A rapid systematic review of breakthrough pain definitions and descriptions

Background

Breakthrough pain is common in life-limiting conditions and at end-of-life. Despite over 30 years of study, there is little consensus regarding the definition and characteristics of breakthrough pain.

Objective

This study aims to update and expand a 2010 systematic review by Haugen and colleagues to identify (1) all definitions of breakthrough pain and (2) all descriptions and classifications of breakthrough pain reported by patients, caregivers, clinicians, and experts.

Design

This rapid systematic review followed the Cochrane Rapid Review Methods Group guidelines. A protocol is published on PROSPERO (CRD42019155583).

Data sources

CINAHL, MEDLINE, PsycINFO, and the Web of Science were searched for breakthrough pain terms from the inception dates of each database to 26th August 2022.

Results

We identified 65 studies that included data on breakthrough pain definitions, descriptions, or classifications from patients (n = 30), clinicians (n = 6), and experts (n = 29), but none with data from caregivers. Most experts proposed that breakthrough pain was a sudden, severe, brief pain occurring in patients with adequately controlled mild-moderate background pain. However, definitions varied and there was no consensus. Pain characteristics were broadly similar across studies though temporal factors varied widely. Experts classified breakthrough pain into nociceptive, neuropathic, visceral, somatic, or mixed types. Patients with breakthrough pain commonly experienced depression, anxiety, and interference with daily life.

Conclusions

Despite ongoing efforts, there is still no consensus on the definition of breakthrough pain. A compromise is needed on breakthrough pain nomenclature to collect reliable incidence and prevalence data and to inform further refinement of the construct.

Clinical Diagnosis and Treatment of Chronic Pain

More than 600 million people globally are estimated to be living with chronic pain. It is one of the most common complaints seen in an outpatient setting, with over half of patients complaining of pain during a visit. Failure to properly diagnose and manage chronic pain is associated with substantial morbidity and mortality, especially when opioids are involved. Furthermore, it is a tremendous financial strain on the healthcare system, as over USD 100 billion is spent yearly in the United States on healthcare costs related to pain management and opioids. This exceeds the costs of diabetes, heart disease, and cancer-related care combined. Being able to properly diagnose, manage, and treat chronic pain conditions can substantially lower morbidity, mortality, and healthcare costs in the United States. This review will outline the current definitions, biopsychosocial model, subclassifications, somatosensory assessments, imaging, clinical prediction models, and treatment modalities associated with chronic pain.

Development and Validation of the Dystonia-Pain Classification System: A Multicenter Study

BACKGROUND

Dystonia is associated with disabling nonmotor symptoms like chronic pain (CP), which is prevalent in dystonia and significantly impacts the quality of life (QoL). There is no validated tool for assessing CP in dystonia, which substantially hampers pain management.

OBJECTIVE

The aim was to develop a CP classification and scoring system for dystonia.

METHODS

A multidisciplinary group was established to develop the Dystonia-Pain Classification System (Dystonia-PCS). The classification of CP as related or unrelated to dystonia was followed by the assessment of pain severity score, encompassing pain intensity, frequency, and impact on daily living. Then, consecutive patients with inherited/idiopathic dystonia of different spatial distribution were recruited in a cross-sectional multicenter validation study. Dystonia-PCS was compared to validated pain, mood, QoL, and dystonia scales (Brief Pain Inventory, Douleur Neuropathique-4 questionnaire, European QoL-5 Dimensions-3 Level Version, and Burke-Fahn-Marsden Dystonia Rating Scale).

RESULTS

CP was present in 81 of 123 recruited patients, being directly related to dystonia in 82.7%, aggravated by dystonia in 8.8%, and nonrelated to dystonia in 7.5%. Dystonia-PCS had excellent intra-rater (Intraclass Correlation Coefficient - ICC: 0.941) and inter-rater (ICC: 0.867) reliability. In addition, pain severity score correlated with European QoL-5 Dimensions-3 Level Version's pain subscore (r = 0.635, P < 0.001) and the Brief Pain Inventory's severity and interference scores (r = 0.553, P < 0.001 and r = 0.609, P < 0.001, respectively).

