Comparing diagnostic criteria for posttraumatic stress disorder in a diverse sample of trauma-exposed youth.

Divergent conceptualization of posttraumatic stress disorder (PTSD) within the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) and International Statistical Classification of Diseases and Related Health Problems (11th ed..; ICD-11) significantly confounds both research and practice. Using a diverse sample of trauma-exposed youth (N = 1,542, age range: 8-20 years), we compared these two diagnostic approaches along with an expanded version of the ICD-11 PTSD criteria that included three additional reexperiencing symptoms (ICD-11+). Within the sample, PTSD was more prevalent using the DSM-5 criteria (25.7%) compared to the ICD-11 criteria (16.0%), with moderate agreement between these diagnostic systems, κ = .57. The inclusion of additional reexperiencing symptoms (i.e., ICD-11+) reduced this discrepancy in prevalence (24.7%) and increased concordance with DSM-5 criteria, κ = .73. All three PTSD classification systems exhibited similar comorbidity rates with major depressive episode (MDE) or generalized anxiety disorder (GAD; 78.0%-83.6%). Most youths who met the DSM-5 PTSD criteria also met the criteria for ICD-11 PTSD, MDE, or GAD (88.4%), and this proportion increased when applying the ICD-11+ criteria (95.5%). Symptom-level analyses identified reexperiencing/intrusions and negative alterations in cognition and mood symptoms as primary sources of discrepancy between the DSM-5 and ICD-11 PTSD diagnostic systems. Overall, these results challenge assertions that nonspecific distress and diagnostically overlapping symptoms within DSM-5 PTSD inflate comorbidity with depressive and anxiety disorders. Further, they support the argument that the DSM-5 PTSD criteria can be refined and simplified without reducing the overall prevalence of psychiatric diagnoses in youth.

The role of resilience in the development of depression, anxiety, and post-traumatic stress disorder after trauma in children and adolescents.

This investigation, conducted within the Texas Childhood Trauma Research Network, investigated the prospective relationships between resiliency and emergent internalizing symptoms among trauma-exposed youth. The cohort encompassed 1262 youth, aged 8-20, from twelve health-related institutions across Texas, who completed assessments at baseline and one- and six-month follow-ups for resiliency, symptoms of depression, generalized anxiety, posttraumatic stress disorder (PTSD), and other demographic and clinical characteristics. At baseline, greater resilience was positively associated with older age, male (vs female) sex assigned at birth, and history of mental health treatment. Unadjusted for covariates, higher baseline resilience was associated with greater prospective depression and PTSD symptoms but not anxiety symptoms. Upon adjusting for demographic and clinical factors, higher baseline resilience was no longer associated with depression, PTSD, or anxiety symptoms. Our analyses demonstrate that the predictive value of resilience on psychopathology is relatively small compared to more readily observable clinical and demographic factors. These data suggest a relatively minor prospective role of resilience in protecting against internalizing symptoms among trauma-exposed youth and highlight the importance of controlling for relevant youth characteristics when investigating a protective effect of resilience on internalizing symptoms.

Comparative effectiveness of dexamethasone in treatment of hospitalized COVID-19 patients in the United States during the first year of the pandemic: Findings from the National COVID Cohort Collaborative (N3C) data repository.

BACKGROUND: Dexamethasone was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial, but evidence is still needed to support its real-world effectiveness in heterogeneous populations of patients with a wide range of comorbidities. METHODS: COVID-19 inpatients represented within the National COVID Cohort Collaborative (N3C) Data Enclave, prior to vaccine availability, were studied. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome defined by use of ECMO or mechanical ventilation. Missing data were imputed with single imputation. Dexamethasone-treated patients were propensity score (PS) matched to non-dexamethasone-treated controls, stratified by remdesivir treatment and based on demographics, baseline laboratory values, comorbidities, and amount of missing data before imputation. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS. RESULTS: Dexamethasone treatment was associated with reduced risk of in-hospital mortality for n = 1,263 treated, matched 1:3 to untreated, patients not receiving remdesivir (OR = 0.77, 95% CI: 0.62 to 0.95, p = 0.017), and for n = 804 treated, matched 1:1 to untreated, patients receiving remdesivir (OR = 0.74, 95% CI: 0.53 to 1.02, p = 0.054). Treatment showed secondary outcome benefit. In sensitivity analyses, treatment effect generally remained similar with some heterogeneity of benefit across quartiles of PS, possibly reflecting concentration of benefit among the more severely affected. CONCLUSIONS: We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability.

