Social Determinants of Health and Delivery of Rehabilitation to Older Adults During ICU Hospitalization.

Importance: Older adults with socioeconomic disadvantage develop a greater burden of disability after critical illness than those without socioeconomic disadvantage. The delivery of in-hospital rehabilitation that can mitigate functional decline may be influenced by social determinants of health (SDOH). Whether rehabilitation delivery differs by SDOH during critical illness hospitalization is not known. Objective: To evaluate whether SDOH are associated with the delivery of skilled rehabilitation during critical illness hospitalization among older adults. Design, Setting, and Participants: This cohort study used data from the National Health and Aging Trends Study linked with Medicare claims (2011-2018). Participants included older adults hospitalized with a stay in the intensive care unit (ICU). Data were analyzed from August 2022 to September 2023. Exposures: Dual eligibility for Medicare and Medicaid, education, income, limited English proficiency (LEP), and rural residence. Main Outcome and Measures: The primary outcome was delivery of physical therapy (PT) and/or occupational therapy (OT) during ICU hospitalization, characterized as any in-hospital PT or OT and rate of in-hospital PT or OT, calculated as total number of units divided by length of stay. Results: In the sample of 1618 ICU hospitalizations (median [IQR] patient age, 81.0 [75.0-86.0] years; 842 [52.0%] female), 371 hospitalizations (22.9%) were among patients with dual Medicare and Medicaid eligibility, 523 hospitalizations (32.6%) were among patients with less than high school education, 320 hospitalizations (19.8%) were for patients with rural residence, and 56 hospitalizations (3.5%) were among patients with LEP. A total of 1076 hospitalized patients (68.5%) received any PT or OT, with a mean rate of 0.94 (95% CI, 0.86-1.02) units/d. After adjustment for age, sex, prehospitalization disability, mechanical ventilation, and organ dysfunction, factors associated with lower odds of receipt of PT or OT included dual Medicare and Medicaid eligibility (adjusted odds ratio, 0.70 [95% CI, 0.50-0.97]) and rural residence (adjusted odds ratio, 0.65 [95% CI, 0.48-0.87]). LEP was associated with a lower rate of PT or OT (adjusted rate ratio, 0.55 [95% CI, 0.32-0.94]). Conclusions and Relevance: These findings highlight the need to consider SDOH in efforts to promote rehabilitation delivery during ICU hospitalization and to investigate factors underlying inequities in this practice.

Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound.

Background: Point-of-care ultrasonography (POCUS) enables access to cardiac imaging directly at the bedside but is limited by brief acquisition, variation in acquisition quality, and lack of advanced protocols. Objective: To develop and validate deep learning models for detecting underdiagnosed cardiomyopathies on cardiac POCUS, leveraging a novel acquisition quality-adapted modeling strategy. Methods: To develop the models, we identified transthoracic echocardiograms (TTEs) of patients across five hospitals in a large U.S. health system with transthyretin amyloid cardiomyopathy (ATTR-CM, confirmed by Tc99m-pyrophosphate imaging), hypertrophic cardiomyopathy (HCM, confirmed by cardiac magnetic resonance), and controls enriched for the presence of severe AS. In a sample of 290,245 TTE videos, we used novel augmentation approaches and a customized loss function to weigh image and view quality to train a multi-label, view agnostic video-based convolutional neural network (CNN) to discriminate the presence of ATTR-CM, HCM, and/or AS. Models were tested across 3,758 real-world POCUS videos from 1,879 studies in 1,330 independent emergency department (ED) patients from 2011 through 2023. Results: Our multi-label, view-agnostic classifier demonstrated state-of-the-art performance in discriminating ATTR-CM (AUROC 0.98 [95%CI: 0.96-0.99]) and HCM (AUROC 0.95 [95% CI: 0.94-0.96]) on standard TTE studies. Automated metrics of anatomical view correctness confirmed significantly lower quality in POCUS vs TTE videos (median view classifier confidence of 0.63 [IQR: 0.44-0.88] vs 0.93 [IQR: 0.69-1.00], p<0.001). When deployed to POCUS videos, our algorithm effectively discriminated ATTR-CM and HCM with AUROC of up to 0.94 (parasternal long-axis (PLAX)), and 0.85 (apical 4 chamber), corresponding to positive diagnostic odds ratios of 46.7 and 25.5, respectively. In total, 18/35 (51.4%) of ATTR-CM and 32/57 (41.1%) of HCM patients in the POCUS cohort had an AI-positive screen in the year before their eventual confirmatory imaging. Conclusions: We define and validate an AI framework that enables scalable, opportunistic screening of under-diagnosed cardiomyopathies using POCUS.

