation of a set of predicted values for age, height and sex using published prediction equations, and using this artificially generated set to derive new regression equations.
Combining original data to derive new regression equations:
An alternative that the ECSC standardisation is to collate available good quality measurements, complying with temporal recommended standards. The first use of collated datasets for deriving predicted values for children was based on 6 data sets from 5 European countries.(5) This study showed that the resulting reference values fit 5 of the 6 data sets; it transpired that the sixth set had been affected by a technical problem. Thus this approach was validated; it led to recommending the American Thoracic Society (ATS) and European Respiratory Society (ERS) to support this technique with a view to deriving reference values based on large groups with a wide age range [4].
Origins of the Global Lung Function Initiative
In 2006 Phillip Quanjer started to collect existing normative datasets to cover as large an age range as possible as well as various ethnic groups, and by 2008 he had accumulated over 30,000 records had been generously made available from all over the world. The pioneering work of Stanojevic et al. [1] which allowed appropriate equations to be developed gave rise to the Global Lung Function Initiative, a Task Force of the European Respiratory Society and American Thoracic Society (insert link to GLI page). Over the next 5 years, more than 150 000 data points in healthy individuals were collated and shared with the GLI.
While the analytical work was performed by the analytical team
the collaborative work of the GLI was only possible with the effective and friendly cooperation, based on mutual respect and trust, with some 70 groups from all over the globe.
References
For more information, go to: lungfunction.org