Faculty Bibliography

OBJECTIVE/UNASSIGNED:To compare the effectiveness of nurse led telephonic palliative care versus specialty outpatient palliative care on quality of life, symptom burden, loneliness, and healthcare use, after attending the emergency department. DESIGN/UNASSIGNED:Pragmatic, randomised clinical trial. SETTING/UNASSIGNED:Emergency Medicine Palliative Care Access (EMPallA) randomised controlled trial enrolling participants from 18 emergency departments in 15 geographically diverse healthcare systems in nine US states, from 1 April 2018 to 30 June 2022. PARTICIPANTS/UNASSIGNED:Of 39 254 eligible patients, 1283 adults who visited the emergency department, were aged ≥50 years, who spoke English or Spanish, and had advanced cancer or end stage organ failure, were randomised to receive nurse led telephonic palliative care (n=639) or specialty outpatient palliative care (n=644). INTERVENTIONS/UNASSIGNED:The nurse led telephonic palliative care arm consisted of weekly or biweekly calls over six months made by registered nurses certified in hospice and palliative care. For the specialty outpatient palliative care arm, patients had one visit each month for six months with a specialty trained hospice and palliative medicine provider. MAIN OUTCOME MEASURES/UNASSIGNED:The primary outcome was change in patient reported quality of life at six months, measured by the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire. Secondary outcomes were change in symptom burden and patient reported loneliness after six months, and healthcare use, measured as the number of emergency department revisits, inpatient days, and hospice use, from enrolment to 12 months. RESULTS/UNASSIGNED:639 patients were assigned to nurse telephonic services and 434 (68%) engaged in care until death, or until they required hospice services or graduated from the programme. For specialty outpatient palliative care, 644 patients were assigned and 344 (53%) attended one or more visits, with an average of 2.7 visits. The mean change in FACT-G scores over six months for the nurse telephonic arm (n=418) was 3.7 (95% confidence interval (CI) 2.3 to 5.1) points compared with 3.1 (1.6 to 4.6) for those in the specialty outpatient care arm (n=409). In the model including all patients who survived to six months (n=1090), the estimated difference in average change in quality of life was 0.71 (95% CI -1.19 to 2.61) points higher in the nurse led telephonic palliative care arm. The analysis did not show any clinically meaningful differences in the change in quality of life between the treatment arms. Also, no important differences between groups were found for secondary outcomes or in subgroup analyses. CONCLUSIONS/UNASSIGNED:The results of the study provided no clear evidence that nurse led telephonic palliative care improved quality of life, or any secondary outcomes, relative to specialty outpatient palliative care. TRIAL REGISTRATION/UNASSIGNED:ClinicalTrials.gov NCT03325985.

Stepped wedge cluster randomized trials (SW-CRTs) are a type of uni-directional crossover designs and are increasingly common in prevention and implementation research. Although sample size formulas have been developed to support the planning of SW-CRTs, almost no prior methods incorporated the baseline measurement of the outcome-a common feature in many randomized trials and, increasingly, in cross-sectional SW-CRTs. In this article, we systematically investigate the possibility of addressing a baseline outcome measurement in designing cross-sectional SW-CRTs. We provide three linear mixed modeling approaches to adjust for the baseline outcome and derive the corresponding variance formula of the treatment effect estimator under each. The derived formulas reveal the efficiency implications of including a baseline outcome measurement, and provide a natural vehicle for the efficiency comparisons across adjustment approaches to generate practical recommendations. We validate the power and sample size methods under each baseline adjustment approach using simulations and provide an illustrative sample size calculation with a baseline outcome using the context of a real SW-CRT.

Factorial study designs can be important for understanding the effectiveness of interventions when multiple interventions are under investigation. In this design setting, a unit of randomization can be assigned to any combination of interventions. The rationale for taking this kind of approach can vary depending on the specific questions targeted by the research. These questions, in turn, have implications for the way in which the analyses will be conducted. The goal in this paper is to describe how we developed a factorial design along with a Bayesian analytic plan for a large cluster-randomized trial-the Emergency Departments LEading the transformation of Alzheimer's and Dementia care (ED-LEAD) study-focused on improving care for persons living with dementia.

