Faculty Bibliography

CONTEXT/BACKGROUND:Health systems have aspired to integrate palliative care (PC) into the emergency department (ED) to improve care quality for over a decade, yet there are very few examples of implemented models in the literature. The coronavirus disease 2019 (COVID-19) pandemic led to an increase in the volume of seriously ill patients in EDs and a consequent rapid increase in PC integration in many EDs. OBJECTIVES/OBJECTIVE:To describe the new PC-ED delivery innovations that emerged during the COVID-19 pandemic. METHODS:For this qualitative study of PC programs in EDs, semistructured interviews were conducted with ED and PC clinicians between June 30, 2020 and August 18, 2020. Participants were asked about PC-ED integration before, during, and after COVID. We conducted a two-phased rapid analysis using a rapid analysis template and consolidated matrix to identify innovations. RESULTS:Using purposive and snowball sampling, we interviewed 31 participants, representing 52 hospitals. Several new innovations in care delivery were identified. These included elements of fully embedded PC, the use of PC extenders, technology both within the electronic medical record and outside it, and innovations in training emergency clinicians in primary PC skills to support care delivery. Most PC efforts focused on increasing goals-of-care conversations. Institutions that implemented these programs reported that they increased PC utilization in the ED, were well received by clinicians, and changed patient's care trajectories. CONCLUSION/CONCLUSIONS:Several new innovations in PC-ED care delivery emerged during COVID. Many innovations leveraged different types of clinicians to deliver care, an increased physical presence of PC in the ED, and used technology to enhance care delivery. These innovations may serve as a framework for institutions as they plan for evolving needs in the ED during and after COVID. Additional research is needed to evaluate the impact of these programs and understand their applicability beyond the pandemic.

PURPOSE/OBJECTIVE:Studies have examined trends in cannabis vaping, but research is needed to examine trends in more frequent use as this may increase risk for adverse health outcomes. METHODS:Data were from 12,561 high school seniors participating in the Monitoring the Future national study. Prevalence of self-reported frequent vaping of cannabis (defined as using ≥10 times in the past month) was compared between 2018 and 2019 cohorts. RESULTS:Frequent vaping of cannabis significantly increased from 2.1% to 4.9%, a 131.4% increase. This increase was larger than the increase for any vaping of cannabis (which increased 85.9%). Notable significant increases occurred among students aged ≥18 years (a 154.9% increase), female students (a 183.5% increase), those who go out 4-7 evenings per week (a 163.0% increase), and those reporting past-year nonmedical prescription opioid use (a 184.7% increase). CONCLUSIONS:Frequent vaping of cannabis is increasing among adolescents in the United States, particularly among selected subgroups.

Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used automated, machine-learning, high-dimensional data pruning, and validated identified biomarkers. The parent cohort consisted of male, never-smoking firefighters with WTC-LI (FEV1, %Pred< lower limit of normal (LLN); n = 100) and controls (n = 127) and had their biomarkers assessed. Cases and controls (n = 15/group) underwent untargeted metabolomics, then feature selection performed on metabolites, cytokines, chemokines, and clinical data. Cytokines, chemokines, and clinical biomarkers were validated in the non-overlapping parent-cohort via binary logistic regression with 5-fold cross validation. Random forests of metabolites (n = 580), clinical biomarkers (n = 5), and previously assayed cytokines, chemokines (n = 106) identified that the top 5% of biomarkers important to class separation included pigment epithelium-derived factor (PEDF), macrophage derived chemokine (MDC), systolic blood pressure, macrophage inflammatory protein-4 (MIP-4), growth-regulated oncogene protein (GRO), monocyte chemoattractant protein-1 (MCP-1), apolipoprotein-AII (Apo-AII), cell membrane metabolites (sphingolipids, phospholipids), and branched-chain amino acids. Validated models via confounder-adjusted (age on 9/11, BMI, exposure, and pre-9/11 FEV1, %Pred) binary logistic regression had AUCROC [0.90(0.84-0.96)]. Decreased PEDF and MIP-4, and increased Apo-AII were associated with increased odds of WTC-LI. Increased GRO, MCP-1, and simultaneously decreased MDC were associated with decreased odds of WTC-LI. In conclusion, automated data pruning identified novel WTC-LI biomarkers; performance was validated in an independent cohort. One biomarker-PEDF, an antiangiogenic agent-is a novel, predictive biomarker of particulate-matter-related lung disease. Other biomarkers-GRO, MCP-1, MDC, MIP-4-reveal immune cell involvement in WTC-LI pathogenesis. Findings of our automated biomarker identification warrant further investigation into these potential pharmacotherapy targets.

