Using machine learning to predict sudden gains in intensive treatment for PTSD.

Sudden gains have been found in PTSD treatment across samples and treatment modality. Sudden gains have consistently predicted better treatment response, illustrating clear clinical implications, though attempts to identify predictors of sudden gains have produced inconsistent findings. To date, sudden gains have not been examined in intensive PTSD treatment programs (ITPs). This study explored the occurrence of sudden gains in a 3-week and 2-week ITP (n = 465 and n = 235), evaluated the effect of sudden gains on post-treatment and follow-up PTSD severity while controlling for overall change, and used three machine learning algorithms to assess our ability to predict sudden gains. We found 31% and 19% of our respective samples experienced a sudden gain during the ITP. In both ITPs, sudden gain status predicted greater PTSD symptom improvement at post-treatment (t2 W=-8.57, t3 W=-14.86, p < .001) and at 3-month follow-up (t2 W=-3.82, t3 W=-5.32, p < .001). However, the effect for follow-up was no longer significant after controlling for total symptom reduction across the ITP (t2 W=-1.59, t3 W=-0.32, p > .05). Our ability to predict sudden gains was poor (AUC <.7) across all three machine learning algorithms. These findings demonstrate that sudden gains can be detected in intensive treatment for PTSD, though their implications for treatment outcomes may be limited. Moreover, despite the use of three machine-learning methods across two fairly large clinical samples, we were still unable to identify variables that accurately predict whether an individual will experience a sudden gain during treatment. Implications for clinical application of these findings and for future studies are discussed.

Appraisal shifts during reappraisal.

How to model the processes involved in regulating emotions via reappraisal? In two studies, we tested whether reappraisal impacts emotions through shifts along appraisal dimensions. In a first experimental study, 437 students imagined reliving a recent distressing event and rated their appraisals and emotions before and after using reappraisal to feel less negative about the event. Between 19% and 49% of changes to different emotions were statistically mediated by shifts along 10 appraisal dimensions. Latent profile analyses suggested that the appraisal shifts reflected four distinct reappraisal tactics. These findings were conceptually replicated in an intensive longitudinal Study 2, where 168 participants rated their appraisals and emotions in relation to a maximum of three emotional events for 7 days, first within an hour of the event and again in the evening when they also reported on emotion regulation use (1142 observations). Between 22% and 46% of changes to different emotions accompanying reappraisal use were statistically mediated by shifts along appraisal dimensions. Appraisal shifts were less significant for unregulated and otherwise regulated emotion changes. Relative to Study 1, the latent profile analyses of Study 2 revealed two similar and four novel reappraisal tactics reflecting a broader range of events and feelings. Across both studies, all appraisal dimensions were involved in at least one tactic and no dimension in all of them, highlighting the suitability of multivariate profiles over univariate dimensions for modelling reappraisal. These findings suggest that appraisal shift profiles can be part of a useful model of cognitive processes underlying reappraisal. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Characteristics associated with non-suicidal self-injury among veterans seeking military sexual trauma-related mental healthcare.

Military sexual trauma (MST) is a serious issue among Veterans; it is associated with increased rates of posttraumatic stress disorder (PTSD) and nonsuicidal self-injury (NSSI), both of which are correlated with poorer mental health outcomes, including increased suicide risk. Additional insight into the characteristics associated with NSSI among Veterans with MST can help identify individuals at increased risk for suicide and other negative outcomes and improve care for Veterans with a history of MST. The current study was comprised of 327 Veterans referred for MST-related mental health services at a VHA hospital. Participants completed a semi-structured interview for clinical symptoms, including NSSI behaviors. Results of a retrospective chart review revealed a high endorsement of lifetime NSSI (26.9%) with cutting behaviors identified as the most frequently endorsed method. Logistic regression showed personality features, history of cumulative sexual trauma, and younger age were uniquely related to lifetime NSSI. These results corroborate previous findings that show elevated rates of NSSI among Veterans with exposure to trauma. This study expands upon previous findings by examining risk factors specific to treatment-seeking Veterans with a history of MST, which can aid clinical care and risk management procedures in Veteran healthcare.

The Effect of Cognitive-Affective Factors on PTSD and Alcohol Use Symptoms: An Investigation on Rumination, Suppression, and Reappraisal.

Background: Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are mental health conditions that often co-occur. The complexity of this comorbidity is well-documented, though the role of malleable cognitive-affective factors in PTSD/AUD warrants further study. Specifically, attaining a more comprehensive understanding of the role of malleable cognitive-affective factors in individuals with symptoms of PTSD/AUD may have important implications for future research, such as in treatment-seeking individuals. Extant examinations of cognitive-affective factors have demonstrated unique associations of cognitive reappraisal, expressive suppression, and rumination in PTSD symptom severity, though these effects had yet to be explored in subgroups of comorbid PTSD/AUD.Methods: In a sample of trauma-exposed individuals (n = 334) recruited to participate through an internet labor market, we first empirically examined latent subgroups of PTSD/AUD symptoms using latent profile analysis, then included expressive suppression, cognitive reappraisal, and four dimensions in the model to elucidate their role in specific profile patterns of PTSD/AUD symptom typologies.Results: Our results support a four-class model of PTSD/AUD symptoms, with unique predictive effects of expressive suppression, problem-focused thoughts, repetitive thoughts, and anticipatory thoughts on latent profile status.Conclusions: These findings may have important implications for future research focused on examining cognitive-affective patterns as they apply to intervention techniques in treatment-seeking individuals with symptoms of PTSD/AUD.

