Reductions in PTSD severity precede reductions in pain intensity among veterans receiving intensive treatment.

OBJECTIVE: Chronic pain often co-occurs with posttraumatic stress disorder (PTSD). The mutual maintenance hypothesis proposes that there may be shared underlying mechanisms of symptoms of pain exacerbating PTSD, and vice versa. The association between PTSD and pain intensity remains understudied. To elucidate the relationship, the present study examined the temporality of changes in PTSD severity and pain intensity in veterans undergoing intensive PTSD treatment. METHOD: Data from 332 veterans undergoing intensive 2-week cognitive processing therapy- (CPT-) based treatment (ITP) with adjunctive components (i.e., mindfulness, art therapy). Random intercepts cross-lagged panel models (RI-CLPMs) were used to examine within-subjects relationships between pain intensity and PTSD severity over the course of the program. RESULTS: Veterans experienced large PTSD severity reductions (Essg = 1.20; p < .001) and small pain severity reductions (Essg = 0.21; p < .001) over the course of treatment, despite pain not being a treatment target. RI-CLPMs revealed that PTSD severity significantly predicted subsequent pain severity. Results indicate the absence of a bidirectional relationship in that changes in pain intensity did not predict later PTSD severity improvement during the ITP. Time trends for both PTSD severity and pain intensity were generally consistent with respect to baseline demographic characteristics. CONCLUSION: Our findings support the mutual maintenance regarding the association between PTSD and pain intensity. Future research should investigate temporal associations in other evidence-based PTSD treatments and formats and evaluate the long-term impacts of PTSD treatment on pain intensity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Changes in emotion regulation difficulties and PTSD symptom severity in an intensive treatment program for PTSD.

BACKGROUND: Greater difficulties in emotion regulation (ER) and decreased use of adaptive ER strategies have been associated with higher levels of posttraumatic stress disorder (PTSD) symptoms. To date, limited research has explored whether ER improves with PTSD treatment or whether such improvements are linked with improvements in PTSD symptoms. METHODS: Veterans and service members with PTSD (N = 223) participated in a 2-week intensive treatment program (ITP) based in Cognitive Processing Therapy (CPT). ER was measured using the Difficulties in Emotion Regulation Short Form (DERS-SF) at baseline and on days 4 and 9 of treatment. PTSD symptoms were reported on the PTSD Symptom Checklist for DSM-5 (PCL-5) at baseline, on days 3, 5, 6, and 8 of treatment, and at post-treatment. RESULTS: DERS-SF scores decreased during treatment (Mchange = 5.12, d = 0.38). Baseline DERS-SF did not predict overall PCL-5 scores across timepoints (p = .377). However, scores on the DERS-SF over time were significantly associated with PCL-5 improvement over the course of treatment (p < .001, R2b = 0.07). Finally, improvements in all subscales of the DERS-SF across time except clarity were significantly associated with improvement in PCL-5 over time. LIMITATIONS: Additional treatment components in the ITP beyond CPT may have contributed to ER improvements. Conclusions are also limited by the use of self-report data. CONCLUSIONS: An intensive CPT-based treatment program for veterans and service members can lead to improved ER in two weeks. ER improvements are associated with PTSD symptom severity during the ITP.

Veterans' 12-month PTSD and depression outcomes following 2- and 3-week intensive cognitive processing therapy-based treatment.

Background: Growing evidence indicates that daily delivery of evidence-based PTSD treatments (e.g. Cognitive Processing Therapy (CPT)), as part of intensive PTSD treatment programmes (ITPs), is feasible and effective. Research has demonstrated that a 2-week CPT-based ITP can produce equivalent outcomes to a 3-week ITP, suggesting shorter treatment can also be highly effective. However, the extent to which ITP length and composition impact longer-term outcomes needs further study.Objective: We examined whether PTSD and depression symptoms 3-, 6-, and 12-months following completion of a 2-week ITP could be considered non-inferior, or equivalent, to those of a 3-week ITP.Method: Data from 638 veterans who participated in a 2-week CPT-based ITP were evaluated against 496 veterans who participated in a 3-week CPT-based ITP. A Bayes factor approach was used to examine whether PTSD and depression severity outcomes of the 2-week ITP could be considered equivalent to the 3-week ITP.Results: Participants across both ITPs reported large PTSD (d = 0.98) and moderate to large depression symptom reductions (d = 0.69) from baseline to 12-month follow-up. The PTSD and depression symptom reductions seen in the 2-week ITP were determined to be equivalent to those of the 3-week ITP.Conclusions: Low follow-up completion was a limitation. Future research might replicate the present findings using samples with greater follow-up rates and explore whether adjunctive services impact other relevant constructs, such as quality of life and functioning.

