Published in

Elsevier, Journal of Pain, 3(15), p. 241-249, 2014

DOI: 10.1016/j.jpain.2014.01.004

Links

Tools

Export citation

Search in Google Scholar

The ACTTION-American Pain Society Pain Taxonomy (AAPT): An Evidence-Based and Multidimensional Approach to Classifying Chronic Pain Conditions

This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

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 US 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 (AAPT). This manuscript 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. AAPT 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 & 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.