CONCLUSIONS

Dystonia-PCS is a reliable tool to categorize and quantify CP impact in dystonia and will help improve clinical trial design and management of CP in patients affected by this disorder. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals

Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on patient-reported information. Blood volume pulse (BVP) is a relatively unexplored physiological measure with the potential to assess pain levels. This study aims to develop an accurate pain intensity classification system based on BVP signals through comprehensive experimental analysis. Twenty-two healthy subjects participated in the study, in which we analyzed the classification performance of BVP signals for various pain intensities using time, frequency, and morphological features through fourteen different machine learning classifiers. Three experiments were conducted using leave-one-subject-out cross-validation to better examine the hidden signatures of BVP signals for pain level classification. The results of the experiments showed that BVP signals combined with machine learning can provide an objective and quantitative evaluation of pain levels in clinical settings. Specifically, no pain and high pain BVP signals were classified with 96.6% accuracy, 100% sensitivity, and 91.6% specificity using a combination of time, frequency, and morphological features with artificial neural networks (ANNs). The classification of no pain and low pain BVP signals yielded 83.3% accuracy using a combination of time and morphological features with the AdaBoost classifier. Finally, the multi-class experiment, which classified no pain, low pain, and high pain, achieved 69% overall accuracy using a combination of time and morphological features with ANN. In conclusion, the experimental results suggest that BVP signals combined with machine learning can offer an objective and reliable assessment of pain levels in clinical settings.

Cancer-Related Pain: A Longitudinal Study of Time to Stable Pain Control and Its Clinicodemographic Predictors

CONTEXT

Multidimensional assessment is pivotal in managing cancer-related pain.

OBJECTIVES

The objectives of this study were to determine time to stable pain control (SPC) and identify its baseline clinicodemographic predictors in patients with cancer pain.

METHODS

This is a prospective longitudinal study of patients attending a cancer pain clinic. Scheduled clinic attendances and weekly investigator-led phone calls enabled monitoring of patients' daily pain diary, opioid use, and other analgesic interventions. Baseline clinicodemographic variables were examined in survival analyses, which included the construction of accelerated failure time models with time ratios [TRs, (95% CIs)], based on time to SPC (pain intensity ≤3 and <3 breakthrough opioid doses over three consecutive days) for variable categories.

RESULTS

Of 319 participants, 22 died before achieving SPC and were censored in the survival analysis. The median survival time (95% CI) to SPC was 22 (19-25) days. In multivariable analysis, compared to their respective reference categories, female sex (P = 0.001), substance abuse (P < 0.001), a neuropathic pain component (P < 0.001), and use of ≥1 adjuvant analgesic (P = 0.022) each had TRs > 1 (1.03-2.54), whereas soft tissue pain (P < 0.001) had a TR = 0.71 (0.62-0.82), reflecting longer and shorter time to SPC, respectively.

CONCLUSION

SPC is achievable for most patients with cancer pain. Recognition of strong predictors of time to SPC, such as substance abuse, a neuropathic pain component, soft tissue pain, and current use of adjuvant analgesia, may help to triage care services based on therapeutic need and guide analgesic interventions.

Cancer Pain Assessment and Classification

More than half of patients affected by cancer experience pain of moderate-to-severe intensity, often in multiple sites, and of different etiologies and underlying mechanisms. The heterogeneity of pain mechanisms is expressed with the fluctuating nature of cancer pain intensity and clinical characteristics. Traditional ways of classifying pain in the cancer population include distinguishing pain etiology, clinical characteristics related to pain and the patient, pathophysiology, and the use of already validated classification systems. Concepts like breakthrough, nociceptive, neuropathic, and mixed pain are very important in the assessment of pain in this population of patients. When dealing with patients affected by cancer pain it is also very important to be familiar to the characteristics of specific pain syndromes that are usually encountered. In this article we review methods presently applied for classifying cancer pain highlighting the importance of an accurate clinical evaluation in providing adequate analgesia to patients.

From "Breakthrough" to "Episodic" Cancer Pain? A European Association for Palliative Care Research Network Expert Delphi Survey Toward a Common Terminology and Classification of Transient Cancer Pain Exacerbations

CONTEXT

Cancer pain can appear with spikes of higher intensity. Breakthrough cancer pain (BTCP) is the most common term for the transient exacerbations of pain, but the ability of the nomenclature to capture relevant pain variations and give treatment guidance is questionable.

OBJECTIVES

To reach consensus on definitions, terminology, and subclassification of transient cancer pain exacerbations.

