Individual treatment selection for patients with posttraumatic stress disorder

Deisenhofer, Anne‐Katharina; Delgadillo, Jaime; Rubel, Julian; Böhnke, Jan R.; Zimmermann, D.; Schwartz, Brian; Lutz, Wolfgang

doi:10.1002/da.22755

Cited by 70 publications

(51 citation statements)

References 58 publications

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“…Unlike traditional single-moderator studies, contemporary studies investigate the combined influence of multiple patient-attributes (e.g., demographic, diagnostic, personality features) to profile patients into subgroups that respond to treatment in similar ways. For example, multivariable prediction algorithms have been developed to identify patients who respond differentially to CBT vs. antidepressant medication (DeRubeis et al, 2014), CBT vs. interpersonal psychotherapy (Huibers et al, 2015), CBT vs. eye-movement desensitization and reprocessing (Deisenhofer et al, 2018), prolonged exposure vs. cognitive processing therapy (Keefe et al, 2018), and CBT vs. psychodynamic therapy (Cohen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies are also leveraging the advantages of machine learning methods in order to optimize feature selection, to discover nonlinear associations and interactions between variables, and to yield prediction models that are more likely to generalise to new samples (e.g., Cohen et al, 2019;Deisenhofer et al, 2018;Delgadillo, Huey, Bennett, & McMillan, 2017;Keefe et al, 2018;Lorenzo-Luaces et al, 2017). Despite the increased sophistication of new studies, this emerging literature is still limited by old problems.…”

Section: Introductionmentioning

confidence: 99%

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Targeted prescription of cognitive–behavioral therapy versus person-centered counseling for depression using a machine learning approach.

Delgadillo¹,

Duhne²

2020

Journal of Consulting and Clinical Psychology

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Targeted prescription of cognitive–behavioral therapy versus person-centered counseling for depression using a machine learning approach.

Delgadillo¹,

Duhne²

2020

Journal of Consulting and Clinical Psychology

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“…Patients reported different profiles of trauma‐related emotions that might require specifically tailored interventions depending on which emotions are most pronounced. Empirically based decision rules should guide differential indications (Deisenhofer et al., ).…”

Section: Discussionmentioning

confidence: 99%

Changes in Trauma‐Related Emotions Following Treatment With Dialectical Behavior Therapy for Posttraumatic Stress Disorder After Childhood Abuse

Görg

Böhnke

Priebe

et al. 2019

Journal of Traumatic Stress

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Dialectical behavior therapy for posttraumatic stress disorder (DBT-PTSD) is a trauma-focused therapy shown to reduce core PTSD symptoms, such as intrusions, hyperarousal, and avoidance. Preliminary data indicate effects on elevated trauma-related emotions (e.g., guilt and shame) and possibly radical acceptance of the traumatic event. However, it is unclear if improvements in these variables are significant after controlling for changes in core PTSD symptoms and to what extent nonclinical levels are obtained. In the current study, 42 individuals who met criteria for PTSD after childhood abuse and were participating in a 3-month residential DBT-PTSD program were evaluated at the start of the exposure phase of DBT-PTSD and the end of treatment; a nonclinical sample with a history of childhood abuse was the reference group. Multivariate analyses of variance and multivariate analyses of covariance controlling for change in core PTSD symptoms were used to evaluate changes in several elevated trauma-related emotions (fear, anger, guilt, shame, disgust, sadness, and helplessness) and in radical acceptance. In a repeated measures multivariate analyses of variance, both elevated trauma-related emotions and radical acceptance significantly improved during DBT-PTSD, λ = 0.34, p < .001; η 2 = .56; t(40) = −5.66, p < .001, SMD = 0.88, even after controlling for changes in PTSD symptoms, λ = 0.35, p < .001, η 2 = .65; = 0.86, p = .018, η 2 = .14, respectively. Posttreatment, 31.0% (for acceptance) to 76.2% (for guilt) of participants showed nonclinical levels of the investigated outcomes, suggesting that both trauma-related emotions and radical acceptance changed after the 3-month residential DBT-PTSD program.

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“…The higher the absolute values of the PAI, the stronger is the predicted benefit of one treatment over another. The interpretation of the PAI can be demonstrated with a recent study that used the PHQ-9 as the primary outcome and found a PAI of 2.5 [70]. This means that if patients had received their "optimal" treatment (out of the two), their PHQ-9 score at 12 weeks would have been 2.5 points lower than if they had obtained their nonoptimal treatment.…”

Section: Personalized Advantage Index (Pai)mentioning

confidence: 99%

Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care

Friedl

Krieger

Chevreul

et al. 2020

JCM

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A variety of effective psychotherapies for depression are available, but patients who suffer from depression vary in their treatment response. Combining face-to-face therapies with internet-based elements in the sense of blended treatment is a new approach to treatment for depression. The goal of this study was to answer the following research questions: (1) What are the most important predictors determining optimal treatment allocation to treatment as usual or blended treatment? and (2) Would model-determined treatment allocation using this predictive information and the personalized advantage index (PAI)-approach result in better treatment outcomes? Bayesian model averaging (BMA) was applied to the data of a randomized controlled trial (RCT) comparing the efficacy of treatment as usual and blended treatment in depressive outpatients. Pre-treatment symptomatology and treatment expectancy predicted outcomes irrespective of treatment condition, whereas different prescriptive predictors were found. A PAI of 2.33 PHQ-9 points was found, meaning that patients who would have received the treatment that is optimal for them would have had a post-treatment PHQ-9 score that is two points lower than if they had received the treatment that is suboptimal for them. For 29% of the sample, the PAI was five or greater, which means that a substantial difference between the two treatments was predicted. The use of the PAI approach for clinical practice must be further confirmed in prospective research; the current study supports the identification of specific interventions favorable for specific patients.

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Individual treatment selection for patients with posttraumatic stress disorder

Abstract: Using a PAI-based algorithm has the potential to improve clinical decision making and to enhance individual patient outcomes, although further replication is necessary before such an approach can be implemented in prospective studies.

Cited by 70 publications

References 58 publications

Targeted prescription of cognitive–behavioral therapy versus person-centered counseling for depression using a machine learning approach.

Targeted prescription of cognitive–behavioral therapy versus person-centered counseling for depression using a machine learning approach.

Changes in Trauma‐Related Emotions Following Treatment With Dialectical Behavior Therapy for Posttraumatic Stress Disorder After Childhood Abuse

Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care

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