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Review
. 2022 Feb 10;5(1):116-120.
doi: 10.1016/j.jhsg.2022.01.004. eCollection 2023 Jan.

Diagnostic Biomarkers for Upper Extremity Chronic Pain Conditions

Cyril S Gary  1   2 , Max E Horowitz  1   3 , Aviram M Giladi  1
Affiliations
Review

Diagnostic Biomarkers for Upper Extremity Chronic Pain Conditions

Cyril S Gary et al. J Hand Surg Glob Online. .

Abstract

Hand surgery patients often experience chronic pain conditions. However, there are few reliable ways to measure pain, making diagnosis and subsequent management of these conditions notably challenging for the hand surgeon. Various diagnostic biomarkers have been actively studied in the chronic pain management field with promising results. This review discusses the development of diagnostic biomarkers for chronic pain conditions of the upper extremity, including complex regional pain syndrome, osteoarthritis, and neuropathic pain. Techniques involving the measurements of heart rate variability, molecular biomarkers including inflammatory and noninflammatory molecules, metabolites, and exosomes, magnetic resonance imagining and electroencephalography, as well as skin biopsy, are discussed. Future potential applications are proposed.

Keywords: Chronic pain; Complex regional pain syndrome; Diagnostic biomarkers; Hand surgery; Heart rate variability; Neuropathic pain; Osteoarthritis; Upper extremity surgery.

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Figures

Figure
Figure
Modalities for analyzing the HRV. Frequency domain measurements estimate the distribution of power into 4 frequency bands: the ultralow frequency band (<0.003 Hz), very LF band (0.003–0.04 Hz), LF band (0.04–0.15 Hz), and HF band (0.15–0.4 Hz). The LF band corresponds to sympathetic input, whereas the HF band corresponds to parasympathetic input. Power spectral density (seconds2/Hz) is the signal energy found in a particular band and can be calculated by summing the area under the curve for a specific frequency range (band). Time domain indices of HRV quantify the amount of variability in measurements of the IBI, which is the time between successive heartbeats. This may be measured as the SD or the root mean square of the IBI (time between successive R waves or R-R interval) on an ECG. Nonlinear analysis involves logarithmic modeling and focuses on 3 clinically relevant measurements: heart rate turbulence that measures the heart rate response to premature beats, deceleration capacity (milliseconds) that measures the ability of the heart to decelerate in certain time scales, and fractal scaling that analyzes fractal organization of the heart.
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