Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Facilitating Harmonization of Variables in Framingham, MESA, ARIC, and REGARDS Studies Through a Metadata Repository.

BACKGROUND: High-quality research in cardiovascular prevention, as in other fields, requires inclusion of a broad range of data sets from different sources. Integrating and harmonizing different data sources are essential to increase generalizability, sample size, and representation of understudied populations-strengthening the evidence for the scientific questions being addressed. METHODS: Here, we describe an effort to build an open-access repository and interactive online portal for researchers to access the metadata and code harmonizing data from 4 well-known cohort studies-the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study, FHS (Framingham Heart Study), MESA (Multi-Ethnic Study of Atherosclerosis), and ARIC (Atherosclerosis Risk in Communities) study. We introduce a methodology and a framework used for preprocessing and harmonizing variables from multiple studies. RESULTS: We provide a real-case study and step-by-step guidance to demonstrate the practical utility of our repository and interactive web page. In addition to our successful development of such an open-access repository and interactive web page, this exercise in harmonizing data from multiple cohort studies has revealed several key themes. These themes include the importance of careful preprocessing and harmonization of variables, the value of creating an open-access repository to facilitate collaboration and reproducibility, and the potential for using harmonized data to address important scientific questions and disparities in cardiovascular disease research. CONCLUSIONS: By integrating and harmonizing these large-scale cohort studies, such a repository may improve the statistical power and representation of understudied cohorts, enabling development and validation of risk prediction models, identification and investigation of risk factors, and creating a platform for racial disparities research. REGISTRATION: URL: https://precision.heart.org/duke-ninds.

Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association.

The evolution of the electronic health record, combined with advances in data curation and analytic technologies, increasingly enables data sharing and harmonization. Advances in the analysis of health-related and health-proxy information have already accelerated research discoveries and improved patient care. This American Heart Association policy statement discusses how broad data sharing can be an enabling driver of progress by providing data to develop, test, and benchmark innovative methods, scalable insights, and potential new paradigms for data storage and workflow. Along with these advances come concerns about the sensitive nature of some health data, equity considerations about the involvement of historically excluded communities, and the complex intersection of laws attempting to govern behavior. Data-sharing principles are therefore necessary across a wide swath of entities, including parties who collect health information, funders, researchers, patients, legislatures, commercial companies, and regulatory departments and agencies. This policy statement outlines some of the key equity and legal background relevant to health data sharing and responsible management. It then articulates principles that will guide the American Heart Association's engagement in public policy related to data collection, sharing, and use to continue to inform its work across the research enterprise, as well as specific examples of how these principles might be applied in the policy landscape. The goal of these principles is to improve policy to support the use or reuse of health information in ways that are respectful of patients and research participants, equitable in impact in terms of both risks and potential benefits, and beneficial across broad and demographically diverse communities in the United States.

Reproductive Experiences and Cardiovascular Disease Care in Pregnancy-Capable and Postmenopausal Individuals: Insights From the American Heart Association Research Goes Red Registry.

To evaluate preconception health and adverse pregnancy outcome (APO) awareness in a large population-based registry. We examined data from the Fertility and Pregnancy Survey of the American Heart Association Research Goes Red Registry to questions regarding prenatal health care experiences, postpartum health, and awareness of the association of APOs with cardiovascular disease (CVD) risk. Among postmenopausal individuals, 37% were unaware that APOs were associated with long-term CVD risk, significantly varying by race-ethnicity. Fifty-nine percent of participants were not educated regarding this association by their providers, and 37% reported providers not assessing pregnancy history during current visits, significantly varying by race-ethnicity, income, and access to care. Only 37.1% of respondents were aware that CVD was the leading cause of maternal mortality. There is an urgent, ongoing need for more education on APOs and CVD risk, to improve the health-care experiences and postpartum health outcomes of pregnant individuals.

Longitudinal Trends in Cardiovascular Risk Factor Profiles and Complications Among Patients Hospitalized for COVID-19 Infection: Results From the American Heart Association COVID-19 Cardiovascular Disease Registry.