INTRODUCTION/BACKGROUND:STRIVE was a 4-year, multicenter, observational, open-label, single-arm study of natalizumab treatment in anti-JC virus antibody-negative (JCV-negative) relapsing-remitting multiple sclerosis (RRMS) patients with disease duration ≤ 3 years. The objective of STRIVE was to examine no evidence of disease activity (NEDA) status and predictors of NEDA in natalizumab-treated patients with early RRMS. METHODS:Proportions of patients with NEDA were evaluated along with baseline predictors of NEDA, annualized relapse rate, 24-week confirmed disability worsening (CDW), magnetic resonance imaging assessments (T2 and gadolinium-enhancing lesions), and serious adverse events. RESULTS:In years 1 and 2, 56.1% (95% confidence interval [CI] 48.7-63.4%) and 73.6% (95% CI 66.2-80.2%) of patients (intent-to-treat population [N = 222]), respectively, achieved NEDA. In years 3 and 4, 84.6% (95% CI 78.0-89.9%) and 91.9% (95% CI 86.4-95.8%) of patients, respectively, achieved Clinical NEDA (no relapses or 24-week CDW). Baseline predictors of NEDA in year 4 were Expanded Disability Status Scale score ≤ 2.0 (odds ratio [OR] = 3.85 [95% CI 1.54-9.63]; p = 0.004) and T2 lesion volume > 4 cc (OR = 0.39 [95% CI 0.15-0.98]; p = 0.046), with the latter also predicting Clinical NEDA in year 4 (OR = 0.21 [95% CI 0.05-0.92]; p = 0.038). The cumulative probability of CDW at year 4 was 19.3%. Serious adverse events were reported in 11.3% of patients. CONCLUSION/CONCLUSIONS:These results support the long-term safety and effectiveness of natalizumab. Baseline predictors of NEDA help to inform benefit-risk assessments of natalizumab treatment in JCV-negative patients with early RRMS. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov identifier NCT01485003.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

This paper describes a sustainable structure to deliver the World Health Organization (WHO) endorsed group Interpersonal Psychotherapy (IPT-G) for Postpartum Adolescent (PPA) mothers living with HIV in Nairobi. It documents the process of mobilizing, training, and engaging Community Health Workers (CHWs) and Key Informants (health facility staff) involved in the Prevention of Mother-To-Child Transmission (PMTCT) in two Primary Health Care (PHC) facilities from informal settlements of Nairobi County. Mainly reporting experiences from the training process utilizing focused group discussions and in-depth interviews involving participants, IPT-G therapists and supervisors we present process findings and acceptability of our IPT-G implementation.

INTRODUCTION/BACKGROUND:Healthcare systems are increasingly interested in identifying patients' housing-related risks, but minimal information exists to inform screening question selection. The primary study aim is to evaluate discordance among 5 housing-related screening questions used in health care. METHODS:This was a cross-sectional multisite survey of social risks used in a convenience sample of adults seeking care for themselves or their child at 7 primary care clinics and 4 emergency departments across 9 states (2018-2019). Housing-related risks were measured using 2 questions from the Accountable Health Communities screening tool (current/anticipated housing instability, current housing quality problems) and 3 from the Children's HealthWatch recommended housing instability screening measures (prior 12-month: rent/mortgage strain, number of moves, current/recent homelessness). The 2-sided Fisher's exact tests analyzed housing-related risks and participant characteristics; logistic regression explored associations with reported health (2019-2020). RESULTS:Of 835 participants, 52% screened positive for ≥1 housing-related risk (n=430). Comparing the tools, 32.8% (n=274) screened discordant: 11.9% (n=99) screened positive by Children's HealthWatch questions but negative by Accountable Health Communities, and 21.0% (n=175) screened positive by the Accountable Health Communities tool but negative by Children's HealthWatch (p<0.001). Worse health was associated with screening positive for current/anticipated housing instability (AOR=0.56, 95% CI=0.32, 0.96) or current/recent homelessness (AOR=0.57, 95% CI=0.34, 0.96). CONCLUSIONS:The 5 housing questions captured different housing-related risks, contributed to different health consequences, and were relevant to different subpopulations. Before implementing housing-related screening initiatives, health systems should understand how specific measures surface distinct housing-related barriers. Measure selection should depend on program goals and intervention resources.

