Faculty Bibliography

Study advisory committees (SACs) provide critical value to clinical trials by providing unique perspectives that pull from personal and professional experiences related to the trial's healthcare topic. The Emergency Medicine Palliative Care Access (EMPallA) study had the privilege of convening a 16-person SAC from the project's inception to completion. The study team wanted to understand the impact this project had on the SAC members. In this narrative, we use reflective dialogue to share SAC members' lived experiences and the impact the EMPallA study has had on members both personally and professionally. We detail the (1) benefits SAC members, specifically patients, and caregivers, have had through working on this project. (2) The importance of recruiting diverse SAC members with different lived experiences and leveraging their feedback in clinical research. (3) Value of community capacity building to ensure the common vision of the clinical trial is promoted.

BACKGROUND/UNASSIGNED:Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. METHODS/UNASSIGNED:To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative treatment benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. RESULTS/UNASSIGNED:We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analysis demonstrates the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. CONCLUSION/UNASSIGNED:The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

INTRODUCTION/BACKGROUND:This study aimed to assess emergency nurses' perceived barriers toward engaging patients in serious illness conversations. METHODS:Using a mixed-method (quant + QUAL) convergent design, we pooled data on the emergency nurses who underwent the End-of-Life Nursing Education Consortium training across 33 emergency departments. Data were extracted from the End-of-Life Nursing Education Consortium post-training questionnaire, comprising a 5-item survey and 1 open-ended question. Our quantitative analysis employed a cross-sectional design to assess the proportion of emergency nurses who report that they will encounter barriers in engaging seriously ill patients in serious illness conversations in the emergency department. Our qualitative analysis used conceptual content analysis to generate themes and meaning units of the perceived barriers and possible solutions toward having serious illness conversations in the emergency department. RESULTS:A total of 2176 emergency nurses responded to the survey. Results from the quantitative analysis showed that 1473 (67.7%) emergency nurses reported that they will encounter barriers while engaging in serious illness conversations. Three thematic barriers-human factors, time constraints, and challenges in the emergency department work environment-emerged from the content analysis. Some of the subthemes included the perceived difficulty of serious illness conversations, delay in daily throughput, and lack of privacy in the emergency department. The potential solutions extracted included the need for continued training, the provision of dedicated emergency nurses to handle serious illness conversations, and the creation of dedicated spaces for serious illness conversations. DISCUSSION/CONCLUSIONS:Emergency nurses may encounter barriers while engaging in serious illness conversations. Institutional-level policies may be required in creating a palliative care-friendly emergency department work environment.

The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel-arm cluster randomized design. While statistical methods have been developed for designing cluster randomized crossover trials, they have exclusively focused on testing the overall average treatment effect, with little attention to differential treatment effects across subpopulations. Recently, interest has grown in understanding whether treatment effects may vary across pre-specified patient subpopulations, such as those defined by demographic or clinical characteristics. In this article, we consider the two-treatment two-period cluster randomized crossover design under either a cross-sectional or closed-cohort sampling scheme, where it is of interest to detect the heterogeneity of treatment effect via an interaction test. Assuming a patterned correlation structure for both the covariate and the outcome, we derive new sample size formulas for testing the heterogeneity of treatment effect with continuous outcomes based on linear mixed models. Our formulas also address unequal cluster sizes and therefore allow us to analytically assess the impact of unequal cluster sizes on the power of the interaction test in cluster randomized crossover designs. We conduct simulations to confirm the accuracy of the proposed methods, and illustrate their application in two real cluster randomized crossover trials.

