Faculty Bibliography

PURPOSE/OBJECTIVE:Breast cancer survivors may demonstrate elevated psychological distress, which can also hinder adherence to survivorship care plans. Our goal was to study heterogeneity of behavioral health and functioning in breast cancer survivors, and identify both risk and protective factors to improve targets for wellness interventions. METHODS:Breast cancer survivors (n = 187) consented to complete self-reported psychological measures and to access their medical records. Latent class analysis (LCA) was used to classify heterogeneous subpopulations based on levels of depression, post-traumatic stress, fear of cancer recurrence, cancer-related pain, and fatigue. Multinomial logistic regression and auxiliary analysis in a 3-step modeling conditional approach was used to identify characteristics of the group based on demographics, treatment history and characteristics, and current medication prescriptions. RESULTS:Three subpopulations of breast cancer survivors were identified from the LCA: a modal Resilient group (48.2%, n = 90), a Moderate Symptoms group (34%, n = 65), and an Elevated Symptoms group (n = 17%, n = 32) with clinically-relevant impairment. Results from the logistic regression indicated that individuals in the Elevated Symptoms group were less likely to have a family history of breast cancer; they were more likely to be closer to time of diagnosis and younger, have received chemotherapy and psychotropic prescriptions, and have higher BMI. Survivors in the Elevated Symptoms group were also less likely to be prescribed estrogen inhibitors than the Moderate Symptoms group. CONCLUSIONS:This study identified subgroups of breast cancer survivors based on behavioral, psychological, and treatment-related characteristics, with implications for targeted monitoring and survivorship care plans. IMPLICATIONS FOR CANCER SURVIVORS/CONCLUSIONS:Results showed the majority of cancer survivors were resilient, with minimal psychological distress. Results also suggest the importance of paying special attention to younger patients getting chemotherapy, especially those without a family history of breast cancer.

BACKGROUND:At-home Ketamine-assisted therapy (KAT) with psychosocial support and remote monitoring through telehealth platforms addresses access barriers, including the COVID-19 pandemic. Large-scale evaluation of this approach is needed for questions regarding safety and effectiveness for depression and anxiety. METHODS:In this prospective study, a large outpatient sample received KAT over four weeks through a telehealth provider. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression, and the Generalized Anxiety Disorder scale (GAD-7) for anxiety. Demographics, adverse events, and patient-reported dissociation were also analyzed. Symptom trajectories were identified using Growth Mixture Modeling, along with outcome predictors. RESULTS:A sample of 1247 completed treatment with sufficient data, 62.8 % reported a 50 % or greater improvement on the PHQ-9, d = 1.61, and 62.9 % on the GAD-7, d = 1.56. Remission rates were 32.6 % for PHQ-9 and 31.3 % for GAD-7, with 0.9 % deteriorating on the PHQ-9, and 0.6 % on the GAD-7. Four patients left treatment early due to side effects or clinician disqualification, and two more due to adverse events. Three patient subpopulations emerged, characterized by Improvement (79.3 %), Chronic (11.4 %), and Delayed Improvement (9.3 %) for PHQ-9 and GAD-7. Endorsing side effects at Session 2 was associated with delayed symptom improvement, and Chronic patients were more likely than the other two groups to report dissociation at Session 4. CONCLUSION/CONCLUSIONS:At-home KAT response and remission rates indicated rapid and significant antidepressant and anxiolytic effects. Rates were consistent with laboratory- and clinic-administered ketamine treatment. Patient screening and remote monitoring maintained low levels of adverse events. Future research should assess durability of effects.

