Publications

OBJECTIVES: Randomized controlled trials (RCTs) of the Collaborative Care Model demonstrate strong evidence for effectively managing depression in a stepped-care approach across diverse patient populations. Despite alignment with the American Society of Clinical Oncology guidelines, which recommend a stepped-care approach for managing depression and anxiety in cancer patients, implementation of collaborative care in cancer centers remains limited and sparse real-world data exist. The Supportive Oncology Collaborative, a program integrating behavioral health and palliative care, was developed at an NCI-designated academic cancer center. This study aims to evaluate depression outcomes within this collaborative care program.

METHODS: A retrospective analysis was conducted on patients with at least 2 Patient Health Questionnaire-9 (PHQ-9) scores recorded within a 12-month period between January 2022 and December 2023 at 1 regional campus. Depression response, defined as a 50% reduction in PHQ-9 scores, was assessed at 12 and 24 weeks. Response rates were compared to those reported in RCTs of collaborative care.

RESULTS: Mean PHQ-9 scores were 17.3 at baseline (n = 47), 11.1 at 12 weeks (n = 43), and 10.1 at 24 weeks (n = 22). Depression response rates were 34.9% at 12 weeks (n = 43) and 54.5% at 24 weeks (n = 22).

SIGNIFICANCE OF RESULTS: We observed depression response rates comparable to those reported in RCTs of collaborative care in individuals with cancer. However, the high proportion of missing data highlights the difficulty of tracking outcomes in real-world clinical settings and the need for further evaluation and strategies to improve data completeness.

BACKGROUND: The benefits of physical activity (PA) for both physical and mental health, including major depressive disorder (MDD), are well established. Mobile devices, such as smartphones, offer a scalable way to monitor PA and its relationship with depressive symptoms in daily life.

OBJECTIVE: This study aimed to investigate the association between passive smartphone-recorded step counts and current depressive symptoms in individuals with and without a lifetime diagnosis of MDD, using a naturalistic bring-your-own-device approach.

METHODS: We used the Remote Monitoring Application in Psychiatry (ReMAP) to collect passive step count data from participants' personal smartphones. The sample included 181 individuals with a lifetime MDD diagnosis, assessed via the structured clinical interview for the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition; DSM-IV), and 195 healthy controls (HCs). Current depressive symptoms were assessed using the Beck Depression Inventory. PA was operationalized as daily and weekly step counts, passively recorded via smartphone sensors. Hierarchical models were applied to examine the association between PA and depression severity.

RESULTS: Patients with MDD exhibited significantly lower daily step counts (mean 3454, SD 2683) compared to HCs (mean 4699, SD 3175; P<.001) and showed reduced diurnal variability (β=-0.36; P=.003). Higher daily step counts were associated with lower Beck Depression Inventory scores across the full sample (β=-0.06, 95% CI -0.09 to -0.02; P=.002), with similar trends in both MDD and HC groups. Weekly step counts also significantly predicted lower concurrent depressive symptoms (β=-0.29, 95% CI -0.43 to -0.14; P<.001), while patients with MDD displayed less variability in weekly activity levels than HCs (β=-0.75; P=.001).

CONCLUSIONS: These findings underscore the potential of mobile devices to be used as effective tools for monitoring PA in patients with MDD, supporting more customized and adaptive approaches to prevention and treatment. They also emphasize the importance of incorporating PA into the clinical management of depression.

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to (1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA; (2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography.

METHODS: The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; LVH controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; LVH controls, 222).

RESULTS: The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91-0.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87-0.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90-0.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91-0.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91-0.95] versus AUC, 0.88 [95% CI, 0.85-0.90]; P<0.001).

CONCLUSIONS: Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.

Epilepsy is a highly prevalent chronic central nervous system disorder that imposes substantial societal and economic burdens. Inconsistent associations of alcohol consumption, identified as a major global health risk factor, with epilepsy risk have been reported. The aim of the present study was to assess the relationship between alcohol use and epilepsy and to identify potential underlying mechanisms, with a particular focus on the role of neutrophil extracellular traps (NETs), using an integrated multiomic approach. We assessed the global risk of alcohol consumption for epilepsy using data from the Global Burden of Disease Study 2021, and we conducted a Mendelian randomization (MR) analysis to evaluate causality. Additionally, we employed machine learning algorithms and protein-protein interaction networks to identify key genes. Our results indicate that alcohol consumption significantly contributes to the risk of epilepsy, as confirmed by MR analysis (odds ratio = 1.30, 95% confidence interval 1.06-1.60; p = 0.011). Functional enrichment analysis revealed pathways related to NET formation, whereas machine learning identified key genes such as myeloperoxidase (MPO) and neutrophil elastase. Animal and molecular experiments confirmed that acute alcohol exposure increases the susceptibility to epileptic seizures, whereas the MPO inhibitor 4-aminobenzoic acid hydrazide showed therapeutic potential for alcohol-induced epilepsy. This study provides novel insights into the role of NETs in alcohol-induced epilepsy and highlights potential therapeutic targets, thereby contributing to the development of innovative treatment strategies for epilepsy prevention and management.

