One of the overlooked parts of teaching pulmonary function interpretation is developing an appreciation for the number and variety of errors that the equipment, patients and technicians can produce and how they affect the reported test results. I routinely run across a couple dozen errors each week while reviewing reports. Most are minor and do not significantly affect the reported results. Many are mundane because they appear so often and a few are interesting because they point out a particular limitation in the equipment, software or testing standards. I’ve kept a file of the more iconic examples of testing errors for years and a while ago a pulmonary staff physician and I used to hold weekly sessions for fellows and residents where we’d present a number of “zingers” to see if they could figure them out. Unfortunately that physician has moved on to a different institution and I’m no longer as available as I used to be so these sessions are no longer held but I think that they or something like them should be held in all teaching hospitals.

The thyroid gland is located across the front of the upper airway a short distance below the larynx. An enlarged thyroid gland is known as a goiter. The most common worldwide cause of goiter is an iodine deficiency. This is much less common in the western nations where factors such as Hashimoto’s thyroiditis, Graves’ disease, multi-nodular thyroid disease, thyroid cancer, pregnancy and the side effects of some medications are the its primary causes. Common respiratory complaints associated with goiter include cough, hoarseness, shortness of breath and stridor.

The current ATS/ERS standards for a positive bronchodilator response are an increase in FEV1 or FVC of ≥ 12% and ≥ 200 ml. These standards are largely based on the ability to detect a change that is far enough above the normal variability in FEV1 and FVC to be considered significant. One problem with this is that the amount of variability that is considered to be “normal” is overly influenced by a relatively small number of subjects that have a high degree of variability.

I was reviewing spirometry reports and noticed several patients in a row with an early termination of exhalation that was not reflected in the reported expiratory time. This is far from the first time I’ve noticed this but seeing several examples more or less at the same time got me curious about how the software was determining expiratory time. The ATS/ERS standards are somewhat mute on how the total expiratory time of a spirometry effort is determined. The closest the standard comes to addressing this is the end-of-test (EOT) criteria:

When assessing the response to bronchodilators the change in FEV1 is used far more frequently than any other spirometry result. Other values such as inspiratory capacity (IC) and peak inspiratory flow (PIF) have also been proposed as indicators, but the ATS/ERS standards includes changes in FVC as well as changes in FEV1 and this is often overlooked. Specifically they: “…recommend using the per cent change from baseline and absolute changes in FEV1 and/or FVC in an individual subject to identify a positive bronchodilator response. Values >12% and 200 mL compared with baseline during a single testing session suggest a ‘‘significant’’ bronchodilatation.”

A while back one of our technicians brought a helium dilution FRC graph to my attention and wanted to know if it showed a system leak. At that time my response was that it definitely wasn’t a leak (leaks don’t show increases in helium) and was probably due to too much oxygen being added to the system at the beginning of the test. A couple of days ago a technician brought a similar graph to me and again I was asked why it looked unusual. I’ve had time to think about this issue since the last time and I’ve come up with an alternate explanation that I think fits the facts a bit better.

My hospital’s Oncology division treats a number of patients with lymphoma and leukemia. It also has an active bone-marrow transplant program and for all of these patients diffusing capacity measurements are a critical part of assessing treatment progress. Since these patients are also frequently anemic, correcting DLCO results for hemoglobin is also critical. For a factor that has as much importance for the interpretation of DLCO results as it does the effect of hemoglobin on DLCO has actually been studied a relatively small number of times. Part of the reason for this is the problem of finding an acceptable model. A reduced or elevated hemoglobin is a consequence of many diseases and conditions. When studying patients longitudinally it is often difficult to separate the changes in DLCO that occur from the disease process and those that occur from changes in hemoglobin. For this reason changes in hemoglobin pre- and post-treatment in anemia and polycythemia have been studied most frequently.

I was reviewing reports today when I ran across one with some glaring errors. There were several things that immediately told me that the reported plethysmographic lung volumes were way off; the VA from the DLCO was almost a liter and a half larger than the TLC and the SVC was only about half the volume of the FVC. When I took a look at the raw test data I saw at least part of the reason why the technician had selected these results to be reported and that was because the SVC quality from most of the efforts was poor. They mostly looked like this:

I’d spent some time researching single-breath tests a while back and of course ran across the Fowler method for measuring anatomic dead space. It’s a relatively simple test but assessing its results as well as the results of alternate dead space measurement techniques turns out to be more complicated than I had remembered. The official definition of anatomic dead space is that it is that part of the inhaled volume that remains in the airways at the end of inhalation and does not participate in gas exchange. An accurate estimate of this volume is important because respiratory dead space (Vd/Vt, discussed previously ) is composed of both anatomical and physiological dead space. The physiological component of the respiratory dead space cannot be determined without knowing the anatomical dead space.

I’ve been planning on putting together a tutorial on characterizing and interpreting the contours of flow-volume loops so I’ve been accumulating flow-volume loops that are examples of different conditions. Lately I was reviewing some of them and noticed that when I tried to compare loops from different individuals with similar baseline conditions that the different sizes of the flow-volume made this difficult. For example, these two loops are both from individuals with normal spirometry.