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 (Ptrend ≤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 Ptrend=0.006). This was driven by an increase in the diagnosis of myocardial infarction and stroke (Ptrend<0.0001 for each). The overall rate of in-hospital mortality was 14.2%, which declined over time (20.8% in March 2020 versus 10.8% in the last epoch; adjusted Ptrend<0.0001). When the analysis was restricted to July 2020 to December 2021, no temporal change in all-cause mortality was seen (adjusted Ptrend=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.

Reproductive Experiences and Cardiovascular Disease Care in Pregnancy Capable and Post-Menopausal Individuals: Insights from the American Heart Association Research Goes Red Registry.

Background: Information on reproductive experiences and awareness of adverse pregnancy outcomes (APOs) and cardiovascular disease (CVD) risk among pregnancy-capable and post-menopausal individuals has not been well described. We sought to evaluate preconception health and APO awareness in a large population-based registry. Methods: Data from the Fertility and Pregnancy Survey of the American Heart Association Research Goes Red Registry (AHA-RGR) were used. Responses to questions pertaining to prenatal health care experiences, postpartum health, and awareness of the association of APOs with CVD risk were used. We summarized responses using proportions for the overall sample and by stratifications, and we tested differences using the Chi-squared test. Results: Of 4,651individuals in the AHA-RGR registry, 3,176 were of reproductive age, and 1,475 were postmenopausal. Among postmenopausal individuals, 37% were unaware that APOs were associated with long-term CVD risk. This varied by different racial/ethnic groups (non-Hispanic White: 38%, non-Hispanic Black: 29%, Asian: 18%, Hispanic: 41%, Other: 46%; P = 0.03). Fifty-nine percent of the participants were not educated regarding the association of APOs with long-term CVD risk by their providers. Thirty percent of the participants reported that their providers did not assess pregnancy history during current visits; this varied by race-ethnicity ( P = 0.02), income ( P = 0.01), and access to care ( P = 0.02). Only 37.1% of the respondents were aware that CVD was the leading cause of maternal mortality. Conclusions: Considerable knowledge gaps exist in the association of APOs with CVD risk, with disparities by race/ethnicity, and most patients are not educated on this association by their health care professionals. There is an urgent and ongoing need for more education on APOs and CVD risk, to improve the health-care experiences and postpartum health outcomes of pregnant individuals.

Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups.

Importance: Stroke is the fifth-highest cause of death in the US and a leading cause of serious long-term disability with particularly high risk in Black individuals. Quality risk prediction algorithms, free of bias, are key for comprehensive prevention strategies. Objective: To compare the performance of stroke-specific algorithms with pooled cohort equations developed for atherosclerotic cardiovascular disease for the prediction of new-onset stroke across different subgroups (race, sex, and age) and to determine the added value of novel machine learning techniques. Design, Setting, and Participants: Retrospective cohort study on combined and harmonized data from Black and White participants of the Framingham Offspring, Atherosclerosis Risk in Communities (ARIC), Multi-Ethnic Study for Atherosclerosis (MESA), and Reasons for Geographical and Racial Differences in Stroke (REGARDS) studies (1983-2019) conducted in the US. The 62 482 participants included at baseline were at least 45 years of age and free of stroke or transient ischemic attack. Exposures: Published stroke-specific algorithms from Framingham and REGARDS (based on self-reported risk factors) as well as pooled cohort equations for atherosclerotic cardiovascular disease plus 2 newly developed machine learning algorithms. Main Outcomes and Measures: Models were designed to estimate the 10-year risk of new-onset stroke (ischemic or hemorrhagic). Discrimination concordance index (C index) and calibration ratios of expected vs observed event rates were assessed at 10 years. Analyses were conducted by race, sex, and age groups. Results: The combined study sample included 62 482 participants (median age, 61 years, 54% women, and 29% Black individuals). Discrimination C indexes were not significantly different for the 2 stroke-specific models (Framingham stroke, 0.72; 95% CI, 0.72-073; REGARDS self-report, 0.73; 95% CI, 0.72-0.74) vs the pooled cohort equations (0.72; 95% CI, 0.71-0.73): differences 0.01 or less (P values >.05) in the combined sample. Significant differences in discrimination were observed by race: the C indexes were 0.76 for all 3 models in White vs 0.69 in Black women (all P values <.001) and between 0.71 and 0.72 in White men and between 0.64 and 0.66 in Black men (all P values ≤.001). When stratified by age, model discrimination was better for younger (<60 years) vs older (≥60 years) adults for both Black and White individuals. The ratios of observed to expected 10-year stroke rates were closest to 1 for the REGARDS self-report model (1.05; 95% CI, 1.00-1.09) and indicated risk overestimation for Framingham stroke (0.86; 95% CI, 0.82-0.89) and pooled cohort equations (0.74; 95% CI, 0.71-0.77). Performance did not significantly improve when novel machine learning algorithms were applied. Conclusions and Relevance: In this analysis of Black and White individuals without stroke or transient ischemic attack among 4 US cohorts, existing stroke-specific risk prediction models and novel machine learning techniques did not significantly improve discriminative accuracy for new-onset stroke compared with the pooled cohort equations, and the REGARDS self-report model had the best calibration. All algorithms exhibited worse discrimination in Black individuals than in White individuals, indicating the need to expand the pool of risk factors and improve modeling techniques to address observed racial disparities and improve model performance.

Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure.

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 < .001) but not in non-Black patients. Conclusions and Relevance: ML models for HF mortality demonstrated superior performance to the traditional and rederived logistic regressions models using race as a covariate. The addition of SDOH parameters improved the prognostic utility of prediction models in Black patients but not non-Black patients in the GWTG-HF registry.

Call to Action for Cardiovascular Disease in Women: Epidemiology, Awareness, Access, and Delivery of Equitable Health Care: A Presidential Advisory From the American Heart Association.

Addressing the pervasive gaps in knowledge and care delivery to reduce sex-based disparities and achieve equity is fundamental to the American Heart Association's commitment to advancing cardiovascular health for all by 2024. This presidential advisory serves as a call to action for the American Heart Association and other stakeholders around the globe to identify and remove barriers to health care access and quality for women. A concise and current summary of existing data across the areas of risk and prevention, access and delivery of equitable care, and awareness and education provides a framework to consider knowledge gaps and research needs critical toward achieving significant progress for the health and well-being of all women.

Research Goes Red: Early Experience With a Participant-Centric Registry.

RATIONALE: Cardiovascular disease remains the leading cause of death in women. To address its determinants including persisting cardiovascular risk factors amplified by sex and race inequities, novel personalized approaches are needed grounded in the engagement of participants in research and prevention. OBJECTIVE: To report on a participant-centric and personalized dynamic registry designed to address persistent gaps in understanding and managing cardiovascular disease in women. METHODS AND RESULTS: The American Heart Association and Verily launched the Research Goes Red registry (RGR) in 2019, as an online research platform available to consenting individuals over the age of 18 years in the United States. RGR aims to bring participants and researchers together to expand knowledge by collecting data and providing an open-source longitudinal dynamic registry for conducting research studies. As of July 2021, 15 350 individuals have engaged with RGR. Mean age of participants was 48.0 48.0±0.2 years with a majority identifying as female and either non-Hispanic White (75.7%) or Black (10.5%). In addition to 6 targeted health surveys, RGR has deployed 2 American Heart Association-sponsored prospective clinical studies based on participants' areas of interest. The first study focuses on perimenopausal weight gain, developed in response to a health concerns survey. The second study is designed to test the use of social media campaigns to increase awareness and participation in cardiovascular disease research among underrepresented millennial women. CONCLUSIONS: RGR is a novel online participant-centric platform that has successfully engaged women and provided critical data on women's heart health to guide research. Priorities for the growth of RGR are centered on increasing reach and diversity of participants, and engaging researchers to work within their communities to leverage the platform to address knowledge gaps and improve women's health.

Community-Level Economic Distress, Race, and Risk of Adverse Outcomes After Heart Failure Hospitalization Among Medicare Beneficiaries.

