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Background: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA)., Methods: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample., Results: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors., Conclusions: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Background: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan., Methods: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample., Results: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables., Conclusions: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Background: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA)., Methods: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample., Results: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics., Limitations: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results., Conclusions: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection., Competing Interests: Conflict of interest In the past 3 years, Dr. Kessler was a consultant for Cambridge Health Alliance, Canandaigua VA Medical Center, Holmusk, Partners Healthcare, Inc., RallyPoint Networks, Inc., and Sage Therapeutics. He has stock options in Cerebral Inc., Mirah, PYM, and Roga Sciences. Dr. Pigeon consulted for CurAegis Technologies and received clinical trial support from Pfizer, Inc. and Abbvie, Inc. Dr. Zubizarreta consulted for Johnson & Johnson Real World Data Analytics. Dr. Cipriani is supported by the National Institute for Health Research (NIHR) Oxford Cognitive Health Clinical Research Facility, by an NIHR Research Professorship (grant RP-2017-08-ST2-006), by the NIHR Oxford and Thames Valley Applied Research Collaboration and by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215-20005); he has also received research, educational and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation and Angelini Pharma. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, or the UK Department of Health. The remaining authors report no conflicts of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The comorbidity of substance use disorder (SUD), depression, and PTSD is common among veterans. Prior research has shown that among veterans with SUD and depression, those with PTSD did not maintain cognitive-behavioral treatment gains as well as those without PTSD. Thus, the current study was designed to evaluate whether adding trauma-focused treatment following an initial group-based integrated cognitive behavioral treatment (ICBT) for SUD and depression improved treatment outcomes. Participants were 123 veterans (89% male) recruited from the VA San Diego Healthcare System. All participants received ICBT in twice weekly, group-delivered sessions for 12 weeks (Phase 1). Participants were then randomized to receive 12 sessions of individual follow-up sessions (Phase 2) utilizing either ICBT or cognitive processing therapy that was modified to integrate SUD treatment (CPT-M). Results indicated that PTSD and depression symptoms slightly improved at the end of Phase 1 group ICBT and further improved through Phase 2 individual treatment (except for participants without PTSD who received CPT-M), with treatment gains maintained one year later. Substance use significantly improved at the end of Phase 1 group ICBT and these improvements were maintained through Phase 2 and the one year follow-up. Participants in the trauma-focused Phase 2 treatment (CPT-M) exhibited similar levels of symptom reduction and maintenance of treatment gains as those in the non-trauma-focused Phase 2 treatment (ICBT). However, there was a slight advantage for Phase 2 CPT-M over Phase 2 ICBT with respect to heavy drinking outcomes for individuals with PTSD. Overall, the combination of group ICBT followed by either CPT-M or ICBT individual therapy appears to be effective for veterans with depression, SUD, and trauma history., (Copyright © 2015. Published by Elsevier Inc.)

The comorbidity of substance use disorder (SUD), depression, and PTSD is common among veterans. Prior research has shown that among veterans with SUD and depression, those with PTSD did not maintain cognitive-behavioral treatment gains as well as those without PTSD. Thus, the current study was designed to evaluate whether adding trauma-focused treatment following an initial group-based integrated cognitive behavioral treatment (ICBT) for SUD and depression improved treatment outcomes. Participants were 123 veterans (89% male) recruited from the VA San Diego Healthcare System. All participants received ICBT in twice weekly, group-delivered sessions for 12 weeks (Phase 1). Participants were then randomized to receive 12 sessions of individual follow-up sessions (Phase 2) utilizing either ICBT or cognitive processing therapy that was modified to integrate SUD treatment (CPT-M). Results indicated that PTSD and depression symptoms slightly improved at the end of Phase 1 group ICBT and further improved through Phase 2 individual treatment (except for participants without PTSD who received CPT-M), with treatment gains maintained one year later. Substance use significantly improved at the end of Phase 1 group ICBT and these improvements were maintained through Phase 2 and the one year follow-up. Participants in the trauma-focused Phase 2 treatment (CPT-M) exhibited similar levels of symptom reduction and maintenance of treatment gains as those in the non-trauma-focused Phase 2 treatment (ICBT). However, there was a slight advantage for Phase 2 CPT-M over Phase 2 ICBT with respect to heavy drinking outcomes for individuals with PTSD. Overall, the combination of group ICBT followed by either CPT-M or ICBT individual therapy appears to be effective for veterans with depression, SUD, and trauma history.

