National Trends in Anticoagulation Therapy for COVID-19 Hospitalized Adults in the United States: Analyses of the National COVID Cohort Collaborative.

BACKGROUND: Anticoagulation (AC) utilization patterns and their predictors among hospitalized coronavirus disease 2019 (COVID-19) patients have not been well described. METHODS: Using the National COVID Cohort Collaborative, we conducted a retrospective cohort study (2020-2022) to assess AC use patterns and identify factors associated with therapeutic AC employing modified Poisson regression. RESULTS: Among 162 842 hospitalized COVID-19 patients, 64% received AC and 24% received therapeutic AC. Therapeutic AC use declined from 32% in 2020 to 12% in 2022, especially after December 2021. Therapeutic AC predictors included age (relative risk [RR], 1.02; 95% confidence interval [CI], 1.02-1.02 per year), male (RR, 1.29; 95% CI, 1.27-1.32), non-Hispanic black (RR, 1.16; 95% CI, 1.13-1.18), obesity (RR, 1.48; 95% CI, 1.43-1.52), increased length of stay (RR, 1.01; 95% CI, 1.01-1.01 per day), and invasive ventilation (RR, 1.64; 95% CI, 1.59-1.69). Vaccination (RR, 0.88; 95% CI, 84-.92) and higher Charlson Comorbidity Index (CCI) (RR, 0.98; 95% CI, .97-.98) were associated with lower therapeutic AC. CONCLUSIONS: Overall, two-thirds of hospitalized COVID-19 patients received any AC and a quarter received therapeutic dosing. Therapeutic AC declined after introduction of the Omicron variant. Predictors of therapeutic AC included demographics, obesity, length of stay, invasive ventilation, CCI, and vaccination, suggesting AC decisions driven by clinical factors including COVID-19 severity, bleeding risks, and comorbidities.

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).

BACKGROUND: Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. METHODS: Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. RESULTS: We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. CONCLUSIONS: The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

Sensation Seeking in Children and Adults with Pediatric-Onset Bipolar Disorder.

Bipolar disorder (BD) is one of the most impairing psychiatric illnesses. Those with pediatric-onset BD tend to have worse outcomes; therefore, accurate conceptualization is important for aspects of care, such as tailored treatment interventions. Sensation seeking behaviors may be a window into the psychopathology of pediatric-onset BD. Participants with BD and healthy controls (HC) ages 7-27 completed self-report assessments, including the Sensation Seeking Scale- V (SSS-V). Among the BD group, there was a significant positive correlation between the Disinhibition subscale and age. Analyses indicated that the BD group scored lower on the Thrill and Adventure Seeking subscale but higher on the Disinhibition scale when compared to the HC group. We found that individuals with pediatric-onset BD are more likely to engage in socially risky behaviors. These results are an important step in understanding sensation seeking characteristics in BD youth and improving treatment, ultimately helping individuals live a more stable life.

Is it time to consider a "time-out" before primary prevention implantable cardioverter-defibrillator in currently or recently hospitalized older patients with heart failure?

