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Published on: July 4, 2018
Clinical potential of pupillary light reflex and heart rate variability parameters as biomarkers for assessing pain
1Department of Otolaryngology, Head and Neck Surgery, Hyogo Prefectural Amagasaki General Medical Center, 6608550 Higashinaniwachou 2-17-77 Hyogo Prefecture, Japan.
Objective:
To investigate the association between subjective pain intensity and objective parameters obtained from two autonomic function tests in a longitudinal study targeting acute pain model in otolaryngology-head and neck region: pupillary light reflex (PLR) and heart rate variability (HRV).
Approach:
We enrolled 35 patients with acute otolaryngology-head and neck region inflammatory disorders at pre-treatment stage. The acute inflammatory disorders were defined as acute tonsillitis, peritonsillar abscess, acute epiglottitis, acute sinusitis, and deep neck space abscess. Patients underwent a numeric rating scale (NRS) to monitor subjective pain intensity, PLR, and HRV as objective tests at 4 time-points during the follow-up term. As main outcome variables, we used 15 analyzable PLR/HRV parameters. To improve robustness of conclusions about the association between NRS and PLR/HRV parameters, we prepared four linear mixed-effects models (LMMs) including predictor variables such as NRS, sociodemographic factors, and individual variability. And then, we selected the better-fit model based on the lowest Akaike's information criterion.
Main Results:
NRS significantly decreased due to treatments. In 14 out of 15 parameters, better-fit models were models including not only sociodemographic factors but also individual variability. We observed significant parameter alterations to one unit change of NRS in five PLR and four HRV parameters.
Significance:
The current study revealed that PLR/HRV parameters can be used as biomarkers reflecting pain relief effects. In addition, the findings suggest the importance of adjusting predictor variables, especially individual variability defined as random effects in LMMs, for obtaining more accurate parameter estimation in the longitudinal study.
