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Adults in rural counties in the United States (US) experience higher rates broadly of cardiovascular disease (CVD) compared with adults in urban counties. Mortality rates specifically due to heart failure (HF) have increased since 2011, but estimates of heterogeneity at the county-level in HF-related mortality have not been produced. The objectives of this study were 1) to quantify nationwide trends by rural-urban designation and 2) examine county-level factors associated with rural-urban differences in HF-related mortality rates. We queried CDC WONDER to identify HF deaths between 2011-2018 defined as CVD (I00-78) as the underlying cause of death and HF (I50) as a contributing cause of death. First, we calculated national age-adjusted mortality rates (AAMR) and examined trends stratified by rural-urban status (defined using 2013 NCHS Urban-Rural Classification Scheme), age (35-64 and 65-84 years), and race-sex subgroups per year. Second, we combined all deaths from 2011-2018 and estimated incidence rate ratios (IRR) in HF-related mortality for rural versus urban counties using multivariable negative binomial regression models with adjustment for demographic and socioeconomic characteristics, risk factor prevalence, and physician density. Between 2011-2018, 162,314 and 580,305 HF-related deaths occurred in rural and urban counties, respectively. AAMRs were consistently higher for residents in rural compared with urban counties (73.2 [95% CI: 72.2-74.2] vs. 57.2 [56.8-57.6] in 2018, respectively). The highest AAMR was observed in rural Black men (131.1 [123.3-138.9] in 2018) with greatest increases in HF-related mortality in those 35-64 years (+6.1%/year). The rural-urban IRR persisted among both younger (1.10 [1.04-1.16]) and older adults (1.04 [1.02-1.07]) after adjustment for county-level factors. Main limitations included lack of individual-level data and county dropout due to low event rates (<20). Differences in county-level factors may account for a significant amount of the observed variation in HF-related mortality between rural and urban counties. Efforts to reduce the rural-urban disparity in HF-related mortality rates will likely require diverse public health and clinical interventions targeting the underlying causes of this disparity.

Background Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. Methods We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. Results Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90 th and 10 th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. Conclusions County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit . Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Of the 15,565 participants in our dataset, 2170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI 0.782–0.844) vs 0.792 (CI 0.760–0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

Emulation of the \"target trial\" (TT), a hypothetical pragmatic randomized controlled trial (RCT), using observational data can be used to mitigate issues commonly encountered in comparative effectiveness research (CER) when randomized trials are not logistically, ethically, or financially feasible. However, cardiovascular (CV) health research has been slow to adopt TT emulation. Here, we demonstrate the design and analysis of a TT emulation using electronic health records to study the comparative effectiveness of the addition of a disease-modifying anti-rheumatic drug (DMARD) to a regimen of methotrexate on CV events among rheumatoid arthritis (RA) patients. We used data from an electronic medical records-based cohort of RA patients from Northwestern Medicine to emulate the TT. Follow-up began 3 months after initial prescription of MTX (2000-2020) and included all available follow-up through June 30, 2020. Weighted pooled logistic regression was used to estimate differences in CVD risk and survival. Cloning was used to handle immortal time bias and weights to improve baseline and time-varying covariate imbalance. We identified 659 eligible people with RA with average follow-up of 46 months and 31 MACE events. The month 24 adjusted risk difference for MACE comparing initiation vs non-initiation of a DMARD was -1.47% (95% confidence interval [CI]: -4.74, 1.95%), and the marginal hazard ratio (HR) was 0.72 (95% CI: 0.71, 1.23). In analyses subject to immortal time bias, the HR was 0.62 (95% CI: 0.29-1.44). In this sample, we did not observe evidence of differences in risk of MACE, a finding that is compatible with previously published meta-analyses of RCTs. Thoughtful application of the TT framework provides opportunities to conduct CER in observational data. Benchmarking results of observational analyses to previously published RCTs can lend credibility to interpretation.