A Deep Learning Approach for Automated Extraction of Functional Status and New York Heart Association Class for Heart Failure Patients During Clinical Encounters.

Introduction: Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials. We developed and validated a deep learning-based natural language processing (NLP) strategy to extract functional status assessments from unstructured clinical notes. Methods: We identified 26,577 HF patients across outpatient services at Yale New Haven Hospital (YNHH), Greenwich Hospital (GH), and Northeast Medical Group (NMG) (mean age 76.1 years; 52.0% women). We used expert annotated notes from YNHH for model development/internal testing and from GH and NMG for external validation. The primary outcomes were NLP models to detect (a) explicit New York Heart Association (NYHA) classification, (b) HF symptoms during activity or rest, and (c) functional status assessment frequency. Results: Among 3,000 expert-annotated notes, 13.6% mentioned NYHA class, and 26.5% described HF symptoms. The model to detect NYHA classes achieved a class-weighted AUROC of 0.99 (95% CI: 0.98-1.00) at YNHH, 0.98 (0.96-1.00) at NMG, and 0.98 (0.92-1.00) at GH. The activity-related HF symptom model achieved an AUROC of 0.94 (0.89-0.98) at YNHH, 0.94 (0.91-0.97) at NMG, and 0.95 (0.92-0.99) at GH. Deploying the NYHA model among 166,655 unannotated notes from YNHH identified 21,528 (12.9%) with NYHA mentions and 17,642 encounters (10.5%) classifiable into functional status groups based on activity-related symptoms. Conclusions: We developed and validated an NLP approach to extract NYHA classification and activity-related HF symptoms from clinical notes, enhancing the ability to track optimal care and identify trial-eligible patients.

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study.

Background: Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk. Methods: Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk. Results: Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. Conclusions: An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.

Long COVID Characteristics and Experience: A Descriptive Study from the Yale LISTEN Research Cohort.

OBJECTIVE: To describe the experience of people with long COVID symptomatology and characterize the psychological, social, and financial challenges they experience. BACKGROUND: The experience of people with long COVID needs further amplification, especially with a comprehensive focus on symptomatology, treatments, and impact on daily life and finances. METHODS: We collected data from individuals aged 18 and older reporting long COVID as participants in the Yale Listen to Immune, Symptom and Treatment Experiences Now (LISTEN) Study. The sample population included 441 participants surveyed between May 2022 and July 2023. We evaluated their demographic characteristics, socioeconomic and psychological status, index infection period, health status, quality of life, symptoms, treatments, pre-pandemic comorbidities, and new-onset conditions. RESULTS: Overall, the median age of the participants with long COVID was 46 years (IQR: 38 to 57 years); 74% were women, 86% were Non-Hispanic White, and 93% were from the United States. Participants reported low health status measured by the Euro-QoL visual analogue scale, with a median score of 49 (IQR: 32 to 61). Participants documented a diverse range of symptoms, with all 96 possible symptom choices being reported. Additionally, participants had tried many treatments (median number of treatments: 19, IQR: 12 to 28). They were also experiencing psychological distress, social isolation, and financial stress. CONCLUSIONS: Despite having tried numerous treatments, participants with long COVID continued to experience an array of health and financial challenges-findings that underscore the failure of the healthcare system to address the medical needs of people with long COVID. These insights highlight the need for crucial medical, mental health, financial, and community support services, as well as further scientific investigation, to address the complex impact of long COVID.

Ischemic Stroke and Reduced Left Ventricular Ejection Fraction: A Multidisciplinary Approach to Optimize Brain and Cardiac Health.

Reduced left ventricular ejection fraction ≤40%, a known risk factor for adverse cardiac outcomes and recurrent acute ischemic stroke, may be detected during an acute ischemic stroke hospitalization. A multidisciplinary care paradigm informed by neurology and cardiology expertise may facilitate the timely implementation of an array of proven heart failure-specific therapies and procedures in a nuanced manner to optimize brain and cardiac health.

