The Added Value of Sensitivity to Nonnoxious Stimuli to Predict an Individual’s Sensitivity to Pain

BACKGROUND: Simple tools are needed to predict postoperative pain. Questionnaire-based tools such as the Pain Sensitivity Questionnaire (PSQ) are validated for this purpose, but prediction could be improved by incorporating other parameters. OBJECTIVES: To explore the potency of sensitivity to nonpainful stimuli and biometric data to improve prediction of pain. STUDY DESIGN: Transversal exploratory study. SETTING: Single clinical investigation center. METHODS. Eighty-five healthy volunteers of both genders underwent a multimodal exploration including biometry, questionnaire-based assessment of anxiety, depression, pain catastrophizing, sensitivity to smell, and the PSQ, followed by a psychophysical assessment of unpleasantness thresholds for light and sound, and sensitivity to mechanical, heat, and cold pain. These last 3 parameters were used to calculate a composite pain score. After a multi-step selection, multivariable analyses identified the explanative factors of experimental pain sensitivity, by including biometric, questionnaire-based, and psychophysical nonnociceptive sensitivity parameters, with the aim of having each domain represented. RESULTS: Female gender predicted mechanical pain, a younger age and dark eyes predicted cold pain, and the PSQ predicted heat pain. Sensitivity to unpleasantness of sound predicted mechanical and heat pain, and sensitivity to unpleasantness of light predicted cold pain. Sensitivity to smell was unrelated. The predictors of the composite pain score were the PSQ, the light unpleasantness threshold, and an interaction between gender and eye color, the score being lower in light-eyed men and higher in all women. The final multivariable multi-domain model was more predictive of pain than the PSQ alone (R2 = 0.301 vs 0.122, respectively). LIMITATIONS: Sensitivity to smell was only assessed by a short questionnaire and could lack relevance. Healthy volunteers were unlikely to elicit psychological risk factors such as anxiety, depression, or catastrophizing. These results have not been validated in a clinical setting (e.g., perioperative). CONCLUSION: The predictive potential of the PSQ can be improved by including information about gender, eye color, and light sensitivity. However, there is still a need for a technique suitable for routine clinical use to assess light sensitivity. KEY WORDS: Personalized medicine, postoperative pain, senses, prediction, photophobia, hyperacusis, eye color, hypervigilance, sensory over-responsivity