ArticleJournal of anesthesia, analgesia and critical care2026
A prospective multimethod investigation of cancer-related pain integrating clinical data and machine learning: results from the RUGGI Study.
Article in Journal of anesthesia, analgesia and critical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07038434 (Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations), which is not on this map. Cited by 1 paper.
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Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
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1 citing paper in PubMed.
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23 authors.
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Abstract
backgroundCancer-related pain is a multidimensional phenomenon, and key determinants of pain intensity remain unclear. The aim of this study was to identify the clinical determinants of cancer-related pain intensity using a convergent, multimethod analytical framework.
methodsProspective observational study of patients with cancer-related pain. Pain was assessed using a 0-10 numeric rating scale (NRS), the Douleur Neuropathique 4, and the Brief Pain Inventory (BPI). Clinical variables included Eastern Cooperative Oncology Group Performance Status (ECOG-PS), metastatic status, comorbidities, and treatments. Multivariable regression, unsupervised clustering, and supervised machine-learning (ML) models were applied.
resultsEighty-six patients were included (median age 64 years). In bivariate analyses, higher pain intensity was associated with opioid use and poorer functional status, while neuropathic pain features were prevalent but not independently associated with pain intensity. Spearman correlation analysis demonstrated significant associations between ECOG-PS and pain intensity, BPI severity, and BPI interference. In multivariable regression, ECOG-PS emerged as a key independent predictor of pain intensity, whereas metastatic status and cancer type were not independently associated. Unsupervised clustering identified two clinically meaningful phenotypes primarily distinguished by pain intensity and functional interference, rather than neuropathic mechanisms. In supervised ML models predicting high pain burden (NRS ≥ 7), ECOG-PS consistently ranked as the most influential feature, although overall predictive performance was modest.
conclusionsFunctional impairment represents a central determinant of cancer-related pain intensity, surpassing pain phenotype and disease-related variables across complementary analytical approaches. These findings support function-centered pain assessment frameworks and underscore the need to systematically integrate functional evaluation into cancer pain management strategies.
trial registrationThe study was prospectively registered on ClinicalTrials.gov (identifier: NCT07038434).
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