BACKGROUND: Trajectories of mortality after primary implantable cardioverter-defibrillator (ICD) placement for older patients with heart failure during or soon after acute hospitalization have not been assessed. OBJECTIVE: The purpose of this study was to compare trajectories of mortality after primary ICD placement during or soon after acute cardiac or noncardiac hospitalization. METHODS: We identified older patients with heart failure undergoing primary ICD placement using 20% Medicare data (2008-2018). Placement settings were as follows: (1) "current-H"-during current hospitalization, (2) "recent-H"-within 90 days of hospitalization, or (3) "chronic stable." Hospitalization was categorized as cardiac vs noncardiac. Interval mortality rates and hazard ratios (HRs) using Cox regression were estimated at 0-30, 31-90, and 91-365 days after ICD placement. RESULTS: Of the 61,710 patients (mean age 76 years; 35% female; 85% white), 19%, 25%, and 56% had ICDs in "current-H," "recent-H," and "chronic stable" settings. Mortality rates (per 100 person-years) were highest during 0-30 days, with 38 (34-42) and 22 (19-24) for "current-H" and "recent-H," which declined to 21 (20-22) and 16 (15-17) during 91-365 days, respectively. Compared to "chronic stable," HRs were highest during 0-30 days post-ICD placement (5.5 [4.5-6.8] for "current-H" and 3.4 [2.8-4.2] for "recent-H") and decreased during 91-365 days ("current-H": 2.0 [1.8-2.1] for "current-H" and 1.6 [1.5-1.7] for "recent-H"). HR pattens were similar for cardiac and noncardiac hospitalizations. CONCLUSION: Primary ICD placement during or soon after hospitalization for any reason was associated with worse mortality with diminishing risks after 90 days. Hospitalization likely identifies a sicker population in whom early mortality with or without ICD may be higher. Our results support careful consideration regarding ICD placement during the 90 days after hospitalization.

Oral health-related quality of life and perceptions of patients following primary root canal treatment or non-surgical retreatment.

This retrospective cohort study compared the effect of primary root canal treatment (RCT) with root canal retreatment (Re-RCT) on patient-reported outcomes in Kuala Lumpur, Malaysia. Forty randomly selected adults participated (RCT n = 20; Re-RCT n = 20). The impact their dentition had on the Oral Health Impact Profile-14 (OHIP-14) was assessed by calculating the prevalence of oral health impact, and the severity score. Focus group discussions using a semi-structured guide were arranged through an online meeting platform. Qualitative content analysis identified common themes, and relevant quotes gathered. The impact on OHIP-14 was limited for both RCT and Re-RCT groups with no significant differences in the prevalence of oral health impact. Significant differences were found for functional limitation (RCT higher) and psychological discomfort (Re-RCT higher). Common themes from the discussions include the importance of retaining teeth, the significance of effective communication between clinicians and patients and that the respondents were satisfied with the treatment.

Time to diagnosis of pulmonary hypertension and diagnostic burden: A retrospective analysis of nationwide US healthcare data.

The main aim of this analysis was to investigate time from symptom onset (chronic unexplained dyspnoea [CUD]) to diagnosis of Group 1 pulmonary hypertension (PH)-pulmonary arterial hypertension (PAH)-and to characterize healthcare resource utilization leading up to diagnosis using a nationwide US claims and an electronic health record (EHR) database from Optum©. Eligible patients were ≥18 years old at first CUD diagnosis (index event) and had a PAH diagnosis on or after index date. Based on administrative codes, PAH was defined as right heart catheterization (RHC), ≥ 2 PAH diagnoses (1 within a year of RHC), and ≥1 post-RHC prescription for PAH treatment. All values are median (1st quartile-3rd quartile) unless otherwise stated. Of 854,722 patients with CUD in the claims database, 582 (0.1%) had PAH. Time from CUD to PAH diagnosis was 2.26 (0.73-4.22) years. PAH patients experienced 3 (2-4) transthoracic echocardiograms (TTEs), 6 (3-12) specialist visits, and 2 (1-4) hospitalizations during the diagnostic interval. Almost one-third of patients (29%) waited 10 months or more to have a TTE. Findings from the EHR database were broadly similar. Resource utilization during the diagnostic interval was also analyzed in an overall PH cohort: findings were generally similar to the PAH cohort (2 [1-3] TTEs, 4 [2-9] specialist visits and 2 [1-4] hospitalizations). These data indicate a delay in the diagnostic pathway for PAH, and illustrate the burden associated with PAH diagnosis.

A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records.