A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression.

Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective: To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants: This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure: DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results: A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images.

Background: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. Objectives: To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging risk biomarkers, and its association with CTRCD. Methods: Across a five-hospital U.S.-based health system (2013-2023), we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), and a control cohort receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (≥0.1, positive screen) and explored its association with i) global longitudinal strain (GLS) measured within 15 days (n=7,271 pairs); ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction [LVEF]<50%), and LVEF<40%. In the ICI cohort we correlated baseline AI-ECG-LVSD predictions with downstream myocarditis. Results: Higher AI-ECG LVSD predictions were associated with worse GLS (-18% [IQR:-20 to -17%] for predictions<0.1, to -12% [IQR:-15 to -9%] for ≥0.5 (p<0.001)). In 1,308 patients receiving AC/TZM (age 59 [IQR:49-67] years, 999 [76.4%] women, 80 [IQR:42-115] follow-up months) a positive baseline AI-ECG LVSD screen was associated with ~2-fold and ~4.8-fold increase in the incidence of the composite CTRCD endpoint (adj.HR 2.22 [95%CI:1.63-3.02]), and LVEF<40% (adj.HR 4.76 [95%CI:2.62-8.66]), respectively. Among 2,056 patients receiving ICI (age 65 [IQR:57-73] years, 913 [44.4%] women, follow-up 63 [IQR:28-99] months) AI-ECG predictions were not associated with ICI myocarditis (adj.HR 1.36 [95%CI:0.47-3.93]). Conclusion: AI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.

Hypertension Trends and Disparities Over 12 Years in a Large Health System: Leveraging the Electronic Health Records.

BACKGROUND: The digital transformation of medical data enables health systems to leverage real-world data from electronic health records to gain actionable insights for improving hypertension care. METHODS AND RESULTS: We performed a serial cross-sectional analysis of outpatients of a large regional health system from 2010 to 2021. Hypertension was defined by systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or recorded treatment with antihypertension medications. We evaluated 4 methods of using blood pressure measurements in the electronic health record to define hypertension. The primary outcomes were age-adjusted prevalence rates and age-adjusted control rates. Hypertension prevalence varied depending on the definition used, ranging from 36.5% to 50.9% initially and increasing over time by ≈5%, regardless of the definition used. Control rates ranged from 61.2% to 71.3% initially, increased during 2018 to 2019, and decreased during 2020 to 2021. The proportion of patients with a hypertension diagnosis ranged from 45.5% to 60.2% initially and improved during the study period. Non-Hispanic Black patients represented 25% of our regional population and consistently had higher prevalence rates, higher mean systolic and diastolic blood pressure, and lower control rates compared with other racial and ethnic groups. CONCLUSIONS: In a large regional health system, we leveraged the electronic health record to provide real-world insights. The findings largely reflected national trends but showed distinctive regional demographics and findings, with prevalence increasing, one-quarter of the patients not controlled, and marked disparities. This approach could be emulated by regional health systems seeking to improve hypertension care.

Factors Associated with Long COVID Recovery among US Adults.

BACKGROUND: While factors associated with long COVID (LC) continue to be illuminated, little is known about recovery. This study used national survey data to assess differences between adults in the United States with LC and those who recovered. METHODS: We used COVID-19 and LC data from the 2022 National Health Interview Survey, a cross-sectional sample of non-institutionalized US adults. Survey analysis was used to account for oversampling and nonresponse bias and to obtain nationally representative estimates. A multivariable logistic regression model was used to identify potential predictors of LC recovery. RESULTS: Among the study sample, 17.7% or an estimated 17.5 American adults reported ever having LC, and among those with LC, 48.5% or an estimated 8.5 million reported having recovered. Multivariable logistic regression analysis showed that Hispanic adults were significantly more likely than White adults to report recovery from LC. At the same time, those with severe COVID-19 symptoms and those who had more than a high school degree, were aged 40 years or older, or were female were less likely to report recovery. CONCLUSION: Significant variations in LC recovery were noted across age, sex, race/ethnicity, and education independent of the severity of COVID-19 symptoms. Further work is needed to elucidate the causes of these differences and identify strategies to increase recovery rates.

Sex differences in symptomatology and immune profiles of Long COVID.

