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Background: The COVID-19 pandemic has evolved through multiple phases characterized by new viral variants, vaccine development, and changes in therapies. It is unknown whether rates of cardiovascular disease (CVD) risk factor profiles and complications have changed over time. Methods: We analyzed the American Heart Association COVID-19 CVD registry, a national multicenter registry of hospitalized adults with active COVID-19 infection. The time period from April 2020 to December 2021 was divided into 3-month epochs, with March 2020 analyzed separately as a potential outlier. Participating centers varied over the study period. Trends in all-cause in-hospital mortality, CVD risk factors, and in-hospital CVD outcomes, including a composite primary outcome of cardiovascular death, cardiogenic shock, new heart failure, stroke, and myocardial infarction, were evaluated across time epochs. Risk-adjusted analyses were performed using generalized linear mixed-effects models. Results: A total of 46 007 patient admissions from 134 hospitals were included (mean patient age 61.8 years, 53% male, 22% Black race). Patients admitted later in the pandemic were younger, more likely obese, and less likely to have existing CVD ( P trend ≤0.001 for each). The incidence of the primary outcome increased from 7.0% in March 2020 to 9.8% in October to December 2021 (risk-adjusted P trend =0.006). This was driven by an increase in the diagnosis of myocardial infarction and stroke ( P trend P trend P trend =0.63). Conclusions: Despite a shifting risk factor profile toward a younger population with lower rates of established CVD, the incidence of diagnosed cardiovascular complications of COVID increased from the onset of the pandemic through December 2021. All-cause mortality decreased during the initial months of the pandemic and thereafter remained consistently high through December 2021.

Background and Purpose: Coronavirus disease 2019 (COVID-19) may be associated with increased risk for ischemic stroke. We present prevalence and characteristics of strokes in patients with laboratory-confirmed severe acute respiratory syndrome coronavirus-2 infection enrolled in the American Heart Association COVID-19 Cardiovascular Disease Registry. Methods: In this quality improvement registry study, we examined demographic, baseline clinical characteristics, and in-hospital outcomes among hospitalized COVID-19 patients. The primary outcomes were ischemic stroke or transient ischemic attack (TIA) and in-hospital death. Results: Among 21 073 patients with COVID-19 admitted at 107 hospitals between January 29, 2020, and November 23, 2020, 160 (0.75%) experienced acute ischemic stroke/TIA (55.3% of all acute strokes) and 129 (0.61%) had other types of stroke. Among nonischemic strokes, there were 44 (15.2%) intracerebral hemorrhages, 33 (11.4%) subarachnoid hemorrhages, 21 (7.3%) epidural/subdural hemorrhages, 2 (0.7%) cerebral venous sinus thromboses, and 24 (8.3%) strokes not otherwise classified. Asians and non-Hispanic Blacks were overrepresented among ischemic stroke/TIA patients compared with their overall representation in the registry, but adjusted odds of stroke did not vary by race. Median time from COVID-19 symptom onset to ischemic stroke was 11.5 days (interquartile range, 17.8); median National Institutes of Health Stroke Scale score was 11 (interquartile range, 17). COVID-19 patients with acute ischemic stroke/TIA had higher prevalence of hypertension, diabetes, and atrial fibrillation compared with those without stroke. Intensive care unit admission and mechanical ventilation were associated with higher odds of acute ischemic stroke/TIA, but older age was not a predictor. In adjusted models, acute ischemic stroke/TIA was not associated with in-hospital mortality. Conclusions: Ischemic stroke risk did not vary by race. In contrast to the association between older age and death from COVID-19, ischemic stroke risk was the highest among middle-aged adults after adjusting for comorbidities and illness severity, suggesting a potential mechanism for ischemic stroke in COVID-19 independent of age-related atherosclerotic pathways.

