Physiological responses to pain in cancer patients: A systematic review

Serena Moscato; Pietro Cortelli; Lorenzo Chiari

doi:10.1016/j.cmpb.2022.106682

Physiological responses to pain in cancer patients: A systematic review

Comput Methods Programs Biomed. 2022 Apr:217:106682. doi: 10.1016/j.cmpb.2022.106682. Epub 2022 Feb 5.

Authors

Serena Moscato¹, Pietro Cortelli², Lorenzo Chiari³

Affiliations

¹ Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, Bologna, Italy. Electronic address: serena.moscato3@unibo.it.
² IRCCS Istituto Delle Scienze Neurologiche Di Bologna, UOC Clinica Neurologica NeuroMet, Ospedale Bellaria, Bologna, Italy; Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.
³ Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" - DEI, University of Bologna, Bologna, Italy; Health Sciences and Technologies, Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy.

PMID: 35172252
DOI: 10.1016/j.cmpb.2022.106682

Abstract

Background and objective: Pain is one of the most debilitating symptoms in persons with cancer. Still, its assessment is often neglected both by patients and healthcare professionals. There is increasing interest in conducting pain assessment and monitoring via physiological signals that promise to overcome the limitations of state-of-the-art pain assessment tools. This systematic review aims to evaluate existing experimental studies to identify the most promising methods and results for objectively quantifying cancer patients' pain experience.

Methods: Four electronic databases (Pubmed, Compendex, Scopus, Web of Science) were systematically searched for articles published up to October 2020.

Results: Fourteen studies (528 participants) were included in the review. The selected studies analyzed seven physiological signals. Blood pressure and ECG were the most used signals. Sixteen physiological parameters showed significant changes in association with pain. The studies were fairly consistent in stating that heart rate, the low-frequency to high-frequency component ratio (LF/HF), and systolic blood pressure positively correlate with the pain.

Conclusions: Current evidence supports the hypothesis that physiological signals can help objectively quantify, at least in part, cancer patients' pain experience. While there is much more to be done to obtain a reliable pain assessment method, this review takes an essential first step by highlighting issues that should be taken into account in future research: use of a wearable device for pervasive recording in a real-world context, implementation of a big-data approach possibly supported by AI, including multiple stratification factors (e.g., cancer site and stage, source of pain, demographic and psychosocial data), and better-defined recording procedures. Improved methods and algorithms could then become valuable add-ons in taking charge of cancer patients.

Keywords: Autonomic signals; Cancer pain; Instrumented assessment; Pain assessment; Physiological signals.

Publication types

Review
Systematic Review

MeSH terms

Algorithms
Cancer Pain*
Health Personnel
Humans
Neoplasms* / complications
Wearable Electronic Devices*