Objective: Low attendance in psychotherapy, particularly among individuals with comorbid disorders, is a pervasive challenge. The present study examined predictors of treatment attendance in a sample of veterans with depression, substance use disorder, and trauma. Methods: This is an analysis of data collected as part of a larger clinical trial involving outpatients at a Veterans Administration dual diagnosis clinic. Individuals were excluded if they had significant memory deficits, schizophrenia, bipolar disorder, or substantial travel constraints. Participants (N = 146) received 12 weeks of group-delivered integrated cognitive behavioral therapy for depression and substance use, followed by randomization to 12 additional weeks of individually delivered cognitive behavioral therapy (n = 62) or cognitive processing therapy (n = 61) modified to address substance use and trauma. Participants, therapists, and researchers were not blinded to group assignment. For this study, we included only the 123 participants who were randomized into the second phase, analyzing predictors of treatment attendance categorized into predisposing factors, enabling factors, and need factors. Results: Participants were primarily male (89%) and Caucasian (76%) and averaged 47 years old (SD = 12). Forty-four percent had alcohol use disorder, 16% had drug use disorder, and 40% had polysubstance use disorder. Most met criteria for PTSD (82%), with 44% having combat-related trauma, 33% sexual trauma, and 28% other trauma. Treatment attendance did not differ between groups. More education was associated with increased group (r =.19, p =.04) and individual session attendance (r =.28, p =.002). Individuals with chronic housing problems attended fewer group sessions (r = −.19, p =.04), while individuals with sexual trauma, compared to those with other traumas, attended more individual sessions (r =.23, p =.01). Number of group sessions attended was positively associated with individual session attendance. Conclusions: Few variables were significantly predictive of treatment attendance, possibly due to the complex nature of comorbid disorders. Including a focus on trauma was not associated with lower attendance. Special consideration may need to be given to education level, homelessness, and trauma when trying to engage and retain patients with comorbid disorders in treatment. This clinical trial is registered at www.clinicaltrials.gov as NCT00958217. [ABSTRACT FROM AUTHOR]

Background: Physical illnesses frequently co-occur with depression and substance use disorders and may impact their improvement. Physical illness symptoms may overlap with or exacerbate somatic symptoms of depression. Individuals may use substances to cope with symptoms of physical illness. Objectives: We examined whether chronic physical health problems moderated changes in depression and substance use among dual diagnosed individuals during and in the year following treatment. Methods: Participants were recruited from a Veterans Affairs dual diagnosis outpatient program between March 2000 and November 2007 and were randomized to either Integrated Cognitive-Behavioral Therapy or Twelve-Step Facilitation Therapy. A total of 214 veterans with assessment data for the variables of interest were included in analyses. Participants completed quarterly depression, substance use, and health assessments over an 18 month period. We used linear-mixed effects models to analyze patterns of change for depression and substance use. Results: Individuals with severe chronic health problems and higher intake depression showed slower improvements in both nonsomatic and somatic depression symptoms. Individuals with severe chronic health problems and higher midtreatment substance use showed less improvement in substance use. Conclusions: Assessing and addressing physical health issues during depression and substance use disorder treatment may improve outcomes. [ABSTRACT FROM AUTHOR]

Objective:There is a high rate of comorbidity among substance dependence, depression, and physical health problems. This study aimed to examine the impact of pre-treatment physical health stressors (acute and chronic conditions) on outcomes of treatment in a sample of veterans with dual disorders (depression and substance dependence) who were randomized to integrated cognitive behavioral therapy versus 12-step interventions.Methods:This study included 205 veterans (89.8% male, mean age = 49.5 years) enrolled in a clinical treatment outcomes trial. Chronic health problems (persistent, ongoing conditions lasting 2 weeks or more; e.g., arthritis, diabetes) and acute health events (occurring on a discrete date; e.g., injury, surgery, myocardial infarction) were coded dichotomously (presence versus absence) and evaluated separately. The impact of physical health stressors on abstinence (defined dichotomously), percentage of days abstinent, and depression symptoms were analyzed at the end of 12 and 24 weeks of treatment. Additionally, associations between intake motivation to change, health stressors, and substance use were examined.Results:Analyses revealed that participants who had experienced a pre-treatment acute health event had higher rates of abstinence at 12 weeks, higher percentage of days abstinent at 24 weeks, and higher depression symptoms at intake. Participants with chronic health difficulties had more severe depression at intake and those participants with severe chronic difficulties had greater depression symptoms across all time points. Chronic health difficulties were related to the “taking steps” factor of motivation to change substance use, but acute health events were not related to motivation to change. Motivation to change was also not related to substance outcomes in our sample.Conclusions:Physical health appears to have a complex relationship with co-occurring depression and substance dependence. Acute health problems predicted lower substance use, whereas chronic health problems were associated with higher depression levels. Explicitly addressing the connection between substance use and health events during treatment may improve addiction treatment outcomes. However, individuals with chronic health problems may benefit from extending treatment or adjunct strategies focused on addressing chronic health concerns. This is an analysis of data collected as part of a clinical trial registered atwww.ClinicalTrials.govas NCT00108407. [ABSTRACT FROM AUTHOR]