When it comes to spirometry, it’s really all about FEV1. FVC and the FEV1/FVC ratio are also important of course, but because FVC is more likely to be underestimated than FEV1 they are less reliable.
Changes in FEV1 are critical in monitoring airway disease. The recent ATS guidelines on Occupational Spirometry indicate that a 15% decrease (adjusted for changes in age) is significant and cause for concern. For diagnosing airways disease however, it is important to know what a normal FEV1 is.
I have been able to find twenty-four different reference equations for FEV1. That’s good in one sense but that quantity also makes it that much more difficult to determine which reference equations should be used. When I graph results it often becomes more apparent what the equations are trying to tell us but in this case I came away a bit more confused instead.
After gender, height and age, ethnicity is considered to be a critical factor in determining normal values. For this reason, I re-graphed the FEV1 results and (somewhat arbitrarily) clustered the different ethnicities based (mostly) on their geographic location.
What I saw was a great deal of overlap between ethnicities and a great deal of variance within ethnicities.
Ethnicity may be important but the more closely you look at ethnicity the less apparent it becomes as to what it actually means. The primary problem with ethnicity and reference equations revolves around the use of standing height as a metric for lung volume.
Ideally, we would like to know what an individual’s lung function “should” be if they were in perfect health. An individual’s “ideal” FEV1 will depend on lung volume and airway caliber, neither of which can be determined with any precision from simple external measurements. There are a number of proportionalities however, between different parts of the human anatomy that are fairly constant across a broad range of human heights and the volume of the thorax is one of these. This relationship was first noted by John Hutchinson over 150 years ago and has been used ever since. The proportionality is not exact, of course, since there is approximately a 30% range in FEV1 across all ethnicities (at least based on these reference equations there is).
Given the way humans have settled over the earth throughout history, it is not surprising that there would be clustering in genes, diet and environment that would cause local populations to have similar relationships between height and FEV1 (and the other lung function values). Historically this has been taken by researchers as a genetic issue, most particularly the distinction between whites and blacks. I (and many others) strongly suspect that diet and environment have as much, if not much greater, effect. Since not only our genes but both diet and environment play a large role in the development and maturity of the human body as well I guess we should be amazed that the range is only 30%.
But 30% is a pretty broad range and can make the difference between calling an FEV1 either normal or abnormal for a very large number of people. Although we categorize these differences under the heading of “ethnicity” what this really means is that there are factors other than height that predict lung volume and airway caliber and we don’t know what they are.
Every lab should choose reference equations that are appropriate to the population they serve but there are no real guidelines on determining this. Probably all too often labs leave their test equipment software set to the manufacturer’s default settings, partly, perhaps in the belief that the equipment manufacturer knows better than they do what reference equations should be used.
There are several factors that I think would help a lab and interpreting physician select reference equations. Although ethnicity should be a consideration it is a slippery concept and frequently lab populations don’t fit neatly into any specific group. A far more important factor should be, I think, the size of the study group. Larger is better and this is because small groups are more prone to selection effects. Next would be good representation of all age groups, particularly the young and the elderly.
The last factor is good statistics. This is not an area that I am particularly qualified to judge, but I’ve seen that the studies with the largest populations and most sophisticated statistical analysis (ie., NHANESIII) show that the decline in FEV1 (and other lung function values) is not linear but accelerates with age. Given the aging of the populations we serve I think this is a critical factor and that reference equations that don’t show this may be causing a mis-interpretation of test results in the elderly.
The apparent rates of decline in FEV1 should be taken with a grain of salt. These are from static population studies and not longitudinal studies. Having said that the ATS Statement on Occupational Spirometry indicates that a normal decline for non-smoking adults is around 29 ml/year and although that value is closely approximated by most of these reference equations, I don’t think that this should be a primary factor in selecting an FEV1 reference equation.
A final thought is that many of the older studies were performed using equipment that may be significantly different from that in use today. Testing standards have also been refined as well and these two factors may account for some of the differences from more recent studies.