OBJECTIVE:To assess telehealth practice for headache visits in the United States. BACKGROUND:The rapid roll out of telehealth during the COVID-19 pandemic impacted headache specialists. METHODS:American Headache Society (AHS) members were emailed an anonymous survey (9/9/20-10/12/20) to complete if they had logged ≥2 months or 50+ headache visits via telehealth. RESULTS:Out of 1348 members, 225 (16.7%) responded. Most were female (59.8%; 113/189). Median age was 47 (interquartile range [IQR] 37-57) (N = 154). The majority were MD/DOs (83.7%; 159/190) or NP/PAs (14.7%; 28/190), and most (65.1%; 123/189) were in academia. Years in practice were 0-3: 28; 4-10: 58; 11-20: 42; 20+: 61. Median number of telehealth visits was 120 (IQR 77.5-250) in the prior 3 months. Respondents were "comfortable/very comfortable" treating via telehealth (a) new patient with a chief complaint of headache (median, IQR 4 [3-5]); (b) follow-up for migraine (median, IQR 5 [5-5]); (c) follow-up for secondary headache (median, IQR 4 [3-4]). About half (51.1%; 97/190) offer urgent telehealth. Beyond being unable to perform procedures, top barriers were conducting parts of the neurologic exam (157/189), absence of vital signs (117/189), and socioeconomic/technologic barriers (91/189). Top positive attributes were patient convenience (185/190), reducing patient travel stress (172/190), patient cost reduction (151/190), flexibility with personal matters (128/190), patient comfort at home (114/190), and patient medications nearby (103/190). Only 21.3% (33/155) of providers said telehealth visit length differed from in-person visits, and 55.3% (105/190) believe that the no-show rate improved. On a 1-5 Likert scale, providers were "interested"/"very interested" in digitally prescribing headache apps (median 4, IQR 3-5) and "interested"/"very interested" in remotely monitoring patient symptoms (median 4, IQR 3-5). CONCLUSIONS:Respondents were comfortable treating patients with migraine via telehealth. They note positive attributes for patients and how access may be improved. Technology innovations (remote vital signs, digitally prescribing headache apps) and remote symptom monitoring are areas of interest and warrant future research.

Left censoring in salivary bioscience data occurs when salivary analyte determinations fall below the lower limit of an assay's measurement range. Conventional statistical approaches for addressing censored values (i.e., recoding as missing, substituting or extrapolating values) may introduce systematic bias. While specialized censored data statistical approaches (i.e., Maximum Likelihood Estimation, Regression on Ordered Statistics, Kaplan-Meier, and general Tobit regression) are available, these methods are rarely implemented in biobehavioral studies that examine salivary biomeasures, and their application to salivary data analysis may be hindered by their sensitivity to skewed data distributions, outliers, and sample size. This study compares descriptive statistics, correlation coefficients, and regression parameter estimates generated via conventional and specialized censored data approaches using salivary C-reactive protein data. We assess differences in statistical estimates across approach and across two levels of censoring (9% and 15%) and examine the sensitivity of our results to sample size. Overall, findings were similar across conventional and censored data approaches, but the implementation of specialized censored data approaches was more efficient (i.e., required little manipulations to the raw analyte data) and appropriate. Based on our review of the findings, we outline preliminary recommendations to enable investigators to more efficiently and effectively reduce statistical bias when working with left-censored salivary biomeasure data.