Estimated Lung Age (ELA)

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.

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Flow-volume loops are timeless

Recently I’ve been trying to help somebody whose spirometry results changed drastically depending on where their tests were performed. When their spirometry was performed on an office spirometer their FVC was less than 60% of predicted and when they were performed in a PFT lab on a multi-purpose test system their FVC was closer to 90% of predicted. Part of the reason for this was that different predicted equations are being used in each location but even so there was about a 1.5 liter difference in FVC.

One important clue is that the reports from the office spirometer showed an expiratory time of around 2 to 2-1/2 seconds while the reports from the PFT lab showed expiratory times from 9 to 12 seconds. The reports from both locations however, only had flow-volume loops and reported expiratory time numerically. There were no volume-time curves so it isn’t possible to verify that the spirometry being performed at either location was measuring time correctly or to say much about test quality.

The shape of a flow-volume loop is often quite diagnostic and many lung disorders are associated with very distinct and specific contours. Volume-time curves, on the other hand, are very old-school and are the original way that spirometry was recorded. The contours of volume-time curves are not terribly diagnostic or distinctive and I suspect they are often included as a report option more because of tradition than any thing else. But volume-time curves are actually a critically important tool for assessing the quality of spirometry and one of the most important reasons for this is because there is no time in a flow-volume loop.

With this in mind, the following flow-volume loop came across my desk yesterday. The FVC, FEV1 and FEV1/FVC ratio were all normal and it was the best of the patient’s efforts.

fvl_timeless

The contour of this flow-volume loop is actually reasonably normal, except possibly for the little blip at the end.
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IC, ERV and the FVC

While reviewing reports today I ran across a couple of lung volume tests from different patients where the SVC was over a liter less than the FVC. Suboptimal SVC measurement can affect both the TLC and the RV and in one case the TLC was slightly below normal (78% of predicted) and in the other the TLC was within normal limits but the RV was over 150% of predicted. Both patients had had lung volume measurements previously and the current TLC was significantly different than it had been before.

I seem to run across this problem at least once a week so I am reasonably used to making manual corrections. I’ve discussed this previously but basically I use the position of the tidal loop within the maximal flow-volume loop obtained during spirometry to determine IC and ERV and then re-calculate TLC and RV accordingly.

fvl_tvl_4

Anyway, for this reason I had tidal loops, and IC and ERV on my mind while I was reviewing other reports. Shortly after this I came across a report that had “fair FVC test quality and reproducibility” in the tech notes so I pulled up the raw spirometry test data and took a closer look.

What I found was that the patient had performed five spirometry efforts and that the FVC and FEV1 was different on each test. All five spirometry efforts met the ATS/ERS criteria for back-extrapolation, expiratory time and end-of-test flow rates. I clicked back and forth between the different spirometry efforts to make sure the right FVC and FEV1 had been selected and when I did I noticed that the position of the tidal loop was shifting left and right and that the closer it was to TLC, the lower the FVC and FEV1 were and vice versa.

fvl_tvl_1

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The dual tracer gas single-breath washout (DTG-SBW) and ventilation inhomogeneity

I’ve been interested in ventilation inhomogeneity for a while and as ways to measure it I have looked at VA/TLC ratios, the Lung Clearance Index (LCI) and the phase III slope of the single-breath N2 washout (SIIIN2). All of these tests are able to provide some information about ventilation inhomogeneity but each has their own limitations and just as importantly although their results have a relatively clear relationship with ventilation inhomogeneity it’s not quite as clear what exactly it is they are measuring. A friend recently pointed me to an on-line article in Chest that discusses the dual-tracer single-breath washout test in patients with COPD. The apparent advantage of this test is that it is able to provide information about the site of the ventilation inhomogeneity. Although dual tracer gases have been used to study airway function for over 50 years the limitation of this technique has been the need to use a mass spectrometer. Some recent advances in technology have made it possible for this type of testing to be performed with a significantly less expensive gas analyzer and this has revived an interest in the dual-tracer gas single-breath washout (DTG-SBW).

The two tracer gases in question are Helium and Sulfur Hexaflouride (SF6). Helium has a density of 4 gm/mol and the density of SF6 is 146 gm/mol, and it is the difference in densities between these two inert and insoluble gases that make this test useful. In order to understand why we need to revisit to the anatomy of the terminal airways.

From Osborne S. Airway resistance and airflow through the tracheobronchial tree. www.SallyOsborne.com.

From Osborne S. Airway resistance and airflow through the tracheobronchial tree. www.SallyOsborne.com.

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Why DIY CPET reports?

