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BACKGROUND: The Get With The Guidelines-Stroke program is a quality improvement initiative designed to enhance adherence to evidence-based stroke care. Since its inception in 2003, over 2800 hospitals in the United States have participated in the program. METHODS: We examined patient characteristics, adherence to performance measures, and in-hospital outcomes in patients hospitalized for acute ischemic stroke, subarachnoid hemorrhage, intracerebral hemorrhage, and transient ischemic attack in The Get With The Guidelines-Stroke hospitals from 2003 through 2022. We quantified temporal changes in performance measure adherence and clinical outcomes over time. Performance measure denominators consisted of patients who were eligible, excluding those with contraindications. RESULTS: Over the 20 years of the program, a total of 7837 849 stroke cases (median age 71 years, 51.0% female; 69.2% ischemic strokes, 3.9% SAHs, 11.5% ICHs, and 15.3% TIAs) were entered into the registry. Except for antithrombotics at discharge, in which the baseline performance was >92%, there was sustained improvement in all performance metrics regardless of type of cerebrovascular event (P

ImportanceGuideline-directed medical therapy (GDMT) remains underutilized on a global level, with significant disparities in access to treatment worldwide. The potential global benefits of quadruple therapy on patients with heart failure with reduced ejection fraction (HFrEF) have not yet been estimated.ObjectiveTo assess the projected population-level benefit of optimal GDMT use globally among patients with HFrEF.Design, setting, and participantsEstimates for HFrEF prevalence, contraindications to GDMT, treatment rates, and the number needed to treat for all-cause mortality at 12 months were derived from previously published sources. Potential lives saved from optimal implementation of quadruple therapy among patients with HFrEF was calculated globally and a sensitivity analysis was conducted to account for uncertainty in the existing data.Main outcomes and measuresAll-cause mortality.ResultsOf an estimated 28.89 million people with HFrEF worldwide, there were 8 235 063 (95% CI, 6 296 020-10 762 972) potentially eligible for but not receiving β-blockers, 20 387 000 (95% CI, 15 867 004-26 184 996) eligible for but not receiving angiotensin receptor-neprilysin inhibitors, 12 223 700 (95% CI, 9 376 895-15 924 973) eligible for but not receiving mineralocorticoid receptor antagonists, and 21 229 170 (95% CI, 16 537 400-27 242 688) eligible for but not receiving sodium glucose cotransporter-2 inhibitors. Optimal implementation of quadruple GDMT could potentially prevent 1 188 277 (95% CI, 767 933-1 914 561) deaths over 12 months. A large proportion of deaths averted were projected in Southeast Asia, Eastern Mediterranean and Africa, and the Western Pacific regions.Conclusions and relevanceImprovement in use of GDMT could result in substantial mortality benefits on a global scale. Significant heterogeneity also exists across regions, which warrants additional study with interventions tailored to country-level differences for optimization of GDMT worldwide.

BACKGROUND: Oral anticoagulants (OACs) are routinely used for the management of atrial fibrillation (AF) while antiplatelet agents are used in coronary artery disease (CAD). However, data regarding the comparative clinical outcomes of OAC monotherapy versus dual antithrombotic therapy (anticoagulant plus antiplatelet agent) in patients with AF and stable CAD are limited. METHODS: A comprehensive search of major databases including PubMed/MEDLINE, Cochrane Library, and Embase was performed from inception to September 1, 2024 to identify randomized control trials (RCTs) that compared OAC monotherapy with dual antithrombotic therapy in patients with AF and stable CAD. The risk ratios (RRs) were estimated with corresponding 95% confidence intervals (CIs) for all outcomes. RESULTS: A total of three RCTs reported data for 3945 patients with AF and stable CAD. The mean age of patients was 73.8 (±11.85) years and the mean follow-up was 22 months. OAC monotherapy was associated with a significantly reduced relative risk of major bleeding (RR: 0.55, 95% CI: 0.32-0.95) compared to dual therapy. The risk of all-cause death (RR: 0.85, 95% CI: 0.49-1.48), cardiovascular death (RR: 0.84, 95% CI: 0.50-1.41), any stroke event (RR: 0.74, 95% CI: 0.46-1.18), and myocardial infarction (RR: 1.57, 95% CI: 0.79-3.12) remained comparable across the two groups. CONCLUSION: OAC monotherapy led to a significant relative risk reduction for major bleeding with similar rates of ischemic events and mortality compared to dual antithrombotic therapy in patients with AF and stable CAD.

