Fall/Winter 2016 Diagnostics Section Bulletin

Fall/Winter 2016 Diagnostics Section Bulletin

Editor
Jeffrey M. Haynes, RRT, RPFT

Pulmonary Function Laboratory
St. Joseph Hospital
Nashua, NH
Work Email: jhaynes@sjhnh.org
Home Email: jhaynes3@comcast.net

Chair:
Katrina Hynes, BAS, RRT, RPFT
Supervisor
Mayo Clinic Pulmonary Evaluation Laboratory
Rochester, MN 55905
(507) 284-4545
Hynes.Katrina@mayo.edu

Former Chair
Matthew J O’Brien, RRT, RPFT
Pulmonary Diagnostic Lab
University of Wisconsin Hospital and Clinics
600 Highland Ave Room E5/520
Madison, WI 53792-5772
(608) 263-7001
Fax: (608) 263-7002
mobrien@uwhealth.org


Technologist’s Notes: Non-Birth Sex Should Not Be Used to Interpret Spirometry Data in Adults

Jeffrey M. Haynes, RRT, RPFT, FAARC

Age, height, race, and sex influence lung size and function. It is well known that normal adult males have larger lungs than females with similar age, height, and ethnicity.

In recent times, social issues faced by transgendered individuals have received much attention and public debate. There has even been debate amongst pulmonary function technologists on which sex should be used when interpreting spirometry data: birth sex or non-birth sex?

While it is important for health care providers to be knowledgeable about and sensitive to issues facing transgendered patients, lung biology must be considered outside of gender identity. An important point to understand is that “gender” and “sex” are not the same thing. The Merriam-Webster dictionary defines gender as “the behavioral, cultural, or psychological traits typically associated with one sex,” whereas the sex of an individual is distinguished “on the basis of their reproductive organs and structures.” In other words, an individual born of male sex, retains male sex despite transitioning to female gender.

Ralph Stumbo, RRT, CPFT, and I recently conducted a retrospective study examining the impact of using non-birth sex (female) on the interpretation of spirometry data in subjects with airflow obstruction. As one might expect, FEV1 was overestimated when expressed as the percent of predicted. Interestingly, no subject was misidentified as non-obstructed when the FEV1/FVC lower limit of normal was defined as a z-score of -1.645.1

Using non-birth sex in transgendered patients with airflow obstruction has a significant impact on spirometry interpretation and puts these subjects at risk for misdiagnosis and suboptimal treatment. We will be presenting our findings at AARC Congress 2016 in San Antonio, TX.

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Reference

  1. Quanjer PH, Stanojevic S, Cole TJ, Baur X, Hall GL, Culver BH, et al. Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations. Eur Respir J 2012;40(6):1324-1343.

Impulse Oscillometry: Another Tool in the Pulmonary Function Technologist’s Toolbox, Part 2: Obtaining the Data and Interpretative Strategies

Dan Alamillo, BS, RRT-NPS, CPFT
Children’s Hospital and Research Center, Oakland, CA

Before beginning the discussion on the interpretation of impulse oscillometry (IOS), I will review how to obtain a suitable study.

Just as with spirometry, proper posture is essential. The patient should either be sitting or standing with his back straight and head slightly elevated in the “sniffing” position. My preference has always been to have the child stand during each trial, but if the child feels more comfortable sitting in a chair, or if you’re working with a really young or anxious child, allowing her to sit on her mother’s lap will suffice as long as the correct posture is achieved. The use of nose-clips is ideal, but with young children, you may find that they may only be amenable to having their mother gently pinch their nose.

The next step is to start the unit and have the patient go onto the mouthpiece, with either his hands or his parent’s hands supporting his cheeks. Then have him quietly breathe at normal tidal volumes. You should have the child attempt this for 30 seconds, but in my experience I consider myself lucky to get 12 to 15 seconds worth of data.

The software will allow you to examine each trial and select the data points that have the least amount of artifacts or spikes in pressure from swallowing, tongue movement, or other factors. There are no formal guidelines in place from the ATS, AARC, or ERS as to the criteria for acceptability, but we can rely on our eye and good old-fashioned statistics to assist, as will be discussed next.

