Skin temperature variability

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Skin Temperature Variability

Skin temperature variability refers to the degree of natural fluctuations that occur in an individual’s skin. While core body temperature is tightly regulated in endotherms (e.g. humans), analysis of skin temperature reveals that it possesses inherent variability which exhibits short-term (minute-to-minute) and long-term (circadian) changes in healthy individuals. These fluctuations reflect the dynamic balance between heat loss and heat production via multiple mechanisms such as vasodilatation and cellular metabolic activity to maintain core body temperature within its physiological range. Assessment of thermoregulation, circadian rhythm, and physiological monitoring in health (e.g., exercise) and disease (e.g., cirrhosis) are all possible applications of skin temperature variability analysis.

Analytical Methods

A wide range of different methods have been used for skin temperature fluctuation analysis, yet the most common techniques include Poincaré plots and entropy analysis.

Poincaré Plots

A Poincaré plot is a graphical representation which analyses the variability and patterns between two consecutive points in a time-series data set. In a traditional Poincaré plot, each data point is plotted against its immediate value (i.e., Tn versus Tn+1). The Poincaré plot can distinguish a complex time-series from random uncorrelated data. Furthermore, it can be used for the measurement of short-term and long-term variability of a time-series, where short-term and long-term variability are quantified by the standard deviation perpendicular to the line of identity (SD1) and the standard deviation parallel the line of identity (SD2).

Extended Poincaré Plots

Extended Poincaré plots consider the correlation between sequential lagged data points in a given time-series (i.e., Tn versus Tn + k, where k represents the lag). The addition of lag in the extended Poincaré plot creates a more multidimensional representation of the data; they reveal higher-order correlations and capture more intricate patterns in the data. This type of analysis has been applied to skin temperature variability as well as other physiological signals (e.g., heart rate variability, respiratory rate variability).

Sample Entropy

Sample entropy is a method that allows to determine the degree of irregularity of a time-series by calculating the probability that epochs of window length “m”, that are similar within a tolerance “r”, remain similar at the next desired point in the time series. To distinguish between a complex time-series and a random time-series, multiscale sample entropy, which combines sample entropy with data with temporal scaling, can be used; multiscale sample entropy incorporates multiple scales or resolutions into the analysis, thereby providing a more comprehensive assessment of the complexity and regularity of the data at different data resolutions. Sample entropy and multiscale entropy have been both adopted for the assessment of skin as well as core body temperature fluctuations.

Clinical Significance

Alterations in skin temperature dynamics occur during the onset of systemic diseases such as sepsis and cirrhosis and may provide information about the disease outcome. Papaioannou et al. reported that multiscale entropy analysis of skin temperature fluctuations in critically ill patients can predict mortality in patients with sepsis. On the other hand, in patients with decompensated cirrhosis, reduced short-term skin temperature variability can predict 1-year mortality independent from current predictors of mortality (e.g., MELD score). These reports indicate how skin temperature variability analysis can potentially serve as an effective bedside tool for a comprehensive understanding of an individual’s health status in critically ill patients.

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