IMPORTANCE: Traditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH). OBJECTIVE: To develop and validate novel machine learning (ML) models for HF mortality that incorporate SDOH. DESIGN, SETTING, AND PARTICIPANTS: This retrospective study used the data from the Get With The Guidelines–Heart Failure (GWTG-HF) registry to identify HF hospitalizations between January 1, 2010, and December 31, 2020. The study included patients with acute decompensated HF who were hospitalized at the GWTG-HF participating centers during the study period. Data analysis was performed January 6, 2021, to April 26, 2022. External validation was performed in the hospitalization cohort from the Atherosclerosis Risk in Communities (ARIC) study between 2005 and 2014. MAIN OUTCOMES AND MEASURES: Random forest-based ML approaches were used to develop race-specific and race-agnostic models for predicting in-hospital mortality. Performance was assessed using C index (discrimination), regression slopes for observed vs predicted mortality rates (calibration), and decision curves for prognostic utility. RESULTS: The training data set included 123 634 hospitalized patients with HF who were enrolled in the GWTG-HF registry (mean [SD] age, 71 [13] years; 58 356 [47.2%] female individuals; 65 278 [52.8%] male individuals. Patients were analyzed in 2 categories: Black (23 453 [19.0%]) and non-Black (2121 [2.1%] Asian; 91 154 [91.0%] White, and 6906 [6.9%] other race and ethnicity). The ML models demonstrated excellent performance in the internal testing subset (n = 82 420) (C statistic, 0.81 for Black patients and 0.82 for non-Black patients) and in the real-world–like cohort with less than 50% missingness on covariates (n = 553 506; C statistic, 0.74 for Black patients and 0.75 for non-Black patients). In the external validation cohort (ARIC registry; n = 1205 Black patients and 2264 non-Black patients), ML models demonstrated high discrimination and adequate calibration (C statistic, 0.79 and 0.80, respectively). Furthermore, the performance of the ML models was superior to the traditional GWTG-HF risk score model (C index, 0.69 for both race groups) and other rederived logistic regression models using race as a covariate. The performance of the ML models was identical using the race-specific and race-agnostic approaches in the GWTG-HF and external validation cohorts. In the GWTG-HF cohort, the addition of zip code–level SDOH parameters to the ML model with clinical covariates only was associated with better discrimination, prognostic utility (assessed using decision curves), and model reclassification metrics in Black patients (net reclassification improvement, 0.22 [95% CI, 0.14-0.30]; P

Background and Purpose: Studies suggest an increased risk of adverse outcomes among patients with acute ischemic stroke (AIS) and coronavirus disease 2019 (COVID-19). Methods: Using Get With The Guidelines–Stroke, we identified 41 971 patients (AIS/COVID-19: 1143; AIS/no COVID-19: 40 828) with AIS hospitalized between February 4, 2020 and June 29, 2020, from 458 Get With The Guidelines–Stroke hospitals with at least one COVID-19 case and evaluated clinical characteristics, treatment patterns, and outcomes. Results: Compared with patients with AIS/no COVID-19, those with AIS/COVID-19 were younger, more likely to be non-Hispanic Black, Hispanic, or Asian, more likely to present with higher National Institutes of Health Stroke Scale scores, and had greater proportions of large vessel occlusions. Rates of thrombolysis and thrombectomy were similar between the groups. Door to computed tomography (median 55 [18–207] versus 35 [14–99] minutes, P P P =0.002) times were longer in the AIS/COVID-19 cohort. In adjusted models, patients with AIS/COVID-19 had decreased odds of discharge with modified Rankin Scale score of ≤2 (odds ratio, 0.65 [95% CI, 0.52–0.81], P P ConclusionS: This analysis demonstrates younger age, greater stroke severity, longer times to evaluation and treatment, and worse morbidity and mortality in patients with AIS/COVID-19 compared with those with AIS/no COVID-19.

Background: Prior prognostic models for acute decompensated HF (ADHF) have incorporated race as a covariate may not have completely account for the social and biological factors that underlie the racial disparities in outcomes. We developed race-specific models to improve risk prediction and better identify race-specific predictors of mortality in patients with ADHF using machine learning (ML) methods. Methods: Race-specific ML-models for in-hospital mortality were developed and validated in ADHF patients enrolled in the GWTG-HF registry between 2007 to 2020 (14,586 Black and 50,351 Non-Black adults) using over 40 candidate variables. External validation was performed among participants from the ARIC study with ADHF (n=1,115 Black and 2,028 Non-Black adults). The discrimination and calibration was compared with a previously validated risk score and cohort-specific logistic regression (LR) models. The changes in model reclassification performance with addition of zip-code level data of socioeconomic (SES) parameters was also assessed. Results: In the GWTG-HF cohort, the ML models had superior performance in both Black and Non-Black participants compared with the traditional GWTG-HF risk score ( Table ). The superior performance of the ML-model was also observed in the external validation cohort as compared with the other LR models ( Table ). Addition of measures of SES did not meaningfully change the performance but improved the reclassification metrics of the ML-models with net up-classification of risk in Black [NRI = 0.21 (0.07, 0.35)] and down-classification in Non-Black patients [NRI = -0.08 (-0.12, -0.04)]. SES was responsible for 21.7% of the total attributable mortality risk in Black adults compared to 0.4% in Non-Black adults. Conclusion: Race-specific, ML-based mortality models demonstrated superior performance when compared to traditional HF mortality risk models. Socioeconomic distress is an important contributor to risk in Black adults.

