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Research Papers

Development of a Non-Invasive Dynamic Pulmonary Function Monitor

[+] Author and Article Information
Michael D. Sokoloff1

Larry Bortner

Physics Department,  University of Cincinnati, Cincinnati, OH 45221larry.bortner@uc.edu

Ralph J. Panos

Pulmonary, Critical Care and Sleep Division,Department of Internal Medicine,  University of Cincinnati, Cincinnati, OH 45221;Pulmonary, Critical Care and Sleep Division,Cincinnati VAMC,Cincinnati, OH 45220e-mail: ralph.panos@uc.edu

1

Corresponding author.

J. Med. Devices 6(2), 021003 (Apr 25, 2012) (10 pages) doi:10.1115/1.4006358 History: Received April 25, 2011; Revised February 17, 2012; Published April 24, 2012; Online April 25, 2012

Characterizing the complexity of airflow limitation in diagnosing and assessing disease severity in asthma, COPD, cystic fibrosis, and other respiratory diseases can help guide clinicians toward the most appropriate treatments. Current technologies allow obstructive lung disease to be measured with about 5%−10% precision. A noninvasive dynamic pulmonary function monitor (DPFM) can quantify ventilation inhomogeneities, such as those originating in partially blocked or constricted small airways, with 1% precision if inert gas concentrations can be measured accurately and precisely over three to four decades of sensitivity. We have studied the precision and linearity of a commercially available mass spectrometer, sampling the gas exhaled by a mechanical lung analog, mimicking a multibreath inert gas washout measurement. The root mean square deviation of the inert gas concentration measured for each “breath,” compared to the expected value for a purely exponential decay, is found to be about 1.1% over three decades of concentration. The corresponding overall impairment, a specific measure of ventilation inhomogeneity, is found to be about 0.2%, which indicates that were inhomogeneities observed, the corresponding impairment could be measured with 1% precision.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Simulated data. The blue points show pure exponential decay with τ1  = 40 s. The dark pink points show the inert gas fraction as a function of time for f1  = 87.5%, f2  = 12.5%, τ1  = 40 s, and τ2  = 200 s.

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Figure 2

Overall impairment, I, as a function of τ2 for f2  = 0.05 (black triangles pointing up), 0.10 (green squares), 0.15 (blue circles), and 0.20 (red triangles pointing down), all for τ1  = 40 s, tb  = 4 s, and f1  + f2  = 1

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Figure 3

Gas concentration measurement sensitivity model used for calculations of DPFM characteristics. The vertical scale shows the fractional precision, ΔC/C, with which the inert gas concentration is assumed to be measured.

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Figure 4

The blue data points represent a typical Monte Carlo experiment generated with initial inert gas concentration of 80% where f1  = 0.68, f2  = 0.12, τ1  = 40 s, and τ2  = 60 s. The green horizontal line corresponds to four decades of measurement sensitivity; the red line to a 400-s measurement.

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Figure 5

Root mean square deviation of f2 as a function of the minimum inert gas concentration used in fit of Monte Carlo data sets generated with τ1  = 40 s, τ2  = 60 s, and f2  = 0.05 (red triangles pointing down), 0.10 (blue circles), 0.15 (green circles), and 0.20 (black triangles pointing up) and f1  + f2  = 0.80

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Figure 6

Root mean square deviation of τ2 as a function of the minimum inert gas concentration used in fit of Monte Carlo data sets generated with τ1  = 40 s, τ2  = 60 s, and f2  = 0.05 (red triangles pointing down), 0.10 (blue circles), 0.15 (green squares), and 0.20 (black triangles pointing up) and f1  + f2  = 0.80

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Figure 7

Root mean square deviation of f2 as a function of the minimum inert gas concentration used in fit of Monte Carlo data sets generated with τ1  = 40 s, f1  = 0.85, f2  = 0.15, τ2  = 85 s (black circles), 60 s (green squares), 55 s (blue triangles pointing up), and 52 s (red triangles pointing down)

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Figure 8

Root mean square deviation of τ2 as a function of the minimum inert gas concentration used in fit of Monte Carlo data sets generated with τ1  = 40 s, f1  = 0.85, f2  = 0.15, τ2  = 85 s (black circles), 60 s (green squares), 55 s (blue triangles pointing up), and 52 s (red triangles pointing down)

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Figure 9

The helium partial pressure reported by the QMS-200 spectrometer, as a function of time. During most of the first 4000 s the “inhaled” gas was pure helium. It was then changed to nitrogen.

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Figure 10

These are the helium partial pressures reported by the QMS-200 spectrometer as functions of time for 40-s intervals (20 measurements each), 1000 s apart. As discussed in the text, there is a clear phase shift between the clocks in the spectrometer and the MLA.

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Figure 11

Average helium concentration measurements with the fit projection superposed. The data points, shown in blue, are each the averages of five data points from Fig. 9, multiplied by 106 . The fit projection, shown in green, is described in the text. The time scale is now measured relative to the first data point in Fig. 9.

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Figure 12

The fractional residuals (data minus fit projection, normalized to predicted values) for the data in Fig. 1. The fit assumes an exponential decay of the helium concentration, with a phase correction for the difference between the MLA and spectrometer clocks parameterized by a Fourier series, as described in the text.

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Figure 13

This is the distribution of fractional residuals plotted in Fig. 1. The central value is consistent with zero and the rms deviation is 1.1%.

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Figure 14

This sketch shows key elements of the gas distribution system, which communicates with the mechanical lung analog: pure gas (either helium or nitrogen) flow through the large PVC tubing at the top. The MLA draws gas into the bellows system of an Ingmar Medical QuickLung through a port at the top, and then expels it. Gas entering or exiting the port passes through check valves that control the direction of the flow. A capillary tube draws a sample of gas from PVC tubing 2.25 in. above the top of the QuickLung unit and sends it to the mass spectrometer. The elements shown here are discussed in greater detail in the text. They are not drawn to scale.

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