This study demonstrated that intensive PTSD treatment programmes for veterans can produce large and lasting PTSD and depression symptoms reductions.A 2-week intensive PTSD treatment programme that offered 37 fewer clinical hours was just as effective as a 3-week programme for veterans, with lasting symptom improvement up to 12 months after treatment.The 2-week programme focused primarily on individual Cognitive Processing Therapy delivered twice per day whereas the 3-week programme combined individual and group CPT and had a much larger number of adjunctive services.

Treating a common comorbidity: Pain outcomes following a 3-week cognitive processing therapy-based intensive treatment for posttraumatic stress disorder address.

Posttraumatic stress disorder (PTSD) commonly co-occurs with pain and has been implicated in the maintenance of chronic pain. However, limited research has examined whether intervening for PTSD can hinder or optimize treatment outcomes for co-occurring pain and PTSD. In the present study, we examined changes in pain, PTSD, and depressive symptoms among 125 veterans completing a 3-week cognitive processing therapy (CPT)-based intensive treatment program (ITP) for PTSD. We also explored whether pretreatment pain interference predicted changes in PTSD and depressive symptom severity and whether larger changes in pain interference over the course of treatment were associated with larger changes in PTSD and depressive symptom severity. Linear mixed models revealed that participants' pain interference decreased throughout treatment, d = 0.15, p = .039. Higher levels of pretreatment pain interference were associated with higher PTSD, p = .001, and depressive symptom severity, p = .014, over time. Larger reductions in pain interference corresponded to more improvement in PTSD symptoms, ß = -.03; p < .001, but not depressive symptoms. These findings indicate that ITPs for PTSD can reduce pain interferences, albeit to a small degree, and that reductions in pain interference can contribute to reductions in PTSD symptom severity. Future studies should examine which treatment components contribute to larger changes in symptom severity for veterans with co-occurring pain and PTSD.

Predicting suicidal ideation 3 months following intensive posttraumatic stress disorder treatment.

OBJECTIVE: This study examined whether posttraumatic stress disorder (PTSD) symptom change during a 3- and 2-week intensive treatment program (ITP)-based in cognitive processing therapy was predictive of reduced suicidal ideation (SI) following treatment. METHOD: Veterans completed either a 3-week (n = 274, Mage = 42.35, SD = 9.43, 64.23% male, 65.33% White) or 2-week (n = 177, Mage = 42.90, SD = 9.81, 57.63% male, 66.67% White) ITP and self-reported PTSD, depression, and SI symptoms prior to, during, and 3 months following treatment. RESULTS: Mixed-effects-based two-stage location scale models assessed change in both overall PTSD severity over the course of the 3- and 2-week ITPs, as well as how this change predicted 3-month follow-up SI. Veterans in both programs reported moderate reductions in SI from baseline to posttreatment (3 weeks: d = 0.49; 2 weeks: d = 0.48). Of the 210 veterans across both programs who endorsed at least some SI at baseline, two-thirds (65.24%) reported reductions in SI posttreatment; three-quarters (74.45%) of these maintained posttreatment SI at 3-month follow-up that was lower than baseline levels. CONCLUSIONS: Both baseline SI and greater individual improvement in PTSD symptom severity during the ITPs were associated with lower SI at 3-month follow-up. Overall, study findings suggest that veterans with PTSD who also endorse SI can be successfully treated using the intensive delivery format and are likely to experience a reduction in SI both during and following treatment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Using the Personalized Advantage Index to determine which veterans may benefit from more vs. less comprehensive intensive PTSD treatment programs.