The current economic burden of cardiovascular (CV)-related hospitalizations grouped by diagnoses and procedures in the United States has not been well characterized. The objective was to identify current trends in CV-related hospitalizations, procedural utilization, and health care costs using the most recent 6 years of hospitalization data. A retrospective analysis of discharge data from the National Inpatient Sample database was conducted to determine trends in CV-related hospitalizations, costs, and procedures for each year from 2016 to the most recent available dataset, 2021. Total CV-related costs were adjusted to and reported in 2023 dollars. In 2021, there were 4,687,370 CV-related hospitalizations at a cost of $108 billion. Heart failure hospitalizations accounted for the highest costs at $18.5 billion, followed by non-ST-elevation myocardial infarction at $11.2 billion and stroke at $10.9 billion. Significant upward trends in costs from 2016 to 2021 were observed for heart failure, stroke, atrial fibrillation, ST-elevation myocardial infarction, chest pain, hypertensive emergency, ventricular tachycardia, aortic dissection, sudden cardiac death, pericarditis, supraventricular tachycardia, and pulmonary heart disease. Over the 6 observational years, total costs increased by over $10 billion, representing a 10% increase. However, the increases were not linear, as there was a significant increase of 6.5% from 2018 to 2019, then a decrease of over 7% from 2019 to 2020, followed by an increase of approximately 6% from 2020 to 2021. By 2030, total CV-related costs are projected to reach $131.3 billion. For all years, coronary procedures were the most performed, followed by extracorporeal membrane oxygenation, non-bypass peripheral vascular surgery, pacemaker placement, and coronary artery bypass graft surgery. Both transcatheter aortic valve replacement and MitraClip procedures demonstrated significant upward trends from 2016 to 2021. Overall, from the years 2016 to 2021, CV-related hospitalizations, costs, and procedures demonstrated upward trends. In conclusion, CV disease remains a high burden in the hospital setting with tremendous health care costs.

Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (training cohort). Demographics, medical comorbidities, vitals, and labs were collected and were used to construct random forest machine learning models to predict in-hospital mortality. Models were compared with logistic regression, and to commonly used heart failure risk scores. The models were subsequently validated in patients hospitalized with a diagnosis of heart failure from a second academic, community medical centre (validation cohort). The entire cohort comprised 21 802 patients, of which 14 539 were in the training cohort and 7263 were in the validation cohort. The median age (25th-75th percentile) was 70 (58-82) for the entire cohort, 43.2% were female, and 6.7% experienced inpatient mortality. In the overall cohort, 7621 (35.0%) patients had heart failure with reduced ejection fraction (EF ≤ 40%), 1271 (5.8%) had heart failure with mildly reduced EF (EF 41-49%), and 12 910 (59.2%) had heart failure with preserved EF (EF ≥ 50%). Random forest models in the validation cohort demonstrated a c-statistic (95% confidence interval) of 0.96 (0.95-0.97), sensitivity (SN) of 87.3%, and specificity (SP) of 90.6% for the prediction of in-hospital mortality. Models for those with HFrEF demonstrated a c-statistic of 0.96 (0.94-0.98), SN 88.2%, and SP 91.0%, and those for patients with HFpEF showed a c-statistic of 0.95 (0.93-0.97), SN 87.4%, and SP 89.5% for predicting in-hospital mortality. The random forest model significantly outperformed logistic regression (c-statistic 0.87, SN 75.9%, and SP 86.9%), and current existing risk scores including the Acute Decompensated Heart Failure National Registry risk score (c-statistic of 0.70, SN 69%, and SP 62%), and the Get With the Guidelines-Heart Failure risk score (c-statistic 0.69, SN 67%, and SP 63%); P

AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD-code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. METHODS AND RESULTS: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VAs External Peer Review Program for HF (EPRP-HF) and non-HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF-related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP-HF as cases (PPV, 0.86; sensitivity, 0.86). From the HF ICD-code universe, we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). CONCLUSIONS: These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation.