Supplemental Digital Content is available in the text., Background and Purpose: The coronavirus disease 2019 (COVID-19) pandemic has created challenges in the delivery of acute stroke care. In this study, we analyze the characteristics, evaluation, treatment, and in-hospital outcomes of patients presenting with acute ischemic stroke (AIS) pre-COVID-19 and during COVID-19. Methods: Get With The Guidelines-Stroke is a national registry of adults with stroke in the United States. Using this registry, we identified patients with a diagnosis of AIS before (n=39 113; November 1, 2019–February 3, 2020) and after (n=41 971; February 4, 2020–June 29, 2020) the first reported case of COVID-19 in the registry. Characteristics, treatment patterns, quality metrics, and in-hospital outcomes were compared between the 2 groups. Results: Stroke presentations decreased by an average of 15.3% per week in the during COVID-19 time period when compared with similar months in 2019. Compared with patients with AIS in the pre-COVID-19 era, patients in the COVID-19 time period had similar rates of intravenous alteplase and endovascular therapy, and similar door to computed tomography, door to needle, and door to endovascular therapy times. In adjusted models, inpatient mortality was similar between those presenting with AIS pre-COVID-19 and during COVID-19 (4.8% versus 5.2%; odds ratio, 1.05 [95% CI, 0.97–1.13]). Conclusions: Among hospitals participating in Get With The Guidelines-Stroke, patients presenting with AIS during COVID-19 received, with few exceptions, similar quality care and experienced similar risk-adjusted outcomes when compared with patients with AIS presenting pre-COVID-19. These findings demonstrate that stroke care in the United States remains robust during the COVID-19 pandemic.

Background Emerging evidence links acute kidney injury (AKI) in patients with COVID‐19 with higher mortality and respiratory morbidity, but the relationship of AKI with cardiovascular disease outcomes has not been reported in this population. We sought to evaluate associations between chronic kidney disease (CKD), AKI, and mortality and cardiovascular outcomes in patients hospitalized with COVID‐19. Methods and Results In a large multicenter registry including 8574 patients with COVID‐19 from 88 US hospitals, data were collected on baseline characteristics and serial laboratory data during index hospitalization. Primary exposure variables were CKD (categorized as no CKD, CKD, and end‐stage kidney disease) and AKI (classified into no AKI or stages 1, 2, or 3 using a modification of the Kidney Disease Improving Global Outcomes guideline definition). The primary outcome was all‐cause mortality. The key secondary outcome was major adverse cardiac events, defined as cardiovascular death, nonfatal stroke, nonfatal myocardial infarction, new‐onset nonfatal heart failure, and nonfatal cardiogenic shock. CKD and end‐stage kidney disease were not associated with mortality or major adverse cardiac events after multivariate adjustment. In contrast, AKI was significantly associated with mortality (stage 1 hazard ratio [HR], 1.72 [95% CI, 1.46–2.03]; stage 2 HR, 1.83 [95% CI, 1.52–2.20]; stage 3 HR, 1.69 [95% CI, 1.44–1.98]; versus no AKI) and major adverse cardiac events (stage 1 HR, 2.17 [95% CI, 1.74–2.71]; stage 2 HR, 2.70 [95% CI, 2.07–3.51]; stage 3 HR, 3.06 [95% CI, 2.52–3.72]; versus no AKI). Conclusions This large study demonstrates a significant association between AKI and all‐cause mortality and, for the first time, major adverse cardiovascular events in patients hospitalized with COVID‐19.

