Predictors of premature treatment termination in a large residential addiction medicine program

Sabrina K Syan; Meenu Minhas; Assaf Oshri; Jean Costello; Sarah Sousa; Andriy V Samokhvalov; Brian Rush; James MacKillop

doi:10.1016/j.jsat.2020.108077

Predictors of premature treatment termination in a large residential addiction medicine program

J Subst Abuse Treat. 2020 Oct:117:108077. doi: 10.1016/j.jsat.2020.108077. Epub 2020 Jul 15.

Authors

Sabrina K Syan¹, Meenu Minhas¹, Assaf Oshri², Jean Costello³, Sarah Sousa³, Andriy V Samokhvalov⁴, Brian Rush⁴, James MacKillop⁵

Affiliations

¹ Peter Boris Centre for Addictions Research, McMaster University & St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada.
² Department of Human Development and Family Sciences, University of Georgia, Athens, GA, USA.
³ Homewood Research Institute, Guelph, ON, Canada.
⁴ Homewood Research Institute, Guelph, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada.
⁵ Peter Boris Centre for Addictions Research, McMaster University & St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Homewood Research Institute, Guelph, ON, Canada. Electronic address: jmackill@mcmaster.ca.

PMID: 32811634
DOI: 10.1016/j.jsat.2020.108077

Abstract

Background: While inpatient programs are a common setting for addiction treatment, patients' premature termination is a major concern. Predicting premature treatment termination has the potential to substantially improve patient outcomes by identifying high-risk profiles and suggesting care paths that might reduce dropout. The current study examined the predictors of premature termination from an inpatient addiction medicine service.

Methods: In 1082 patients admitted to a large inpatient addiction medicine service, we used intake assessments of severity of alcohol use disorder, illicit drug use disorder, post-traumatic stress disorder (PTSD), anxiety disorders, and major depressive disorder to predict planned termination (n = 922) or premature termination (n = 160). We used two complementary analytic approaches-traditional binary logistic regression and a data-driven latent profile analysis (LPA).

Results: Binary logistic regression revealed that alcohol use severity, illicit drug use severity, and PTSD severity significantly predicted termination status, although alcohol use severity notably exhibited an inverse relationship. The LPA revealed four distinct profiles, with one profile exhibiting a significantly higher rate of premature termination and another exhibiting a significantly lower rate of premature termination. The high-risk profile was characterized by high drug severity, high comorbid psychopathology (PTSD, depression, and anxiety symptoms), but low alcohol severity. The low-risk profile was characterized by high alcohol severity, but low drug use and low comorbid psychopathology.

Conclusions: These results provide converging evidence that illicit drug severity and psychiatric severity, and particularly PTSD, were associated with premature termination. Moreover, the LPA revealed distinct latent subgroups of patients with meaningfully higher and lower risk of premature termination, suggesting that addiction services should develop strategies for identifying high-risk individuals or develop care paths for high-risk symptom clusters. Approaches that are trauma-informed or otherwise focus on the management of comorbid psychiatric conditions may be particularly appropriate for reducing premature termination.

Keywords: Drop-out; Recovery; Relapse; Substance use disorders; Treatment.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Addiction Medicine*
Anxiety Disorders / epidemiology
Comorbidity
Depressive Disorder, Major*
Humans
Stress Disorders, Post-Traumatic* / epidemiology
Substance-Related Disorders* / epidemiology