AIMS: In the follow-up of patients with mitral regurgitation (MR), assessment of left ventricular (LV) dilatation using standard echocardiography often yields inconsistent results. We investigated whether measuring 3D LV end-systolic volume (3DLVESV) improves risk stratification in moderate or greater MR.

METHODS AND RESULTS: In the prospective 3D Echocardiography and Cardiovascular Prognosis in Mitral Regurgitation (3D-PRIME) study, 227 patients -142 with primary (PMR) and 85 secondary MR (SMR)- underwent 2D/3D echocardiography. 3DLVESV was increased if ≥41.5/35 mL/m², and LV end-systolic diameter (LVESD) enlarged if ≥39.8/34.8 mm in men/women. The primary outcome was a composite of MR progression towards intervention, death, or heart failure hospitalization (HFH). Death or HFH was a secondary outcome.At baseline, 28% of PMR and 54% of SMR patients had increased 3DLVESV. After 21 (15-25) months, increased 3DLVESV was associated with 1.9-fold (1.2-3.2) higher adjusted risk of the primary outcome in PMR, and 4.1-fold (1.6-10.7) higher risk of death or HFH in SMR (P < 0.05). 3DLVESV and LVESD concordantly identified LV dilatation in 20% of PMR patients and were discordant in 27%. Both patients with increased 3DLVESV only, and those with increased both 3DLVESV and LVESD, had high risk of the primary outcome after adjusting for recommendations for intervention in PMR: HR 7.1 (2.9-16.9) and 4.9 (2.1-11.1), respectively (P < 0.001).

CONCLUSION: Increased 3DLVESV is associated with a higher risk of adverse events in patients with significant MR. In PMR, evaluating LV dilatation using both 3DLVESV and LVESD may enhance risk stratification and aid in patient selection for close follow-up.

CLINICALTRIALSGOV IDENTIFIER: NCT04442828, 17 April 2020.

This Perspective explores the transformative potential of piezoelectric biomaterials in addressing one of the most persistent challenges in craniomaxillofacial (CMF) bone regeneration: the reliable healing of large, irregular, and functionally demanding skeletal defects. Traditional approaches─autologous grafts, allografts, and inert synthetic scaffolds─are limited by donor-site morbidity, immunogenicity, mechanical insufficiency, and inadequate bioactivity. In contrast, piezoelectric materials offer a dynamic alternative, generating endogenous-like electrical cues in response to mechanical stress, thereby mimicking the body's natural bone homeostasis and healing mechanisms. Bone's intrinsic piezoelectricity plays a critical role in cellular behavior through electromechanical signaling. Inspired by this, exogenous piezoelectric scaffolds─composed of polymers (e.g., PVDF, PLA), ceramics (e.g., BaTiO3, HA), or their composites─have been engineered to convert physiological strain into localized electric potentials that activate osteogenic pathways and modulate immune responses. In vitro and in vivo studies consistently demonstrate enhanced osteoblast proliferation, differentiation, mineralization, and macrophage polarization toward pro-healing phenotypes. Notably, BaTiO3/PLA membranes, magnetically or mechanically activated composites, and 3D-printed piezoelectric scaffolds have shown accelerated bone formation and vascularization in preclinical CMF defect models. Beyond bone repair, these materials exhibit antimicrobial and immunomodulatory properties, making them uniquely suited for complex, load-bearing, and inflamed environments such as mandible or periodontal defects. The convergence of advanced fabrication techniques (e.g., electrospinning, 3D/4D printing) and smart materials design now enables patient-specific, bioactive implants capable of real-time mechanotransduction. While no human trials have yet been reported, the clinical trajectory is supported by existing FDA-approved piezoelectric devices and growing preclinical validation. Future progress hinges on overcoming translational barriers, including regulatory clearance, scalable manufacturing, and mechanical reliability under functional loads. Ultimately, piezoelectric biomaterials represent a next-generation paradigm for CMF regeneration, combining mechanical support, immunomodulation, and bioelectric stimulation to enable personalized and robust bone regeneration.

OBJECTIVES: Mobile health (mHealth) technology has been utilized to offer self-management tools to people with pain, including symptom tracking. Existing mobile tracking applications (apps) for chronic pain management have demonstrated reliability, feasibility, improved coping, and reduced health care utilization. Unfortunately, adherence in using a pain app can be problematic with many not using or discontinuing its use early. The current study aimed to investigate the impact that pain self-efficacy, pain conceptualizations, and patient perception of pain care providers, have on engagement with a mobile pain tracking app.

METHODS: Seventy-six (N=76) individuals with chronic pain downloaded a pain app and completed questionnaires assessing their pain and use of a pain app 3 months after they had downloaded the app. Associations with engagement with the app, defined as the number of daily diaries completed, and demographic and self-report questionnaire data were examined.

RESULTS: Results showed that engagement with the app was unrelated to self-efficacy and pain conceptualization but significantly related to positive perceptions of their pain care providers. Patients with more severe pain were found to have lower self-efficacy, less understanding of the biopsychosocial model of pain, and lower satisfaction with their involvement in their pain care decisions. Surprisingly, those who engaged more with the app demonstrated lower self-efficacy as compared with those who used the pain app less.