BACKGROUND: This study aimed to develop a machine learning (ML) model to identify patients who are likely to have pulmonary hypertension (PH), using a large patient-level US-based electronic health record (EHR) database. METHODS: A gradient boosting model, XGBoost, was developed using data from Optum's US-based de-identified EHR dataset (2007-2019). PH and disease control adult patients were identified using diagnostic, treatment and procedure codes and were randomly split into the training (90%) or test set (10%). Model features included patient demographics, physician visits, diagnoses, procedures, prescriptions, and laboratory test results. SHapley Additive exPlanations values were used to determine feature importance. RESULTS: We identified 11,279,478 control and 115,822 PH patients (mean age, respectively: 62 and 68 years, both 53% female). The final model used 165 features, with the most important predictive features including diagnosis of heart failure, shortness of breath and atrial fibrillation. The model predicted PH with an area under the receiver operating characteristic curve (AUROC) of 0.92. AUROC remained above 0.80 for the prediction of PH up to and beyond 18 months before diagnosis. Among the PH patients, we also identified 955 pulmonary arterial hypertension (PAH) and 1432 chronic thromboembolic pulmonary hypertension (CTEPH) patients, and the range of AUROCs obtained for these cohorts was 0.79-0.90 and 0.87-0.96, respectively. CONCLUSIONS: This model to detect PH based on patients' EHR records is viable and performs well in subgroups of PAH and CTEPH patients. This approach has the potential to improve patient outcomes by reducing diagnostic delay in PH.

10 Lessons Learned in Launching a Division-Wide Measurement-Based Care Initiative.

Mental health problems are a major source of morbidity and mortality for children and adolescents, affecting 15% to 20% of those under 18 years of age in the US.1 Half of all mental health conditions start by age 14 years, although most cases remain undetected and untreated.2 Despite knowing much about mental health conditions affecting children, many speculate that the lack of standardized approaches to patient care contribute to poor outcomes, including substantial diagnostic variation, few remissions, risk for relapse or recidivism, and, ultimately, greater mortality due to an inability to accurately predict who will make a suicide attempt.3-5 Studies support this over-reliance on the "art of medicine" (ie, subjective judgment without use of standardized measures), finding that only 17.9% of psychiatrists and 11.1% of psychologists in the US routinely administer symptom rating scales to their patients, despite studies suggesting that when using clinical judgment alone, mental health providers detect deterioration for only 21.4% of patients.4.

Predicting COVID-19 prognosis in hospitalized patients based on early status.

IMPORTANCE: COVID-19 remains the fourth leading cause of death in the United States. Predicting COVID-19 patient prognosis is essential to help efficiently allocate resources, including ventilators and intensive care unit beds, particularly when hospital systems are strained. Our PLABAC and PRABLE models are unique because they accurately assess a COVID-19 patient's risk of death from only age and five commonly ordered laboratory tests. This simple design is important because it allows these models to be used by clinicians to rapidly assess a patient's risk of decompensation and serve as a real-time aid when discussing difficult, life-altering decisions for patients. Our models have also shown generalizability to external populations across the United States. In short, these models are practical, efficient tools to assess and communicate COVID-19 prognosis.

CRP Monitoring in Early Hospitalization: Implications for Predicting Outcomes in Patients with COVID-19.

Elevated C-reactive protein (CRP) levels have been associated with poorer COVID-19 outcomes. While baseline CRP levels are higher in women, obese individuals, and older adults, the relationship between CRP, sex, body mass index (BMI), age, and COVID-19 outcomes remains unknown. To investigate, we performed a retrospective analysis on 824 adult patients with COVID-19 admitted during the first pandemic wave, of whom 183 (22.2%) died. The maximum CRP value over the first five hospitalization days better predicted hospitalization outcome than the CRP level at admission, as a maximum CRP > 10 mg/dL independently quadrupled the risk of death (p < 0.001). Males (p < 0.001) and patients with a higher BMI (p = 0.001) had higher maximum CRP values, yet CRP levels did not impact their hospitalization outcome. While CRP levels did not statistically mediate any relation between sex, age, or BMI with clinical outcomes, age impacted the association between BMI and the risk of death. For patients 60 or over, a BMI < 25 kg/m2 increased the risk of death (p = 0.017), whereas the reverse was true for patients <60 (p = 0.030). Further impact of age on the association between BMI, CRP, and the risk of death could not be assessed due to a lack of statistical power but should be further investigated.