Who will respond to intensive PTSD treatment? A machine learning approach to predicting response prior to starting treatment.

Despite the established effectiveness of evidence-based PTSD treatments, not everyone responds the same. Specifically, some individuals respond early while others respond minimally throughout treatment. Our ability to predict these trajectories at baseline has been limited. Predicting which individuals will respond to a certain type of treatment can significantly reduce short- and long-term costs and increase the ability to preemptively match individuals with treatments to which they are most likely to respond. In the present study, we examined whether veterans' responses to a 3-week Cognitive Processing Therapy-based intensive PTSD treatment program could be accurately predicted prior to the first session. Using a sample of 432 veterans, and a wide range of demographic and clinical data collected during intake, we assessed six machine learning and statistical methods and their ability to predict fast and minimal responders prior to treatment initiation. For fast response classification, gradient boosted models (GBM) had the highest AUC-PR (0.466). For minimal response classification, elastic net (EN) had the highest mean CV AUC-PR (0.628). Using the best performing classifiers, we were able to predict both fast and minimal responders prior to starting treatment with relatively high AUC-ROC of 0.765 (GBM) and 0.826 (EN), respectively. These results may inform treatment modifications, although the accuracy may not be sufficient for clinicians to base inclusion/exclusion decisions entirely on the classifiers. Future research should evaluate whether these classifiers can be expanded to predict to which treatment type(s) an individual is most likely to respond based on various clinical, circumstantial, and biological features.

Comparing Veterans with Posttraumatic Stress Disorder Related to Military Sexual Trauma or Other Trauma Types: Baseline Characteristics and Residential Cognitive Processing Therapy Outcomes.

The Veterans Health Administration (VHA) has called for improved assessment and intervention for survivors of military sexual trauma (MST) to mitigate deleterious sequalae, including posttraumatic stress disorder (PTSD). Research on the impact of MST-related PTSD (MST-IT) on men is limited, and few studies have examined the differential effects of treatment across genders and MST-IT. Additionally, studies have utilized varying definitions of MST (e.g., sexual assault only vs. including sexual harassment), contributing to disparate outcomes across studies. Utilizing data from 343 veterans seeking residential cognitive processing therapy (CPT) for PTSD in VHA, this study examined the impact of MST-IT and gender on differences in demographic characteristics; pre-treatment severity of PTSD (overall and clusters), depression, and negative posttraumatic cognitions (NPCs); and post-treatment severity of these variables after accounting for pre-treatment severity. Results from 2x2 factorial ANOVAs found no differences in pre-treatment depression or overall PTSD by MST-IT, gender, or their interaction; however, MST-IT survivors presented with greater pre-treatment avoidance, global NPCs, and self-blame. Results from hierarchical linear regression models found only pre-treatment symptom severity significantly predicted post-treatment severity for overall PTSD and all NPCs. These findings suggest veteran survivors of MST-IT appear to benefit similarly from CPT delivered in a VHA residential PTSD program compared to veterans with other index traumas, regardless of gender. Although there were minimal post-treatment differences in PTSD and NPCs by MST-IT status and gender, residual symptoms related to negative cognitions and mood appear to differ across gender and MST-IT status. Specifically, in individuals without MST-IT, post-treatment PTSD symptoms of negative alterations in cognition and mood were higher in men than women. Moreover, women with MST-IT reported more symptoms of depression than both men with MST-IT and women without MST-IT. These findings suggest depressive symptoms decrease through residential PTSD treatment differentially by MST-IT status and gender and warrant further examination.

Fear of pain as a predictor of concurrent and downstream PTSD symptoms.

BACKGROUND: Pain anxiety has been associated with more severe posttraumatic stress disorder (PTSD) symptoms. However, the unique role of individual domains of pain anxiety has yet to be explored in the prediction of PTSD severity. This study examined whether specific pain anxiety domains (i.e., cognitive anxiety, escape/avoidance, fear of pain, and physiological anxiety) predict both concurrent and downstream PTSD symptoms above and beyond other PTSD risk factors. METHOD: Participants were 63 survivors of traumatic events with moderate to high baseline pain treated in the emergency department and assessed for PTSD symptoms and pain anxiety at 3- and 12-months. RESULTS: Three-month pain anxiety domains of fear of pain and physiological anxiety (inversely related) significantly predicted concurrent 3-month PTSD symptoms above and beyond other established PTSD risk factors (i.e., sex, age, pain, and trauma type). However, only 3-month fear of pain significantly predicted 12-month PTSD symptoms. CONCLUSIONS: Findings highlight the relevance of specific pain anxiety domains in concurrent and future PTSD symptoms and suggest the importance of evaluating pain anxiety among patients with PTSD. Interventions focused on increasing willingness to experience and tolerate fear of pain may help mitigate this risk, thereby improving outcomes for individuals with acute PTSD symptoms.