Strong sex differences in the frequencies and manifestations of Long COVID (LC) have been reported with females significantly more likely than males to present with LC after acute SARS-CoV-2 infection 1-7 . However, whether immunological traits underlying LC differ between sexes, and whether such differences explain the differential manifestations of LC symptomology is currently unknown. Here, we performed sex-based multi-dimensional immune-endocrine profiling of 165 individuals 8 with and without LC in an exploratory, cross-sectional study to identify key immunological traits underlying biological sex differences in LC. We found that female and male participants with LC experienced different sets of symptoms, and distinct patterns of organ system involvement, with female participants suffering from a higher symptom burden. Machine learning approaches identified differential sets of immune features that characterized LC in females and males. Males with LC had decreased frequencies of monocyte and DC populations, elevated NK cells, and plasma cytokines including IL-8 and TGF-ß-family members. Females with LC had increased frequencies of exhausted T cells, cytokine-secreting T cells, higher antibody reactivity to latent herpes viruses including EBV, HSV-2, and CMV, and lower testosterone levels than their control female counterparts. Testosterone levels were significantly associated with lower symptom burden in LC participants over sex designation. These findings suggest distinct immunological processes of LC in females and males and illuminate the crucial role of immune-endocrine dysregulation in sex-specific pathology.

Cost-effectiveness of bevacizumab therapy in the care of patients with hereditary hemorrhagic telangiectasia.

No FDA or EMA approved therapies exist for bleeding due to hereditary hemorrhagic telangiectasia (HHT), the second-most-common inherited bleeding disorder worldwide. The current standard-of-care (SOC) includes iron and red cell supplementation, alongside the necessary hemostatic procedures, none of which target underlying disease pathogenesis. Recent evidence has demonstrated that bleeding pathophysiology is amenable to systemic antiangiogenic therapy with the anti-VEGF bevacizumab. Despite its high cost, the addition of longitudinal bevacizumab to the current SOC may reduce overall healthcare resource utilization and improve patient quality-of-life. We conducted the first cost-effectiveness analysis of IV bevacizumab in patients with HHT with the moderate-to-severe phenotype, comparing 1) bevacizumab added to SOC versus 2) SOC alone. The primary outcome was the incremental net monetary benefit (iNMB) reported over a lifetime time horizon and across accepted willingness-to-pay thresholds, in USD per quality-adjusted-life-year (QALY). Bevacizumab therapy accrued 9.3 QALYS while generating $428,000 in costs, compared to 8.3 QALYs and $699,000 in costs accrued in the SOC strategy. The iNMB of bevacizumab therapy versus the standard of care was $433,000. No parameter variation and no scenario analysis, including choice of iron supplementation product, changed the outcome of bevacizumab being a cost-saving strategy. Bevacizumab therapy also saved patients an average of 133 hours spent receiving HHT-specific care per year of life. In probabilistic sensitivity analysis, bevacizumab was favored in 100% of all 10,000 Monte Carlo iterations across base-case and all scenario analyses. Bevacizumab should be considered for more favorable formulary placement in the care of patients with moderate-to-severe HHT.

Optimizing antithrombotic therapy in patients with coexisting cardiovascular and gastrointestinal disease.

Balancing the safety and efficacy of antithrombotic agents in patients with gastrointestinal disorders is challenging because of the potential for interference with the absorption of antithrombotic drugs and for an increased risk of bleeding. In this Review, we address considerations for enteral antithrombotic therapy in patients with cardiovascular disease and gastrointestinal comorbidities. For those with gastrointestinal bleeding (GIB), we summarize a general scheme for risk stratification and clinical evidence on risk reduction approaches, such as limiting the use of concomitant medications that increase the risk of GIB and the potential utility of gastrointestinal protection strategies (such as proton pump inhibitors or histamine type 2 receptor antagonists). Furthermore, we summarize the best available evidence and potential gaps in our knowledge on tailoring antithrombotic therapy in patients with active or recent GIB and in those at high risk of GIB but without active or recent GIB. Finally, we review the recommendations provided by major medical societies, highlighting the crucial role of teamwork and multidisciplinary discussions to customize the antithrombotic regimen in patients with coexisting cardiovascular and gastrointestinal diseases.

Cost-effectiveness of rapid vs. in-house vs. send-out ADAMTS13 testing for immune thrombotic thrombocytopenic purpura.

