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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

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Predicting Outpatient Follow-Up Retention After Inpatient Treatment in Patients With Alcohol Use Disorder: A

Jennifer J Barb1, Lillian C King1, Alexandria N Hughes1

  • 1Translational Biobehavioral and Health Promotion Branch, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA.

Addiction Biology
|June 13, 2026
PubMed
Summary

Patient retention in alcohol use disorder (AUD) treatment is key for recovery. Surprisingly, positive urgency and positive life events predicted fewer outpatient visits, highlighting the need to integrate motivational factors into aftercare planning.

Keywords:
alcohol use disordermachine learningoutpatient AUD treatmentrandom forest

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Area of Science:

  • Psychiatry
  • Psychology
  • Data Science

Background:

  • Patient retention is crucial for successful recovery from alcohol use disorder (AUD).
  • Understanding factors influencing continued engagement in outpatient care post-inpatient treatment is vital for improving AUD treatment outcomes.

Purpose of the Study:

  • To identify predictors of outpatient treatment engagement, measured by follow-up visit frequency, among individuals with AUD after inpatient care.
  • To integrate machine learning and regression techniques to analyze clinical, psychological, and physiological variables.

Main Methods:

  • A five-step analytic framework combining random forest modeling (RFM) and Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed.
  • Clinical, psychological, and physiological data from 119 AUD patients post-inpatient treatment were analyzed.
  • RFM identified potential predictors, while LASSO validated and selected significant variables.

Main Results:

  • Positive urgency and positive life events were the strongest negative predictors of outpatient visit frequency.
  • Higher alcohol use severity and elevated hemoglobin levels were associated with fewer follow-up visits.
  • Increased depressive symptom severity positively predicted greater outpatient engagement.

Conclusions:

  • Affective traits, alcohol use severity, and physiological factors significantly determine outpatient engagement after inpatient AUD treatment.
  • Unexpectedly, factors like positive urgency and positive life events correlated with reduced attendance, suggesting a potential diminished perceived need for care.
  • Findings underscore the necessity of incorporating psychological and motivational assessments into post-discharge plans to enhance patient retention and early recovery in AUD treatment.