BACKGROUND:Offering medications for opioid use disorder (MOUD) in carceral settings significantly reduces overdose. However, it is unknown to what extent individuals in jails continue MOUD once they leave incarceration. We aimed to assess the relationship between in-jail MOUD and MOUD continuity in the month following release. METHODS:We conducted a retrospective cohort study of linked NYC jail-based electronic health records and community Medicaid OUD treatment claims for individuals with OUD discharged from jail between 2011 and 2017. We compared receipt of MOUD within 30 days of release, among those with and without MOUD at release from jail. We tested for effect modification based on MOUD receipt prior to incarceration and assessed factors associated with treatment discontinuation. RESULTS:Of 28,298 eligible incarcerations, 52.8 % received MOUD at release. 30 % of incarcerations with MOUD at release received community-based MOUD within 30 days, compared to 7 % of incarcerations without MOUD (Risk Ratio: 2.62 (2.44-2.82)). Most (69 %) with MOUD claims prior to incarceration who received in-jail MOUD continued treatment in the community, compared to 9 % of those without prior MOUD. Those who received methadone (vs. buprenorphine), were younger, Non-Hispanic Black and with no history of MOUD were less likely to continue MOUD following release. CONCLUSIONS:MOUD maintenance in jail is strongly associated with MOUD continuity upon release. Still, findings highlight a gap in treatment continuity upon-reentry, especially among those who initiate MOUD in jail. In the wake of worsening overdose deaths and troubling disparities, improving MOUD continuity among this population remains an urgent priority.

BACKGROUND:Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. METHODS:To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. RESULTS:We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. CONCLUSION/CONCLUSIONS:The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

CONTEXT/BACKGROUND:Outpatient Palliative Care (OPC) benefits persons living with serious illness, yet barriers exist in utilization. OBJECTIVES/OBJECTIVE:To identify factors associated with OPC clinic utilization. METHODS:Emergency Medicine Palliative Care Access is a multicenter, randomized control trial comparing two models of palliative care for patients recruited from the Emergency Department (ED): nurse-led telephonic case management and OPC (one visit a month for 6 months). Patients were aged 50+ with advanced cancer or end-stage organ failure and recruited from 19 EDs. Using a mixed effects hurdle model, we analyzed patient, provider, clinic and healthcare system factors associated with OPC utilization. RESULTS:Among the 603 patients randomized to OPC, about half (53.6%) of patients attended at least one clinic visit. Those with less than high school education were less likely to attend an initial visit than those with a college degree or higher (aOR 0.44; CI 0.23, 0.85), as were patients who required considerable assistance (aOR 0.45; CI 0.25, 0.82) or had congestive heart failure only (aOR 0.46; CI 0.26, 0.81). Those with higher symptom burden had a higher attendance at the initial visit (aOR 1.05; CI 1.00, 1.10). Reduced follow up visit rates were demonstrated for those of older age (aRR 0.90; CI 0.82, 0.98), female sex (aRR 0.84; CI 0.71, 0.99), and those that were never married (aRR 0.62; CI 0.52, 0.87). CONCLUSION/CONCLUSIONS:Efforts to improve OPC utilization should focus on those with lower education, more functional limitations, older age, female sex, and those with less social support. TRIAL REGISTRATION CLINICALTRIALS. GOV IDENTIFIER/UNASSIGNED:NCT03325985.

The objective of this study was to estimate the associations of jail-initiated medication for opioid use disorder (MOUD) and patient navigation (PN) with opioid use disorder (OUD) at 6 months post-release. Three randomized trials (combined N = 330) were combined to assess whether MOUD (extended-release naltrexone or interim methadone) initiated prior to release from jail with or without PN would reduce the likelihood of a DSM-5 diagnosis of OUD 6 months post-release relative to enhanced treatment-as-usual (ETAU). Across the three studies, assignment to MOUD compared to ETAU was not associated with an OUD diagnosis at 6 months post-release (69% vs. 75%, respectively, OR = 0.67, 95% CI: 0.42 to 1.20). Similarly, PN compared to MOUD without PN was not associated with an OUD diagnosis (63% vs 77%, respectively, OR = 0.61, 95% CI: 0.27 to 1.53). Results underscore the need to further optimize the effectiveness of MOUD for patients initiating treatment in jail, beginning with an emphasis on post-release treatment adherence.