Cigarette smoking raises the probability that an individual will get lung cancer, chronic bronchitis and/or emphysema (among many other things). Nicotine is addictive and smokers often need significant motivation in order to quit. Lung age is a tool that was designed to give smokers an additional incentive to do this. The concept is fairly simple and that is by reformulating an FEV1 reference equation it is possible to take an individual’s actual FEV1 and estimate the age of their lungs (ELA). Because cigarette smoking can cause airway obstruction it tends to mimic premature lung aging which means that when a smoker’s FEV1 is used to calculate an ELA it can be significantly greater than their real or chronological lung age (CLA).
This idea was first proposed by Morris and Temple in 1985. Using Morris et al’s 1971 spirometry reference equations they studied the effect of calculating an estimated lung age (ELA) using observed FVC, FEV1 and FEF25-75 values both singly and in combinations and found that the FEV1 had the lowest standard error. The ELA calculation based on Morris et al’s FEV1 reference equations has achieved a degree of popularity and is available on at least one personal spirometer (Pulmolife, sold by Carefusion, MDSpiro and Vitalograph) and as an on-line calculator from a couple different websites (Chestx-ray.com and Lung Foundation of Australia).
Interestingly, the effectiveness of ELA towards quitting smoking has been studied only a handful of times. One often-quoted study of smoking cessation (Parkes et al) saw double the quit rate (13.6% vs 6.4%) when ELA was used as an intervention but the study’s methodology has since been criticized and it’s results have not been duplicated.
As importantly, the Morris and Temple’s lung age calculation is based on a single reference equation and there has been no attempt to adjust it for ethnicity. Significant discrepancies between the Morris and Temple ELA and an ELA equation derived from a study relevant to a local population were first pointed out in a paper by Newbury et al in 2010. In particular many smokers who had an ELA greater than their CLA using an equation based on the local population had an ELA that was less than CLA when using the Morris and Temple equation.
In a later paper Newbury et al compared the Morris and Temple ELA equation with a number of equations derived from the most commonly used FEV1 reference equations and continued to find significant discrepancies. This has been seconded by the study of Ben Saad et al which found that none of the ELA equations were relevant for a North African population.
The fact is that significant discrepancies exist between all ELA reference equations and this is because they act to magnify small differences in FEV1. The normal decline in FEV1 per year (very approximately 0.025 L/year) is small relative to the FEV1 volume which in turn means that the differences in predicted FEV1 from even the most commonly used reference equations within the same ethnicity can be equal to decades in ELA.
It is at least partly for this reason that Hansen et al proposed using the FEV1/FVC and FEV1/FEV6 ratio rather than the FEV1 to calculate the difference between ELA and CLA. Hansen et al based their ELA equations on the NHANESIII data set but indicated their belief that FEV1/FVC and FEV1/FEV6 ratios are relatively universal. Although this approach appears to make sense it too has been criticized as circular reasoning because the equations were validated using the same data set they were derived from.
Although lung age appears to be a simple and effective way to present facts to a patient, the lung age calculation is overly prone to both over- and under-estimation. In a very real sense the ELA calculations magnifies the differences between reference equations. Given the breadth of the normal range this means that even when using the most appropriate reference equations many normal individuals will have an ELA that is elevated relative to their CLA and many individuals with airway obstruction will have a ELA less than their CLA.
Note: One of the most interesting aspects about ELA calculations is how they increase the apparent differences between spirometry reference equations. I’ve discussed the broad range of normal mean values in the available FEV1 and FEV1/FVC ratio reference equations previously but ELA calculations make this much more obvious. Developing and selecting reference equations is a chronic problem without any easily evident solution and ELA calculations makes this even more obvious.
In addition, FEV1 can be reduced in non-smokers due to causes as diverse as asthma, obesity, pulmonary fibrosis and neuromuscular diseases. Are these patients going to be told what their lung age is? And if not, how do you know what proportion of a smoker’s lung age is due to causes other than smoking? For that matter, given the normal scatter in FEV1 the likelihood that any individual’s ELA is less than their CLA (and vice versa) before they even start smoking is 50% so without having a baseline FEV1 on hand, ELA is always going to be speculative.
