Classification of pain in cancer patients – a systematic literature review

Abstract

One of the aims of the European Palliative Care Research Collaborative (EPCRC) is to achieve consensus on a classification system for cancer pain. We performed a systematic literature review to identify existing classification systems and domains/items used to classify cancer patients with pain. In a systematic search in the databases Medline and Embase, covering 1986-2006, 692 hits were obtained. 92 papers were evaluated to address pain classification. Six standardised classification systems were identified; three of them systematically developed and partially validated. Both pain characteristics and patient characteristics relevant for cancer pain classification were included in the classification systems. All but one of the standardised systems aim at predicting treatment response or adequacy of treatment. Several domains and items used to describe cancer pain but not formally described as part of a classification system were also identified and systematized. The existing approaches to pain classification in cancer patients are different, mostly not thoroughly validated, and none is widely applied. An internationally accepted classification system for cancer pain could improve research and cancer pain management. This systematic review suggests a need for developing an international consensus on how to classify pain in cancer patients.

Pain following cancer treatment: Guidelines for the clinical classification of predominant neuropathic, nociceptive and central sensitization pain

“…Still, extensive validation of the clinical classification guideline of pain in cancer survivors beyond the primary publication is required. Such a validation process may include, but should not be limited to: systematic literature reviews, meta-analysis, repeatable input from patients and clinical experts and empirical data collections [13]. The latter may include interobserver reliability testing, cross-sectional as well as longitudinal studies comparing the outcome of the clinical algorithm with psychophysiological pain testing (e.g.…”

“…the temporal summation of pain and conditioned pain modulation paradigms) as well as functional brain imaging findings. Such a validation process should preferentially be performed within an international collaboration consisting of clinical experts and clinical researchers [13], to optimize external validity and wide acceptance of the study findings.…”

“…This is important for clinicians because neuropathic and mixed cancer pain (i.e. a mixture of nociceptive, neuropathic and/or central sensitization pain) are considered to be more difficult to treat than pure nociceptive pain [13,14], and the classification of the correct pain mechanism is relevant regarding choice of cancer pain treatment [13]. Indeed, in contrast to symptomatic and/or diagnosis-based treatments, mechanismbased treatments may have better outcomes [15].…”

Cancer-related neuropathic pain at the outpatient pain clinic of the Clinical centre of Serbia: Cancer-related neuropathic pain

“…Autori navode da se, sa obzirom na postojanje i neneuropatske komponente u malignom bolu, kliničko ispoljavanje neuropatske komponente razlikuje od nemalignog bola, te tako neuropatska komponenta ostaje često prikrivena dominantnim bolom nocioceptivnog porekla 5 . U različitim radovima se navodi različita prevalenca neuropatskog bola kod pacijenata sa malignim bolom, 15-39% [6][7][8][9] . U revijalnom članku Kurito GB i saradnika, objašnjena je povezanost između vrste maligne bolesti i primenjene terapije sa ispoljenim simptomima i znakovima neuropatije 6 .…”

An Efficient Hybrid Classification Algorithm – An Example from Palliative Care

“…As of today, there is no consensus about the set of classes that should be used for pain classification in palliative care [9]. Our domain is open and changing, which is why we study methods of machine learning and decision support that are able to produce useful results without making very strong commitments about the domain.…”