The risk and burden of thromboembolic and hemorrhagic events in patients with malignant gliomas receiving bevacizumab.

PURPOSE: Bevacizumab has evolved as an integral treatment option for patients with high-grade gliomas. Little is known about clinical risk factors that predispose patients with high-grade gliomas receiving bevacizumab to VTE or ICH. We sought to characterize the clinical risk factors associated with risk of either event. METHODS: In this multi-institutional retrospective study, we first evaluated patients with high-grade gliomas who were treated with bevacizumab at University of Texas MD Anderson Cancer Center from 2015-2021. We compared clinical and treatment-related factors among three cohorts: those who developed VTE, ICH, or neither. We further compared survival outcomes of these patients from the time of bevacizumab initiation. Then to further confirm our results in a non-cancer center hospital setting we evaluated patients from two Ascension Seton Hospitals in Austin, Texas which are affiliated with Dell Medical School at the University of Texas at Austin from 2017-2022. RESULTS: We found that the presence of cerebral macrobleeding, defined as a magnetic susceptibility of > 1 cm3 on magnetic resonance imaging, was highly associated with risk of developing ICH after initiation of bevacizumab. Development of ICH was significantly associated with poorer survival outcomes. We did not find a statistically significant effect of VTE on survival after bevacizumab initiation. CONCLUSION: In order to stratify the risk for developing ICH before the initiation of bevacizumab, we recommend to assess for the presence of cerebral macrobleeding as it is associated with ICH development.

Hidden Flaws Behind Expert-Level Accuracy of Multimodal GPT-4 Vision in Medicine.

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

Clinical and molecular determinants of bleeding-related adverse outcomes in high-grade glioma.

PURPOSE: Cancer is an independent risk factor for the development of venous thromboembolism (VTE). However, patients with high-grade glioma (HGG) including glioblastoma (GBM) are at a particularly high risk of VTE with an incidence up to 20-30% per year. Patients are often placed on anticoagulation if they are found to have VTE. However, patients with primary brain tumors such as HGG are at increased risk for intracerebral hemorrhage (ICH) even without the administration of anticoagulation. The combination of risk factors for ICH with anticoagulation and HGG complicates decision-making. Currently it is not known which of the direct oral anticoagulants (DOACs) are safest for patients with HGG in terms of adverse bleeding-related outcomes such as ICH. Furthermore, a deeper understanding of the clinical and molecular determinants of bleeding-related adverse outcomes in HGG is not fully characterized. METHODS: In this retrospective study, we identified and gathered data on 75 consecutive patients with pathology-confirmed HGG with hospital encounters at two academic medical center hospitals in Austin between July 1, 2017 and June 30, 2022. We compared clinical and treatment-related factors among cohorts who had received various forms of anticoagulation or no anticoagulation. RESULTS: Patients who were on rivaroxaban (3/7 (43%)) had a statistically significant association with more bleeding-related adverse events compared to those on apixaban (0/12 (0%)) or enoxaparin (0/5 (0%), p = 0.022) even though the groups were similar in characteristics including total time on the respective anticoagulation. Patients on anticoagulation vs those never on anticoagulation did not differ in terms of their studied demographic and clinical characteristics. Intriguingly, logistic regression analysis revealed that patients Astrocytoma, isocitrate dehydrogenase (IDH) mutant, grade 4 had a significant association with more adverse bleeding-related events even when controlling for other relevant factors (Odds Ratio compared to reference GBM: 49.4, 95% CI: 2.8, 2084.7; p = 0.013). CONCLUSION: In this study we found that the use of rivaroxaban was associated with more bleeding-related events compared to apixaban and enoxaparin in patients with high-grade glioma. In this study we also found that the diagnosis of astrocytoma, IDH mutant, grade 4 was associated with more bleeding events. However, this is based on a small study and there is a need for larger studies to further evaluate these results.