Who am I? Key influences on the formation of academic identity within a faculty development program.

INTRODUCTION: Professional identity encompasses how individuals understand themselves, interpret experiences, present themselves, wish to be perceived, and are recognized by the broader professional community. For health professional and health science educators, their 'academic' professional identity is situated within their academic community and plays an integral role in their well being and productivity. This study aims to explore factors that contribute to the formation and growth of academic identity (AI) within the context of a longitudinal faculty development program. METHODS: Using a qualitative case study approach, data from three cohorts of a 2-year faculty development program were explored and analyzed for emerging issues and themes related to AI. RESULTS: Factors salient to the formation of AI were grouped into three major domains: personal (cognitive and emotional factors unique to each individual); relational (connections and interactions with others); and contextual (the program itself and external work environments). DISCUSSION: Faculty development initiatives not only aim to develop knowledge, skills, and attitudes, but also contribute to the formation of academic identities in a number of different ways. Facilitating the growth of AI has the potential to increase faculty motivation, satisfaction, and productivity. Faculty developers need to be mindful of factors within the personal, relational, and contextual domains when considering issues of program design and implementation.

An anti-PD-1-GITR-L bispecific agonist induces GITR clustering-mediated T cell activation for cancer immunotherapy.

Costimulatory receptors such as glucocorticoid-induced tumor necrosis factor receptor-related protein (GITR) play key roles in regulating the effector functions of T cells. In human clinical trials, however, GITR agonist antibodies have shown limited therapeutic effect, which may be due to suboptimal receptor clustering-mediated signaling. To overcome this potential limitation, a rational protein engineering approach is needed to optimize GITR agonist-based immunotherapies. Here we show a bispecific molecule consisting of an anti-PD-1 antibody fused with a multimeric GITR ligand (GITR-L) that induces PD-1-dependent and FcγR-independent GITR clustering, resulting in enhanced activation, proliferation and memory differentiation of primed antigen-specific GITR+PD-1+ T cells. The anti-PD-1-GITR-L bispecific is a PD-1-directed GITR-L construct that demonstrated dose-dependent, immunologically driven tumor growth inhibition in syngeneic, genetically engineered and xenograft humanized mouse tumor models, with a dose-dependent correlation between target saturation and Ki67 and TIGIT upregulation on memory T cells. Anti-PD-1-GITR-L thus represents a bispecific approach to directing GITR agonism for cancer immunotherapy.

Twelve tips for ethical approval for research in health professions education.

BACKGROUND: A growing number of faculty are engaging in research in health professions education. Suggestions continue to be made in the literature for a clear and less onerous pathway for the ethical review of this work. AIM: We aim to provide advice about the ethics application process for those conducting research in health professions education. METHODS: We used critical reflection of our experiences as research ethics board (REB) members, applying for, reviewing and consulting about the ethics application process in both UK and Canadian health contexts in addition to evidence and advice that is available in the literature to inform the tips provided. RESULTS: Twelve tips are offered to help faculty understand and navigate through the ethics application process. CONCLUSION: Health professionals have an important role to play in advancing the field of health professions education, and despite issues identified with current review pathways, REB review is in place to ensure that this work is undertaken safely and ethically. We believe the tips offered in this article will help faculty identify, and devise plans to address, some ethical issues that are common in health professions education research.

Relationships of power: implications for interprofessional education.