Dispositional optimism mediates relations between childhood maltreatment and PTSD symptom severity among trauma-exposed adults.

BACKGROUND: Experiences of maltreatment in childhood, such as abuse and neglect, have been associated with poorer psychological well-being in adulthood, including elevated risk of revictimization and increased likelihood of developing posttraumatic stress disorder (PTSD) symptoms. Maltreatment has also been associated with reduced resources related to resilience, such as optimism, which may act as a protective factor for mental health. OBJECTIVES: In this study, we examined the mediating role of dispositional optimism in the relationship between childhood maltreatment and PTSD symptom severity from recent trauma in a sample of adults (n = 108) who presented to their local emergency department following trauma. METHODS: We analyzed six models to account for cumulative childhood maltreatment as well as five primary subtypes of maltreatment: emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect. RESULTS: Greater dispositional optimism mediated relations between total maltreatment and decreased PTSD severity (Bab = .056, SE = .029, 95 % CI [.009, .121]). Optimism also mediated relations between all maltreatment subtypes and PTSD severity, except sexual abuse. CONCLUSIONS: These results may suggest optimism and positive psychology interventions as effective techniques in reducing the likelihood of PTSD development and severity in trauma-exposed individuals.

A machine learning approach to modeling PTSD and difficulties in emotion regulation.

Despite evidence for the association between emotion regulation difficulties and posttraumatic stress disorder (PTSD), less is known about the specific emotion regulation abilities that are most relevant to PTSD severity. This study examined both item-level and subscale-level models of difficulties in emotion regulation in relation to PTSD severity using supervised machine learning in a sample of U.S. adults (N=570). Participants were recruited via Amazon's Mechanical Turk (MTurk) and completed self-report measures of emotion regulation difficulties and PTSD severity. We used five different machine learning algorithms separately to train each statistical model. Using ridge and elastic net regression results in the testing sample, emotion regulation predictor variables accounted for approximately 28% and 27% of the variance in PTSD severity in the item- and subscale-level models, respectively. In the item-level model, four predictor variables had notable relative importance values for PTSD severity. These items captured secondary emotional responding, experiencing emotions as out-of-control, difficulties modulating emotional arousal, and low emotional granularity. In the subscale-level model, lack of access to effective emotion regulation strategies, lack of emotional clarity, and emotional nonacceptance subscales had the highest relative importance to PTSD severity. Results from analyses modeling a probable diagnosis of PTSD based on DERS items and subscales are presented in supplemental findings. Findings have implications for developing more efficient, targeted emotion regulation interventions for PTSD.

The mediating effect of rumination between posttraumatic stress disorder symptoms and anger reactions.

OBJECTIVE: The relationship between posttraumatic stress disorder (PTSD) and anger is well established. However, further investigation into the underlying mechanisms of this PTSD-anger relationship is needed. Rumination, a construct with a known association to PTSD symptoms, theoretically may mediate this PTSD-anger relationship. DESIGN: We conducted a mediational model using self-report measures of PTSD symptoms, rumination, and anger reactions from a nonclinical, trauma-exposed sample (N = 339) through Amazon's Mechanical Turk. RESULTS: Rumination mediated overall PTSD symptoms and anger reactions, controlling for age and gender identity. Further analyses examined this mediating role between PTSD subscale scores and anger. Rumination mediated all PTSD subscales and anger when modeled separately. After adjusting for other PTSD symptoms, only 2 subscales' relationship with anger remained mediated by rumination: negative alterations in cognitions/mood and physiological arousal. CONCLUSIONS: Rumination should be assessed in the context of PTSD and anger symptoms, specifically physiological arousal and negative moods/cognitions symptoms in PTSD. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Emotional avoidance and social support interact to predict depression symptom severity one year after traumatic exposure.

Individuals exposed to a traumatic event commonly develop symptoms of depression, a psychiatric disorder associated with a number of negative clinical and public health consequences. Both intrapersonal and interpersonal risk factors have been associated with heightened risk for depression following traumatic event exposure; however, less is known about how these risk factors may interact to predict trauma-exposed individuals' risk of subsequently developing depression symptoms. This study examined the interactive influence of emotional avoidance (an intrapersonal risk factor) and perceived social support (an interpersonal risk factor) on the development of depression symptoms over a one-year period among N = 46 individuals recruited shortly after visiting a hospital emergency department for treatment following exposure to a traumatic event. Results revealed a significant main effect of emotional avoidance on 12-month depression symptoms. The main effect was qualified by an emotional avoidance by perceived social support interaction: the relation of emotional avoidance to 12-month depression symptoms was positive and significant only for individuals with low levels of perceived social support. Results highlight the need to consider both intrapersonal and interpersonal risk factors, as well as their interaction, when predicting which individuals may be most at risk to develop depression following traumatic event exposure.