Importance Statin use prior to hospitalization for Coronavirus Disease 2019 (COVID-19) is hypothesized to improve inpatient outcomes including mortality, but prior findings from large observational studies have been inconsistent, due in part to confounding. Recent advances in statistics, including incorporation of machine learning techniques into augmented inverse probability weighting with targeted maximum likelihood estimation, address baseline covariate imbalance while maximizing statistical efficiency. Objective To estimate the association of antecedent statin use with progression to severe inpatient outcomes among patients admitted for COVD-19. Design, setting and participants We retrospectively analyzed electronic health records (EHR) from individuals ≥ 40-years-old who were admitted between March 2020 and September 2022 for ≥ 24 h and tested positive for SARS-CoV-2 infection in the 30 days before to 7 days after admission . Exposure Antecedent statin use—statin prescription ≥ 30 days prior to COVID-19 admission. Main outcome Composite end point of in-hospital death, intubation, and intensive care unit (ICU) admission. Results Of 15,524 eligible COVID-19 patients, 4412 (20%) were antecedent statin users. Compared with non-users, statin users were older (72.9 (SD: 12.6) versus 65.6 (SD: 14.5) years) and more likely to be male (54% vs. 51%), White (76% vs. 71%), and have ≥ 1 medical comorbidity (99% vs. 86%). Unadjusted analysis demonstrated that a lower proportion of antecedent users experienced the composite outcome (14.8% vs 19.3%), ICU admission (13.9% vs 18.3%), intubation (5.1% vs 8.3%) and inpatient deaths (4.4% vs 5.2%) compared with non-users. Risk differences adjusted for labs and demographics were estimated using augmented inverse probability weighting with targeted maximum likelihood estimation using Super Learner . Statin users still had lower rates of the composite outcome (adjusted risk difference: − 3.4%; 95% CI: − 4.6% to − 2.1%), ICU admissions (− 3.3%; − 4.5% to − 2.1%), and intubation (− 1.9%; − 2.8% to − 1.0%) but comparable inpatient deaths (0.6%; − 1.3% to 0.1%). Conclusions and relevance After controlling for confounding using doubly robust methods, antecedent statin use was associated with minimally lower risk of severe COVID-19-related outcomes, ICU admission and intubation, however, we were not able to corroborate a statin-associated mortality benefit. Key points Question Is statin use prior to hospital admission for COVID-19 associated with reducing severe inpatient outcomes? Findings In this observational study using electronic health records from a multi-hospital health system in Chicago, we used robust statistical methods to account for confounding and found that adults 40 years or older who were prescribed statins prior to admission for COVID-19 had minimally lower rates of intubation and admission to the intensive care unit. However, inpatient mortality was comparable between statins users and non-users. Meaning Consistent with current COVID-19 treatment guidelines, we did not find evidence supporting the utilization of statins for clinically significant reduction in severe inpatient COVID-19 outcomes.

Background Mindfulness-based interventions have been shown to improve psychological outcomes including stress, anxiety, and depression in general population studies. However, effectiveness has not been sufficiently examined in racially and ethnically diverse community-based settings. We will evaluate the effectiveness and implementation of a mindfulness-based intervention on depressive symptoms among predominantly Black women at a Federally Qualified Health Center in a metropolitan city. Methods In this 2-armed, stratified, individually randomized group-treated controlled trial, 274 English-speaking participants with depressive symptoms ages 18–65 years old will be randomly assigned to (1) eight weekly, 90-min group sessions of a mindfulness-based intervention (M-Body), or (2) enhanced usual care. Exclusion criteria include suicidal ideation in 30 days prior to enrollment and regular (>4x/week) meditation practice. Study metrics will be assessed at baseline and 2, 4, and 6 months after baseline, through clinical interviews, self-report surveys, and stress biomarker data including blood pressure, heart rate, and stress related biomarkers. The primary study outcome is depressive symptom score after 6 months. Discussion If M-Body is found to be an effective intervention for adults with depressive symptoms, this accessible, scalable treatment will widely increase access to mental health treatment in underserved, racial/ethnic minority communities. Trial registration ClinicalTrials.gov NCT03620721. Registered on 8 August 2018.