Interprofessional education (IPE) is considered a key mechanism in enhancing communication and practice among health care providers, optimizing participation in clinical decision making and improving the delivery of care. An important, though under-explored, factor connected to this form of education is the unequal power relations that exist between the health and the social care professions. Drawing on data from the evaluation of a large multi-site IPE initiative, we use Witz's model of professional closure (1992) to explore the perspectives and the experiences of participants and the power relations between them. A subset of interviews with a range of different professionals (n = 25) were inductively analyzed to generate emerging themes related to perceptions of professional closure and power. Findings from this work highlight how professionals' views of interprofessional interactions, behaviours and attitudes tend to either reinforce or attempt to restructure traditional power relationships within the context of an IPE initiative.

Simulated interprofessional education: an analysis of teaching and learning processes.

Simulated learning activities are increasingly being used in health professions and interprofessional education (IPE). Specifically, IPE programs are frequently adopting role-play simulations as a key learning approach. Despite this widespread adoption, there is little empirical evidence exploring the teaching and learning processes embedded within this type of simulation. This exploratory study provides insight into the nature of these processes through the use of qualitative methods. A total of 152 clinicians, 101 students and 9 facilitators representing a range of health professions, participated in video-recorded role-plays and debrief sessions. Videotapes were analyzed to explore emerging issues and themes related to teaching and learning processes related to this type of interprofessional simulated learning experience. In addition, three focus groups were conducted with a subset of participants to explore perceptions of their educational experiences. Five key themes emerged from the data analysis: enthusiasm and motivation, professional role assignment, scenario realism, facilitator style and background and team facilitation. Our findings suggest that program developers need to be mindful of these five themes when using role-plays in an interprofessional context and point to the importance of deliberate and skilled facilitation in meeting desired learning outcomes.

Neophyte facilitator experiences of interprofessional education: implications for faculty development.

The facilitation of learners from different professional groups requires a range of interprofessional knowledge and skills (e.g. an understanding of possible sources of tension between professions) in addition to those that are more generic, such as how to manage a small group of learners. The development and delivery of interprofessional education (IPE) programs tends to rely on a small cohort of facilitators who have typically gained expertise through 'hands-on' involvement in facilitating IPE and through mentorship from more experienced colleagues. To avoid burn-out and to meet a growing demand for IPE, a larger number of facilitators are needed. However, empirical evidence regarding effective approaches to prepare for this type of work is limited. This article draws on data from a multiple case study of four IPE programs based in an urban setting in North America with a sample of neophyte facilitators and provides insight into their perceptions and experiences in preparing for and delivering IPE. Forty-one semi-structured interviews were conducted before (n = 20) and after (n = 21) program delivery with 21 facilitators. Findings indicated that despite participating in a three-fold faculty development strategy designed to support them in their IPE facilitation work, many felt unprepared and continued to have a poor conceptual understanding of core IPE and interprofessional collaboration principles, resulting in problematic implications (e.g. 'missed teachable moments') within their IPE programs. Findings from this study are discussed in relation to the IPE, faculty development and wider educational literature before implications are offered for the future delivery of interprofessional faculty development activities.

Outcomes of COVID-19 in Patients With Cancer: Report From the National COVID Cohort Collaborative (N3C).