A stepwise approach has always worked best for me and I have always advocated this approach when explaining IOS to our residents or pulmonary fellows. When looking at a completed study the technologist should carefully examine the following:

  1. Quality assurance (statistics)
  2. Graphs
  3. Data
  4. Putting it all together

Quality assurance

I always talk about quality assurance first because it makes no sense to trudge through a report if you know the data obtained are not acceptable or reproducible. To make this determination, the software in the IOS system will calculate two important metrics. The first is the coefficient of variation (CV) and indicates the amount of variation in each of the measurements of the same wavelength (Hertz). The CV is simply the standard deviation of each measurement divided by the mean. An IOS test with a CV less than 10% is usually considered reproducible.

The next metric is called the coherence and is a good indicator of the quality and reliability of the impedance measurements (resistance and reactance) at a certain wavelength. Coherence derivation is made from a sophisticated equation, but the computer program mercifully does it for us. For those out there who have had heated discussions about the number  or have refused to set a ventilator rate to a non-prime number, then this one is for you:

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Figure 1. Coherence Derivation

For the rest of us, coherence provides an estimate of the quality and reliability of the impedance measurements at the various frequencies. An acceptable study will have a coherence value that is greater than 0.60 at 5 Hertz and greater than 0.80 at the higher frequencies.

Examine the charts

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Figure 2: Good data with the graphs indicating a bronchodilator response.

Once you have a good study, then a simple examination of the graphs generated from the data will help you visualize what the numbers will be telling you. It is also one last opportunity to check the quality of the study, as some studies have been known to be “reproducibly bad,” in that the data may have acceptable statistics. If it doesn’t look right, it will warrant a careful examination of the raw data.

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Figure 3: Bad data as reactance (left side of the graph) starts at 0 cmH2O at the lowest frequency when it should be negative.

Examine the data

In my previous article we discussed, at some length, the impendence data that are obtained in an IOS study. Now is the time for us to look at that data and determine if there exists any abnormal pulmonary lung function. To accomplish this, one must know what the “normal” is and the following tables should assist.

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Table 1: Normal IOS resistance values1,2,3

It should be noted the reason for the slight disparity in normal values for the R5-R20 gradient is that healthy, young children (less than seven years of age) have more compliant chest walls and thus exhibit a greater frequency dependence across the frequency spectrum when measuring resistance to air flow.

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Table 2: Normal IOS reactance values1,4

As you may recall from part 1 of this paper, other reactance values can be determined from the reactance curves, such as when the lungs transition from passive distention into active stretching (FRES) and the area under the reactance curve from the reactance at 5 Hertz (X5) to the FRES (AX). Currently, there are no normative sets of reference values for these parameters, although they have been used for trending purposes when gauging a bronchodilator response or response to a therapy plan. Clearly, further research is needed in this area.

With reference to gauging a patient’s response, there have been reports to suggest that the R5, when compared to a baseline value, can be used as a surrogate for spirometry in both bronchodilator response and in bronchoprovocation studies. (See Tables 3 & 4)

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Table 3: Positive bronchodilator response with regards to IOS5,6,7,8,9

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Table 4: Spirometric equivalent for bronchial challenge testing as measured by IOS10,11,12

Now that we understand the normal values and the assessment of the airway responses to stimuli, let’s look at what some of the abnormal values can tell us about our patient’s airways. To this end, by looking at both the resistance/reactance curves and data the clinician can begin to understand the nature of any airways dysfunction.

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Figure 4. IOS graph with distal obstruction

Obstructions along the lower airways will typically exhibit frequency dependence in which both the resistance (right side) and reactance (left side) will change with changes in frequency. This type of obstructive physiology will have the following resistance and reactance values:

  • R5 > 150% of predicted.
  • R5-R20% > 30% (children) or > 20% (adults).
  • R20 can be either normal or elevated depending on whether the obstruction is purely distal of if there is also proximal disease present.
  • X5 may be normal but will require a significant increase in Hertz to reach the FRES. As a result, the AX will also be large.
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Figure 5. IOS graph with proximal obstruction

A proximal obstructive physiology will present as a frequency independent curve for the resistace data and as a frequency dependent curve for reactance and will have the following values:

  • R5 > 150% of predicted
  • R5-R20% < 30% (children) or < 20% (adults)
  • R20 > 150% of predicted
  • Normal reactance (X) data

Combined obstruction:

As alluded to previously, if you suspect a combined obstruction, it can be teased out in the R20 value. In other words, the resistance values of your data will have the hallmark data points of both obstructive diseases.