A prospective, one-armed, safety non-inferiority trial with historical controls was performed at a single-center, quaternary, children’s hospital. Inclusion criteria were children aged 3 months–18 years after pediatric cardiac surgery resulting in a two-ventricle repair between 7/2020 and 7/2021. Eligible patients were compared with patients from a 5-year historical period (selected using a database search). The intervention was that “regular risk” patients received no diuretics and pre-specified “high risk” patients received 5 days of twice per day furosemide at discharge. 61 Subjects received the intervention. None were readmitted for pleural effusions, though 1 subject was treated for a symptomatic pleural effusion with outpatient furosemide. The study was halted after an interim analysis demonstrated that 4 subjects were readmitted with pericardial effusion during the study period versus 2 during the historical control (2.9% versus 0.2%, P  = 0.003). We found no evidence that limited post-discharge diuretics results in an increase in readmissions for pleural effusions. This conclusion is limited as not enough subjects were enrolled to definitively show that this strategy is not inferior to the historical practice. There was a statistically significant increase in readmissions for pericardial effusions after implementation of this study protocol which can lead to serious complications and requires further study before conclusions can be drawn regarding optimal diuretic regimens.

We take steps toward causally interpretable meta-analysis by describing methods for transporting causal inferences from a collection of randomized trials to a new target population, one trial at a time and pooling all trials. We discuss identifiability conditions for average treatment effects in the target population and provide identification results. We show that the assumptions that allow inferences to be transported from all trials in the collection to the same target population have implications for the law underlying the observed data. We propose average treatment effect estimators that rely on different working models and provide code for their implementation in statistical software. We discuss how to use the data to examine whether transported inferences are homogeneous across the collection of trials, sketch approaches for sensitivity analysis to violations of the identifiability conditions, and describe extensions to address nonadherence in the trials. Last, we illustrate the proposed methods using data from the Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis Trial.

Remote patient monitoring (RPM) for hypertension enables automatic transmission of blood pressure (BP) and pulse into the electronic health record (EHR), but its effectiveness in primary care is unknown. This pragmatic matched cohort study using EHR data compared BP outcomes between individuals prescribed RPM and temporally-matched controls from six primary care practices. We retrospectively created a cohort of 288 Medicare-enrolled patients prescribed BP RPM (cases) and 1152 propensity score-matched controls (1:4). Matching was based on age, sex, systolic blood pressure (SBP), marital status, and other characteristics. Outcomes at 3, 6, 9 and 12 months were: controlling high BP (most recent BP < 140/90 mm Hg), antihypertensive medication intensification, and most recent SBP assessed using: all measurements, and office measurements only. At baseline, RPM-prescribed patients and controls had similar ages and systolic BP. BP control diverged at 3 months (RPM: 72.2%, control: 51%, p < 0.001). This difference persisted but decreased over follow-up. After 12 months, the RPM-prescribed cohort had greater BP control (RPM: 71.5%, control: 58.1%, p < 0.001) and lower SBP (132.3 versus 136.5 mm Hg, p = 0.003) using all measurements, but they did not differ using only office measurements (12 month BP control: 60.8% versus 58.1%, p = 0.44; SBP: 135.9 versus 136.5 mm Hg, p = 0.91). At 12 months, the most recent BP measurements were more current for RPM-prescribed patients (median [IQR] 8 [0–109] versus 134 [56–239] days). Net increases in antihypertensive medications by 12 months were similar. Implementation of RPM in primary care could inform hypertension management strategies and increase hypertension control. Registration: ClinicalTrials.gov identifier: NCT05562921.

This pragmatic matched cohort study using EHR data extended the follow up to 18 months for BP outcomes comparing individuals prescribed remote patient monitoring ( n  = 288) and temporally-matched controls ( n  = 1152) from six primary care practices. After 18 months, the RPM-prescribed cohort had greater BP control < 140/90 mm Hg (RPM cohort: 71.5%, control cohort: 51.9%, p  < 0.001) and lower systolic BP (131.6 versus 136.0 mm Hg, p  = 0.004) using office and home measurements. BP control at 18 months assessed by office measurements only was also higher in the RPM group (62.2% versus 51.9%, p  = 0.004).