Design of a multicenter randomized controlled trial of a post-discharge suicide prevention intervention for high-risk psychiatric inpatients: The Veterans Coordinated Community Care Study.

BACKGROUND: The period after psychiatric hospital discharge is one of elevated risk for suicide-related behaviors (SRBs). Post-discharge clinical outreach, although potentially effective in preventing SRBs, would be more cost-effective if targeted at high-risk patients. To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention. METHODS: The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (n = 850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects. RESULTS: Recruitment is underway and will end September 2024. Six-month follow-up will end and analysis will begin in Summer 2025. CONCLUSION: Results will provide information about the effectiveness of CLASP versus TAU in reducing post-discharge SRBs and provide guidance to VHA clinicians and policymakers about the implications of targeted use of CLASP among high-risk psychiatric inpatients in the months after hospital discharge. CLINICAL TRIALS REGISTRATION: ClinicalTrials.Gov identifier: NCT05272176 (https://www. CLINICALTRIALS: gov/ct2/show/NCT05272176).

Retrievable dual-chamber leadless pacemaker implant (Aveir DR) in an adult patient with congenital heart disease.

Leadless pacemakers have demonstrated potential as a transvenous pacing option in Adult Congenital Heart Disease patients. Aveir™ single-chamber (VR) leadless pacemakers have demonstrated safety in patients without congenital heart disease in a dual chamber approach. We present a case of dual-chamber pacing using the Aveir dual-chamber (DR) leadless pacemaker in a patient with repaired dextro-transposition of the great arteries with ventricular septal defect (VSD) surgical closure. A 26-year-old male patient with a history of transposition of the great arteries status post arterial switch and VSD repair neonatally had complicated second degree atrioventricular block and sinus node dysfunction necessitating pacemaker placement. Epicardial single-chamber ventricular pacemaker was placed neonatally, which was switched to dual-chamber pacemaker at age 17 due to malfunction. Recent fracture of pacemaker leads led to implantation of new dual chamber leadless pacemaker. Removal of previous pacemaker leads via mechanical extraction occurred and implantation of Aveir DR leadless pacemaker was performed under anesthesia via right femoral vein access without complication. Follow-up demonstrated Aveir VR threshold of 1.0V@0.2 ms, R-wave of 8.9mV, impedance of 490Ω, and the Aveir AR threshold of 0.75V@0.2 ms, P-wave of 3.7mV, and impedance of 400Ω. This case demonstrates safety and efficacy of dual chamber leadless pacemaker implantation in an ACHD patient.

Predicting Suicides Among US Army Soldiers After Leaving Active Service.

Importance: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions. Objective: To develop a model based on administrative data for regular US Army soldiers that can predict suicides 1 to 120 months after leaving active service. Design, Setting, and Participants: In this prognostic study, a consolidated administrative database was created for all regular US Army soldiers who left service from 2010 through 2019. Machine learning models were trained to predict suicides over the next 1 to 120 months in a random 70% training sample. Validation was implemented in the remaining 30%. Data were analyzed from March 2023 through March 2024. Main outcome and measures: The outcome was suicide in the National Death Index. Predictors came from administrative records available before leaving service on sociodemographics, Army career characteristics, psychopathologic risk factors, indicators of physical health, social networks and supports, and stressors. Results: Of the 800 579 soldiers in the cohort (84.9% male; median [IQR] age at discharge, 26 [23-33] years), 2084 suicides had occurred as of December 31, 2019 (51.6 per 100 000 person-years). A lasso model assuming consistent slopes over time discriminated as well over all but the shortest risk horizons as more complex stacked generalization ensemble machine learning models. Test sample area under the receiver operating characteristic curve ranged from 0.87 (SE = 0.06) for suicides in the first month after leaving service to 0.72 (SE = 0.003) for suicides over 120 months. The 10% of soldiers with highest predicted risk accounted for between 30.7% (SE = 1.8) and 46.6% (SE = 6.6) of all suicides across horizons. Calibration was for the most part better for the lasso model than the super learner model (both estimated over 120-month horizons.) Net benefit of a model-informed prevention strategy was positive compared with intervene-with-all or intervene-with-none strategies over a range of plausible intervention thresholds. Sociodemographics, Army career characteristics, and psychopathologic risk factors were the most important classes of predictors. Conclusions and relevance: These results demonstrated that a model based on administrative variables available at the time of leaving active Army service can predict suicides with meaningful accuracy over the subsequent decade. However, final determination of cost-effectiveness would require information beyond the scope of this report about intervention content, costs, and effects over relevant horizons in relation to the monetary value placed on preventing suicides.

Long-term outcomes in advanced anterior skull base malignancy: a single quaternary institution experience.