BackgroundSince January 2020, COVID-19 has affected more than 100 million people in the U.S. Previous studies on racial and ethnic disparities related to characteristics and outcomes of COVID-19 patients have been insightful. However, appropriate epidemiologic age-standardization of the disease burden and disparities for hospitalization data are lacking.ObjectiveTo identify and describe racial and ethnic disparities for primary COVID-19 hospitalizations in the U.S. in 2020.MethodsIn this nationally representative observational study, we use the National Inpatient Sample to quantify racial and ethnic disparities in COVID-19 hospitalizations. Descriptive statistics for patient characteristics, common comorbidities, age-standardized hospitalization rates, inpatient complications, and mortality among COVID-19 hospitalizations were contrasted by race and ethnicity.ResultsThere were 1,058,815 primary COVID-19 hospitalizations in 2020. Of those, 47.2% were female, with median age of 66 (IQR, 54, 77). Overall inpatient mortality rate was 11.1%. When compared to White patients, Black, Hispanic, and Native American patients had higher age-standardized hospitalization rate ratios of 2.42 (95% CI 2.40-2.43), 2.26 (2.25-2.28), and 2.51 (2.46-2.56), respectively. Non-White patients had increased age-adjusted rates for procedures and complications. Factors associated with inpatient mortality include age, male sex, Hispanic or Native American race or ethnicity, lower income, Medicaid, heart failure, arrhythmias, coagulopathy, and chronic liver disease.ConclusionsMarginalized populations in the U.S. had over twice the COVID-19 hospitalization rate relative to White patients. Age-adjusted mortality rates were highest for Black, Hispanic, and Native American patients. Careful consideration for vulnerable populations is encouraged during highly communicable respiratory pandemics.

STUDY OBJECTIVE: The association of prior to admission guideline-directed medical therapy (GDMT) use in patients hospitalized with Heart Failure with Reduced Ejection Fraction (HFrEF, ejection fraction ≤40 %) and Coronavirus Disease 2019 (COVID-19) with in-hospital outcomes has not been well studied. DESIGN/SETTING/PARTICIPANTS/INTERVENTIONS/OUTCOME MEASURES: Using the American Heart Associations Get With The Guidelines Heart Failure Registry, we identified HFrEF patients presenting with acute decompensated heart failure (ADHF) and compared rates of GDMT prescription between those presenting prior to and during the pandemic. In a subgroup of patients with a concomitant COVID-19 diagnosis, we evaluated the association of prior to admission GDMT use with in-hospital mortality and severe COVID-19. RESULTS: 23,899 patients were admitted with HFrEF during the pandemic (2/16/20-3/24/21) and 26,459 patients were admitted in the year prior (2/16/19-2/15/20). In this overall cohort, prior to admission ACEI/ARB/ARNI (45.6 % vs 48.1 %, p

