Development of the Addiction Dimensions for Assessment and Personalised Treatment (ADAPT)

John Marsden*, Brian Eastwood, Robert Ali, Pete Burkinshaw, Gagandeep Chohan, Alex Copello, Daniel Burn, Michael Kelleher, Luke Mitcheson, Steve Taylor, Nick Wilson, Chris Whiteley, Edward Day

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Background: Convergent research reveals heterogeneity in substance use disorders (SUD). The Addiction Dimensions for Assessment and Personalised Treatment (ADAPT) is designed to help clinicians tailor therapies. Methods: Multicentre study in 21 SUD clinics in London, Birmingham (England) and Adelaide (Australia). 132 clinicians rated their caseload on a beta version with 16 ordinal indicators of addiction severity, health and social problem complexity, and recovery strengths constructs. In Birmingham, two in-treatment outcomes were recorded after 15-months: 28-day drug use (Treatment Outcome Profile; n= 703) and Global Assessment of Functioning (GAF; DSM-IV Axis V; n= 695). Following item-level screening (inter-rater reliability [IRR]; n= 388), exploratory structural equation models (ESEM), latent profile analysis (LPA), and mixed-effects regression evaluated construct, concurrent and predictive validity characteristics, respectively. Results: 2467 patients rated (majority opioid or stimulant dependent, enrolled in opioid medication assisted or psychological treatment). IRR-screening removed two items and ESEM models identified and recalibrated remaining indicators (root mean square error of approximation 0.066 [90% confidence interval 0.055-0.064]). Following minor re-specification and satisfactory measurement invariance evaluation, ADAPT factor scores discriminated patients by sample, addiction therapy and drug use. LPA identified three patient sub-types: Class 1 (moderate severity, moderate complexity, high strengths profile; 46.9%); Class 2 (low severity, low complexity, high strengths; 25.4%) and Class 3 (high severity, high complexity, low strengths; 27.7%). Class 2 had higher GAF (z= 4.30). Class 3 predicted follow-up drug use (z= 2.02) and lower GAF (z= 3.51). Conclusion: The ADAPT is a valid instrument for SUD treatment planning, clinical review and outcome evaluation. Scoring and application are discussed.

Original languageEnglish
Pages (from-to)121-131
Number of pages11
JournalDrug and Alcohol Dependence
Volume139
DOIs
Publication statusPublished - 2014

Bibliographical note

Funding Information:
Costs of the working group discussion process and the analysis were supported by the English National Treatment Agency for Substance Misuse (NTA) and the Health and Wellbeing Directorate, Public Health England (PHE). The contents of this article are solely the responsibility of the authors and do not necessarily reflect the views or stated policy of AonA, DASSA, Blenheim CDP, BSMHT, ELFT, KC/IOP, PHE or SLaM. The ADAPT (V1.0) is free for use for all non-commercial applications.

Funding Information:
Resource costs to the Adelaide, Birmingham and London collaborators for this study were supported by Drug and Alcohol Services South Australia (DASSA) ; Birmingham and Solihull Mental Health Foundation Trust (BSMHT) ; Blenheim CDP ; East London NHS Foundation Trust (ELFT) ; Action on Addiction (AonA, via an untied educational grant from Reckitt-Benckiser Pharmaceuticals); South London and Maudsley NHS Foundation Trust (SLaM) ; King's College London (Institute of Psychiatry [KCL/IOP]) , and the King's Clinical Trials Unit at King's Health Partners.

Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.

Keywords

  • ADAPT
  • Assessment
  • Personalised
  • Substance use disorder
  • Treatment

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