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Research Papers

Sensitivity of Digital Thermal Monitoring Parameters to Reactive Hyperemia

[+] Author and Article Information
Mohammad W. Akhtar

Department of Mechanical Engineering, University of Houston, Houston, TX 77204mwakhtar3@uh.edu

Stanley J. Kleis1

Department of Mechanical Engineering, University of Houston, Houston, TX 77204kleis@uh.edu

Ralph W. Metcalfe

Department of Mechanical Engineering, University of Houston, Houston, TX 77204metcalfe@uh.edu

Morteza Naghavi

 Fairway Medical Technologies, Inc., 710 N. Post Oak Road, Suite 204, Houston, TX 77024mn@vp.org

1

Corresponding author.

J Biomech Eng 132(5), 051005 (Mar 25, 2010) (14 pages) doi:10.1115/1.4001137 History: Received November 17, 2008; Revised December 29, 2009; Posted January 28, 2010; Published March 25, 2010; Online March 25, 2010

Both structural and functional evaluations of the endothelium exist in order to diagnose cardiovascular disease (CVD) in its asymptomatic stages. Vascular reactivity, a functional evaluation of the endothelium in response to factors such as occlusion, cold, and stress, in addition to plasma markers, is the most widely accepted test and has been found to be a better predictor of the health of the endothelium than structural assessment tools such as coronary calcium scores or carotid intima-media thickness. Among the vascular reactivity assessment techniques available, digital thermal monitoring (DTM) is a noninvasive technique that measures the recovery of fingertip temperature after 2–5 min of brachial occlusion. On release of occlusion, the finger temperature responds to the amount of blood flow rate overshoot referred to as reactive hyperemia (RH), which has been shown to correlate with vascular health. Recent clinical trials have confirmed the potential importance of DTM as an early stage predictor of CVD. Numerical simulations of a finger were carried out to establish the relationship between DTM and RH. The model finger consisted of essential components including bone, tissue, major blood vessels (macrovasculature), skin, and microvasculature. The macrovasculature was represented by a pair of arteries and veins, while the microvasculature was represented by a porous medium. The time-dependent Navier–Stokes and energy equations were numerically solved to describe the temperature distribution in and around the finger. The blood flow waveform postocclusion, an input to the numerical model, was modeled as an instantaneous overshoot in flow rate (RH) followed by an exponential decay back to baseline flow rate. Simulation results were similar to clinically measured fingertip temperature profiles in terms of basic shape, temperature variations, and time delays at time scales associated with both heat conduction and blood perfusion. The DTM parameters currently in clinical use were evaluated and their sensitivity to RH was established. Among the parameters presented, temperature rebound (TR) was shown to have the best correlation with the level of RH with good sensitivity for the range of flow rates studied. It was shown that both TR and the equilibrium start temperature (representing the baseline flow rate) are necessary to identify the amount of RH and, thus, to establish criteria for predicting the state of specific patient’s cardiovascular health.

Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 5

Steady-state temperatures (Tss) at the sensor location as a function of Qs for an upward air velocity of 0.1 m/s

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Figure 6

Steady-state temperature distribution (internal and external to the finger, shown in three planes) for Qs=10 cc/min and air velocity=0.1 m/s (see Fig. 2)

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Figure 7

Artery surface temperature (°C) during the recovery stage, toccl,end=time at the end of occlusion (Qs=10 cc/min, b=4, tc=60 s, Tambient=22°C, air velocity=0.1 m/s)

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Figure 8

Fingertip temperature response during DTM; measured (o—occluded arm, x—control arm) and simulated (-) for conditions Tambient=24.8°C, Qs=35 cc/min, b=1.5, tc=120 s, air velocity=0.02 m/s

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Figure 9

DTM response (Qs=10 cc/min, b=4, tc=60 s, Tambient=22°C, air velocity=0.1 m/s)

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Figure 11

DTM response for various flow rates with (a) no RH and with (b) RH, b=4, and tc=60 s, Tambient=22°C, air velocity=0.1 m/s for the input flow waveform

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Figure 12

TR dependence on Qs for b=2,4,6; curve fit lines shown only for trend purposes and do not represent actual data points

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Figure 13

NP dependence on Qs for b=2,4,6; curve fit lines shown only for trend purposes and do not represent actual data points

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Figure 14

TTR (min) dependence on Qs for b=2,4,6; curve fit lines shown only for trend purposes and do not represent actual data points

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Figure 15

AUC dependence on Qs for b=2,4,6; curve fit lines shown only for trend purposes and do not represent actual data points

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Figure 16

TR dependence on start temperature for b=2,4,6; curve fit lines shown only for trend purposes and do not represent actual data points

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Figure 1

Fingertip temperature response during DTM (clinical data, sampled data averaged over 1 s interval, symbols added for clarity)

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Figure 2

Finger model components and steady-state temperature (°C) distribution at the sensor plane with the finger enclosed in an air cavity (simulation conditions same as Fig. 6)

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Figure 3

Histogram of grid quality (measured by equiangle skew): (a) sensor plane for 3D simulations and (b) for 2D simulations

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Figure 4

Input flow rate to simulate RH: (a) schematic and (b) from radial Doppler velocimetry

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Figure 10

Contours of temperature in the sensor plane at (a) 0 s, (b) 1 s, (c) 21 s,(d) 42 s, (e) 72 s, (f) 92 s, (g) 102 s, and (h) 112 s from start of occlusion; (i) 1 s, (j) 5 s, (k) 9 s, (l) 15 s, (m) 25 s, (n) 100 s, (o) 300 s, and (p) 500 s postocclusion; Qs=10 cc/min, b=4, and tc=60 s, Tambient=22°C, air velocity=0.1 m/s (the bottom and top surfaces are clipped for magnification of the distribution in and around the finger)

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