Background: Intensive PTSD treatment programs (ITPs) are highly effective but tend to differ greatly in length and the number of adjunctive services that are provided in conjunction with evidence-based PTSD treatments. Individuals' treatment response to more or less comprehensive ITPs is poorly understood.Objective: To apply a machine learning-based decision-making model (the Personalized Advantage Index (PAI)), using clinical and demographic factors to predict response to more or less comprehensive ITPs.Methods: The PAI was developed and tested on a sample of 747 veterans with PTSD who completed a 3-week (more comprehensive; n = 360) or 2-week (less comprehensive; n = 387) ITP.Results: Approximately 12.32% of the sample had a PAI value that suggests that individuals would have experienced greater PTSD symptom change (5 points) on the PTSD Checklist for DSM-5 in either a more- or less comprehensive ITP. For individuals with the highest 25% of PAI values, effect sizes for the amount of PTSD symptom change between those in their optimal vs. non-optimal programs was d = 0.35.Conclusions: Although a minority was predicted to have benefited more from a program, there generally was not a substantial difference in predicted outcomes. Less comprehensive and thus more financially sustainable ITPs appear to work well for most individuals with PTSD.

A Personalized Advantage Index (PAI) was developed for a 3-week (more comprehensive) and a 2-week (less comprehensive) intensive PTSD treatment program to predict treatment responses.Using the PAI, approximately 12% of the sample was predicted to have experienced meaningfully greater in another program than the one in which they participated.Findings suggest a less comprehensive and more financially sustainable 2-week intensive PTSD treatment program would work well for most veterans in the present study.

Examining trauma cognition change trajectories among initial PTSD treatment non-optimal responders: a potential avenue to guide subsequent treatment selection.

BACKGROUND: Despite their general effectiveness, 14-50% of individuals do not fully respond to evidence-based treatments for posttraumatic stress disorder (PTSD). Although changes in negative posttrauma cognitions (NPCs) are considered a likely PTSD treatment mechanism, less is known about how NPCs change among individuals who continue to be symptomatic following treatment (non-optimal responders). OBJECTIVE: The objective of this study was to examine NPC change trajectories among individuals who were determined to be non-optimally responsive to intensive PTSD treatment. METHOD: Using a 3-week Cognitive Processing Therapy-based intensive PTSD treatment sample (ITP; N = 243), the present study examined the number of distinct NPC change trajectories among non-optimal responders via Group Based Trajectory Modeling and assessed predictors of non-optimal responders' NPC change trajectory membership. Analyses were replicated in a separate 2-week ITP sample (N = 215). RESULTS: In both non-optimal responder samples, two trajectories emerged; a no NPC change group which represented those with an overall lack of NPC change throughout treatment and an NPC change group which represented those with an overall reduction of NPCs occurring primarily later in treatment. Changes in PTSD symptom severity during treatment was the only consistent predictor of NPC change trajectory group membership among treatment non-optimal responders across ITPs. CONCLUSIONS: Findings suggest NPC change among non-optimal responders is nuanced and may inform subsequent intervention selection, resulting in testable hypotheses for future research.

Throughout intensive PTSD treatment, non-optimal responders exhibited two distinct negative posttrauma cognition change trajectories: (1) no change, and (2) slow change.Changes in PTSD symptom severity during treatment consistently predicted non-optimal responders' trajectory of change in negative posttrauma cognitions.Additional research is needed to explore how nuances of negative posttrauma cognition change may inform subsequent treatment intervention in initial non-optimal responders.

Advantages of out-of-plane pulse compression gratings.

Diffraction gratings for pulse compression generally perform best at the Littrow angle, but reflection gratings require a nonzero deviation angle to separate the incident and diffracted beams, so they cannot be used at the Littrow angle. In this paper, we show both theoretically and experimentally that most practical multilayer dielectric (MLD) and gold reflection grating designs can be used with quite substantial beam-deviation angles-as large as even 30°-by mounting the grating out-of-plane and choosing the optimal polarization. The impact of polarization when mounting out-of-plane is explained and quantified.