ELA is an interesting concept but for all these reasons I believe that far too often it will be misleading and should not be used. I particularly disagree with the fact that the Morris and Temple ELA equation (without any correction for ethnicity!) is built into a number of spirometry systems.
So without ELA what role should pulmonary function labs play in getting smokers to quit?
Smoking is an addiction and no matter what technique has been used the number of successful quitters is small, usually on the order of 10%. Quitting smoking requires significant self-motivation and being confrontational is unlikely to improve an individual’s chances of succeeding. I’ve read that some pulmonologists will refuse to treat smokers with COPD who do not quit and although I understand some of the reasoning behind this I think it is more likely to make the individual in question more hopeless, not more motivated. Being judgmental is not the role that anybody in a PFT lab should take (no matter what the medical disorder is and regardless of how self-inflicted it may or may not be). We have to be informational instead and that means that testing should be as accurate as we can make it and that test results should be discussed neutrally and not used as a scare tactic. As a reminder less than 20% of smokers will actually develop COPD and this is something we need to be honest about when discussing smoking with patients.
Given human nature, the possibility of future (and even current) health risks are probably not as much a motivation as the impact smoking has on an individual’s finances. Smoking is expensive and there are a large number of on-line calculators that show this cost in a variety of different ways (Google “cost of smoking calculator” to see). Presently, in the US the average cost of a pack of cigarettes is over $6, which means that for a 1 PPD smoker, the weekly cost of their habit is over $40 and annual cost is over $2200. If that isn’t a reason to quit, I’m not sure what is.
ELA Reference Equations:
| Male: | |
| Reference: | Equation: |
| [A] | ELA = %FVC + 50.7 – (33.3 × FEV1) |
| [B] | ELA = ((2.87 x height x 0.394) – (31.25 x FEV1) – 39.375 |
| [C] | ELA = (1.56 x height) – (33.69 x FEV1) – 85.62 |
| [D1] | ELA = (1.483 x height) – (34.483 x FEV1) – 85.8621 |
| [D2] | ELA = (0.01303 – SQRT(0.0001697 + 0.000688 x (0.00014098 x height2 – FEV1 + 0.5536))) / -0.000344 |
| [D3] | ELA = (2.081 + 0.5846 x height3 – FEV1) / 0.01599 x height |
| [D4] | ELA = (0.00183 + SQRT((-0.001832) – 4 x 0.00011 x (9.37674 – (2.10839 x Ln(height)) + Ln(FEV1)))) / (2 x 0.00011) |
| [E] | ELA = ((0.036 x height) – 1.178 – FEV1) / 0.028 |
| [F] | ELA = 209.195 – 0.455 x Height – 11.521 x Observed FEV1 (L) – 0.602 x Observed FEV1/FVC (%) + 1.956 x Observed FEF50 (L/s) |
| Female: | |
| Reference: | Equation: |
| [A] | ELA = (0.84 × %FVC)+ 50.2 – (40 × FEV1) |
| [B] | ELA = ((3.56 x height x 0.394) – (40 x FEV1) – 77.28 |
| [C] | ELA = (1.33 x height) – (31.98 x FEV1) – 74.65 |
| [D1] | ELA = (1.58 x height) – (40 x FEV1) – 104 |
| [D2] | ELA = (0.00361 – SQRT(0.000013 + 0.000776*(0.00011496 x height2 – FEV1 + 0.4333)))/-0.000388 |
| [D3] | ELA = (1.597 + 0.5552 x height3 – FEV1) / 0.01574 x height |
| [D4] | ELA = (0.00422 + SQRT((-0.004222) – (4 x 0.00015 x (Ln(FEV1) + 8.49717 – 1.90019*Ln(height))))) / (2 x 0.00015) |
| [E] | ELA = ((0.022 x height) – 0.005 – FEV1) / 0.022 |
| [F] | ELA = 234.441 – 0.792 x Height – 7.295 x Observed FEV1 (L) – 0.610 x· Observed FEV1/FVC (%) + 0.301 x Observed PEF (L/s) + 2.647 x Observed FEF50 (L/s) |
Note: Height is in cm.