BACKGROUND: Advanced skull base malignancies are a heterogenous subset of head and neck cancers, and management is often complex. In recent times, there has been a paradigm shift in surgical technique and the advent of novel systemic options. Our goal was to analyse the long-term outcomes of a single quaternary head and neck and skull base service. METHODS: A retrospective review of 127 patients with advanced anterior skull base malignancies that were treated at our institution between 1999 and 2015 was performed. Multiple variables were investigated to assess their significance on 5 and 10-year outcomes. RESULTS: The mean age was 60.9 (± 12.6 SD). Sixty-four percent were males and 36% were females. Ninety percent of patients had T4 disease. Median survival time was 133 months. The 5-year overall survival (OS) was 66.2%, disease-specific survival (DSS) was 74.7%, and recurrence-free survival (RFS) was 65.0%. The 10-year OS was 55.1%, DSS was 72.1%, and RFS was 53.4%. Histological type and margin status significantly affected OS & DSS. CONCLUSION: Surgical management of advanced skull base tumours has evolved over the last few decades at our institution with acceptable survival outcomes and complication rates. Histological diagnosis and margin status are the main predictors of survival. The addition of neoadjuvant systemic agents in current trials may improve outcomes.

A phase Ib study to assess the safety of the human papillomavirus DNA vaccine (AMV002) in combination with durvalumab for HPV-associated oropharyngeal squamous cell carcinoma.

Background: Programmed cell death ligand 1 (PD-L1) inhibitors have limited efficacy as monotherapy in patients with recurrent/metastatic (R/M) Human Papilloma Virus (HPV) oropharyngeal squamous cell carcinoma (OPSCC). A phase I study of the therapeutic HPV-16 DNA vaccine AMV002 in curatively treated patients with OPSCC demonstrated a measurable immune response against HPV while being associated with high safety and tolerability. This prospective phase Ib single centre pilot study aims to test the safety and tolerability of combined PD-L1 inhibitor, Durvalumab, with AMV002 in 12 patients with recurrent OPSCC. Methods: Participants had evidence of R/M HPV-associated OPSCC. They received three intradermal administrations of AMV002 with Durvalumab followed by Durvalumab maintenance. Safety and tolerability data was the primary endpoint. The study was conducted with ethical approval (HREC/2018/QMS/47293) in Brisbane, Australia. Findings: The most common adverse event (AE) related to vaccine administration was erythema at the injection site. There were no grade 3 or 4 vaccine related AEs. There was one presumed immune-related grade 3 elevation in lipase secondary to Durvalumab with no intervention required. No patient ceased study due to treatment-related AEs. At week 16, objective response rate was 8% (N=1) and disease control rate was 17% (N=2). At a median follow up of 25.6 (20.0-26.6) months there was one long term complete response while all other participants developed progressive disease. Of the 11 evaluated patients, 9, (82%) had E6 and/or E7-specific T cell responses to the vaccine. Conclusion: The combination of AMV002 therapeutic HPV-16 vaccine and Durvalumab was found to be safe and well tolerated with no increased safety signals generated. T cell responses to vaccine were observed but further work will be required to improve efficacy.

Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Characterising B cell expression and prognostic significance in human papillomavirus positive oropharyngeal cancer.

OBJECTIVES: The incidence of human papillomavirus positive oropharyngeal cancer (HPV+OPC) is increasing, and new biomarkers are required to better define prognostic groups and guide treatment. Infiltrating T cells have been well studied in head and neck cancer, however the presence and role of B cells and tertiary lymphoid structures (TLS) in the tumor microenvironment has not, even though the interplay between T and B cells is increasingly being recognised. MATERIALS AND METHODS: Using CD20 immunohistochemistry (IHC) to identify B cells and TLS in a cohort of 159 HPV + OPC patients, we semi-quantitatively scored abundance and location (intra-tumoral or stromal) and correlated findings with patient survival. RESULTS: 32% (51/157) of patients had high intra-tumoral (IT) abundance of CD20+ B cells (≥5%) and this was prognostic for improved overall survival (OS) with an adjusted hazard ratio (HR) of 0.2 (95 % CI 0.0-0.7, p = 0.014). We validated our results in an independent cohort comprising 171 HPV + OPC where 14% (23/171) were IT CD20+ high, again showing improved survival with an adjusted HR for OS of 0.2 (95 % CI 0.0-1.4, p = 0.003). Neither stromal abundance nor the presence of TLS were prognostic in either cohort. B cells were subtyped by multispectral IHC, identifying CD20+CD27+ cells, consistent with memory B cells, as the predominant subtype. Combined with validated biomarker CD103, a marker of tissue-resident memory T cells, IT CD20+ B cells abundance was able to prognostically stratify patients further. CONCLUSIONS: CD20+ B cell abundance has the potential to be used as a biomarker to identify good and poor prognosis HPV + OPC patients.

Estimated Average Treatment Effect of Psychiatric Hospitalization in Patients With Suicidal Behaviors: A Precision Treatment Analysis.