BackgroundHow housing insecurity might affect patients with heart failure (HF) is not well characterized. Housing insecurity increases risks related to both communicable and non-communicable diseases. For patients with HF, housing insecurity likely increases the risk for worse outcomes and rehospitalizations.Methods and resultsWe analyzed U.S. HF hospitalizations using the 2020 National Inpatient Sample (NIS) and Nationwide Readmissions Database (NRD) to evaluate the impacts of housing insecurity on HF outcomes and hospital utilization. Individuals were identified as having housing insecurity using diagnostic ICD-10 codes. Demographics and comorbidities were compared between HF patients with and without housing insecurity. An adjusted logistic regression was performed to evaluate the relationships between housing insecurity and socioeconomic status on in-hospital mortality. Using a Cox proportional hazards model, HF patients with and without housing insecurity were evaluated for the risk of all-cause and HF-specific readmissions over time. Of the 1,003,270 hospitalizations for HF in the U.S. in 2020, 16,150 were identified as having housing insecurity (1.6%) and 987,120 were identified as having no housing insecurity (98.4%). The median age of patients with housing insecurity hospitalized for HF was 57, as compared to 73 in the population with no housing insecurity. A higher proportion of patients in the housing insecurity group were Black (35% vs 20.1%) or Hispanic (11.1% vs 7.3%). Patients with housing insecurity were more likely to carry a diagnosis of alcohol use disorder (15.2% vs 3.3%) or substance use disorder (70.2% vs 17.8%), but were less likely to use tobacco (18.3% vs 28.7%). Patients with housing insecurity were over 4.5 times more likely to have Medicaid (52.4% vs 11.3%). Median length of stay did not differ between patients with housing insecurity versus those without. Patients with housing insecurity were more likely to discharge Against Medical Advice (11.4% vs 2.03%). After adjusting for patient characteristics, housing insecurity was associated with lower in-hospital mortality (OR 0.60, 95% CI 0.39 - 0.92). Housing insecurity was associated with a higher risk of all-cause readmissions at 180 days (HR 1.13, 95% CI 1.12 - 1.14). However, there was no significant difference in the risk of HF-specific readmissions at 180 days (HR 1.07, 95% CI 0.998 - 1.14) CONCLUSIONS: Patients with HF and housing insecurity have distinct demographic characteristics. They are also more likely to be readmitted after their initial hospitalization when compared to those without housing insecurity. Identifying and addressing specific comorbid conditions for patients with housing insecurity who are hospitalized for HF may allow clinicians to provide more focused care, with the goal of preventing morbidity, mortality, and unnecessary readmissions.

The Get With The Guidelines-Stroke program which, began 20 years ago, is one of the largest and most important nationally representative disease registries in the United States. Its importance to the stroke community can be gauged by its sustained growth and widespread dissemination of findings that demonstrate sustained increases in both the quality of care and patient outcomes over time. The objectives of this narrative review are to provide a brief history of Get With The Guidelines-Stroke, summarize its major successes and impact, and highlight lessons learned. Looking to the next 20 years, we discuss potential challenges and opportunities for the program.

BACKGROUND: Low-dose colchicine has been shown to lower major adverse cardiovascular events (MACE) among those with cardiovascular disease (CVD). It remains unclear how long a CVD patient needs to live to potentially benefit from colchicine. Our study aimed to determine the time to benefit (TTB) of colchicine in individuals with CVD. METHODS: Literature searches were performed in PubMed for the cardiovascular outcome trial of colchicine in patients with CVD until October 12, 2023. The primary outcome measured was MACE. Reconstructed individual participant data (IPD) and the stratified Cox proportional hazards model were used to calculate the hazard ratio (HR) and 95 % confidence interval (CI) to estimate the efficacy of colchicine, and Weibull survival curves were fitted to estimate TTB for specific absolute risk reduction (ARR) thresholds (0.002, 0.005, and 0.01). RESULTS: Four trials randomizing 11,594 adults aged between 59.8 and 66.5 years were included (follow-up duration: 12-28.6 months). Compared with placebo, colchicine reduced the risk of MACE (HR 0.68, 95 % CI: 0.60 to 0.78) but had no impact on cardiovascular and all-cause mortality. A TTB of 11.0 months (95 % CI: 0.59 to 21.3) was estimated to be needed to prevent 1 MACE in 100-colchicine-treated patients. The TTB for acute coronary syndrome was similar compared to stable coronary artery disease (10.7 vs. 11.2 months for ARR = 0.010). CONCLUSIONS: By using reconstructed IPD, this pooled analysis demonstrated that colchicine was associated with reduced nonfatal MACE, and the TTB was approximately 11.0 months to prevent 1 MACE per 100 patients.