SARS-CoV-2 Humoral Immune Responses in Convalescent Individuals Over 12 Months Reveal Severity-Dependent Antibody Dynamics.

Background: Understanding the kinetics and longevity of antibody responses to SARS-CoV-2 is critical to informing strategies toward reducing Coronavirus disease 2019 (COVID-19) reinfections, and improving vaccination and therapy approaches. Methods: We evaluated antibody titers against SARS-CoV-2 nucleocapsid (N), spike (S), and receptor binding domain (RBD) of spike in 98 convalescent participants who experienced asymptomatic, mild, moderate or severe COVID-19 disease and in 17 non-vaccinated, non-infected controls, using four different antibody assays. Participants were sampled longitudinally at 1, 3, 6, and 12 months post-SARS-CoV-2 positive PCR test. Findings: Increasing acute COVID-19 disease severity correlated with higher anti-N and anti-RBD antibody titers throughout 12 months post-infection. Anti-N and anti-RBD titers declined over time in all participants, with the exception of increased anti-RBD titers post-vaccination, and the decay rates were faster in hospitalized compared to non-hospitalized participants. <50% of participants retained anti-N titers above control levels at 12 months, with non-hospitalized participants falling below control levels sooner. Nearly all hospitalized and non-hospitalized participants maintained anti-RBD titers above controls for up to 12 months, suggesting longevity of protection against severe reinfections. Nonetheless, by 6 months, few participants retained >50% of their 1-month anti-N or anti-RBD titers. Vaccine-induced increases in anti-RBD titers were greater in non-hospitalized relative to hospitalized participants. Early convalescent antibody titers correlated with age, but no association was observed between Post-Acute Sequelae of SARS-CoV-2 infection (PASC) status or acute steroid treatment and convalescent antibody titers. Interpretation: Hospitalized participants developed higher anti-SARS-CoV-2 antibody titers relative to non-hospitalized participants, a difference that persisted throughout 12 months, despite the faster decline in titers in hospitalized participants. In both groups, while anti-N titers fell below control levels for at least half of the participants, anti-RBD titers remained above control levels for almost all participants over 12 months, demonstrating generation of long-lived antibody responses known to correlate with protection from severe disease across COVID-19 severities. Overall, our findings contribute to the evolving understanding of COVID-19 antibody dynamics. Funding: Austin Public Health, NIAAA, Babson Diagnostics, Dell Medical School Startup.

Characterizing patterns of substance use in trauma exposed youth.

OBJECTIVE: Previous work investigating the impact of childhood trauma on substance use and co-occurring psychiatric disorders has primarily been conducted in adults or on specific trauma types. This limits understanding of traumas impact in childhood and how different types of traumas play a role. We sought to characterize substance use in a sample of trauma-exposed youth in the context of psychiatric comorbidities. METHOD: 1152 youth from the Texas Childhood Trauma Research Network (TX-CTRN) that were exposed to at least one trauma meeting DSM-5 Criterion A were assessed for current substance use and psychiatric diagnoses. Latent class analysis was used to identify patterns of substance use. To characterize these patterns, we examined if demographics, number of trauma types experienced, or childhood psychiatric disorders predicted class membership. RESULTS: We identified four primary patterns of substance use: Non-use (66.1%), predominantly alcohol use (19.7%), predominantly cannabis use (4.5%), and polysubstance use (9.7%). Compared to the non-users, polysubstance users tended to be older, Non-Hispanic White, have experienced more types of trauma. They were also more likely to have fulfilled diagnostic criteria for suicidality and ADHD. Comparisons among the substance using classes were more nuanced. CONCLUSION: The findings highlight the need for universal assessments of trauma, substance misuse, and mental health symptoms in youth as the presence or absence of their co-occurrence has implications for treatment.

Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses.

A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, where such outcomes are already obvious to the user. To alleviate biases in human decision-making, it is worth considering a broad differential diagnosis, going beyond the most likely options. We introduce a new task, "less likely brainstorming," that asks a model to generate outputs that humans think are relevant but less likely to happen. We explore the task in two settings: a brain MRI interpretation generation setting and an everyday commonsense reasoning setting. We found that a baseline approach of training with less likely hypotheses as targets generates outputs that humans evaluate as either likely or irrelevant nearly half of the time; standard MLE training is not effective. To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans. We compare our method with several state-of-the-art controlled text generation models via automatic and human evaluations and show that our models' capability of generating less likely outputs is improved.

