Do the Global Lung Function Initiative 2012 equations fit my population?

Abstract

Knowing whether a patient's lung function result is similar to what can be expected of a healthy individual is critical for the correct interpretation of pulmonary function test results. Since lung function changes with growth and ageing and differs according to sex and ethnicity, there are now >300 published reference equations available for spirometry alone. Consequently, individual pulmonary function laboratories are left with a challenging decision: which reference equation do I choose? While it is recommended that reference equations are population specific, there are obvious logistical challenges to collect normative data from a large and representative population such that the resulting range of normal values are not biased. Alternatively, laboratories can choose to use a published reference equation derived from a population similar to the one being tested. Several studies have shown that not all lung function reference equations produce the same results [1-3]. For instance, some equations are based on smaller samples of the population, while others are based on larger and representative population surveys. In some studies, spirometry curves are reviewed rigorously for quality control, while in others they are not. Some data are collected on antiquated equipment no longer available today, while others contain novel outcome measures. Furthermore, within each commercial spirometer, users have an overwhelming number of options: to use a single reference equation, or multiple reference equations combined into a single prediction set. The "stitching" of different equations results in artificial jumps in predicted values that could lead to erroneous interpretation of results and unnecessary intervention [2, 4, 5]. Most importantly, often the physicians interpreting results are unaware of which equations were used, and how the choice of equation might affect their interpretation [6-8]. Another common practice is to extrapolate reference equations beyond the age range of the intended age range, which has also been shown to bias interpretation of results [9].

The multi‐ethnic global lung initiative 2012 (GLI-2012) norms reflect contemporary adult's Algerian spirometry

“…Their outcomes are habitually reported as percentage predicted where predicted data are acquired from a healthy non-smoker norm population [4–6] . Nevertheless, predicted normal data from diverse sources may change widely, and as the variability of tests fluctuates with “time of life”, the use of percentage predicted leads to an age bias [7–9] . The age bias can be avoided by the use of sex, age, height and ethnicity specific Z-score [10] .…”

“…Furthermore, the methodology for performing spirometric tests ( eg ; protocol and equipment) must be stringent [4, 25] . The external validation of the GLI-2012 norms is recommended [9, 10] and further evaluations of applicability from other parts of the world (particularly the Arab one) are required in order to verify the appropriateness in these areas. Hitherto, there is no publication evaluating the applicability of the GLI-2012 norms for Algerian adults.…”

Choosing and using lung function prediction equations

“…In this issue of the ERJ, QUANJER and STANOJEVIC [6] have also outlined some reasons as to why local populations may appear to give an imperfect fit for the GLI-2012 predictions. The strength of GLI is that…”

“…How patients' results are then used in relation to their predicted values is where there is potential scope for more error than that related to the choice of equations. QUANJER and STANOJEVIC [6] reiterate that using "percentage of predicted" for determining if a result is unusual is completely unacceptable, because the method retains age, sex and size bias. The lower limit of normal (LLN) should be used to decide if a result is unusual and the LLN has been taken to be the lower fifth percentile (z-score < −1.645) for subjects referred for lung function testing [8].…”

“…European and North American populations are well represented by GLI and so the need for further large sample validation of these populations is not a pressing issue. However, QUANJER and STANOJEVIC [6] indicate that the large data resource behind the GLI project needs additional data of satisfactory quality from under-represented populations, for example from India, Africa and Latin America to name just three, in order to be sure that these populations are better able to be assessed in the future. A further difficulty is obtaining adequate sampling of representative people of advanced age with normal lungs; with the ever-increasing numbers of people surviving to advanced age, this continues to be an area of uncertainty that needs addressing [7].…”