OBJECTIVES/OBJECTIVE:To assess the acceptability of an infection-control prevention strategy during the COVID-19 pandemic for skilled nursing facilities (SNFs) staff and residents, to describe implementation challenges and potential solutions, and to explore intervention impact perceptions. DESIGN/METHODS:Qualitative investigation of a multimethod pragmatic clinical trial designed to develop and test a novel cohorting intervention to mitigate infection transmission in SNFs during the COVID-19 pandemic. SETTING AND PARTICIPANTS/METHODS:We used a purposive sample of staff and residents in 2 New York City nonprofit SNFs participating in the intervention trial. METHODS:We developed a novel cohorting strategy to minimize infection transmission by using infection risk (vaccination or prior infection history) to assign staff to specific residents. We employed a qualitative descriptive approach using content thematic analysis of semistructured interviews with staff and residents to identify themes of acceptability, challenges, and potential impact. RESULTS:Seventy-one staff and 16 residents completed semistructured interviews. Staff and residents supported the intervention. However, both identified challenges, including concerns that switching assignments could lead to staffing issues and care discontinuity. Staff identified communication about the intervention as both a challenge and potential solution, citing a desire for clear and consistent communication across all employees. Staff felt the intervention had potential impact in reducing infection risk fear, promoting effective protective equipment usage, and encouraging more interaction and less isolation than standard prevention practices. CONCLUSIONS AND IMPLICATIONS/CONCLUSIONS:We developed a novel and acceptable preventive strategy that may increase residents' flexibility and reduce their isolation as well as staff stress. Clear, consistent, and proactive communication may improve intervention acceptability and perceptions about impact. Similar infection-control protocols might be adapted for different long-term care infection risks. Communication at all staffing levels and with residents is essential to ensure the intervention is acceptable to both. Future trials could include social isolation and staff burnout as potential outcomes.

BACKGROUND:Healthy Days at Home (HDaH) is a patient-centered outcome measure quantifying the number of days individuals spend at home without hospitalizations or emergency department (ED) visits, while maintaining functional independence. This study examines the association between HDaH and prognosis among US older adults with serious life-limiting illnesses (commonly heart failure, chronic obstructive pulmonary disease, advanced cancer, and end-stage kidney disease) and explores how this relationship differs by cancer status. METHODS:For this prospective cohort design study, we pooled Medicare Claims data of older adults (aged 66 or greater) with serious life-limiting illnesses who visited one of 30 EDs participating in the Primary Palliative Care for Emergency Medicine study between 2015 and 2019. The main exposure was prognosis, measured using the Gagne index, a short-term predictor of mortality. We controlled for age, sex, race/ethnicity, and used cancer diagnosis as a secondary predictor and stratification variable. The outcome, HDaH, was defined as 180 days minus the days a patient spent in healthcare institutions, including hospitals, skilled nursing facilities, and hospice care. We used generalized linear mixed-effects models with a log (180) offset to estimate the adjusted rate ratios (aRR) and 95% confidence intervals. RESULTS:The cohort included 122,579 seriously ill older adults,11% (n = 13,452) of whom had cancer. The median (IQR) HDaH was 115 (26-174) days. Each unit increase in Gagne index score was associated with a 6.0% decrease in the rate of HDaH (aRR: 0.94; 95% CI: 0.94 to 0.94), a pattern observed in both cancer and non-cancer groups. Cancer diagnosis was associated with 7.0% increase in HDaH (aRR: 1.07; 95% CI: 1.07 to 1.07). CONCLUSION/CONCLUSIONS:While poor prognosis is associated with fewer healthy days at home, cancer diagnosis is associated with more healthy days at home. Our findings highlight the need for tailored care models to reduce hospitalizations and increase HDaH for patients with serious life-limiting illnesses other than cancer.