Evaluating large language models on medical evidence summarization.

Recent advances in large language models (LLMs) have demonstrated remarkable successes in zero- and few-shot performance on various downstream tasks, paving the way for applications in high-stakes domains. In this study, we systematically examine the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains. We conduct both automatic and human evaluations, covering several dimensions of summary quality. Our study demonstrates that automatic metrics often do not strongly correlate with the quality of summaries. Furthermore, informed by our human evaluations, we define a terminology of error types for medical evidence summarization. Our findings reveal that LLMs could be susceptible to generating factually inconsistent summaries and making overly convincing or uncertain statements, leading to potential harm due to misinformation. Moreover, we find that models struggle to identify the salient information and are more error-prone when summarizing over longer textual contexts.

Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality.

This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID-19 patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospital dataset to predict the mortality of COVID-19 patients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) and Locally Interpretable Model Agnostic Explanations (LIME), to compare the global and local interpretation of feature importance. This paper demonstrates the advantages of using XAI which shows the feature importance and decisive capability. Furthermore, we use the XAI methods to cross-validate their interpretations for individual patients. The XAI models reveal that Medicare financial class, older age, and gender have high impact on the mortality prediction. We find that LIME's local interpretation does not show significant differences in feature importance comparing to SHAP, which suggests pattern confirmation. This paper demonstrates the importance of XAI methods in cross-validation of feature attributions.

Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances.

Deep neural networks (DNNs) have rapidly become a de facto choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify hard-to-learn (HTL) training samples, and improve pathology localization by attending them explicitly, during training in supervised, semi-supervised, and weakly-supervised settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning [15] - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by ~2-3%.

Designing an interprofessional dementia specialty clinic: Conceptualization and evaluation of a patient-centered model.

The need for blueprints to design specialty care interprofessional collaboration (IPC) models is urgent, given the expanding aging population and current challenges in dementia diagnosis and treatment. We describe key steps creating an interprofessional outpatient dementia specialty clinic, efforts to sustain the model, and evaluation of interprofessional effectiveness and clinician satisfaction. The conception for the Comprehensive Memory Center was informed by qualitative research methodologies including focus groups, interviews, and literature reviews. Quantitative evaluation included satisfaction surveys and team effectiveness measures. The IPC model diverges from typical dementia practices through its interprofessional team, visit structure, approach to decision-making, in-house services, and community collaborations. Team retreats and workshops helped build clinician knowledge of interprofessional values and practices to sustain the IPC model. In the first 3.5 years, we served nearly 750 patients and their caregivers. Team evaluation results revealed that increased access to consultation and sharing the workload and emotional burden were beneficial. The majority of team members preferred the IPC model to traditional models of clinical care.

A Content Analysis of Self-Reported Financial Relationships in Biomedical Research.

INTRODUCTION: Financial conflicts of interest (fCOI) present well documented risks to the integrity of biomedical research. However, few studies differentiate among fCOI types in their analyses, and those that do tend to use preexisting taxonomies for fCOI identification. Research on fCOI would benefit from an empirically-derived taxonomy of self-reported fCOI and data on fCOI type and payor prevalence. METHODS: We conducted a content analysis of 6,165 individual self-reported relationships from COI statements distributed across 378 articles indexed with PubMed. Two coders used an iterative coding process to identify and classify individual fCOI types and payors. Inter-rater reliability was κ = 0.935 for fCOI type and κ = 0.884 for payor identification. RESULTS: Our analysis identified 21 fCOI types, 9 of which occurred at prevalences greater than 1%. These included research funding (24.8%), speaking fees (20.8%), consulting fees (18.8%), advisory relationships (11%), industry employment (7.6%), unspecified fees (4.8%), travel fees (3.2%), stock holdings (3.1%), and patent ownership (1%). Reported fCOI were held with 1,077 unique payors, 22 of which were present in more than 1% of financial relationships. The ten most common payors included Pfizer (4%), Novartis (3.9%), MSD (3.8%), Bristol Myers Squibb (3.2%), AstraZeneca (3.1%), GSK (3%), Boehringer Ingelheim (2.9%), Roche (2.8%), Eli LIlly (2.5%), and AbbVie (2.4%). CONCLUSIONS: These results provide novel multi-domain prevalence data on self-reported fCOI and payors in biomedical research. As such, they have the potential to catalyze future research that can assess the differential effects of various types of fCOI. Specifically, the data suggest that comparative analyses of the effects of different fCOI types are needed and that special attention should be paid to the diversity of payor types for research relationships.