While awaiting confirmatory results, empiric therapy for patients suspected to have immune thrombotic thrombocytopenic purpura (iTTP) provides benefits and also accrues risks and costs. Rapid assays for ADAMTS13 may be able to avoid the cost and risk exposure associated with empiric treatment. We conducted the first cost-effectiveness evaluation of testing strategies with rapid versus traditional ADAMTS13 assays in patients with intermediate to high-risk PLASMIC scores, with and without caplacizumab use. We built a Markov cohort simulation with four clinical base-case analyses: 1) Intermediate-risk PLASMIC score with caplacizumab, 2) Intermediate-risk PLASMIC score without caplacizumab, 3) High-risk PLASMIC score with caplacizumab, 4) High-risk PLASMIC score without caplacizumab. Each of these evaluated three testing strategies: 1) rapid assay (<1-hour turnaround), 2) in-house FRET-based assay (24-hour turnaround), and 3) send-out FRET-based assay (72-hour turnaround). The primary outcome was the incremental net monetary benefit (iNMB) reported over a 3-day time horizon and across accepted willingness-to-pay thresholds in USD per quality-adjusted life-year (QALY). While accruing the same amount of QALYs, the rapid assay strategy saved up to $46,820 (95% CI $41,961-$52,486) per-patient-tested. No parameter variation changed the outcome. In probabilistic sensitivity analyses, the rapid assay strategy was favored in 100% (three base-cases and scenario analyses) and 99% (one base-case and scenario analysis) across 100,000 Monte Carlo iterations within each. Rapid ADAMTS13 testing for patients with intermediate- or high-risk PLASMIC scores yields significant per-patient cost savings, achieved by reducing the costs associated with unnecessary therapeutic plasma exchange and caplacizumab therapy in patients without iTTP.

Burden of Ischemic and Hemorrhagic Stroke Across the US From 1990 to 2019.

Importance: Stroke is a leading cause of death and disability in the US. Accurate and updated measures of stroke burden are needed to guide public health policies. Objective: To present burden estimates of ischemic and hemorrhagic stroke in the US in 2019 and describe trends from 1990 to 2019 by age, sex, and geographic location. Design, Setting, and Participants: An in-depth cross-sectional analysis of the 2019 Global Burden of Disease study was conducted. The setting included the time period of 1990 to 2019 in the US. The study encompassed estimates for various types of strokes, including all strokes, ischemic strokes, intracerebral hemorrhages (ICHs), and subarachnoid hemorrhages (SAHs). The 2019 Global Burden of Disease results were released on October 20, 2020. Exposures: In this study, no particular exposure was specifically targeted. Main Outcomes and Measures: The primary focus of this analysis centered on both overall and age-standardized estimates, stroke incidence, prevalence, mortality, and DALYs per 100 000 individuals. Results: In 2019, the US recorded 7.09 million prevalent strokes (4.07 million women [57.4%]; 3.02 million men [42.6%]), with 5.87 million being ischemic strokes (82.7%). Prevalence also included 0.66 million ICHs and 0.85 million SAHs. Although the absolute numbers of stroke cases, mortality, and DALYs surged from 1990 to 2019, the age-standardized rates either declined or remained steady. Notably, hemorrhagic strokes manifested a substantial increase, especially in mortality, compared with ischemic strokes (incidence of ischemic stroke increased by 13% [95% uncertainty interval (UI), 14.2%-11.9%]; incidence of ICH increased by 39.8% [95% UI, 38.9%-39.7%]; incidence of SAH increased by 50.9% [95% UI, 49.2%-52.6%]). The downturn in stroke mortality plateaued in the recent decade. There was a discernible heterogeneity in stroke burden trends, with older adults (50-74 years) experiencing a decrease in incidence in coastal areas (decreases up to 3.9% in Vermont), in contrast to an uptick observed in younger demographics (15-49 years) in the South and Midwest US (with increases up to 8.4% in Minnesota). Conclusions and Relevance: In this cross-sectional study, the declining age-standardized stroke rates over the past 3 decades suggest progress in managing stroke-related outcomes. However, the increasing absolute burden of stroke, coupled with a notable rise in hemorrhagic stroke, suggests an evolving and substantial public health challenge in the US. Moreover, the significant disparities in stroke burden trends across different age groups and geographic locations underscore the necessity for region- and demography-specific interventions and policies to effectively mitigate the multifaceted and escalating burden of stroke in the country.