  • R5 > 150 % of predicted — distal element
  • R5-R20% > 20% (children) or > 30% in adults — distal element
  • R20 > 150% of predicted — proximal element

Putting it all together: case studies

Now that we have the basics down, let’s make use of this knowledge with some actual IOS studies.

Case 1: A four-year-old boy was taken to the Urgent Care Clinic after his mother noticed that his “regular” nighttime cough had become non-stop. She noted that he frequently complains of being unable to keep up with his friends at school, and after coming down with a cold, has been “making funny sounds” when he breathes. He presented with the following: diffuse wheezing throughout with bilateral crackles at both bases; HR: 162; RR: 32; SpO2: 94%. The patient was speaking in 3-4 word sentences, but indicated that he felt no distress (actually said, “I’m good”.). A peak flow was attempted but that only made the wheezing worse. An astute 2nd year resident, having just completed her pulmonary elective rotation and had an IOS study completed on her, asked if we could see the patient in our clinic. The following is the actual IOS study.

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Figure 6. IOS graphs and data from a child with acute asthma

The post bronchodilator study was conducted after the patient received a four-hour dose at 10 mg/hour and dexamethasone.

Statistical Data:

PRE:    R5 (CV=5), R10 (CV=6), R20 (CV=8), FRES (CV=6)

POST: R5 (CV=6), R10 (CV=4), R20 (CV=8), FRES (CV=6)

Analysis of the Data:

PRE

Statistical data shows this to be an acceptable and reproducible study.

Both curves show frequency dependence.

R5 > 150% of predicted.

R5-R20% > 30%.

R20 < 150%.

X5 elevated with FRES being reached at 46.94 Hz. (unable to calculate AX).

Outcome: Significant obstruction of the distal airways

POST

Statistical data shows this to be an acceptable and reproducible study.

Both curves show frequency dependence.

R5 < 150% of predicted.

R5-R20% < 30%.

R20 < 150%.

X5 normal with FRES being reached at 18.48 Hz. AX = 16.63.

Outcome: Normal function

The patient was admitted for further observation. His “cold” was untreated asthma that became an exacerbation. He recovered well and he and his family were eventually discharged home after receiing extensive asthma education and training. I still see the little guy every now and then through the clinics and he is a year older, wiser, and now the proud owner of two MDIs with a spacer who is only too happy to show you his technique. He has since graduated to spirometry, and when he asks me, “how’s it going?” I tell him, “I’m good.”

Suggested reading

The inspiration for this article and my talk on this subject at the 2014 AARC Congress came from one of the best review articles written on IOS, which I stumbled across while learning more about this funny little modality we had sitting quietly in one of our testing rooms. You can read the abstract here.

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Figure 7. IOS device

References

  1. Clement J, Dumoulin B, Gubbelmans R, Hendricks S, van de Woestijne KP. Reference values of total respiratory resistance and reactance between 4 and 26 Hz in children and adolescents aged 4-20 years. Bull Eur Physiopath Respir 1987;23:441-448.
  2. Frei J, Jutla J, Kramer G, Hatzakis GE, Ducharme FM, Davis GM. Impulse oscillometry: reference values in children 100 to 150 cm in height and 3 to 10 years of age. Chest 2005;128:1266-1273.
  3. Morgan W. Mansfield L, Wolf J, Souhrada JF. The measurement of total respiratory resistance in small children. J Asthma 1982;19:233-240.
  4. Dencker M, Malmberg LP, Valind S, et al. Reference values for respiratory impedance by using impulse oscillometry in children aged 2-11 years. Clin Physiol Funct Imaging 2006;26:247-250.
  5. Oostveen E, MacLeod D, Lorino H, et al. The forced oscillation technique in clinical practice: methodology, recommendations and future developments. Eur Respir J 2003;22:1026-1041.
  6. Ortiz G, Mendez R. The effects of inhaled albuterol and salmeterol in 2 to 5-year old asthmatic children as measured by impulse oscillometry. J Asthma 2002;39:531-536.
  7. Nieto A, Pamies R, Oliver F, Medina A, Caballero L, Mazon A. Montelokast improves pulmonary function measured by impulse oscillometry in children with asthma (Mio study). Respir Med 2006;100:1180-1185.
  8. Song TW, Kim KW, Kim ES, Kim KE, Sohn MH. Correlation between spirometry and impulse oscillometry in children with asthma. Acta Paediatr 2008;97:51-54.
  9. Oostveen E, Dom S, Desager K, Hagendorens M, DeBacker W, Weyler J. Lung function and bronchodilator response in 4-year old children with different wheezing phenotypes. Eur Respir J 2010;35:865-872.
  10. Mansur AH, Manney S, Ayers JG. Methacholine-induced asthma symptoms correlate with impulse oscillometry but not spirometry. Respir Med 2008;102:42-49.
  11. Pairon JC, Iwatsubo Y, Hubert C, Lorino H, Nouaigui H, Gharbi R, et al. Measurement of bronchial responsiveness by forced oscillation technique in occupational epidemiology. Eur Respir J 1994;7:484-489.
  12. Nielsen KG, Bisgaard H. Cold air challenge and specific airway resistance in preschool children. Paediatr Respir Rev 2005;6:255-266.