The simple answer to the question I posed at the beginning of this blog is that ethnicity does matters, and it matters a lot, but the real and more complex answer is that it is not at all clear what ethnicity is even when we think we know what it is. The Global Lung Initiative released their preliminary spirometry reference equations based on results from PFT Labs around the world a year or so ago but has indicated they await further data in order to continue to refine them. Despite the fact their reference equations come from an exceptionally large and diverse population it is interesting to note that they have already been criticized for not meeting the needs of a local population.
I think that continuing to focus on ethnicity is not giving us the right answers. We need to start looking for metrics that can be used in place of, or along with, height that don’t depend on ethnicity. I will admit that I don’t have any clear notion what we should be looking for. Sitting height has been used several times but has not been shown to be a significant improvement over standing height. Attempting to measure chest breadth and depth is frustrated by the tissue (muscle and fat) that overlays the ribcage and also doesn’t say anything about the anatomical position of the diaphragm. Perhaps some combination of standing height, sitting height, chest measurements and BMI would be better than standing height alone. This would place a greater burden on PFT labs to make these measurements, but I think the improvement in precision would be worth it.
FEV1 Study Populations:
| Reference | Ethnicity: | #Female: | Female Ages: | #Male: | Male Ages: |
| [A] | White | 927 | 21-80 | 476 | 21-80 |
| Black | 772 | 21-80 | 422 | 21-80 | |
| Hispanic | 872 | 21-80 | 506 | 21-80 | |
| [B] | White | 327 | 20-79 | 300 | 20-79 |
| [C] | White | 471 | 20-84 | 517 | 20-84 |
| [D] | White | 97 | Not stated | 102 | Not stated |
| [E] | White | 176 | 20-69 | 86 | 25-70 |
| [F] | White | 1129 | 27-82 | 1106 | 27-82 |
| [G] | Japanese-American | 0 | 6349 | 45-68 | |
| [H] | American Indian | 253 | 45-74 | 190 | 45-74 |
| [I] | Asian Indian | 137 | 20-80 | 226 | 16-80 |
| [J] | Chinese | 595 | 18-80 | 494 | 18-80 |
| [K] | Malaysian | 614 | 20-69 | 1385 | 20-69 |
| [L] | White | 102 | 18-70 | 110 | 18-70 |
| [M] | Black | 205 | 18-90 | 206 | 18-85 |
| [N] | Jewish-Ashkenazi | 663 | 20-74 | 1154 | 21-79 |
| Jewish-Sephardic | 547 | 20-69 | 786 | 21-84 | |
| [O] | Black | 117 | 18-47 | 143 | 18-47 |
| [P] | White | 96 | 18 to >70 | 83 | 18 to >70 |
| [Q] | Chinese | 0 | 440 | 18-80 | |
| [R] | White | 270 | 25 to >75 | 373 | 20 to >75 |
| [S] | Iranian | 255 | 17-82 | 295 | 17-82 |
| [T] | Saudi | 292 | 18-65 | 175 | 18-65 |
| [U] | Filipino | 153 | 16-68 | 130 | 17-78 |
| [V] | Korean | 694 | 20+ | 926 | 20+ |
| [W] | White | 7009 | 18-80 | 4565 | 18-80 |
| [X] | Iranian | 1110 | 21-80 | 1302 | 21-80 |