When I first started performing CPETs in the 1970’s a patient’s exhaled gas was collected at intervals during the test in Douglas bags and I had a worksheet that I’d use to record the patient’s respiratory rate, heart rate and SaO2. After the test was over I’d analyze the gas concentrations with a mass spectrometer and the gas volumes with a 300 liter Tissot spirometer and then use the results from these to hand calculate VO2, VCO2, Rq, tidal volume and minute volume. These results were then passed on to the lab’s medical director who’d use them when dictating a report.

Around 1990 the PFT lab I was in at the time acquired a metabolic cart for CPET testing. This both decreased the amount of work I had to do to perform a CPET and significantly increased the amount of information we got from a test. The reporting software that came with the metabolic cart however, was very simplistic and neither the lab’s medical director or I felt it met our needs so I created a word processing template, manually transcribed the results from the CPET system printouts and used it to report results.

Twenty five years and 3 metabolic carts later I’m still using a word processing template to report CPET results.

Why?

Well, first the reporting software is still simplistic and using it we still can’t get a report that we think meets our needs (and it’s also not easy to create and modify reports which is a chronic complaint I have about all PFT lab software I’ve ever worked with). Second, there are some values that we think are important that the CPET system’s reporting software does not calculate and there is no easy way to get it on a report as part of the tabular results. Finally, and most importantly, I need to check the results for accuracy.

You’d think that after all these years that you wouldn’t need to check PFT and CPET reports for mathematical errors but unfortunately that’s not true. For example, these results are taken from a recent CPET:

Time: VO2 (LPM): VCO2 (LPM): Reported Rq: “Real” Rq:
Baseline: 0.296 0.220 0.74 0.74
00:30 0.302 0.214 0.77 0.71
01:00 0.363 0.277 0.77 0.76
01:30 0.395 0.306 0.78 0.77
02:00 0.424 0.353 0.99 0.83
02:30 0.459 0.403 0.92 0.88
03:00 0.675 0.594 0.89 0.88
03:30 0.618 0.584 0.94 0.94
04:00 0.836 0.822 1.00 0.98

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The FVC/DLCO ratio. Will the real percent predicted please stand up?

Recently a reader asked me a question about the FVC/DLCO ratio. To be honest I’d never heard of this ratio before which got me intrigued so I spent some time researching it. What I found leaves me concerned that a lack of understanding about reference equations may invalidate several dozen interrelated studies published over the last two decades.

Strictly speaking the FVC/DLCO ratio is the %predicted FVC/%predicted DLCO ratio (which is usually abbreviated FVC%/DLCO%) and it appears to be used exclusively by specialists involved in the treatment of systemic sclerosis and related disorders. Specifically, the ratio is used to determine whether or not a patient has pulmonary hypertension. The basic idea is that (quoting from a letter to the editor):

“As we know, in ILD both FVC and DLCO fall and their fall is proportionate, whereas in pulmonary arterial hypertension DLCO falls significantly and disproportionately to FVC.”

A variety of algorithms using the FVC%/DLCO% have been devised. Inclusion factors are usually an FVC < 70% of predicted and a DLCO (corrected for hemoglobin) < 60% of predicted. A number of different threshold values for FVC%/DLCO% have been published ranging from 1.4 to 2.2 with the differences appearing to be dependent on study population characteristics and the type of statistical analysis performed. It is thought that individuals meeting the inclusion factors with an FVC%/DLCO% ratio above the threshold most probably have pulmonary hypertension.

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When no change is a change, or is it?

I was reviewing a spirometry report last week and when I went to compare the results with the patient’s last visit I noticed that the FVC and FEV1 hadn’t changed significantly. However, the previous results were from 2009 and when the percent predicted is considered there may have been a significant improvement.

2009 Observed: %Predicted:
FVC: 2.58 87%
FEV1: 1.60 72%
FEV1/FVC: 62 82%
2016 Observed: %Predicted:
FVC: 2.82 104%
FEV1: 1.65 82%
FEV1/FVC: 59 79%

The answer to whether or not there was an improvement would appear to depend on what changes you’d normally expect to see in the FVC and FEV1 over a time span of 7 years. The FVC and FEV1 normally peaks around age 20 to 25 and then declines thereafter.

fvc_predicted_l

fev1_predicted_l

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Top 10 spirometry errors and mistakes

A couple of days ago my medical director and I had a short discussion about teaching pulmonary fellows to read PFTs and agreed that in order to be good at interpreting PFTs it isn’t the basic algorithms that are hard, it’s gaining an understanding of test quality and testing problems. My medical director then suggested this topic. At first I wasn’t sure I could find 10 errors but after spending a couple hours digging through my teaching files I managed to come up with just a few more than that. So strictly speaking it’s not a top 10 list but I kept the title because I liked it.