Low DLCO and Exercise Desaturation

Jeffrey M. Haynes, RRT, RPFT, FAARC

Diffusing capacity of the lung for carbon monoxide (DLCO) reflects a multitude of physiologic functions in the lungs. Lung diffusion is affected by structural factors including lung volume, alveolar capillary membrane thickness, alveolar capillary surface area, and pulmonary capillary volume. Functional factors include ventilation, V/Q matching, alveolar gas composition, membrane diffusivity, hemoglobin content, hemoglobin binding properties, and the partial pressure of capillary gases.

In patients with interstitial lung disease, a low DLCO expressed as a percent of predicted is predictive of exercise oxygen desaturation. In one study, a DLCO cut-off point of 56% of predicted was highly predictive of exercise oxygen desaturation.1 (See figure 1).

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Figure 1. Receiver operating characteristics (ROC) curves for the prediction of exercise oxygen desaturation. For each parameter, the greater area under the line is associated with more predictive value.1

 In my laboratory, we have a protocol for DLCO stipulating that any patient with a DLCO ≤ 50% of predicted (who is not already on domiciliary oxygen) automatically undergoes ambulatory oximetry. This protocol frequently identifies patients who would benefit from supplemental oxygen therapy. In fact, I tested the patient below approximately 12 hours prior to writing this article.

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Figure 2. DLCO from a patient with interstitial lung disease.

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Figure 3. Exercise oxygen saturation histogram

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Figure 4. Exercise oxygen saturation graph

Reference

  1. Someya F, Mugii N, Hasegawa M, Yahata T, Nakagawa T. Predictors of exercise-induced oxygen desaturation in systemic sclerosis patients with interstitial lung disease. Respir Care 2014;59(1):75-80.

Positive Flow Error

Jeffrey M. Haynes, RRT, RPFT, FAARC

I am an admitted “zero flow” button pusher addict. I am paranoid because flow-sensing pneumotachs are susceptible to both positive and negative flow errors. A positive flow error results in exaggerated expiratory flows and volumes, whereas a negative flow error results in underestimated expiratory flows and volumes. The graphics below show a positive flow error.

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Figure 1. Data and F/V loop with a positive flow error

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Figure 2. Volume-time curve with a positive flow error

Technologists need to be vigilant about possible positive and negative flow errors. Don’t be afraid to damage your zero flow button. Another strategy is to start testing without the patient attached, and only start testing when a horizontal volume line is viewed on the screen.


Wipe Your Shoes Before You Enter the Body Box!

Jeffrey M. Haynes, RRT, RPFT, FAARC

I grew up in New Hampshire. Like many northern states, we have muddy springs and snowy, never-ending winters. As a boy, walking into the house without wiping my shoes was a big no-no. My mother made sure I obeyed this rule: “Jeffrey Michael, wipe your feet!”

This rule also applies to body plethysmographs. Figure 1 shows the door seal of a body box after testing a patient in March.

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Figure 1. Dirty body box door seal

This patient only had spirometry testing, so the dirty door seal didn’t affect his testing. Obviously, this door seal needed to be cleaned, but I decided to re-calibrate the plethysmograph first to examine the affect of the dirty seal. As shown below in figure 2, there was a marked increase in the body box leak factor.

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Figure 2. Increased body box leak with a dirty door seal

Lesson-of-the-day: keep you door seals clean and wipe your shoes before getting into a body box.


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Bulletin deadlines: Winter Issue: December 1; Spring Issue: March 1; Summer Issue: June 1; Spring/Summer Issue: February 1, 2017.