Geolocation Patterns, Wi-Fi Connectivity Rates, and Psychiatric Symptoms Among Urban Homeless Youth: Mixed Methods Study Using Self-report and Smartphone Data.

BACKGROUND: Despite significant research done on youth experiencing homelessness, few studies have examined movement patterns and digital habits in this population. Examining these digital behaviors may provide useful data to design new digital health intervention models for youth experiencing homelessness. Specifically, passive data collection (data collected without extra steps for a user) may provide insights into lived experience and user needs without putting an additional burden on youth experiencing homelessness to inform digital health intervention design. OBJECTIVE: The objective of this study was to explore patterns of mobile phone Wi-Fi usage and GPS location movement among youth experiencing homelessness. Additionally, we further examined the relationship between usage and location as correlated with depression and posttraumatic stress disorder (PTSD) symptoms. METHODS: A total of 35 adolescent and young adult participants were recruited from the general community of youth experiencing homelessness for a mobile intervention study that included installing a sensor data acquisition app (Purple Robot) for up to 6 months. Of these participants, 19 had sufficient passive data to conduct analyses. At baseline, participants completed self-reported measures for depression (Patient Health Questionnaire-9 [PHQ-9]) and PTSD (PTSD Checklist for DSM-5 [PCL-5]). Behavioral features were developed and extracted from phone location and usage data. RESULTS: Almost all participants (18/19, 95%) used private networks for most of their noncellular connectivity. Greater Wi-Fi usage was associated with a higher PCL-5 score (P=.006). Greater location entropy, representing the amount of variability in time spent across identified clusters, was also associated with higher severity in both PCL-5 (P=.007) and PHQ-9 (P=.045) scores. CONCLUSIONS: Location and Wi-Fi usage both demonstrated associations with PTSD symptoms, while only location was associated with depression symptom severity. While further research needs to be conducted to establish the consistency of these findings, they suggest that the digital patterns of youth experiencing homelessness offer insights that could be used to tailor digital interventions.

More is not always better: 2 weeks of intensive cognitive processing therapy-based treatment are noninferior to 3 weeks.

OBJECTIVE: Although there is mounting evidence that massed treatment for PTSD is both feasible and effective, many questions remain about the optimal length of intensive treatment programs (ITPs), as well as the role of adjunctive services, such as psychoeducation, mindfulness, and yoga. Our setting recently transitioned from a three-week ITP to a two-week program. Adjunctive services were reduced, but the amount of individual CPT between programs remained similar. The present study examined the effectiveness of a two-week ITP based on twice daily individual CPT sessions and evaluated the program's noninferiority to an established three-week ITP using a Bayesian analytical approach. METHOD: Bayesian linear mixed regression models were used to explore PTSD and depression changes over time, as well as predictors of change. Noninferiority of the two-week ITP to a three-week ITP was also established using a Bayes factor approach. RESULTS: Results indicate that program participants change meaningfully in both PTSD and depression severity over the course of treatment, and that changes in posttraumatic cognitions predict subsequent changes in these outcomes. Further, the two-week ITP can be considered noninferior to the three-week ITP in both clinical outcomes and overall satisfaction. CONCLUSIONS: In the context of intensive PTSD treatment, the content of the ITP appears to matter more than its overall length. Shorter programs have the potential to increase access and treatment capacity. Our findings demonstrate the importance of continuous and rigorous program evaluation. Limitations as well as future directions for research, such as identifying the most effective treatment components, are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The role of time since trauma on treatment outcomes of veterans in two intensive posttraumatic stress disorder treatment programs.

