Faculty Bibliography

OBJECTIVES/OBJECTIVE:We explored medications for opioid use disorder treatment (MOUD) utilization in six New York City public hospitals that implemented the "Consultation for Addiction Care and Treatment in Hospitals (CATCH)" program. METHODS:CATCH rolled out between October 2018 and February 2020. Data from the electronic health record were analyzed for the first year post-implementation. Eligible cases included adults with an opioid-related diagnosis admitted to inpatient departments served by CATCH, with a stay of ≥1 night. Patients were classified as receiving an MOUD order if there was at least 1 order of buprenorphine, methadone, or naltrexone. Logistic regression modeled the impact of CATCH consults on MOUD orders, controlling for demographic and clinical characteristics with hospital as a random effect. RESULT/RESULTS:Among 2117 eligible patients, 71.4% were male, with a mean age of 51.2 years, and 27.2% identified as Black, 21.2% as White, and 34.5% as Hispanic. MOUD was ordered in 60.9% of admissions, and 41.5% had a completed CATCH consult. Patients identified as Black had lower odds of receiving a MOUD order than those identified as White (OR: 0.52, 95% CI: 0.38-0.71; P < 0.001). Patients with a CATCH consult had higher odds of receiving a MOUD order (OR: 3.22, 95% CI: 2.54-4.07; P < 0.001). CONCLUSION/CONCLUSIONS:Majority of patients in our sample received a MOUD order, with higher odds among those with a CATCH consult. Further research is needed on the drivers of racial disparities in MOUD, and other contextual, organizational, and population-specific barriers and facilitators contributing to receipt of hospital-based addiction consult services and MOUD.

This paper presents a Bayesian regression model relating scalar outcomes to brain functional connectivity represented as symmetric positive definite (SPD) matrices. Unlike many proposals that simply vectorize the matrix-valued connectivity predictors, thereby ignoring their geometric structure, the method presented here respects the Riemannian geometry of SPD matrices by using a tangent space modeling. Dimension reduction is performed in the tangent space, relating the resulting low-dimensional representations to the responses. The dimension reduction matrix is learned in a supervised manner with a sparsity-inducing prior imposed on a Stiefel manifold to prevent overfitting. Our method yields a parsimonious regression model that allows uncertainty quantification of all model parameters and identification of key brain regions that predict the outcomes. We demonstrate the performance of our approach in simulation settings and through a case study to predict Picture Vocabulary scores using data from the Human Connectome Project.

BACKGROUND:The optimal management of patients with intermediate-risk pulmonary embolism (PE), who have right heart dysfunction (determined by a combination of imaging and cardiac biomarkers) but a normal blood pressure, is uncertain. These patients suffer from reduced functional capacity and a lower quality of life over the long-term, despite use of anticoagulant therapy. Catheter-directed therapy (CDT) is a promising treatment for acute PE that rapidly removes thrombus and potentially improves cardiac dysfunction. However, CDT has risk and is costly, and it is not known whether it improves long-term cardiorespiratory fitness and/or quality of life compared with anticoagulation alone. METHODS:) with cardiopulmonary exercise testing at 3 months and reduce New York Heart Association (NYHA) Class at 12 months compared with No-CDT. These 2 primary efficacy outcomes will be analyzed sequentially using a "gatekeeping" procedure; for NYHA class to be compared, peak oxygen consumption must first be shown to be significantly increased by CDT. Safety and cost-effectiveness will also be assessed. CONCLUSION/CONCLUSIONS:When completed, PE-TRACT will provide important evidence regarding the benefits and risks of CDT to treat intermediate-risk PE compared with anticoagulation alone. TRIAL REGISTRATION/BACKGROUND:clinicaltrials.gov: NCT05591118.

IMPORTANCE/UNASSIGNED:Medications for opioid use disorder (MOUD) are highly effective, but only 22% of individuals in the US with opioid use disorder receive them. Hospitalization potentially provides an opportunity to initiate MOUD and link patients to ongoing treatment. OBJECTIVE/UNASSIGNED:To study the effectiveness of interprofessional hospital addiction consultation services in increasing MOUD treatment initiation and engagement. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This pragmatic stepped-wedge cluster randomized implementation and effectiveness (hybrid type 1) trial was conducted in 6 public hospitals in New York, New York, and included 2315 adults with hospitalizations identified in Medicaid claims data between October 2017 and January 2021. Data analysis was conducted in December 2023. Hospitals were randomized to an intervention start date, and outcomes were compared during treatment as usual (TAU) and intervention conditions. Bayesian analysis accounted for the clustering of patients within hospitals and open cohort nature of the study. The addiction consultation service intervention was compared with TAU using posterior probabilities of model parameters from hierarchical logistic regression models that were adjusted for age, sex, and study period. Eligible participants had an admission or discharge diagnosis of opioid use disorder or opioid poisoning/adverse effects, were hospitalized at least 1 night in a medical/surgical inpatient unit, and were not receiving MOUD before hospitalization. INTERVENTIONS/UNASSIGNED:Hospitals implemented an addiction consultation service that provided inpatient specialty care for substance use disorders. Consultation teams comprised a medical clinician, social worker or addiction counselor, and peer counselor. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The dual primary outcomes were (1) MOUD treatment initiation during the first 14 days after hospital discharge and (2) MOUD engagement for the 30 days following initiation. RESULTS/UNASSIGNED:Of 2315 adults, 628 (27.1%) were female, and the mean (SD) age was 47.0 (12.4) years. Initiation of MOUD was 11.0% in the Consult for Addiction Treatment and Care in Hospitals (CATCH) program vs 6.7% in TAU, engagement was 7.4% vs 5.3%, respectively, and continuation for 6 months was 3.2% vs 2.4%. Patients hospitalized during CATCH had 7.96 times higher odds of initiating MOUD (log-odds ratio, 2.07; 95% credible interval, 0.51-4.00) and 6.90 times higher odds of MOUD engagement (log-odds ratio, 1.93; 95% credible interval, 0.09-4.18). CONCLUSIONS/UNASSIGNED:This randomized clinical trial found that interprofessional addiction consultation services significantly increased postdischarge MOUD initiation and engagement among patients with opioid use disorder. However, the observed rates of MOUD initiation and engagement were still low; further efforts are still needed to improve hospital-based and community-based services for MOUD treatment. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov Identifier: NCT03611335.

