Skin blood flow dynamics and its role in pressure ulcers

Abstract

Pressure ulcers are a significant healthcare problem affecting the quality of life in wheelchair bounded or bed-ridden people and are a major cost to the healthcare system. Various assessment tools such as the Braden scale have been developed to quantify the risk level of pressure ulcers. These tools have provided an initial guideline on preventing pressure ulcers while additional assessments are needed to improve the outcomes of pressure ulcer prevention. Skin blood flow function that determines the ability of the skin in response to ischemic stress has been proposed to be a good indicator for identifying people at risk of pressure ulcers. Wavelet spectral and nonlinear complexity analyses have been performed to investigate the influences of the metabolic, neurogenic and myogenic activities on microvascular regulation in people with various pathological conditions. These findings have contributed to the understanding of the role of ischemia and viability on the development of pressure ulcers. The purpose of the present review is to provide an introduction of the basic concepts and approaches for the analysis of skin blood flow oscillations, and present an overview of the research results obtained so far. We hope this information may contribute to the development of better clinical guidelines for the prevention of pressure ulcers.

Figure 1

Schematic diagram of the architecture of the skin vasculature.

Figure 2

Skin blood flow (SBF) response in the sacral skin of a heathy subject. (a) Skin blood flow under externally pressure shows a decrease between 10^th and 14^th min. After the removel of the pressure, skin blood flow shows an increase (reactive hyperemia). (b) SBF response to a rapid local heating to 42 °C shows a biphasic vasodilation. The first peak is mediated by axon relfex and the second pleateau is mediated by nitric oxide. (c) SBF response to local cooling to 25 °C. Skin blood flow decreaes in response to cooling. PU, perfusion unit.

Figure 3

Fourier spectrum of the LDF blood flow signal shown in Figure 2b during 1–10min. (a) The wavelet amplitude spectrum of the signal. (b) The power spectrum of the signal. PSD, power spectral density.

Figure 4

An example of wavelet analysis of the LDF blood flow signal shown in Figure 2a. (a) Instantaneous frequencies corresponding to the local maxima of absolute wavelet coefficients. (b) Wavelet amplitude spectrum.

Figure 5

Schematic representation of self-similar BFO on different time scales. The bottom panel shows a 10 min SBF signal (Figure 2b during 1–10 min), the middle panel shows a 2 min segment of the signal, and the top panel shows a 20 sec segment of the signal. SBF, skin blood flow; PU, perfusion unit.

Figure 6

Illustration of detrended fluctuation analysis of LDF blood flow signals. For the signal shown in Figure 2b during the pre-heating period (1–10 min), the scaling regions n₁ < n < n₂ and n > n₂ are not distinguishable (blue line), whereas the LDF signal during the maximal vasodilation period (51–60 min) exhibited three distinct scaling regions with two crossovers (pink line). In this figure, represents the observation window size, represents the corresponded fluctuation of BFO, and α₁,α₂, and α₃ represent the scaling exponents.