Research on the impact of time since trauma (TST) on posttraumatic stress disorder (PTSD) treatment outcomes lacks consensus and has not been examined in cognitive processing therapy (CPT)-based intensive PTSD treatment programs (ITPs). Furthermore, little is known about how TST impacts other trauma-related outcomes, such as depressive symptoms and negative posttrauma cognitions. We examined whether TST predicted severity and changes in PTSD and depressive symptoms and negative posttrauma cognitions, controlling for trauma type (combat or military sexual trauma), age, sex, and race, in two separate samples of veterans with PTSD who completed 2-week (n = 132) or 3-week (n = 407) CPT-based ITPs. In the 3-week sample, PTSD symptom reduction differed based on TST; however, these differences lacked clinical significance, TST x Time R2 b = .002, and were not replicated in the 2-week sample, R2 b < .001. TST did not significantly predict depressive symptoms, R2 b = .005, or negative posttrauma cognition severity or changes, R2 b = .002, in the 3-week sample. In the 2-week sample, linear mixed-effects models indicated that TST also did not significantly predict PTSD or depressive symptoms, R2 b s < .001, or negative posttrauma cognition severity or changes, R2 b s = .002. These findings suggest that TST is not a clinically relevant predictor of PTSD symptoms, depressive symptoms, or negative posttrauma cognitions among individuals engaged in CPT-based ITPs. Future research should investigate the association between TST and trauma-related outcomes in more trauma type- and age-diverse samples within different intensive treatment settings.

Moving toward precision PTSD treatment: predicting veterans' intensive PTSD treatment response using continuously updating machine learning models.

BACKGROUND: Considerable heterogeneity exists in treatment response to first-line posttraumatic stress disorder (PTSD) treatments, such as Cognitive Processing Therapy (CPT). Relatively little is known about the timing of when during a course of care the treatment response becomes apparent. Novel machine learning methods, especially continuously updating prediction models, have the potential to address these gaps in our understanding of response and optimize PTSD treatment. METHODS: Using data from a 3-week (n = 362) CPT-based intensive PTSD treatment program (ITP), we explored three methods for generating continuously updating prediction models to predict endpoint PTSD severity. These included Mixed Effects Bayesian Additive Regression Trees (MixedBART), Mixed Effects Random Forest (MERF) machine learning models, and Linear Mixed Effects models (LMM). Models used baseline and self-reported PTSD symptom severity data collected every other day during treatment. We then validated our findings by examining model performances in a separate, equally established, 2-week CPT-based ITP (n = 108). RESULTS: Results across approaches were very similar and indicated modest prediction accuracy at baseline (R2 ~ 0.18), with increasing accuracy of predictions of final PTSD severity across program timepoints (e.g. mid-program R2 ~ 0.62). Similar findings were obtained when the models were applied to the 2-week ITP. Neither the MERF nor the MixedBART machine learning approach outperformed LMM prediction, though benefits of each may differ based on the application. CONCLUSIONS: Utilizing continuously updating models in PTSD treatments may be beneficial for clinicians in determining whether an individual is responding, and when this determination can be made.

Evaluating changes in negative posttrauma cognition as a mechanism of PTSD severity changes in two separate intensive treatment programs for veterans.

BACKGROUND: A wealth of evidence has illustrated that reductions in negative posttrauma cognitions (NPCs) predict improvement in posttraumatic stress disorder (PTSD) symptoms during treatment. Yet, the specific temporal arrangement of changes in these constructs is less well understood. This study examined the temporal association between NPC changes and PTSD symptom changes in two distinct intensive PTSD treatment samples. METHODS: Data from 502 veterans who completed a 3-week CPT-based intensive PTSD treatment program was used to test the extent to which lagged NPC measurement predicted the next occurring PTSD severity measurement using linear mixed effects regression models. PTSD severity was assessed every other day during treatment. NPCs were assessed at three treatment timepoints. A second sample of 229 veterans who completed a 2-week CPT-based intensive PTSD treatment program was used to replicate these findings. RESULTS: Across both intensive PTSD treatment programs, NPCs generally increased from intake the end of the first treatment week, which was followed by gradual decreases in NPCs throughout the rest of both programs. Change in NPCs during both the 3-week (b = .21, p < .001, R2 = .38) and the 2-week programs (b = 0.20, p < .001, R2 = .24) were significant predictors of change in PTSD symptom severity. However, the reverse was true as well, with change in PTSD severity predicting latter change in NPCs during both the 3-week (b = 1.51, p < .001, R2 = .37) and 2-week (b = 1.37, p < .001, R2 = .33) programs, further raising questions about temporality of the association between NPCs and PTSD symptom severity during treatment. CONCLUSIONS: The present study demonstrated that changes in NPCs may not temporally precede changes in PTSD symptom severity in PTSD treatment samples. Instead, we observed earlier PTSD symptom changes and a bidirectional association between the two constructs across both samples. Clinically, the study supports the continued focus on NPCs as an important treatment target as they are an important indicator of successful PTSD treatment, even if they may not be a direct mechanism of treatment-based changes in PTSD severity. Future research should attempt to identify alternative mechanisms of change in CPT.

