A quantitative review of the ubiquitous relapse curve

Kirshenbaum, Ari P.; Olsen, Darlene M.; Bickel, Warren K.

doi:10.1016/j.jsat.2008.04.001

Cited by 98 publications

(72 citation statements)

References 35 publications

(12 reference statements)

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“…Across all three drugs, most patients relapsed early post-treatment, and thereafter, relapse rates decelerated dramatically. In a recent quantitative review of treatment outcome studies on nicotine and alcohol, Kirshenbaum, Olsen, and Bickel (2009) demonstrated that the basic shape of the relapse curve tends to be uniform-'the rate of relapse decelerates after initial abstinence has been achieved, and therefore, the amount of accumulated time abstinent may be the transcending variable that operates to shape the relapse curve' (p. 8). In short 'abstinence begets abstinence' (p. 9).…”

Section: Temporal Course Of Relapse Patternsmentioning

confidence: 99%

Eating disorders, obesity and addiction

Wilson

2010

Euro Eating Disorders Rev

138

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An addiction model of both eating disorders and obesity has received increasing attention in the popular and scientific literature. The addiction is viewed as a brain disease that must be directly targeted if treatment is to succeed. Evidence from laboratory feeding studies, epidemiology, genetic and familial research, psychopathological mechanisms, and treatment outcome research on cognitive behaviour therapy (CBT) is inconsistent with the clinical validity or utility of the addiction model of eating disorders. Neurobiological research has shown commonalities in brain reward processes between obesity and substance abuse disorders. Yet emphasis on apparent similarities overlooks important differences between obesity and drug addiction. Interest in obesity as a brain disease should not detract from a public health focus on the 'toxic food environment' that is arguably responsible for the obesity epidemic and related nutrition-based chronic disease.

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Section: Temporal Course Of Relapse Patternsmentioning

confidence: 99%

Eating disorders, obesity and addiction

Wilson

2010

Euro Eating Disorders Rev

138

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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%

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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“…Without specific treatment, approximately 80-90 percent of all patients who underwent detoxification treatment will relapse within the next 6 months (Boothby & Doering 2005). Interestingly, the majority of relapses (approximately 60-70 percent) appear within the first 3 months after detoxification (Kirshenbaum et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Predicting the future relapse of alcohol-dependent patients from structural and functional brain images

et al. 2015

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In alcohol dependence, individual prediction of treatment outcome based on neuroimaging endophenotypes can help to tailor individual therapeutic offers to patients depending on their relapse risk. We built a prediction model for prospective relapse of alcohol-dependent patients that combines structural and functional brain images derived from an experiment in which 46 subjects were exposed to alcohol-related cues. The patient group had been subdivided post hoc regarding relapse behavior defined as a consumption of more than 60 g alcohol for male or more than 40 g alcohol for female patients on one occasion during the 3-month assessment period (16 abstainers and 30 relapsers). Naïve Bayes, support vector machines and learning vector quantization were used to infer prediction models for relapse based on the mean and maximum values of gray matter volume and brain responses on alcohol-related cues within a priori defined regions of interest. Model performance was estimated by leave-one-out cross-validation. Learning vector quantization yielded the model with the highest balanced accuracy (79.4 percent, p < 0.0001; 90 percent sensitivity, 68.8 percent specificity). The most informative individual predictors were functional brain activation features in the right and left ventral tegmental areas and the right ventral striatum, as well as gray matter volume features in left orbitofrontal cortex and right medial prefrontal cortex. In contrast, the best pure clinical model reached only chance-level accuracy (61.3 percent). Our results indicate that an individual prediction of future relapse from imaging measurement outperforms prediction from clinical measurements. The approach may help to target specific interventions at different risk groups.

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A quantitative review of the ubiquitous relapse curve

Cited by 98 publications

References 35 publications

Eating disorders, obesity and addiction

Eating disorders, obesity and addiction

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

Predicting the future relapse of alcohol-dependent patients from structural and functional brain images

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