In rape trauma PTSD, patient characteristics indicate which trauma-focused treatment they are most likely to complete

Keefe, John R.; Stirman, Shannon Wiltsey; Cohen, Zachary D.; DeRubeis, Robert J.; Smith, Brian N.; Resick, Patricia A.

doi:10.1002/da.22731

Cited by 53 publications

(34 citation statements)

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“…Following (Keefe et al, 2018;, two machine learning techniques were used to select pre-treatment variables in each dataset to build a treatment-modality interaction PAI model. Although the STEPd dataset was used before to develop a PAI model (Huibers et al, 2015), in that study less advanced methods were applied to build a prediction model (i.e., no imputation of missing data, stepwise variable selection approach and leave-one-out cross validation).…”

Section: Transformation Of the Outcome Variablementioning

confidence: 99%

“…Similar studies have been conducted since, investigating individual advantages in CBT versus IPT for MDD (Huibers et al, 2015), CBT versus psychodynamic treatment for MDD (Cohen et al, 2019), and CBT versus CBT with integrated exposure and emotionfocused elements for MDD (Friedl et al, 2020). The PAI has also been studied in the contexts of supportive-expressive therapy, antidepressants and placebo for MDD (Zilcha-Mano et al, 2016), eye movement desensitization and reprocessing versus cognitive therapy for posttraumatic stress disorder (PTSD, Deisenhofer et al, 2018), prolonged exposure versus cognitive processing therapy for PTSD (Keefe et al, 2018), antidepressants versus placebo for MDD (Webb et al, 2019), and CBT versus a positive psychology intervention for MDD (Lopez-Gomez et al, 2019). Overall, these studies indicated that different treatments may have different clinically relevant effects for a given individual, and that use of the PAI may improve outcomes by optimizing treatment selection.…”

Section: Introductionmentioning

confidence: 99%

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Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression

Bronswijk

Bruijniks

Lorenzo‐Luaces

et al. 2020

Psychotherapy Research

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Optimizing treatment selection may improve treatment outcomes in depression. A promising approach is the Personalized Advantage Index (PAI), which predicts the optimal treatment for a given individual. To determine the generalizability of the PAI, models needs to be externally validated, which has rarely been done. Method: PAI models were developed within each of two independent trials, with substantial between-study differences, that both compared CBT and IPT for depression (STEPd: n = 151 and FreqMech: n = 200). Subsequently, both PAI models were tested in the other dataset. Results: In the STEPd study, post-treatment depression was significantly different between individuals assigned to their PAI-indicated treatment versus those assigned to their non-indicated treatment (d = .57). In the FreqMech study, posttreatment depression was not significantly different between patients receiving their indicated treatment versus those receiving their non-indicated treatment (d = .20). Cross-trial predictions indicated that post-treatment depression was not significantly different between those receiving their indicated treatment and those receiving their non-indicated treatment (d = .16 and d = .27). Sensitivity analyses indicated that cross-trial prediction based on only overlapping variables didn't improve the results. Conclusion: External validation of the PAI has modest results and emphasizes between-study differences and many other challenges.

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Section: Transformation Of the Outcome Variablementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression

Bronswijk

Bruijniks

Lorenzo‐Luaces

et al. 2020

Psychotherapy Research

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“…To construct a powerful prediction algorithm, we used two techniques to identify predictors for long-term depression severity from the 29 variables available: a model-based recursive partitioning method followed by bootstrap resampling in conjunction with backwards elimination (R packages "mobForest" and "bootstepAIC," Rizopoulos & Rizopoulos, 2009; prevous applications of this method: Keefe et al, 2018;Zilcha-Mano et al, 2016). The modelbased recursive partitioning technique is based on a random forest algorithm .…”