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.

BACKGROUND: The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. OBJECTIVE: We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. METHODS: This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. RESULTS: Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). CONCLUSIONS: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

Changes in guilt cognitions in intensive PTSD treatment among veterans who experienced military sexual trauma or combat trauma.

Unresolved trauma-related guilt has been identified as a factor that can intensify posttraumatic stress disorder PTSD symptomology and is associated with many negative mental health outcomes. Evidence-based treatments, such as Cognitive Processing Therapy (CPT), have been shown to successfully reduce trauma-related guilt. However, less is known about how trauma-related guilt cognitions change over the course of PTSD treatment and, more specifically, intensive PTSD treatments. The current study examined whether guilt cognitions (i.e., hindsight bias/responsibility, insufficient justification, wrongdoing) changed over the course of a 3-week CPT-based intensive treatment program (ITP), whether guilt cognition changes predicted PTSD and depression symptom reductions over time, and whether guilt cognition changes and their association with PTSD and depression symptom changes differed based on whether they worked on combat trauma or military sexual trauma. Data were collected from 360 veterans enrolled in a 3-week ITP. Results from linear mixed effects models suggested that trauma-related guilt cognitions reduced significantly over the course of treatment (ps < .001), changes in wrongdoing (p = .032) and hindsight bias/responsibility (p = .003) were significant predictors of PTSD symptom reductions and hindsight bias/responsibility (p = .032) was the only significant predictor of depression symptom reduction. Overall differences in guilt cognitions over time based on cohort type were only significant for insufficient justification (p = .001). These findings suggest that changes in hindsight bias/responsibility demonstrated the largest effect size (d = 0.1.14), implying that hindsight bias/responsibility may be one of the most important guilt cognitions to target. This study also highlights the importance of the relationship between trauma-related guilt and PTSD and depression symptoms. Future research should examine whether changes in guilt cognitions precede changes in PTSD and depression symptoms, and if addressing certain types of guilt cognitions is more important to achieving PTSD and depression symptom reductions.

The Identification of Subphenotypes and Associations with Health Outcomes in Patients with Opioid-Related Emergency Department Encounters Using Latent Class Analysis.

The emergency department (ED) is a critical setting for the treatment of patients with opioid misuse. Detecting relevant clinical profiles allows for tailored treatment approaches. We sought to identify and characterize subphenotypes of ED patients with opioid-related encounters. A latent class analysis was conducted using 14,057,302 opioid-related encounters from 2016 through 2017 using the National Emergency Department Sample (NEDS), the largest all-payer ED database in the United States. The optimal model was determined by face validity and information criteria-based metrics. A three-step approach assessed class structure, assigned individuals to classes, and examined characteristics between classes. Class associations were determined for hospitalization, in-hospital death, and ED charges. The final five-class model consisted of the following subphenotypes: Chronic pain (class 1); Alcohol use (class 2); Depression and pain (class 3); Psychosis, liver disease, and polysubstance use (class 4); and Pregnancy (class 5). Using class 1 as the reference, the greatest odds for hospitalization occurred in classes 3 and 4 (Ors 5.24 and 5.33, p < 0.001) and for in-hospital death in class 4 (OR 3.44, p < 0.001). Median ED charges ranged from USD 2177 (class 1) to USD 2881 (class 4). These subphenotypes provide a basis for examining patient-tailored approaches for this patient population.

Building strong therapeutic relationships quickly: The effect of the perceived working alliance on veterans' intensive PTSD treatment outcomes.