BACKGROUND: In light of high burden of heart failure (HF) in China, studies of prognostic implication of HF stages are important. We aimed to evaluate the relationship between HF stages and mortality risk in Chinese community populations. METHODS: Nationwide representative populations aged ≥35 years (n = 23,284, mean age 56.9 years, women 53.2%) were enrolled from 2012 to 2016. According to the international HF guidelines, participants were divided into stage A, B and C, and those who did not qualify these stages were categorized as apparently-healthy group. Association between HF stages and all-cause, cardiovascular [CV] and non-CV death was evaluated using multivariable-adjusted Cox proportional regression analysis. FINDINGS: During a median follow-up of 4.7 years (109,902.8 person-years), 1314 deaths occurred. Age-adjusted incidence rate of all-cause death was 5.3 in apparently-healthy, 7.8 in stage A, 8.6 in stage B and 24.6 in stage C groups per 1000 person-years. In reference to apparently-healthy group, adjusted hazard ratio for all-cause death was 1.90 (95% CI: 1.47-2.45), 2.43 (95% CI: 1.89-3.13) and 6.40 (95% CI: 4.56-8.99) for stage A, B and C. Advancing HF stages were associated with increasing risks for all-cause, CV and non-CV death (P-trend

BACKGROUND: Oral anti-coagulants (OAC) are the intervention for the prevention of stroke, which consistently improve clinical outcomes and survival among patients with atrial fibrillation (AF). The main purpose of this study is to identify problems in OAC utilization among hospitalized patients with AF in China. METHODS: Using data from the Improving Care for Cardiovascular Disease in China-Atrial Fibrillation (CCC-AF) registry, guideline-recommended OAC use in eligible patients was assessed. RESULTS: A total of 52,530 patients with non-valvular AF were enrolled from February 2015 to December 2019, of whom 38,203 were at a high risk of stroke, 9717 were at a moderate risk, and 4610 were at a low risk. On admission, only 20.0% (6075/30,420) of patients with a diagnosed AF and a high risk of stroke were taking OAC. The use of pre-hospital OAC on admission was associated with a lower risk of new-onset ischemic stroke/transient ischemic attack among the diagnosed AF population (adjusted odds ratio: 0.54, 95% confidence interval: 0.43-0.68; P  70% of AF patients were at a high risk of stroke. Although poor performance on guideline-recommended OAC use was found in this study, over time the CCC-AF project has made progress in stroke prevention in the Chinese AF population.Registration:ClinicalTrials.gov, NCT02309398.

Heart failure (HF) is a common disease with increasing prevalence around the world. There is high morbidity and mortality associated with poorly controlled HF along with increasing costs and strain on healthcare systems due to a high rate of rehospitalization and resource utilization. Despite the establishment of clear evidence-based guideline directed medical therapies (GDMT) proven to improve HF morbidity and mortality, there remains significant clinical inertia to optimizing HF patients on GDMT. Only a minority of HF patients are prescribed on all four classes of GDMT. To bridge the gap between the vulnerable population of HF patients and lifesaving GDMT, HF implementation is of increasing importance. HF implementation involves strategies and techniques to improve GDMT optimization along with other modalities to improve HF management. HF implementation meets patients where they are, including at the time of acute decompensation in the inpatient setting, at the vulnerable discharge stage, and at the chronic management stage in the outpatient setting. Inpatient HF implementation strategies include protocolized rapid titration of GDMT, site-level audit-and-feedback, virtual GDMT optimization teams, and electronic health record notifications and alerts. Discharge HF implementation strategies include education at patient and provider levels, discharge summaries, and HF transitional programs. Outpatient HF implementation strategies include digital innovations such as electronic health record utilization and mobile applications, population level strategies such as registries and clinical dashboards), changes in HF team structure and member roles, remote monitoring with implanted devices and telemonitoring, and hospital at home care model. With a growing population of HF patients, there is an increasing need for novel and creative HF implementation and monitoring methods.