BACKGROUND:There is a need to understand the relationship between COVID-19 Convalescent Plasma (CCP) anti-SARS-CoV-2 IgG levels and clinical outcomes to optimize CCP use. This study aims to evaluate the relationship between recipient baseline clinical status, clinical outcomes, and CCP antibody levels. METHODS:The study analyzed data from the COMPILE study, a meta-analysis of pooled individual patient data from 8 randomized clinical trials (RCTs) assessing the efficacy of CCP vs. control, in adults hospitalized for COVID-19 who were not receiving mechanical ventilation at randomization. SARS-CoV-2 IgG levels, referred to as 'dose' of CCP treatment, were retrospectively measured in donor sera or the administered CCP, semi-quantitatively using the VITROS Anti-SARS-CoV-2 IgG chemiluminescent immunoassay (Ortho-Clinical Diagnostics) with a signal-to-cutoff ratio (S/Co). The association between CCP dose and outcomes was investigated, treating dose as either continuous or categorized (higher vs. lower vs. control), stratified by recipient oxygen supplementation status at presentation. RESULTS:A total of 1714 participants were included in the study, 1138 control- and 576 CCP-treated patients for whom donor CCP anti-SARS-CoV2 antibody levels were available from the COMPILE study. For participants not receiving oxygen supplementation at baseline, higher-dose CCP (/control) was associated with a reduced risk of ventilation or death at day 14 (OR = 0.19, 95% CrI: [0.02, 1.70], posterior probability Pr(OR < 1) = 0.93) and day 28 mortality (OR = 0.27 [0.02, 2.53], Pr(OR < 1) = 0.87), compared to lower-dose CCP (/control) (ventilation or death at day 14 OR = 0.79 [0.07, 6.87], Pr(OR < 1) = 0.58; and day 28 mortality OR = 1.11 [0.10, 10.49], Pr(OR < 1) = 0.46), exhibiting a consistently positive CCP dose effect on clinical outcomes. For participants receiving oxygen at baseline, the dose-outcome relationship was less clear, although a potential benefit for day 28 mortality was observed with higher-dose CCP (/control) (OR = 0.66 [0.36, 1.13], Pr(OR < 1) = 0.93) compared to lower-dose CCP (/control) (OR = 1.14 [0.73, 1.78], Pr(OR < 1) = 0.28). CONCLUSION/CONCLUSIONS:Higher-dose CCP is associated with its effectiveness in patients not initially receiving oxygen supplementation, however, further research is needed to understand the interplay between CCP anti-SARS-CoV-2 IgG levels and clinical outcome in COVID-19 patients initially receiving oxygen supplementation.

We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of the L components of a mixture model which incorporates scalar and functional covariates. This process can be thought as a hierarchical model with the first level modelling a scalar response according to a mixture of parametric distributions and the second level modelling the mixture probabilities by means of a generalized linear model with functional and scalar covariates. The traditional approach of treating functional covariates as vectors not only suffers from the curse of dimensionality, since functional covariates can be measured at very small intervals leading to a highly parametrized model, but also does not take into account the nature of the data. We use basis expansions to reduce the dimensionality and a Bayesian approach for estimating the parameters while providing predictions of the latent classification vector. The method is motivated by two data examples that are not easily handled by existing methods. The first example concerns identifying placebo responders on a clinical trial (normal mixture model) and the other predicting illness for milking cows (zero-inflated mixture of the Poisson model).

BACKGROUND:Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework can be directly used to analyze microbiome as a mediator between health disparity and clinical outcome, due to the non-manipulable nature of the exposure and the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. METHODS:Considering the modifiable and quantitative features of the microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g., ethnicity or region) to the outcome through the microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups and innovatively and successfully extends the existing microbial mediation methods, which are originally proposed under potential outcome or counterfactual outcome study design, to address health disparities. RESULTS:Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating the microbiome's contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between ethnicities or regions. 20.63%, 33.09%, and 25.71% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 18, and 16 species are identified to play the mediating role respectively. CONCLUSIONS:The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles. Video Abstract.

INTRODUCTION/BACKGROUND:Cognitive activity and awareness during cardiac arrest (CA) are reported but ill understood. This first of a kind study examined consciousness and its underlying electrocortical biomarkers during cardiopulmonary resuscitation (CPR). METHODS:) monitoring into CPR during in-hospital CA (IHCA). Survivors underwent interviews to examine for recall of awareness and cognitive experiences. A complementary cross-sectional community CA study provided added insights regarding survivors' experiences. RESULTS:=43%) normal EEG activity (delta, theta and alpha) consistent with consciousness emerged as long as 35-60 minutes into CPR. CONCLUSIONS:Consciousness. awareness and cognitive processes may occur during CA. The emergence of normal EEG may reflect a resumption of a network-level of cognitive activity, and a biomarker of consciousness, lucidity and RED (authentic "near-death" experiences).