Background: In response to the public health emergency created by the coronavirus disease 2019 (COVID-19) pandemic, American Heart Association volunteers and staff aimed to rapidly develop and launch a resource for the medical and research community to expedite scientific advancement through shared learning, quality improvement, and research. In Methods and Results: Participating hospitals are enrolling consecutive hospitalized patients with active COVID-19 disease, regardless of CVD status. This hospital quality improvement program will allow participating hospitals and health systems to evaluate patient-level data including mortality rates, intensive care unit bed days, and ventilator days from individual review of electronic medical records of sequential adult patients with active COVID-19 infection. Participating sites can leverage these data for onsite, rapid quality improvement, and benchmarking versus other institutions. After 9 weeks, >130 sites have enrolled in the program and >4000 records have been abstracted in the national dataset. Additionally, the aggregate dataset will be a valuable data resource for the medical research community. Conclusions: The AHA COVID-19 CVD Registry will support greater understanding of the impact of COVID-19 on cardiovascular disease and will inform best practices for evaluation and management of patients with COVID-19.

On April 1, 2020, the confirmed global burden of COVID-19 was more than 900,000 with 46,413 deaths Despite worldwide calls for social distancing and containment, the incidence of the disease continues to increase COVID-19 is a respiratory tract infection caused by the novel coronavirus (SARS-CoV2) Preliminary analyses from the Chinese Center for Disease Control and Prevention indicated an overall case fatality rate (CFR) of 2 3%;however the CFR was higher in older adults (14 8% in those 80+/- years) and 49% of all critical cases Those with preexisting conditions (cardiovascular disease (CVD), diabetes, chronic respiratory disease, hypertension, and cancer) also had higher CRFs In the United States, 116 million adults have hypertension, 26 million US adults have diabetes mellitus, 9% have preexisting CVD and may therefore be at greater risk of complications or adverse health outcomes from COVID-19 infection

Background Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions. Methods We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR). Results There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03). Conclusions An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.

Key Points Question What factors are associated with observed trends in the in-hospital mortality rates in the United States during the first 9 months of the COVID-19 pandemic? Findings In this cohort study of 20 736 patients, in-hospital mortality rates decreased in the US between March and November 2020, even after accounting for the changing mix in patient age, sex, comorbidities, and disease severity at the time of admission. Hospital and intensive care unit length of stay and use of mechanical ventilation decreased over time, whereas the use of glucocorticoids and remdesivir increased. Meaning Changes in age, sex, comorbidities, and disease severity among patients with COVID-19 do not fully explain the decrease in the in-hospital mortality rates observed during the first 9 months of the COVID-19 pandemic., Importance In-hospital mortality rates from COVID-19 are high but appear to be decreasing for selected locations in the United States. It is not known whether this is because of changes in the characteristics of patients being admitted. Objective To describe changing in-hospital mortality rates over time after accounting for individual patient characteristics. Design, Setting, and Participants This was a retrospective cohort study of 20 736 adults with a diagnosis of COVID-19 who were included in the US American Heart Association COVID-19 Cardiovascular Disease Registry and admitted to 107 acute care hospitals in 31 states from March through November 2020. A multiple mixed-effects logistic regression was then used to estimate the odds of in-hospital death adjusted for patient age, sex, body mass index, and medical history as well as vital signs, use of supplemental oxygen, presence of pulmonary infiltrates at admission, and hospital site. Main Outcomes and Measures In-hospital death adjusted for exposures for 4 periods in 2020. Results The registry included 20 736 patients hospitalized with COVID-19 from March through November 2020 (9524 women [45.9%]; mean [SD] age, 61.2 [17.9] years); 3271 patients (15.8%) died in the hospital. Mortality rates were 19.1% in March and April, 11.9% in May and June, 11.0% in July and August, and 10.8% in September through November. Compared with March and April, the adjusted odds ratios for in-hospital death were significantly lower in May and June (odds ratio, 0.66; 95% CI, 0.58-0.76; P, This cohort study of patients included in the US American Heart Association COVID-19 Cardiovascular Disease Registry evaluates whether any changes in the in-hospital COVID-19 mortality rates during the first 9 months of the pandemic are associated with individual characteristics of patients with COVID-19.