PURPOSE: Variation in risk of adverse clinical outcomes in patients with cancer and COVID-19 has been reported from relatively small cohorts. The NCATS' National COVID Cohort Collaborative (N3C) is a centralized data resource representing the largest multicenter cohort of COVID-19 cases and controls nationwide. We aimed to construct and characterize the cancer cohort within N3C and identify risk factors for all-cause mortality from COVID-19. METHODS: We used 4,382,085 patients from 50 US medical centers to construct a cohort of patients with cancer. We restricted analyses to adults ≥ 18 years old with a COVID-19-positive or COVID-19-negative diagnosis between January 1, 2020, and March 25, 2021. We followed N3C selection of an index encounter per patient for analyses. All analyses were performed in the N3C Data Enclave Palantir platform. RESULTS: A total of 398,579 adult patients with cancer were identified from the N3C cohort; 63,413 (15.9%) were COVID-19-positive. Most common represented cancers were skin (13.8%), breast (13.7%), prostate (10.6%), hematologic (10.5%), and GI cancers (10%). COVID-19 positivity was significantly associated with increased risk of all-cause mortality (hazard ratio, 1.20; 95% CI, 1.15 to 1.24). Among COVID-19-positive patients, age ≥ 65 years, male gender, Southern or Western US residence, an adjusted Charlson Comorbidity Index score ≥ 4, hematologic malignancy, multitumor sites, and recent cytotoxic therapy were associated with increased risk of all-cause mortality. Patients who received recent immunotherapies or targeted therapies did not have higher risk of overall mortality. CONCLUSION: Using N3C, we assembled the largest nationally representative cohort of patients with cancer and COVID-19 to date. We identified demographic and clinical factors associated with increased all-cause mortality in patients with cancer. Full characterization of the cohort will provide further insights into the effects of COVID-19 on cancer outcomes and the ability to continue specific cancer treatments.

The ties that bind: a network approach to creating a programme in faculty development.

CONTEXT: Current trends in medical education reflect the changing health care environment. An increasingly large and diverse student population, a move to more distributed models of education, greater community involvement and an emphasis on social accountability, interprofessional education and student-centred approaches to learning necessitate new approaches to faculty development to help faculty members respond effectively to this rapidly changing landscape. METHODS: Drawing upon the tenets of network theory and the broader organisational literature, we propose a 'fishhook' model of faculty development programme formation. The model is based on seven key factors which supported the successful formation of a centralised programme for faculty development that addressed many of the contemporary issues in medical education. These factors include: environmental readiness; commitment and vision of a mobiliser; recruitment of key stakeholders and leaders to committees; formation of a collaborative network structure; accumulation of networking capital; legitimacy, and flexibility. DISCUSSION: Our aim in creating this model is to provide a guide for other medical schools to consider when developing similar programmes. The model can be adapted to reflect the local goals, settings and cultures of other medical education contexts.

Cytokine profiling in active and quiescent SLE reveals distinct patient subpopulations.

BACKGROUND: Patients with SLE display marked clinical and immunlogical heterogeneity. The purpose of the study was to investigate patterns of serum cytokines in patients with active and stable systemic lupus erythematosus (SLE) and to determine how they relate to clinical phenotype. METHODS: Serum levels of 10 cytokines were measured retrospectively in a cohort of patients with SLE and in healthy controls using a high-sensitivity multiplex bead array. Disease activity was determined using the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) and British Isles Lupus Assessment Group (BILAG-2004) indices. Logistic regression models were used to determine the association between cytokine levels and active SLE. Principal component analysis (PCA) and cluster analysis was then used to identify subgroups of patients on the basis of cytokine levels. RESULTS: Serum chemokine (C-X-C motif) ligand 10 (CXCL10) and CXCL13 were significantly higher in patients with SLE compared to healthy controls. Two cytokines (pentraxin-related protein (PTX3) and CXCL10) were significantly higher in patients with active disease after adjustment for potential confounding factors. Measurement of four cytokines (CXCL10, IL-10, IL-21 and PTX3) significantly improved the performance of a model to identify patients with clinically active disease. Cluster analysis revealed that the patients formed 3 distinct groups, characterised by higher levels of interferon alpha (IFNα) and B lymphocyte stimulator (BLyS) (group 1), increased CXCL10 and CXCL13 (group 2) or low levels of cytokines (group 3). Group 2 had significantly lower serum complement and higher anti-double-stranded DNA antibodies and increased prevalence of inflammatory arthritis. CONCLUSIONS: Multiplex analysis has identified a serum cytokine signature for active SLE. Within the SLE population distinct cytokine subgroups were identified, with differing clinical and immunological phenotypes that appeared stable over time. Assessment of cytokine profiles may reveal unique insights into disease heterogeneity.