Section: Variable Selectionmentioning

confidence: 99%

A prognostic index for long-term outcome after successful acute phase cognitive therapy and interpersonal psychotherapy for major depressive disorder

et al. 2018

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Background Major depressive disorder (MDD) has a highly recurrent nature. After successful treatment, it is important to identify individuals who are at risk of an unfavorable long‐term course. Despite extensive research, there is no consensus yet on the clinically relevant predictors of long‐term outcome in MDD, and no prediction models are implemented in clinical practice. The aim of this study was to create a prognostic index (PI) to estimate long‐term depression severity after successful and high quality acute treatment for MDD. Methods Data come from responders to cognitive therapy (CT) and interpersonal psychotherapy (IPT) in a randomized clinical trial ( n = 85; CT = 45, IPT = 40). Primary outcome was depression severity, assessed with the Beck Depression Inventory II, measured throughout a 17‐month follow‐up phase. We examined 29 variables as potential predictors, using a model‐based recursive partitioning method and bootstrap resampling in conjunction with backwards elimination. The selected predictors were combined into a PI. Individual PI scores were estimated using a cross‐validation approach. Results A total of three post‐treatment predictors were identified: depression severity, hopelessness, and self‐esteem. Cross‐validated PI scores evidenced a strong correlation ( r = 0.60) with follow‐up depression severity. Conclusion Long‐term predictions of MDD are multifactorial, involving a combination of variables that each has a small prognostic effect. If replicated and validated, the PI can be implemented to predict follow‐up depression severity for each individual after acute treatment response, and to personalize long‐term treatment strategies.

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“…More symptomatic veterans appear to be at increased risk of premature termination, consistent what has been observed in outpatient populations. Future studies should qualitatively investigate veterans’ reasons for treatment dropout, examine the role of medication usage and compliance, determine symptom changes across the course of treatment and how they relate to program completion, and examine moderators of treatment completion (e.g., Keefe et al, 2018) and receipt of TFPs in order to enhance treatment completion and outcome.…”

Section: Discussionmentioning

confidence: 99%

Predictors of Dropout From Residential Treatment for Posttraumatic Stress Disorder Among Military Veterans

et al. 2019

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Background: Successful psychotherapy for posttraumatic stress disorder (PTSD) necessitates initial and sustained engagement. However, treatment dropout is common, with rates of 50–70% depending on the setting, type of treatment and how dropout is calculated. Dropout from residential treatment is less understood and could be impacted by participation of more symptomatic patient populations and reduced day-to-day barriers to engagement. Gaining insight into predictors of treatment dropout is critical given that individuals with greater symptoms are the most in need of successful treatments but also at higher risk of unsuccessful psychotherapy episodes. Aim: The aim of the current study was to examine predictors of treatment dropout among veterans receiving residential treatment for PTSD. Methods: The study included 3,965 veterans who initiated residential PTSD treatment within a Department of Veterans Affairs program during Fiscal Year 2015 and completed self-report measures of demographics and psychiatric symptoms at admission. Results: In our sample ( N = 3,965, 86.5% male, mean age = 45.5), 27.5% did not complete the residential program ( n = 1,091). Controlling for age, marital status, combat/non-combat trauma, and facility, generalized estimating equation modeling analysis indicated greater PTSD symptoms and physical functioning at admission were associated with reduced likelihood of completing the residential program. There were significant differences in trauma-focused psychotherapy received by individuals who dropped out of residential treatment and those who did not. Among veterans who dropped out, 43.6% did not get any trauma-focused psychotherapy; 22.3% got some, but less than 8 sessions; and 34.1% got at least 8 sessions; compared to 37.3%, 4.8%, and 57.9%, respectively, among program completers. Conclusion: Dropout rates from residential PTSD programs indicate that at least one in four veterans do not complete residential treatment, with more symptomatic individuals and those who do not receive trauma-focused therapy being less likely to complete.

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In rape trauma PTSD, patient characteristics indicate which trauma-focused treatment they are most likely to complete

Cited by 53 publications

References 53 publications

Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression

Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression

A prognostic index for long-term outcome after successful acute phase cognitive therapy and interpersonal psychotherapy for major depressive disorder

Predictors of Dropout From Residential Treatment for Posttraumatic Stress Disorder Among Military Veterans

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