Predictors of heavy drinking during and following treatment.

Witkiewitz, Katie

doi:10.1037/a0022889

Cited by 71 publications

(73 citation statements)

References 70 publications

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“…In our study, as in previous ones [44], background variables (sociodemographics, personality disorders, and alcohol drinking status and its consequences at baseline) have less posttreatment predictive power than distal variables (alcohol drinking status and its consequences after 6 months of treatment, type of treatment used, and relapse prevention intervention). Eighteen of the 27 most important variables reported in table 4 are distal ones, i.e.…”

Section: Discussionsupporting

confidence: 71%

“…18 months after the start of treatment, covers only a part of the 5-year period recommended as a definition for stable remission [49]. Knowing that significant individual variability has been found in drinking trajectories following treatment [44], a larger sample may be needed to identify predictors of outcomes that are important for specific subgroups of patients associated with background variables. In our study, biomarkers were used as outcome variables and also as predictors of outcomes; however, the fact that we did not use the glycoprotein carbohydrate-deficient transferrin (CDT) conjointly with liver function tests is also a limitation as this combination enhances sensitivity [33].…”

Section: Discussionmentioning

confidence: 99%

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Predictors of Posttreatment Drinking Outcomes in Patients with Alcohol Dependence

Flórez¹,

Sáiz²,

García‐Portilla³

et al. 2014

Eur Addict Res

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Aim: This cohort study examined how predictors of alcohol dependence treatment outcomes work together over time by comparing pretreatment and posttreatment predictors. Methods: A sample of 274 alcohol-dependent patients was recruited and assessed at baseline, 6 months after treatment initiation (end of the active intervention phase), and 18 months after treatment initiation (end of the 12-month research follow-up phase). At each assessment point, the participants completed a battery of standardized tests [European Addiction Severity Index (EuropASI), Obsessive Compulsive Drinking Scale (OCDS), Alcohol Timeline Followback (TLFB), Fagerström, and International Personality Disorder Examination (IPDE)] that measured symptom severity and consequences; biological markers of alcohol consumption were also tested at each assessment point. A sequential strategy with univariate and multivariate analyses was used to identify how pretreatment and posttreatment predictors influence outcomes up to 1 year after treatment. Results: Pretreatment variables had less predictive power than posttreatment ones. OCDS scores and biological markers of alcohol consumption were the most significant variables for the prediction of posttreatment outcomes. Prior pharmacotherapy treatment and relapse prevention interventions were also associated with posttreatment outcomes. Conclusions: The findings highlight the positive impact of pharmacotherapy during the first 6 months after treatment initiation and of relapse prevention during the first year after treatment and how posttreatment predictors are more important than pretreatment predictors.

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Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Predictors of Posttreatment Drinking Outcomes in Patients with Alcohol Dependence

Flórez¹,

Sáiz²,

García‐Portilla³

et al. 2014

Eur Addict Res

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“…A complex array of factors appears to contribute to the likelihood of lapse to use during substance use disorder treatment (Poling et al, 2007; Witkiewitz, 2011). In a large clinical trial of treatment for prescription opioid dependence, a brief, 3-item craving scale was associated with the odds of opioid use in the following week.…”

Section: Discussionmentioning

confidence: 99%

Assessing craving and its relationship to subsequent prescription opioid use among treatment-seeking prescription opioid dependent patients

McHugh

Fitzmaurice

Carroll

et al. 2014

Drug and Alcohol Dependence

100

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Background Craving is viewed as a core feature of substance use disorders and has been shown to predict future drug use, particularly over the short term. Accordingly, craving is often assessed in treatment settings as a marker of risk for subsequent drug use. The identification of the briefest measure that maintains predictive validity is of particular value for both clinical and research settings to minimize assessment burden while maintaining utility for the prediction of use. Methods Data from a multi-site clinical trial of treatment for prescription opioid dependence were examined to evaluate whether a brief, 3-item craving scale administered each week predicted urine-confirmed self report of prescription opioid use in the subsequent week. Logistic regression models examining the association between craving and presence or absence of opioid use in the following week were conducted, controlling for opioid use in the previous week, treatment condition, and lifetime history of heroin use. Results Greater craving was associated with a higher odds of prescription opioid use in the following week. For each one-unit increase on this 10-point scale, the odds of using opioids in the subsequent week was 17% higher. In addition to an item assessing urges, items assessing cue-induced craving and perceived likelihood of relapse in an environment where drugs were previously used contributed uniquely to this association. Conclusions A brief measure of prescription opioid craving predicted prescription opioid use among individuals in treatment. This measure offers an efficient strategy to inform the assessment of risk for use in this population.

