Prediction of pain using electrocardiographic‐derived autonomic measures: A systematic review

Abstract

Background and ObjectivePain is a major clinical challenge, and understanding the pathophysiology is critical for optimal management. The autonomic nervous system reacts to pain stimuli, and autonomic dysfunction may predict pain sensation. The most used assessment of autonomic function is based on electrocardiographic measures, and the ability of such measures to predict pain was investigated.Databases and Data TreatmentEnglish articles indexed in PubMed and EMBASE were reviewed for eligibility and included when they reported electrocardiographic‐derived measures’ ability to predict pain response. The quality in prognostic studies (QUIPS) tool was used to assess the quality of the included articles.ResultsThe search revealed 15 publications, five on experimental pain, five on postoperative pain, and five on longitudinal clinical pain changes, investigating a total of 1069 patients. All studies used electrocardiographically derived parameters to predict pain assessed with pain thresholds using quantitative sensory testing or different scales. Across all study modalities, electrocardiographic measures were able to predict pain. Higher parasympathetic activity predicted decreased experimental, postoperative, and long‐term pain in most cases while changes in sympathetic activity did not consistently predict pain.ConclusionsMost studies demonstrated that parasympathetic activity could predict acute and chronic pain intensity. In the clinic, this may be used to identify which patients need more intensive care to prevent, for example postoperative pain and develop personalized chronic pain management.SignificancePain is a debilitating problem, and the ability to predict occurrence and severity would be a useful clinical tool. Basal autonomic tone has been suggested to influence pain perception. This systematic review investigated electrocardiographic‐derived autonomic tone and found that increased parasympathetic tone could predict pain reduction in different types of pain.