Factors Associated With Long COVID: Insights From Two Nationwide Surveys.

BACKGROUND: Long COVID is a multisystemic condition that affects the lives of millions of people globally, yet factors associated with it are poorly defined. Our purpose in this study was to identify factors associated with long COVID. METHODS: This cross-sectional study used data from the 2022 Behavioral Risk Factor Surveillance System (BRFSS) and the 2022 National Health Interview Survey (NHIS). We restricted the sample to individuals aged 18 and older who reported a positive COVID-19 test or doctor's diagnosis. Individuals who reported symptoms of at least 3 months were assumed to have long COVID. We identified demographic and clinical characteristics associated with long COVID, in unadjusted and adjusted analyses. RESULTS: The study included 124,313 individuals in the BRFSS cohort and 10,131 in the NHIS cohort who reported a COVID-19 infection, with 26,783 (21.5%) and 1797 (17.7%) reporting long COVID, respectively. We found middle age, female sex, lack of a college degree, and severity of acute COVID-19 infection to be associated with long COVID. In contrast, non-Hispanic Asian and Black Americans were less likely to report long COVID compared with non-Hispanic White individuals. These findings were consistent across datasets. CONCLUSIONS: Several demographic features were associated with long COVID, which may be the result of social, clinical, or biological influences.

Fibrinolytic Agents in Thromboembolic Diseases: Historical Perspectives and Approved Indications.

Fibrinolytic agents catalyze the conversion of the inactive proenzyme plasminogen into the active protease plasmin, degrading fibrin within the thrombus and recanalizing occluded vessels. The history of these medications dates to the discovery of the first fibrinolytic compound, streptokinase, from bacterial cultures in 1933. Over time, researchers identified two other plasminogen activators in human samples, namely urokinase and tissue plasminogen activator (tPA). Subsequently, tPA was cloned using recombinant DNA methods to produce alteplase. Several additional derivatives of tPA, such as tenecteplase and reteplase, were developed to extend the plasma half-life of tPA. Over the past decades, fibrinolytic medications have been widely used to manage patients with venous and arterial thromboembolic events. Currently, alteplase is approved by the U.S. Food and Drug Administration (FDA) for use in patients with pulmonary embolism with hemodynamic compromise, ST-segment elevation myocardial infarction (STEMI), acute ischemic stroke, and central venous access device occlusion. Reteplase and tenecteplase have also received FDA approval for treating patients with STEMI. This review provides an overview of the historical background related to fibrinolytic agents and briefly summarizes their approved indications across various thromboembolic diseases.

The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities.

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce.

How to Use Quasi-Experimental Methods in Cardiovascular Research: A Review of Current Practice.

BACKGROUND: Quasi-experimental methods (QEMs) are a family of techniques used to estimate causal relationships when randomized controlled trials are unfeasible or unethical. They offer a powerful alternative to observational studies by introducing random assignment of individuals or groups into their design, thereby offering stronger means of establishing causation. The use of QEMs in cardiovascular research has not been systematically examined to determine steps toward improving and expanding their use. METHODS: We identified 4 main techniques using a systematic search strategy from 2016 to 2021: instrumental variable analysis, interrupted time series analysis, difference-in-differences analysis, and regression discontinuity designs. QEMs are examined as alternatives to randomized controlled trials and traditional observational studies; as more observational data becomes available to researchers, there are more opportunities to apply these techniques. Eligible articles were selected based on publication in high-ranked journals. The quality of eligible articles was appraised using the Joanna Briggs Institute checklist for quasi-experimental studies. RESULTS: Data from 380 studies were extracted based on our inclusion criteria. Forty-two of these studies were published in the top 10 medical or top 20 cardiovascular disease journals, and 25 studies were included after quality appraisal. The review identifies the main features and limitations associated with each technique, providing readers with practical guidance on how to apply these to their research. A graphical decision aid was developed to facilitate the routine use of QEMs. CONCLUSIONS: The use of QEMs in cardiovascular research published in contemporary, high-impact articles was examined. Findings are biased toward this segment of literature, which represents the latest developments in this growing area of cardiovascular research. The decision aid is a novel schematic that researchers can adopt into practice.