References:
Ben Saad H, Elhraiech A, Mabrouk KH, Mdalla SB, Essghaier M, Maatoug C, Abdelghami A, Bouslah H, Charrada A, Rouatbi S. Estimated lung age in healthy North African adults cannot be predicted using reference equations derived from other populations. Egyptian J Chest Dis Tuberculosis 2013; 62: 789-804.
Hansen JE, Sun X-G, Wasserman K. Meeting Abstract. Use of %FEV1/FEV6 and increased lung age to persuade smokers to quit. Chest 2009; 136(4): 56S
Hansen JE, Sun X-G, Wasserman K. Calculating gambling odds and lung ages for smokers. Eur Respir J 2010; 35: 776-780.
Hansen JE. Letter to the Editor: Measuring the lung age of smokers. Primary Care Respir J 2010; 19(3): 286-287.
Hansen JE. Letter to the Editor: Lung age is a useful concept and calculation. Primary Care Respir J 2010; 19(4): 400-401.
[A] Ishida Y, Ichikawa YE, Fukakusa M, Kawatsu A, Masuda K. Novel equations better predict lung age: a retrospective analysis using two cohorts of participants with medical check-up examinations in Japan. Primary Care Respir J 2015; 25: 15011.
The ELA reference equations in [A] were derived from:
The Report of the Special Committee of Pulmonary Physiology of the Japanese Respiratory Society (JRS). Reference values for spirogram and blood gas analysis in non-smoking healthy adults in Japan. J Japan Respir Soc 2001; 39:1–17
Morris JF, Koski A, Johnson LC. Spirometric standards for healthy nonsmoking adults. Am Rev Resp Dis 1971; 103: 57-67.
[B] Morris JF, Temple W. Spirometric “lung age” estimation for motivating smoking cessation. Prev Med 1985; 14: 655-662.
[C] Newbury W, Newbury J, Briggs, Crockett A. Exploring the need to update lung age equations. Primary Care Respir J 2010; 19(3): 242-247.
[D] Newbury W, Lorimer M, Crockett A. Newer equations better predict lung age in smokers: a retrospective analysis using a cohort of randomly selected participants. Primary Care Respir J 2012; 21(2): 78-84.
The authors of [D] derived a series of lung age equations from:
[D1] Quanjer P, Tammeling G, Cotes J, Pedersen O, Peslin R, Yernault J-C. Lung volumes and forced ventilatory flows. Report Working Party Standardization of Lung Function Tests, European Community for Steel and Coal. Official Statement of the European Respiratory Society. Eur Respir J 1993; 6(Suppl 16): 5-40.
[D2] Hankinson J, Odencrantz J, Fedan K. Spirometric reference values from a sample of the general U.S. population. Am J Respir Crit Care Med 1999; 159: 179-87.
[D3] Gore C, Crockett A, Pederson D, Booth M, Bauman A, Owen N. Spirometric standards for healthy adult lifetime nonsmokers in Australia. Eur Respir J 1995; 8: 773-782.
[D4] Falaschetti E, Laiho J, Primatesta P, Purdon S. Prediction equations for normal and low lung function from the Health Survey for England. Eur Respir J 2004; 23: 456-463.
Parkes G, Greenhalgh T, Griffin M, Dent R. Effect on smoking quit rate of telling patients their lung age: the Step2Quit randomised controlled trial. Brit Med J 2008; 336(7644): 598-604.
Quanjer PH, Enright PL. Editorial: Should we use lung age? Primary Care Respir J 2010; 19(3): 197-199.
[E] Toda R et al. Validation of “lung age” measured by spirometry and handy electronic FEV1/FEV6 meter in pulmonary diseases. Inter Med 2009; 48: 513-521.
[F] Yamaguchi K, Omori H, Onoue A, Katoh T, Ogata Y, Kawashima H, Onizawa S, Tsuji T, Aoshiba K, Nagai A. Novel regression equations predicting lung age from varied spirometric parameters. Respir Physiol Neurobiol 2012; 183: 108–114.
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