BACKGROUND:Evidence-based pharmacological treatments for posttraumatic stress disorder (PTSD) are few and of limited efficacy. Previous work suggests that angiotensin type 1 receptor inhibition facilitates fear inhibition and extinction, important for recovery from PTSD. This study tests the efficacy of the angiotensin type 1 receptor antagonist losartan, an antihypertensive drug, repurposed for the treatment of PTSD. METHODS:A randomized controlled trial was conducted for 10 weeks in 149 men and women meeting DSM-5 PTSD criteria. Losartan (vs. placebo) was flexibly titrated from 25 to 100 mg/day by week 6 and held at highest tolerated dose until week 10. Primary outcome was the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) change score at 10 weeks from baseline. A key secondary outcome was change in CAPS-5 associated with a single nucleotide polymorphism of the ACE gene. Additional secondary outcomes included changes in the PTSD Checklist for DSM-5 and the Patient Health Questionnaire-9, and proportion of responders with a Clinical Global Impressions-Improvement scale of "much improved" or "very much improved." RESULTS:Both groups had robust improvement in PTSD symptoms, but there was no significant difference on the primary end point, CAPS-5 measured as week 10 change from baseline, between losartan and placebo (mean change difference, 0.9, 95% confidence interval, -3.2 to 5.0). There was no significant difference in the proportion of Clinical Global Impressions-Improvement scale responders for losartan (58.6%) versus placebo (57.9%), no significant differences in changes in PTSD Checklist for DSM-5 or Patient Health Questionnaire-9, and no association between ACE genotype and CAPS-5 improvement on losartan. CONCLUSIONS:At these doses and durations, there was no significant benefit of losartan compared with placebo for the treatment of PTSD. We discuss implications for failure to determine the benefit of a repurposed drug with strong a priori expectations of success based on preclinical and epidemiological data.

The loss of a loved one is a potentially traumatic event that can result in disparate outcomes and symptom patterns. Machine learning methods offer computational tools to probe this heterogeneity and understand grief psychopathology in its complexity. In this article, we examine the latest contributions to the scientific study of bereavement reactions garnered through the use of computational methods. We focus on findings originating from trajectory modeling studies, as well as the recent insights originating from the network analysis of prolonged grief symptoms. We also discuss applications of artificial intelligence for the accurate identification of major depression and post-traumatic stress, as examples for their potential applications to the study of loss reactions.

PURPOSE/OBJECTIVE:Breast cancer survivors may be at risk for increased rates of emotional distress and poorer quality of life. Survivorship care plans (SCPs) promoting wellness activities may support well-being; however, survivors may not receive or engage in their SCPs. This study aimed to assess receipt and participation in SCP activities as well as barriers to engagement amongst breast cancer survivors. METHODS:Breast cancer survivors (n = 187; 99% female, Mean age = 57.7) consented and completed self-reported assessments of SCP recommendations, engagement and interest in wellness activities, and potential barriers to engagement. RESULTS:A minority of participants recalled receiving an SCP (21%). The most physician recommended (62%) and completed (53%) activity was exercise. Interest in adding other wellness activities to the SCP was high, with reported interest levels of approximately 50% for several activities (e.g., mind body, nutrition, psychotherapy interventions). Fully half reported that having a physician-designed plan would influence participation in activities. The most common reported barriers to SCP activity engagement were lack of time (82%), work/school (65%), and lack of information (65%). CONCLUSION/CONCLUSIONS:Few survivors recalled receiving a formal SCP, and lack of information about wellness activities was a commonly reported barrier to participation. Interest in wellness activities was generally high and may indicate the need for more formal prescription or motivation enhancement techniques to promote SCP engagement. There may be a clinical need to emphasize SCP recommendations to enhance recall and increase engagement in wellness activities that may reduce psychological distress and improve quality of life.

Losing a loved one is one of life's greatest stressors. Although most bereaved individuals navigate through a period of intense acute grief that lessens with time, approximately 10% will develop a prolonged grief condition. This review provides an overview of the course of grief and describes risk factors for developing prolonged grief disorder. The evolution of the prolonged grief disorder diagnosis, including the latest criteria sets for ICD-11 and DSM-5, as well as common comorbid conditions and differential diagnosis are discussed. Clinically useful self-report and clinician-rated measures for assessing symptom constructs and overall prolonged grief disorder severity, evidence-based psychotherapies (such as complicated grief treatment), as well as evidence about pharmacologic approaches are presented. Finally, the authors discuss important future directions, including a potential increase in prolonged grief disorder cases due to the COVID-19 pandemic.