DISCUSSION: These findings highlight the importance of the patient-provider relationship in engaging with mHealth technology for pain management. Results further imply that longer-term use of mHealth tools may not be perceived as adaptive or clinically helpful for certain individuals.

INTRODUCTION: Cognition is a common symptom dimension in major mood disorders and is associated with impairments in daily life functioning. Assessments that capture cognitive difficulties reflective of those that people experience in the real world are therefore much needed; however, most cognitive assessments lack ecological validity. A recently developed, fully immersive VR platform for cognitive assessment (CAVIR) has proven to be feasible, well-tolerated, sensitive to cognitive impairment in psychiatric populations, and associated with measures of daily functioning. Here we aimed to assess the validity of a newly developed English language version of CAVIR in people with primary mood disorders (PMD) and controls (HC).

METHOD: We enrolled 40 people with PMD including Bipolar I Disorder, Bipolar II Disorder, and Major Depressive Disorder, and 40 healthy controls. Participants were administered the CAVIR, the MATRICS Consensus Cognitive Battery (MCCB), symptom ratings, and measures of daily functioning (FAST, UPSA-B).

RESULTS: Patients scored worse than controls on the CAVIR composite and all subtests (p = 0.02-p < 0.0001), except the executive functioning task (p = 0.85). Comparing the composite and domain scores of CAVIR to their corresponding domains on the MCCB revealed modest to moderate, significant correlations on the composite and all domains except executive functioning. The CAVIR was associated with both performance-based (UPSA-B) and interview rated (FAST) measures of functioning.

CONCLUSIONS: This newly translated English language version of CAVIR performed very similarly to the original version and was sensitive to cognitive impairments in people with PMD. CAVIR composite and most subtests were correlated with an established paper and pencil cognitive battery and were associated with measures of functioning. The CAVIR is self-administered, quick, and requires minimal training, making it a useful tool for assessing cognition.

BACKGROUND: Climate-related disruptions to the health system may impact cancer outcomes. This may be particularly true for prostate cancer, which greatly contributes to cancer burden while existing on a risk spectrum, leading some men to delay treatment even in advanced cases.

METHODS: The study included men diagnosed with metastatic prostate cancer from 2010 to 2020 within SEER-supported counties that experienced a climate-related natural disaster from 2012 to 2018. A smaller subgroup of "major" disasters was classified based on individual assistance from FEMA. Year of natural disaster was considered the index date, with 2-year pre- and post-disaster periods compared. Age-standardized incidence-based metastatic prostate cancer mortality (IBM) rates were extracted from SEER and adjusted for demographics. Counties were then compared 147 SEER counties without any climate-related natural disaster.

RESULTS: There were 222 counties across 11 states experiencing a single disaster within the study period, covering an estimated 27,787,120 people. Compared to the index year, prostate cancer IBM was 15% higher (RR: 1.15, 95% CI 1.02-1.30) 1-year post-disaster and 28% higher (RR: 1.28, 95% CI 1.11-1.49) 2 years post-disaster. Associations were stronger among counties (n = 50) experiencing a "major" disaster (RR: 1.21, 95% CI: 1.05-1.40) and 35% (RR: 1.35, 95% CI: 1.17-1.55) at 1 and 2 years. In non-exposed counties, this pattern was absent.

CONCLUSIONS: We report a significant, dose-dependent change in mortality from metastatic prostate cancer following a climate-related natural disaster. The reasons are speculative but may include delayed diagnosis, care fragmentation, and interruptions for treatments for advanced disease including chemotherapy and radio-hormonal therapy.

INTRODUCTION: Online sales of e-cigarettes challenge the enforcement of state tobacco control (TC) policies. Using key informant (KI) interviews, we examined barriers and facilitators to implementing TC policies in the context of e-cigarettes sold online.

METHODS: From April 2023 to December 2024, we invited state representatives from all 50 states and the District of Columbia to participate in KI interviews. Guided by Bullock's (2021) policy implementation framework, we examined licensure, minimum legal sales age (MLSA), flavor bans, and taxation. Data were analyzed using team-based, iterative coding.

RESULTS: We conducted 74 semi-structured interviews with 90 KIs across 41 states, of which online sales emerged as a key theme in 41 interviews with 60 KIs across 34 states. KIs noted how online sales interacted with existing legal frameworks and enforcement methods, often creating challenges, including jurisdictional ambiguity over out-of-state or international sellers, limited capacity to monitor the emergence of new online sellers, statutory language not designed for digital commerce, supply chain ambiguity, and logistical difficulties in conducting online decoy buys. KIs noted several potential enforcement facilitators, including the Prevent All Cigarette Trafficking Act (e.g., monthly delivery sales reports), consumer complaint systems, internet surveillance, directories of products authorized for sale, and interstate coordination of data sharing. Some states pursued legal action against online retailers using consumer protection laws.

CONCLUSIONS: Online e-cigarette sales present a regulatory challenge for TC implementation, requiring policy adaptation, cross-jurisdictional coordination, enhanced monitoring, and research to guide effective regulation of the digital marketplace.