Stepped wedge cluster randomized trials (SWCRTs) often face challenges related to potential confounding by time. Traditional frequentist methods may not provide adequate coverage of an intervention's true effect using confidence intervals, whereas Bayesian approaches show potential for better coverage of intervention effects. However, Bayesian methods remain underexplored in the context of SWCRTs. To bridge this gap, we propose two innovative Bayesian hierarchical penalized spline models. Our first model accommodates large numbers of clusters and time periods, focusing on immediate intervention effects. To evaluate this approach, we compared this model to traditional frequentist methods. We then extend our approach to account for time-varying intervention effects, conducting a comprehensive comparison with an existing Bayesian monotone effect curve model and alternative frequentist methods. The proposed models were applied in the Primary Palliative Care for Emergency Medicine stepped wedge trial to evaluate the effectiveness of the intervention. Through extensive simulations and real-world application, we demonstrate the robustness of our proposed Bayesian models. Notably, the Bayesian immediate effect model consistently achieves the nominal coverage probability, providing more reliable interval estimations while maintaining high estimation accuracy. Furthermore, our proposed Bayesian time-varying effect model represents a significant advancement over the existing Bayesian monotone effect curve model, offering improved accuracy and reliability in estimation while also achieving higher coverage probability than alternative frequentist methods. To the best of our knowledge, this marks the first development of Bayesian hierarchical spline modeling for SWCRTs. Our proposed models offer promising tools for researchers and practitioners, enabling more precise evaluation of intervention impacts.

BACKGROUND:Drug overdose deaths have increased fivefold over the last 20 years, primarily fueled by synthetic opioids, which led the Centers for Disease Control and Prevention to declare an opioid overdose epidemic. Responding to this epidemic, we designed and implemented opioid overdose prevention (OOP) training for medical students to help promote effective naloxone usage. Previously, we compared online and in-person versions of OOP training over 2 years of training. To better establish the evidence for online training, we performed a randomized controlled non-inferiority trial comparing in-person with online opioid prevention training. METHODS:Third-year medical students were randomized into groups to receive either in-person or online training in preparation for clinical rotations. Students randomized to receive online training were provided a link to the training modules. Students randomized to receive in-person training were trained in an in-person setting. We performed a non-inferiority per-protocol analysis with the primary outcome of knowledge using a non-inferiority margin of a -9.1% difference between groups. RESULTS:A total of 205 students were randomized, 103 students to in-person training and 102 to online training. Eighty-three in-person students and 104 online students were included. The online group had a higher post-training knowledge score compared to the in-person group by 0.44 points (0-11 point scale) with a 95% CI of (-0.04, 0.93) that did not cross the margin of non-inferiority. CONCLUSIONS:Online training for OOP was effective and non-inferior to in-person training. Online OOP training may be considered an alternative to in-person training.