Importance: Psychiatric hospitalization is the standard of care for patients presenting to an emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect of hospitalization in reducing subsequent suicidal behaviors is poorly understood and likely heterogeneous. Objectives: To estimate the association of psychiatric hospitalization with subsequent suicidal behaviors using observational data and develop a preliminary predictive analytics individualized treatment rule accounting for heterogeneity in this association across patients. Design, Setting, and Participants: A machine learning analysis of retrospective data was conducted. All veterans presenting with suicidal ideation (SI) or suicide attempt (SA) from January 1, 2010, to December 31, 2015, were included. Data were analyzed from September 1, 2022, to March 10, 2023. Subgroups were defined by primary psychiatric diagnosis (nonaffective psychosis, bipolar disorder, major depressive disorder, and other) and suicidality (SI only, SA in past 2-7 days, and SA in past day). Models were trained in 70.0% of the training samples and tested in the remaining 30.0%. Exposures: Psychiatric hospitalization vs nonhospitalization. Main Outcomes and Measures: Fatal and nonfatal SAs within 12 months of ED/UC visits were identified in administrative records and the National Death Index. Baseline covariates were drawn from electronic health records and geospatial databases. Results: Of 196 610 visits (90.3% men; median [IQR] age, 53 [41-59] years), 71.5% resulted in hospitalization. The 12-month SA risk was 11.9% with hospitalization and 12.0% with nonhospitalization (difference, -0.1%; 95% CI, -0.4% to 0.2%). In patients with SI only or SA in the past 2 to 7 days, most hospitalization was not associated with subsequent SAs. For patients with SA in the past day, hospitalization was associated with risk reductions ranging from -6.9% to -9.6% across diagnoses. Accounting for heterogeneity, hospitalization was associated with reduced risk of subsequent SAs in 28.1% of the patients and increased risk in 24.0%. An individualized treatment rule based on these associations may reduce SAs by 16.0% and hospitalizations by 13.0% compared with current rates. Conclusions and Relevance: The findings of this study suggest that psychiatric hospitalization is associated with reduced average SA risk in the immediate aftermath of an SA but not after other recent SAs or SI only. Substantial heterogeneity exists in these associations across patients. An individualized treatment rule accounting for this heterogeneity could both reduce SAs and avert hospitalizations.

Workforce Diversity, Equity, and Inclusion: A Crucial Component of Professionalism in Psychiatry.

Documented disparities have profoundly impacted the training and careers of physicians from socially and historically marginalized groups, including women, people with disabilities, people who identify with racial and ethnic minority groups, and the lesbian, gay, bisexual, transgender, and queer or questioning+ community. Professionalism is a core component of medical training and practice, yet a focus on workforce diversity, equity, and inclusion is often absent. This report aims to encourage the adoption of workforce diversity, equity, and inclusion as a crucial component of professionalism, with an emphasis on the field of psychiatry.

Development of a model to predict antidepressant treatment response for depression among Veterans.

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.

Refining stereotactic body radiation therapy as a bridge to transplantation for hepatocellular carcinoma: An institutional experience.

BACKGROUND: Stereotactic body radiotherapy (SBRT) has been established as a safe and effective treatment for hepatocellular carcinoma (HCC). Currently, there are no consensus guidelines to advise optimal patient selection and radiotherapy planning parameters to minimise the risk of surgical and medical complications after liver transplant (LT) in patients who have had prior SBRT for HCC, whilst optimising treatment benefit. METHODS: We performed a retrospective analysis of all adult patients who received liver SBRT as a bridge to LT at a tertiary institution between 2017 and 2019. RESULTS: Nine patients received SBRT as bridging therapy to LT. HCC location varied from peripheral to central/hilar regions and HCC diameter was 13-54 mm. Median time between SBRT and LT was 141 days (range 27-461 days). Median operating time was 360 min (range 270-480 min). Four patients (44%) had visible SBRT reaction or fibrosis at the time of LT. SBRT reaction resulted in clinical impact in one patient (11%) only, where vascular clamping of the IVC was required for 10 min. CONCLUSION: SBRT is a safe and effective treatment for HCC enabling patients to remain within LT criteria, even for lesions not amenable to other more conventional bridging therapies. We describe a preliminary decision pathway to guide the optimal use of SBRT as a bridge to LT developed in our institution.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

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.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Development of a model to predict psychotherapy response for depression among Veterans.

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.

A prospective observational cohort study examining the development of head and neck lymphedema from the time of diagnosis.

INTRODUCTION: Head and neck lymphedema can occur in the internal or external structures of the head and neck region. Little is known about the development of this condition over the course of treatment for head and neck cancer. This study aimed to observe the development of internal and external lymphedema from diagnosis to 12 weeks postacute treatment. METHODS: A single center, prospective observational cohort study assessed participants for external lymphedema, internal lymphedema, quality of life, and symptom burden. Assessments were conducted prior to starting radiotherapy (RT), at the end of RT, 6 and 12 weeks after RT. RESULTS: Forty-six participants were recruited. External lymphedema as measured by percentage water content, increased from 41.9 at baseline (95% CI: 39.3-44.4) to 50.4 (95% CI: 46.0-54.8) at 12 weeks following RT (p-value < .001). After adjusting for changes in weight and participant age at baseline, a general increase in tape measurements was observed over time with significant increases from baseline to 12 weeks post-RT for all measurement points. By 12 weeks post-RT, all participants had lymphedema present in eight of 13 internal sites assessed. CONCLUSIONS: Internal and external head and neck lymphedema was observed to increase from baseline to 12 weeks after completion of RT without abatement. People with head and neck cancer should be educated about the potentially extended duration of this treatment side effect. Further research is required to determine the point at which swelling symptoms recede.