Intensive treatment programs (ITPs) are successful at reducing posttraumatic stress disorder (PTSD) and depression symptoms in veterans. However, the role of the working alliance in the context of ITPs is largely unexplored. The purpose of this study was to examine veteran-rated working alliance with their individual cognitive processing therapy (CPT) provider as a predictor of changes in PTSD and depression symptoms as well as negative posttrauma cognitions in two unique ITP formats. Data were collected from 128 veterans who completed a 2-week ITP, involving 2 × individual CPT/day, as well as 73 veterans who completed a 3-week ITP, involving 1 × group CPT/day and 1 × individual CPT/day. Both ITPs included adjunctive wellness, skills, and psychoeducation services in addition to CPT. Linear mixed-effects models were used to determine whether changes in working alliance predicted changes in PTSD and depression symptoms. Stronger veteran-reported working alliance with their individual CPT therapist, most notably agreement on tasks, predicted significant reductions in both the 2-week and 3-week programs in PTSD (ps = .012 and .002, respectively) and depression symptoms (ps = .009 and .007, respectively) and negative posttrauma cognitions (ps = .009 and .016, respectively). These results highlight the importance of veterans' perceived working alliance with their individual treatment therapists in ITPs. Results suggest that a strong working alliance that is meaningful for treatment outcomes can be developed quickly despite the brevity of this intensive treatment format. Future research should examine ways to facilitate the development of a strong working alliance prior to or as early as possible in ITPs. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Improving mental, physical, and social functioning through participation in a 3-week cognitive processing therapy-based intensive PTSD treatment.

Research has supported the utility of brief intensive treatment programs (ITPs) which utilize interventions, such as Cognitive Processing Therapy (CPT), for reducing severity of symptoms among veterans with posttraumatic stress disorder (PTSD). These treatments have produced large overall reductions in PTSD severity and demonstrated the persistence of these gains following treatment. However, the potential effects of ITPs on mental, physical, and social functioning following treatment completion has been largely unexplored. We utilized data from 204 veterans and 5 service members who completed a 3-week CPT-based ITP and 3-month follow-up assessments. We used a two-stage mixed effects location-scale model approach to initially model each participant's amount of PTSD change over time and used these estimates to predict mental, physical, and social functioning three months following treatment. Veterans reported moderate improvements in mental, physical, and social functioning from pre-treatment to 3-month follow-up (ds = 0.52,.42,.55, and.47, respectively). Results indicated that reductions in PTSD severity during treatment, rather than fluctuation in symptom reporting from one assessment to the next, significantly predicted improved mental, physical, and social functioning at follow-up. This study supports the ability of ITPs to enact meaningful improvement in functioning among veterans with PTSD in a short timeframe.

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.

Treating posttraumatic stress disorder at home in a single week using 1-week virtual massed cognitive processing therapy.

Posttraumatic stress disorder (PTSD) treatments are increasingly delivered in massed formats and have shown comparable results to standard, weekly treatment. To date, massed cognitive processing therapy (CPT), delivered daily, has been delivered primarily in combination with adjunctive services and among veteran populations, but it has not been rigorously evaluated as a standalone intervention. The present study evaluated 1-week massed CPT delivered virtually (i.e., via telehealth) to a community sample of trauma-exposed individuals (N = 24). Using a single-arm open-label design, participants received CPT twice per day for 5 days. The results indicated that most participants completed treatment (n = 23, 95.8%), and no adverse events were reported. Participants exhibited large reductions in clinician-rated, d = 2.01, and self-reported PTSD symptoms, d = 2.55, as well as self-reported depressive symptoms, d = 1.46. On average, participants reported a 5-point PTSD symptom reduction and 1-point reduction in depressive symptoms for each treatment day. Reductions in PTSD and depressive symptoms were maintained at 3-month follow-up. Overall, 1-week massed CPT delivered virtually was shown to be feasible and to result in rapid symptom reductions that were sustained over time. Virtual massed CPT has the potential to increase access to effective treatments and help trauma survivors restore aspects of their lives in short amounts of time.