BACKGROUND:The linear mixed-effects model (LME) is a conventional parametric method mainly used for analyzing longitudinal and clustered data in genetic studies. Previous studies have shown that this model can be sensitive to parametric assumptions and provides less predictive performance than non-parametric methods such as random effects-expectation maximization (RE-EM) and unbiased RE-EM regression tree algorithms. These longitudinal regression trees utilize classification and regression trees (CART) and conditional inference trees (Ctree) to estimate the fixed-effects components of the mixed-effects model. While CART is a well-known tree algorithm, it suffers from greediness. To mitigate this issue, we used the Evtree algorithm to estimate the fixed-effects part of the LME for handling longitudinal and clustered data in genome association studies. METHODS:In this study, we propose a new non-parametric longitudinal-based algorithm called "Ev-RE-EM" for modeling a continuous response variable using the Evtree algorithm to estimate the fixed-effects part of the LME. We compared its predictive performance with other tree algorithms, such as RE-EM and unbiased RE-EM, with and without considering the structure for autocorrelation between errors within subjects to analyze the longitudinal data in the genetic study. The autocorrelation structures include a first-order autoregressive process, a compound symmetric structure with a constant correlation, and a general correlation matrix. The real data was obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling used body mass index (BMI) as the phenotype and included predictor variables such as age, sex, and 25,640 single nucleotide polymorphisms (SNPs). RESULTS:The results demonstrated that the predictive performance of Ev-RE-EM and unbiased RE-EM was nearly similar. Additionally, the Ev-RE-EM algorithm generated smaller trees than the unbiased RE-EM algorithm, enhancing tree interpretability. CONCLUSION/CONCLUSIONS:The results showed that the unbiased RE-EM and Ev-RE-EM algorithms outperformed the RE-EM algorithm. Since algorithm performance varies across datasets, researchers should test different algorithms on the dataset of interest and select the best-performing one. Accurately predicting and diagnosing an individual's genetic profile is crucial in medical studies. The model with the highest accuracy should be used to enhance understanding of the genetics of complex traits, improve disease prevention and diagnosis, and aid in treating complex human diseases.

BACKGROUND:Loneliness is a quality-of-life (QoL) concern for patients facing serious, life-limiting illnesses. Discerning risk factors of loneliness in palliative care patients allows providers to take preventative action and develop holistic treatment plans. METHODS:A planned sub-study of patients who completed the previously developed Three-Item Loneliness Scale upon enrollment into the multicenter, randomized clinical trial Emergency Medicine Palliative Care Access (EMPallA) with the objective of investigating the association of multimorbidity with loneliness in patients with late-stage illnesses. The EMPallA study included patients who were at least 50 years old and diagnosed with at least one end-stage illness (advanced cancer, advanced congestive heart failure (CHF), end-stage renal disease (ESRD), or advanced chronic obstructive pulmonary disease (COPD)). RESULTS:We analyzed 1,212 surveys using a mixed-effects logistic regression model. Our findings suggest those with a single illness are less likely to be lonely than those with multimorbidity (odds ratio [OR] = 0.5, 95% CI 0.3 to 0.8). Additionally, older age was associated with less loneliness (OR comparing age by 10-year increments is 0.7 [95% CI: 0.6 to 0.9]), after adjusting for disease type, education level, race, sex, immigrant status, having a caregiver, COVID-19 period, language, and site geographic location. CONCLUSIONS:Patients suffering from multimorbidity self-report being "very lonely" more often than patients with a single advanced illness; furthermore, advanced illness patients who were middle-aged (versus elderly) were 25% more likely to report being "very lonely." TRIAL REGISTRATION/BACKGROUND:Clinicaltrials.gov identifier: NCT03325985. Registered October 30, 2017.

BACKGROUND/UNASSIGNED:Chronic kidney disease (CKD) impacts more than 800 million people. It causes significant suffering and disproportionately impacts marginalized populations in the United States. Kidney palliative care has the potential to alleviate this distress, but has not been tested. This pilot study evaluates the feasibility of a randomized clinical trial (RCT) testing the efficacy of integrated kidney palliative and CKD care in an urban safety-net hospital. METHODS/UNASSIGNED:, and are receiving care at our safety net hospital. Participants will be randomized in permuted blocks of two or four to either the intervention group, who will receive monthly ambulatory care visits for six months with a palliative care provider trained in kidney palliative care, or to usual nephrology care. Primary outcomes are feasibility of recruitment, retention, fidelity to the study visit protocol, and the ability to collect outcome data. These outcomes include symptom burden, quality of life, and engagement in advance care planning. DISCUSSION/UNASSIGNED:This pilot RCT will provide essential data on the feasibility of testing integrated palliative care in CKD care in an underserved setting. These outcomes will inform a larger, fully powered trial that tests the efficacy of our kidney palliative care approach. CLINICAL TRIAL REGISTRATION/UNASSIGNED:NCT04998110.