Effect of adjunctive benzodiazepines on clinical outcomes in lithium- or quetiapine-treated outpatients with bipolar I or II disorder: Results from the Bipolar CHOICE trial

Bobo, William V.; Reilly-Harrington, Noreen A.; Ketter, Terence A.; Brody, Benjamin D.; Kinrys, Gustavo; Kemp, David E.; Shelton, Richard C.; McElroy, Susan L.; Sylvia, Louisa G.; Kocsis, James H.; McInnis, Melvin G.; Friedman, Edward S.; Singh, Vivek; Tohen, Mauricio; Bowden, Charles L.; Deckersbach, Thilo; Calabrese, Joseph R.; Thase, Michael E.; Nierenberg, Andrew A.; Rabideau, Dustin J.; Schoenfeld, David; Faraone, Stephen V.; Kamali, Masoud

doi:10.1016/j.jad.2014.02.046

Cited by 16 publications

(10 citation statements)

References 14 publications

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“…Literature on anxiety comorbidity with BD shows a trend toward lithium nonresponsiveness . Additionally, a recent analysis in the Bipolar CHOICE dataset found that benzodiazepine users experienced significantly less improvement in CGI‐BP than benzodiazepine nonusers; while this study did not examine comorbid anxiety disorder as a predictor of outcome in BD patients, a majority of benzodiazepine users in this analysis did have a comorbid anxiety disorder diagnosis. SAD, agoraphobia, and panic disorder comorbidity with BD is associated with poor symptomatic outcome and reduced overall function.…”

Section: Discussionmentioning

confidence: 83%

Predictive modeling for response to lithium and quetiapine in bipolar disorder

Kim

Dufour

et al. 2019

Bipolar Disorders

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Objectives: Lithium and quetiapine are known to be effective treatments for bipolar disorder. However, little information is available to inform prediction of response to these medications. Machine-learning methods can identify predictors of response by examining variables simultaneously. Further evaluation of models on a test sample can estimate how well these models would generalize to other samples.Methods: Data (N = 482) were drawn from a randomized clinical trial of outpatients with bipolar I or II disorder who received adjunctive personalized treatment plus either lithium or quetiapine. Elastic net regularization (ENR) was used to generate models for lithium and quetiapine; these models were evaluated on a test set. Results:Predictions from the lithium model explained 17.4% of the variance in actual observed scores of patients who received lithium in the test set, while predictions from the quetiapine model explained 32.1% of the variance of patients that received quetiapine. Of the baseline variables selected, those with the largest parameter estimates were: severity of mania; attention-deficit/hyperactivity disorder (ADHD) comorbidity; nonsuicidal self-injurious behavior; employment; and comorbidity with each of two anxiety disorders (social phobia/society anxiety and agoraphobia).Predictive accuracy of the ENR model outperformed the simple and basic theoretical models.Conclusion: ENR is an effective approach for building optimal and generalizable models. Variables identified through this methodology can inform future research on predictors of response to lithium and quetiapine, as well as future modeling efforts of treatment choice in bipolar disorder. K E Y W O R D S bipolar disorder, lithium, machine learning, predictive modeling, quetiapine | 429 KIM et al.

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Section: Discussionmentioning

confidence: 83%

Predictive modeling for response to lithium and quetiapine in bipolar disorder

Kim

Dufour

et al. 2019

Bipolar Disorders

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“…Literature on anxiety comorbidity with BD shows a trend toward lithium nonresponsiveness 42 . Additionally, a recent analysis in the Bipolar CHOICE dataset found that benzodiazepine users experienced significantly less improvement in CGI-BP than benzodiazepine non-users 43 ; while this study did not examine comorbid anxiety disorder as a predictor of outcome in BD patients, a majority of benzodiazepine users in this analysis did have a comorbid anxiety disorder diagnosis. SAD, agoraphobia, and panic disorder comorbidity with BD is associated with poor symptomatic outcome and reduced overall function.…”

Section: Discussionmentioning

confidence: 77%

Predictive modeling for response to lithium and quetiapine in bipolar disorder

Kim¹,

Dufour²,

Xu³

et al. 2017

Preprint

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Objectives: Lithium and quetiapine are known to be effective treatments for bipolar disorder. However, little information is available to inform prediction of response to these medications. Machine-learning methods can identify predictors of response by examining variables simultaneously. Further evaluation of models on a test sample can estimate how well these models would generalize to other samples. Methods: Data (N=482) were drawn from a randomized clinical trial of outpatients with bipolar-I or II disorder who received adjunctive personalized treatment plus either lithium or quetiapine. Elastic Net Regularization (ENR) was used to generate models for lithium and quetiapine; these models were evaluated on a test set. Results: Predictions from the lithium model explained 17.4% of the variance in actual observed scores of patients who received lithium in the test set, while predictions from the quetiapine model explained 32.1% of the variance of patients that received quetiapine. Of the baseline variables selected, those with the largest parameter estimates were: severity of mania; attention-deficit/hyperactivity disorder (ADHD) comorbidity; non-suicidal self-injurious behavior; employment; and comorbidity with each of two anxiety disorders (social phobia/society anxiety and agoraphobia). Predictive accuracy of the ENR model outperformed the simple and basic theoretical models.Conclusion: ENR is an effective approach for building optimal and generalizable models. Variables identified through this methodology can inform future research on predictors of response to lithium and quetiapine, as well as future modeling efforts of treatment choice in BD.

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“… 106 The recently published CHOICE (Clinical and Health Outcomes Initiative in Comparative Effectiveness) trial showed that adjunctive BDZs do not significantly affect the clinical outcome in patients treated with lithium or quetiapine. 107 …”

Section: Resultsmentioning

confidence: 99%

Bridging the gap between education and appropriate use of benzodiazepines in psychiatric clinical practice

Dell’Osso¹,

Albert²,

Atti³

et al. 2015

NDT

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More than half a century after their discovery, benzodiazepines (BDZs) still represent one of the largest and most widely prescribed groups of psychotropic compounds, not only in clinical psychiatry but also in the entire medical field. Over the last two decades, however, there has been an increased focus on the development of antidepressants and antipsychotics on the part of the pharmaceutical industry, clinicians, and researchers, with a reduced interest in BDZs, in spite of their widespread clinical use. As a consequence, many psychiatric residents, medical students, nurses, and other mental health professionals might receive poor academic teaching and training regarding these agents, and have the false impression that BDZs represent an outdated chapter in clinical psychopharmacology. However, recent advances in the field, including findings concerning epidemiology, addiction risk, and drug interactions, as well as the introduction of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition with related diagnostic changes, strongly encourage an updated appraisal of the use of BDZs in clinical practice. During a recent thematic event convened with the aim of approaching this topic in a critical manner, a group of young Italian psychiatrists attempted to highlight possible flaws in current teaching pathways, identify the main clinical pros and cons regarding current use of BDZs in clinical practice, and provide an updated overview of their use across specific clinical areas and patient populations. The main results are presented and discussed in this review.

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Effect of adjunctive benzodiazepines on clinical outcomes in lithium- or quetiapine-treated outpatients with bipolar I or II disorder: Results from the Bipolar CHOICE trial

Cited by 16 publications

References 14 publications

Predictive modeling for response to lithium and quetiapine in bipolar disorder

Predictive modeling for response to lithium and quetiapine in bipolar disorder

Predictive modeling for response to lithium and quetiapine in bipolar disorder

Bridging the gap between education and appropriate use of benzodiazepines in psychiatric clinical practice

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