OBJECTIVE:Posttraumatic stress disorder and prolonged grief disorder (PGD) arise following major life stressors and may share some overlapping symptomatology. This study aimed to examine the presence and response to treatment of posttraumatic stress symptoms (PTSS) in bereaved adults with a primary diagnosis of PGD. METHODS:A randomized controlled trial of 395 adults with PGD (defined as an Inventory of Complicated Grief score ≥ 30 plus confirmation on structured clinical interview) randomly assigned participants to either complicated grief treatment (CGT) with citalopram, CGT plus placebo, citalopram, or placebo between March 2010 and September 2014. This secondary analysis examined the presence of PTSS (per the Davidson Trauma Scale) at baseline and change in PTSS with treatment using longitudinal mixed-effects regression and examined the role of violent compared to nonviolent deaths (loss type). RESULTS:High levels of PTSS were present at baseline, regardless of loss type, and were associated with increased functional impairment (P < .001). CGT with placebo demonstrated efficacy for PTSS compared to placebo in both threshold (OR = 2.71; 95% CI, 1.13-6.52; P = .026) and continuous (P < .001; effect size d = 0.47) analyses, and analyses were suggestive of a greater effect for CGT plus citalopram compared to citalopram alone (threshold analysis: OR = 2.84; 95% CI, 1.20-6.70; P = .017; continuous analysis: P = .053; d = 0.25). In contrast, citalopram did not differ from placebo, and CGT plus citalopram did not differ from CGT plus placebo. CONCLUSIONS:Bereavement-related PTSS are common in bereaved adults with PGD in the context of both violent and nonviolent death and are associated with poorer functioning. CGT shows efficacy for PTSS, while the antidepressant citalopram does not. TRIAL REGISTRATION:: ClinicalTrials.gov identifier: NCT01179568.

There has been a marked increase of network studies of Major Depressive Disorder (MDD). Despite rapidly growing contributions, their findings have yet to be systematically aggregated and examined. We therefore conducted a systematic review of depression network studies using PRISMA guidelines. A total of 254 clinical and population studies were collected from ISI's Web of Science and PsycINFO, between January 2010 to May 2020. A total of 23 between-subject studies were included for review, resulting in 58 cross-sectional networks. To determine their most critical symptoms and their connections, we analyzed strength centrality rankings, and aggregated the most robust symptoms connections into a summary network. Results indicated substantial variability between study samples, depression measures, and network features. Fatigue and Depressed Mood were the most central symptoms, while Weight changes tended to have the weakest centrality. Depressed Mood and Fatigue formed two separated symptoms communities characterized by recurrent connections, with Mood-Anhedonia as the most frequent edge of MDD. Network analysis informed our understanding of MDD, suggesting the critical role of Fatigue and Depressed Mood. The study's findings are discussed in their clinical and methodological implications, including future directions for network studies of MDD.

AbstractObjective: To design a Natural Language Processing (NLP) algorithm capable of detecting suicide content from patients' written communication to their therapists, to support rapid response and clinical decision making in telehealth settings. Method: A training dataset of therapy transcripts for 1,864 patients was established by detecting patient content endorsing suicidality using a proxy-model anchored on therapists' suicide prevention interventions; human expert raters then assessed the level of suicide risk endorsed by patients identified by the proxy-model (i.e., no risk, risk factors, ideation, method, or plan). A bag-of-words classification model was then iteratively built using the annotations from the expert raters to detect suicide risk level in 85,216 labeled patients' sentences from the training dataset. Results: The final NLP model identified risk-related content from non-risk content with good accuracy (AUC = 82.78). Conclusions: Risk for suicide could be reliably identified by the NLP algorithm. The risk detection model could assist telehealth clinicians in providing crisis resources in a timely manner. This modeling approach could also be applied to other psychotherapy research tasks to assist in the understanding of how the psychotherapy process unfolds for each patient and therapist.