Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain

• Published in: BMC palliative care 2024-08, Vol.23 (1), p.198-14, Article 198
• Main Authors: Cascella, Marco, Di Gennaro, Piergiacomo, Crispo, Anna, Vittori, Alessandro, Petrucci, Emiliano, Sciorio, Francesco, Marinangeli, Franco, Ponsiglione, Alfonso Maria, Romano, Maria, Ovetta, Concetta, Ottaiano, Alessandro, Sabbatino, Francesco, Perri, Francesco, Piazza, Ornella, Coluccia, Sergio
• Format: Article
• Language: English
• Subjects: Adult; Aged; Aged, 80 and over; Artificial intelligence; Automatic Pain Assessment; Breakthrough Cancer Pain; Cancer Pain; Cancer Pain - diagnosis; Cancer therapies; Care and treatment; Cohort Studies; Diagnosis; Electrocardiography; Electrocardiography - methods; Female; Galvanic Skin Response - physiology; Health aspects; Heart rate; Humans; Male; Metastasis; Middle Aged; Narcotics; Nervous system; Opioids; Pain; Pain management; Pain Management - methods; Pain Management - standards; Pain Measurement - methods; Palliative Care; Palliative treatment; Physiology; Quality of life; Skin; Time series
• ISSN: 1472-684X; 1472-684X
• DOI: 10.1186/s12904-024-01526-z

Tailoring effective strategies for cancer pain management requires a careful analysis of multiple factors that influence pain phenomena and, ultimately, guide the therapy. While there is a wealth of research on automatic pain assessment (APA), its integration with clinical data remains inadequately explored. This study aimed to address the potential correlations between subjective and APA-derived objectives variables in a cohort of cancer patients. A multidimensional statistical approach was employed. Demographic, clinical, and pain-related variables were examined. Objective measures included electrodermal activity (EDA) and electrocardiogram (ECG) signals. Sensitivity analysis, multiple factorial analysis (MFA), hierarchical clustering on principal components (HCPC), and multivariable regression were used for data analysis. The study analyzed data from 64 cancer patients. MFA revealed correlations between pain intensity, type, Eastern Cooperative Oncology Group Performance status (ECOG), opioids, and metastases. Clustering identified three distinct patient groups based on pain characteristics, treatments, and ECOG. Multivariable regression analysis showed associations between pain intensity, ECOG, type of breakthrough cancer pain, and opioid dosages. The analyses failed to find a correlation between subjective and objective pain variables. The reported pain perception is unrelated to the objective variables of APA. An in-depth investigation of APA is required to understand the variables to be studied, the operational modalities, and above all, strategies for appropriate integration with data obtained from self-reporting. This study is registered with ClinicalTrials.gov, number (NCT04726228), registered 27 January 2021, https://classic. gov/ct2/show/NCT04726228?term=nct04726228&draw=2&rank=1.