METHODS

The most frequent authors on BTCP literature were identified using the same search strategy as in a systematic review and invited to participate in a two-round Delphi survey. Topics with a low degree of consensus on BTCP classification were refined into 20 statements. The participants rated their degree of agreement with the statements on a numeric rating scale (0-10). Consensus was defined as a median numeric rating scale score of ≥7 and an interquartile range of ≤3.

RESULTS

Fifty-two authors had published three or more articles on BTCP over the past 10 years. Twenty-seven responded in the first round and 24 in the second round. Consensus was reached for 13 of 20 statements. Transient cancer pain exacerbations can occur without background pain, when background pain is uncontrolled, and regardless of opioid treatment. There exist cancer pain exacerbations other than BTCP, and the phenomenon could be named "episodic pain." Patient-reported treatment satisfaction is important with respect to assessment. Subclassification according to pain pathophysiology can provide treatment guidance.

CONCLUSION

Significant transient cancer pain exacerbations include more than just BTCP. Patient input and pain classification are important factors for tailoring treatment.

The ACTTION-American Pain Society Pain Taxonomy (AAPT): an evidence-based and multidimensional approach to classifying chronic pain conditions

Current approaches to classification of chronic pain conditions suffer from the absence of a systematically implemented and evidence-based taxonomy. Moreover, existing diagnostic approaches typically fail to incorporate available knowledge regarding the biopsychosocial mechanisms contributing to pain conditions. To address these gaps, the Analgesic, Anesthetic, and Addiction Clinical Trial Translations Innovations Opportunities and Networks (ACTTION) public-private partnership with the U.S. Food and Drug Administration and the American Pain Society (APS) have joined together to develop an evidence-based chronic pain classification system called the ACTTION-APS Pain Taxonomy. This paper describes the outcome of an ACTTION-APS consensus meeting, at which experts agreed on a structure for this new taxonomy of chronic pain conditions. Several major issues around which discussion revolved are presented and summarized, and the structure of the taxonomy is presented. ACTTION-APS Pain Taxonomy will include the following dimensions: 1) core diagnostic criteria; 2) common features; 3) common medical comorbidities; 4) neurobiological, psychosocial, and functional consequences; and 5) putative neurobiological and psychosocial mechanisms, risk factors, and protective factors. In coming months, expert working groups will apply this taxonomy to clusters of chronic pain conditions, thereby developing a set of diagnostic criteria that have been consistently and systematically implemented across nearly all common chronic pain conditions. It is anticipated that the availability of this evidence-based and mechanistic approach to pain classification will be of substantial benefit to chronic pain research and treatment.

PERSPECTIVE

The ACTTION-APS Pain Taxonomy is an evidence-based chronic pain classification system designed to classify chronic pain along the following dimensions: 1) core diagnostic criteria; 2) common features; 3) common medical comorbidities; 4) neurobiological, psychosocial, and functional consequences; and 5) putative neurobiological and psychosocial mechanisms, risk factors, and protective factors.

Reliability of the Bryce/Ragnarsson spinal cord injury pain taxonomy

BACKGROUND/OBJECTIVE

Pain is a common secondary complication of spinal cord injury (SCI). However, the literature offers varying estimates of the numbers of persons with SCI who develop pain. The variability in these numbers is caused in part by differences in the classification of pain; there is currently no commonly accepted classification system for pain affecting persons after SCI. This study investigated the interrater reliability of the Bryce/Ragnarsson SCI pain taxonomy (BR-SCI-PT). The hypothesis was that, when used by physicians with minimal training in the BR-SCI-PT, it would have high interrater reliability for the categorization of reported pains.

METHODS

One hundred thirty-five vignettes, each of which described a person with SCI with one or more different etiologic subtypes of pain, were evaluated by 5 groups of up to 10 physicians with SCI subspecialization (39 respondents total). Physician classifications were compared with those made by the investigators.

RESULTS

Of 179 pain descriptions, 83% were categorized correctly to one of the 15 BR-SCI-PT pain types; 93% were categorized correctly with respect to level (above/at/below neurological level of injury), whereas 90% were categorized correctly as being either nociceptive or neuropathic. Subjects expressed a generally high confidence in the correctness of their classifications.

CONCLUSIONS

Substantial interrater agreement was achieved in determining subtypes of pain within the BR-SCI-PT. The agreement was improved for categorizing within less restrictive categories (ie, with respect to the neurological level of injury and whether the pain was nociceptive or neuropathic).