Female FEV1 Reference Equations:
| [A] White | 0.4333-(0.00361*age)-(0.000194*age^2)+(0.00010846*ht^2) |
| [B] White | -1.901+(0.037*ht)-(0.025*age) |
| [C] White | (0.089*(ht/2.54)-(0.025*Age)-1.932) |
| [D] White | (0.085*(ht/2.54))-(0.025*age)-1.692 |
| [E] White | -1.405+(0.0309*ht)-(0.0201*age) |
| [F] White | -1.747-(0.0263*age)+(3.619*(ht/100)) |
| [L] White | -2.73-(0.031*age)+(4.47*(ht/100)) |
| [P] White | (3.321*(ht/100))-(0.025*age)-1.394 |
| [R] White | (0.0314*ht)-(0.0203*age)-1.353 |
| [W] White | EXP(-8.397+(1.865*LN(ht))+(0.0057*age)-(0.00015*age^2)) |
| [A] Hispanic | 0.4529-(0.01178*age)-(0.000113*age^2)+(0.00012154*ht^2) |
| [H] American Indian | (0.0358*ht)-(0.0262*age)-1.774 |
| [N] Jewish-A | -1.574-(0.021*age)+(3.247*(ht/100)) |
| [N] Jewish-S | -1.636-(0.021*age)+(3.247*(ht/100)) |
| [O] Black | -1.6158-(0.0178*age)+(0.0298*ht) |
| [S] Iranian | (0.039489*ht)-(0.023593*age)-2.498 |
| [T] Saudi | (0.036*ht)-(0.018*age)-2.482 |
| [X] Iranian | (0.03715*ht)-(0.0238*age)-2.072 |
| [M] Black | -1.398-(0.023*age)+(2.848*(ht/100)) |
| [A] black | 0.3433-(0.01283*age)+(0.00010846*ht^2) |
| [I] Asian Indian | 0.401+(0.021*ht)-(0.021*age) |
| [J] Chinese | -1.272-(0.0199*age)+(0.02825*ht) |
| [K] Malaysian | (0.0294*ht)-(0.0238*age)-1.609 |
| [U] Filipino | -1.0375+(0.0256*ht)-(0.0187*age) |
| [V] Korean | (0.03558*ht)-(0.000192*age^2)-2.4114 |
Male FEV1 Reference Equations:
| [A] White | 0.5536-(0.01303*age)-(0.000172*age^2)+(0.00014098*ht^2) |
| [B] White | -2.832+(0.047*ht)-(0.03*age) |
| [C] White | (0.092*(ht/2.54))-(0.032*age)-1.26 |
| [D] White | (0.093*(ht/2.54))-(0.032*age)-1.343 |
| [E] White | -6.5147+(0.0665*ht)-(0.0292*age) |
| [F] White | -4.261-(0.0296*age)+(5.465*(ht/100)) |
| [L] White | -2.16-(0.031*age)+(4.47*(ht/100)) |
| [P] White | (3.961*(ht/100))-(0.033*age)-1.558 |
| [R] White | (0.0338*ht)-(0.0252*age)-0.789 |
| [W] White | EXP(-8.957+2.014*LN(ht)+(0.00281*age)-(0.000105*age^2)) |
| [A] Hispanic | 0.6306-(0.02928*age)+(0.00015104*ht^2) |
| [G] Japanese-American | -1.845-(0.018*age)+(0.035*ht) |
| [H] American Indian | (0.0599*ht)-(0.024*age)-5.65 |
| [S] Iranian | (0.043822*ht)-(0.028801*age)-2.425 |
| [T] Saudi | (0.036*ht)-(0.019*age)-1.886 |
| [N] Jewish-A | -3.302-(0.026*age)+(4.686*(ht/100)) |
| [N] Jewish-S | -3.302-(0.026*age)+(4.686*(ht/100)) |
| [X] Iranian | (0.045302*ht)-(0.02566*age)-2.78204 |
| [A] Black | 0.3411-(0.02309*age)+(0.00013194*ht^2) |
| [M] Black | -1.933-(0.027*age)+(3.609*(ht/100)) |
| [O] Black | -3.6679-(0.0331*age)+(0.0501*ht) |
| [I] Asian Indian | -1.936+(0.035*ht)-(0.026*age) |
| [J] Chinese | -2.404-(0.0254*age)+(0.03978*ht) |
| [K] Malaysian | (0.0353*ht)-(0.0315*age)-1.78 |
| [U] Filipino | -3.2068+(0.0436*ht)-(0.0205*age) |
| [V] Korean | (0.04578*ht)-(0.0002484*age^2)-3.4132 |
| [Q] Chinese | -1.86083-(0.02755*age)+(0.03733*ht) |
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