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Using Latent Profile Analysis to Characterize Clinical Heterogeneity and Impulsivity in a Large Residential Addiction

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Identifying patient subgroups in addiction treatment is key. Five distinct profiles emerged, varying in substance use and psychiatric severity, informing personalized care for better outcomes.

Keywords:
LPASUDaddictionmental healthpsychiatric comorbidities

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Area of Science:

  • Addiction Medicine
  • Psychiatry
  • Data Science

Background:

  • Clinical heterogeneity in substance use disorder (SUD) treatment poses challenges.
  • Standardized treatments may not meet individual patient needs.
  • Tailored approaches are needed to improve treatment outcomes.

Purpose of the Study:

  • To systematically characterize clinical heterogeneity in patients with SUD.
  • To identify distinct patient profiles based on substance use and co-occurring psychiatric conditions.
  • To inform the development of personalized addiction treatment strategies.

Main Methods:

  • Latent profile analysis (LPA) was used.
  • Data from 1519 inpatient SUD treatment patients (April 2018-March 2020).
  • Clustering based on substance use, depression, anxiety, and PTSD severity.

Main Results:

  • Five distinct patient profiles were identified.
  • Profiles varied in SUD severity, psychiatric comorbidities, and age.
  • Higher psychiatric severity was observed in alcohol and cannabis-focused profiles.
  • Impulsive behavior was higher in polysubstance and high-severity cannabis/psychiatric profiles.

Conclusions:

  • LPA effectively identified five patient clusters with significant heterogeneity.
  • Tailored treatment pathways addressing specific profile needs are recommended.
  • Personalized approaches focusing on commonalities within clusters can enhance SUD treatment effectiveness.