Methods for development and application of data standards in an ontology-driven information model for measuring, managing, and computing social determinants of health for individuals, households, and communities evaluated through an example of asthma.

OBJECTIVE: To describe methods to approach application of data standards to integrate social determinants of health (SDoH) into EHRs through evaluation of a case of clinical decision support for pediatric asthma. MATERIALS AND METHODS: We identified a list of environmental factors important for managing pediatric asthma. We identified and integrated data from local outdoor air quality monitors with elements available from the clinic's EHR and self-reported indoor air quality questionnaire data. We assessed existing SDoH frameworks, assessment tools, and terminologies to identify representative data standards for these environmental SDoH measures. RESULTS: We found many-to-many relationships between the multiple framework domains, the environmental exposure measures collected, and existing standards. The majority of concepts did not accurately align with environmental exposure measurements. We propose an ontology-driven information framework methodology to apply standards for SDoH measurements to support measuring, managing, and computing SDoH data. DISCUSSION: To support methods of integrating SDoH data in the EHR via an ontology-driven information framework, a common SDoH ecosystem should be developed descriptively and prescriptively integrating framework domains, assessment tools, and standard ontologies to support future data sharing, aggregation, and interoperability. A hierarchical object-oriented information model should be adopted to manage SDoH to extend beyond patient-centered orientation of EHRs to orient to households and communities. CONCLUSION: SDoH data pose unique challenges and opportunities in collecting, measuring, and managing health information. Future work is needed to define data standards for implementing SDoH in a hierarchical, object-oriented information model representing multiple units of orientation including individuals, households, and communities.

Comparative Effectiveness of Dexamethasone in Treatment of Hospitalized COVID-19 Patients during the First Year of the Pandemic: The N3C Data Repository.

Background: Dexamethasone, a widely available glucocorticoid, was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial; however, evidence is still needed to support its real-world effectiveness in patients with a wide range of comorbidities and in diverse care settings. Objectives: To conduct a comparative effectiveness analysis of dexamethasone use with and without remdesivir in hospitalized COVID-19 patients using electronic health record data. Methods: We conducted a retrospective real-world effectiveness analysis using the harmonized, highly granular electronic health record data of the National COVID Cohort Collaborative (N3C) Data Enclave. Analysis was restricted to COVID-19 patients in an inpatient setting, prior to vaccine availability. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome as defined by use of ECMO or mechanical ventilation during stay. Missing data were imputed with single imputation. Matching of dexamethasone-treated patients to non-dexamethasone-treated controls was accomplished using propensity score (PS) matching, stratified by remdesivir treatment and based on demographics, baseline laboratory values, and comorbidities. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS. Results: Regression analysis revealed a statistically significant association between dexamethasone use and reduced risk of in-hospital mortality for those not receiving remdesivir (OR=0.77, 95% CI:0.62 to 0.95, p=0.017), and a borderline statistically significant risk for those receiving remdesivir (OR=0.74, 95% CI: 0.53 to 1.02, p=0.054). Treatment also showed secondary outcome benefit. In sensitivity analyses, treatment effect size generally remained similar with some heterogeneity of benefit across strata of PS. Conclusions: We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability.

EchoGen: A New Benchmark Study on Generating Conclusions from Echocardiogram Notes.

Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length. In this work, we focus on echocardiogram notes that is longer and more complex compared to previous note types. We formally define the task of echocardiography conclusion generation (EchoGen) as generating a conclusion given the findings section, with emphasis on key cardiac findings. To promote the development of EchoGen methods, we present a new benchmark, which consists of two datasets collected from two hospitals. We further compare both standard and state-of-the-art methods on this new benchmark, with an emphasis on factual consistency. To accomplish this, we develop a tool to automatically extract concept-attribute tuples from the text. We then propose an evaluation metric, FactComp, to compare concept-attribute tuples between the human reference and generated conclusions. Both automatic and human evaluations show that there is still a significant gap between human-written and machine-generated conclusions on echo reports in terms of factuality and overall quality.

Trends in Oral Anticoagulant Use Among 436 864 Patients With Atrial Fibrillation in Community Practice, 2011 to 2020.

Background Among patients with nonvalvular atrial fibrillation (AF) and an elevated stroke risk, guidelines recommend direct oral anticoagulants (DOACs) over warfarin for stroke prevention. Changes in DOAC use over the past decade have not been well described. Methods and Results We evaluated trends in use of DOACs and warfarin from 2011 to 2020 among adults with AF and a CHA2DS2-VASc score ≥2 based on electronic health record data from 88 health systems in the United States contributing to Cerner Real World Data. The use of DOACs and warfarin was described over time, by age, sex, race, and ethnicity, and at the health-system level. We identified 436 864 patients with AF at risk for stroke (median age, 78 years; 52.1% men). From 2011 to 2020, overall anticoagulation rates increased from 56.3% to 64.7%, as DOAC use increased steadily (from 4.7% to 47.9%), while warfarin use declined (from 52.4% to 17.7%). DOAC uptake was similar across age, sex, and race and ethnicity groups but varied by health system. In 2020, the median health-system-level proportion of patients with AF on a DOAC was 49% (interquartile range, 40%-54%). Conclusions Over the past decade, anticoagulation rates for patients with AF have increased modestly as DOACs largely replaced warfarin, though significant gaps remain: One in 3 high-risk patients with AF is not on any anticoagulant. While DOAC adoption was generally consistent across major demographic groups, use between health systems remained highly variable, suggesting that provider and system factors influence DOAC uptake use more than patient-level factors.

Real-world opioid use among patients with migraine enrolled in US commercial insurance and risk factors associated with migraine progression.

BACKGROUND: Migraineurs may be categorized as having episodic migraine (EM: < 15 headache days/month) or chronic migraine (CM: ≥ 15 days/month for > 3 months with ≥ 8 days/month having features of migraine). Opioid use has been linked to progression from EM to CM. OBJECTIVE: To describe the utilization of opioid prescriptions among patients with migraine, to determine the association between opioid use and migraine progression, and to explore demographic and clinical risk factors for migraine progression. METHODS: This retrospective cohort study used Optum's deidentified Clinformatics Data Mart Database from January 2015 to December 2018. Adult patients with a migraine diagnosis and continuous health plan enrollment were included. Opioid use was measured by average daily morphine equivalent dose, also known as morphine milligram equivalent (MME). Descriptive statistics were used to summarize the opioid use by patient demographic and clinical characteristics. A Cox proportional hazards model with stepwise selection was used to determine the risk factors of new-onset CM. RESULTS: Overall, 35% of patients with migraine (27,331 of 78,134) received prescription opioids (> 0 MME/day) during the 12-month follow-up period. Higher opioid dosage was found in patients who had CM and comorbidities of interest. Compared with patients with EM, patients with CM were twice as likely to receive at least 20 MME/day (CM 3.8% vs EM 1.9%) and had a higher median opioid day supply (CM 20 vs EM 10) during follow-up. About 7% of patients with CM with at least 1 opioid prescription had at least 50 MME/day in any 90-day period during follow-up. A significant association was found between MME level and the likelihood of new-onset CM. Additional significant risk factors of migraine progression included younger age, female sex, South and West regions, and having a diagnosis of medication overuse headache, depression, back pain, or fibromyalgia (all P < 0.05). CONCLUSIONS: Despite guidelines and the availability of more migraine-specific treatments, opioids are still commonly prescribed to patients with migraines in real-world practice, especially for those with CM. In this study population, a higher risk of new-onset CM was associated with receiving higher opioid doses.