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“…Irrespective of the type of intervention employed, at least 60% of those treated for an AUD will relapse to a period of hazardous alcohol consumption, typically within 6 months of treatment (Meyerhoff and Durazzo, 2010, Witkiewitz, 2011, Kirshenbaum et al, 2009). Resumption of hazardous alcohol consumption levels within 6 months of treatment is associated with extended periods of relapse and clinically significant impairments of psychosocial functioning (e.g., unemployment, relationship/marital discord, legal entanglements) over the ensuing 1–3 years (Durazzo et al, 2008, Maisto et al, 2006, Maisto et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, demographic, behavioral and psychiatric variables (e.g., education, alcohol consumption level, frequency of mood disorders) that were associated with relapse in previous reports from our group and others (Durazzo et al, 2008, Greenfield et al, 2003, Witkiewitz, 2011) were also concurrently examined as predictors to determine their association with post-treatment drinking status. Finally, in Relapsers, we determined the variables associated with duration of abstinence before relapse onset.…”

Section: Introductionmentioning

confidence: 99%

Psychiatric, Demographic, and Brain Morphological Predictors of Relapse After Treatment for an Alcohol Use Disorder

Durazzo

Meyerhoff

2016

Alcoholism Clin & Exp Res

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Background Relapse in alcohol use disorders (AUD) is related to a complex interplay among multiple biological, psychiatric, psychological and psychosocial factors, which may change dynamically during and after treatment. At treatment entry for AUD, morphological abnormalities in anterior frontal regions and the insula have been observed in those who ultimately relapse following treatment. The goal of this study was to determine if measures anterior frontal and insula measures of regional brain thickness, surface area and volume predict post-treatment drinking status (i.e., relapser or abstainer) over an extended period after outpatient treatment for AUD, while concurrently considering common psychiatric, psychological and psychosocial factors previously associated with relapse. Methods Alcohol dependent individuals (n = 129) were followed for 18 months after treatment to determine post-treatment drinking status [Abstainers (n = 47) or Relapsers (n = 82)]. Brain morphometrics were derived from FreeSurfer. Receiver operator characteristic (ROC) curve analysis was used to identify the regional brain thickness, surface area and volume (all scaled to intracranial volume), demographic, psychiatric, other substance use (e.g., cigarette smoking) and alcohol consumption variables, obtained at entry into treatment, that best predicted post-treatment drinking status. Survival analyses determined variables that were related to duration of abstinence after treatment. Results ROC analyses indicated that mood disorders, education, and volumes of the right caudal ACC, right rostral ACC and total right frontal gray matter were significant predictors of post-treatment drinking status. Among Relapsers, survival analyses showed smokers and individuals with a comorbid medical condition relapsed earlier after treatment. Additionally, a greater frequency of smokers relapsed within 6 months of treatment. Conclusions Results reinforce that relapse in AUD is a function of multiple biological, psychiatric, psychological and psychosocial factors. Effective treatment of depressive disorders and cigarette smoking concurrent with AUD-focused interventions may promote better treatment outcomes.

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Predictors of heavy drinking during and following treatment.

Cited by 71 publications

References 70 publications

Predictors of Posttreatment Drinking Outcomes in Patients with Alcohol Dependence

Predictors of Posttreatment Drinking Outcomes in Patients with Alcohol Dependence

Assessing craving and its relationship to subsequent prescription opioid use among treatment-seeking prescription opioid dependent patients

Psychiatric, Demographic, and Brain Morphological Predictors of Relapse After Treatment for an Alcohol Use Disorder

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