Spirometry errors and mistakes seem to fall into four categories: demographics, reference equations, testing and interpretation.

Demographics:

Normal values are based on an individual’s age, height and gender. When this information is entered incorrectly the normal reference values will also be incorrect. These errors often go uncaught because whoever reviews and interprets reports usually isn’t the same person who sees the patient and performs the tests. This type of error often doesn’t get corrected until the results are uploaded into a hospital information system or the patient returns for a second (or third or fourth) visit.

1. Wrong gender.

Pulmonary function reference equations are gender specific and for individuals with the same age and height, men will have a larger FVC and FEV1 than women do. When a patient’s demographics information is manually entered into a PFT system it’s always possible for somebody to enter the wrong gender. When this happens the predicted values will be either over- or under-estimated. This happens in my lab at least a half a dozen times a year and it’s why when I review reports I try to check the patient’s gender right after reading their name.

This is also a problem area for individuals who have gone through gender reassignment (transsexuals). An individual’s physiologic/developmental gender needs to be used to generate predicted values but this may be at odds with their gender recorded in a hospital’s information system. Some PFT lab systems populate their demographics information from their hospital’s information system when an order is received and it may or may not be possible to alter gender once this has happened. In other cases, an individual’s demographics may be cross-referenced when PFT results are uploaded into hospital information system and may throw an error if the wrong gender is present.

2. Wrong height

All lung volumes and capacities scale with height. Like any other manual entry height can be mis-entered and the most common error I’ve seen is for somebody to enter 60 inches when they meant 6 feet 0 inches.

Height can also be mis-measured if the patient isn’t asked to remove their shoes or to stand straight, or if the patient is asked for their height and it isn’t even measured. An error of an inch or two probably won’t make a big difference in a patient’s predicted values (particularly given the discrepancies between different reference equations) but for somebody who’s on the edge of normal and abnormal it can make a significant difference in how a report is interpreted.

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Which DLCO should be reported?

I like to think my lab is better than most but every so often something comes along that makes me realize I’m probably only fooling my self.

Earlier this week I was reviewing the DLCO test data for a patient with interstitial lung disease. At first glance the spirometry and DLCO results pretty much matched the diagnosis and I had already seen they weren’t significantly different from the last visit. The technician had written “fair DLCO reproducibility” which was reason enough to review the test data but I’ve actually been making a point of taking a careful look at all DLCO tests, not just the questionable ones, for the last couple of weeks. I took one look at the test data, put my head in my hands, and counted to ten before continuing.

Reported: %Predicted: Test #1: Test #2: Test #3:
DLCO: 13.22 66% 10.08 92.17 16.36
Vinsp (L): 2.17 2.20 2.15
VA (L): 3.45 66% 2.89 2.93 4.02
DL/VA: 3.78 91% 3.49 31.5 4.07
CH4: 60.84 60.94 43.15
CO: 34.46 0.51 23.13

Even though the averaged DLCO results were similar to the last visit, the two tests they were averaged from were quite different. Reproducibility was not fair, it was poor. But far more than that, something was seriously wrong with the second test and the technician hadn’t told anybody that they’d had problems with the test system. {SIGH}. It’s awful hard to fix a problem when you don’t even know there is one in the first place.

I usually review reports in the morning the day after the tests have been performed, so the patient was long gone by the time I saw the results. This left me with a problem that I’m sure we’ve all had at one time or another and that was whether any of the DLCO results were reportable.
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When is a change in FVC significant?

Most of the COPD patients that are seen in my lab tend to have little change in their FEV1 from visit to visit but their FVC often changes significantly. A change in FVC is usually related to how long a patient is able to exhale and this in turn is usually related to how well they are feeling at the time. This would seem to imply that a significant change in FVC, particularly for a patient with COPD, is, if not clinically significant, at least clinically important even when the FEV1 hasn’t changed.

The problem with this is that expiratory time can be affected by things other than how the patient is feeling. Dyspnea and fatigue, of course. As importantly some technicians are better at motivating patients than other technicians so it can also be related to which technician is performing their tests. Even when the same technician is involved however, there is no guarantee that the level of motivation or a patient’s response to that motivation will be the same.

So how do you know if a change in FVC clinically significant or not?

Recently a spirometry report from a patient with very severe COPD came across my desk. When comparing the results to those of the last visit I could see that there had been a small (but not significant) increase in FEV1 but at the same time there had been a large (and significant) increase in FVC.

Visit 1: Observed: %Predicted:
FVC (L): 1.28 36%
FEV1(L): 0.53 19%
FEV1/FVC: 41 53%
Visit 2: Observed: %Predicted:
FVC (L): 1.93 55%
FEV1(L): 0.60 22%
FEV1/FVC: 31 40%

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