BACKGROUND: Socioeconomic disadvantage is a strong determinant of adverse outcomes in patients with heart failure. However, the contribution of community-level economic distress to adverse outcomes in heart failure may differ across races and ethnicities. METHODS: Patients of self-reported Black, White, and Hispanic race and ethnicity hospitalized with heart failure between 2014 and 2019 were identified from the Medicare MedPAR Part A 100% Files. We used patient-level residential ZIP code to quantify community-level economic distress on the basis of the Distressed Community Index (quintile 5: economically distressed versus quintiles 1-4: nondistressed). The association of continuous and categorical measures (distressed versus nondistressed) of Distressed Community Index with 30-day, 6-month, and 1-year risk-adjusted mortality, readmission burden, and home time were assessed separately by race and ethnicity groups. RESULTS: The study included 1 611 586 White (13.2% economically distressed), 205 840 Black (50.6% economically distressed), and 89 199 Hispanic (27.3% economically distressed) patients. Among White patients, living in economically distressed (versus nondistressed) communities was significantly associated with a higher risk of adverse outcomes at 30-day and 1-year follow-up. Among Black and Hispanic patients, the risk of adverse outcomes associated with living in distressed versus nondistressed communities was not meaningfully different at 30 days and became more prominent by 1-year follow-up. Similarly, in the restricted cubic spline analysis, a stronger and more graded association was observed between Distressed Community Index score and risk of adverse outcomes in White patients (versus Black and Hispanic patients). Furthermore, the association between community-level economic distress and risk of adverse outcomes for Black patients differed in rural versus urban areas. Living in economically distressed communities was significantly associated with a higher risk of mortality and lower home time at 1-year follow-up in rural areas but not urban areas. CONCLUSIONS: The association between community-level economic distress and risk of adverse outcomes differs across race and ethnic groups, with a stronger association noted in White patients at short- and long-term follow-up. Among Black patients, the association of community-level economic distress with a higher risk of adverse outcomes is less evident in the short term and is more robust and significant in the long-term follow-up and rural areas.

Genetic Variants Associated With Unexplained Sudden Cardiac Death in Adult White and African American Individuals.

Importance: Unexplained sudden cardiac death (SCD) describes SCD with no cause identified. Genetic testing helps to diagnose inherited cardiac diseases in unexplained SCD; however, the associations between pathogenic or likely pathogenic (P/LP) variants of inherited cardiomyopathies (CMs) and arrhythmia syndromes and the risk of unexplained SCD in both White and African American adults living the United States has never been systematically examined. Objective: To investigate cases of unexplained SCD to determine the frequency of P/LP genetic variants of inherited CMs and arrhythmia syndromes. Design, Setting, and Participants: This genetic association study included 683 African American and White adults who died of unexplained SCD and were included in an autopsy registry. Overall, 413 individuals had DNA of acceptable quality for genetic sequencing. Data were collected from January 1995 to December 2015. A total of 30 CM genes and 38 arrhythmia genes were sequenced, and variants in these genes, curated as P/LP, were examined to study their frequency. Data analysis was performed from June 2018 to March 2021. Main Outcomes and Measures: The frequency of P/LP variants for CM or arrhythmia in individuals with unexplained SCD. Results: The median (interquartile range) age at death of the 413 included individuals was 41 (29-48) years, 259 (62.7%) were men, and 208 (50.4%) were African American adults. A total of 76 patients (18.4%) with unexplained SCD carried variants considered P/LP for CM and arrhythmia genes. In total, 52 patients (12.6%) had 49 P/LP variants for CM, 22 (5.3%) carried 23 P/LP variants for arrhythmia, and 2 (0.5%) had P/LP variants for both CM and arrhythmia. Overall, 41 P/LP variants for hypertrophic CM were found in 45 patients (10.9%), 9 P/LP variants for dilated CM were found in 11 patients (2.7%), and 10 P/LP variants for long QT syndrome were found in 11 patients (2.7%). No significant difference was found in clinical and heart characteristics between individuals with or without P/LP variants. African American and White patients were equally likely to harbor P/LP variants. Conclusions and Relevance: In this large genetic association study of community cases of unexplained SCD, nearly 20% of patients carried P/LP variants, suggesting that genetics may contribute to a significant number of cases of unexplained SCD. Our findings regarding both the association of unexplained SCD with CM genes and race-specific genetic variants suggest new avenues of study for this poorly understood entity.

Trends in Patient Characteristics and COVID-19 In-Hospital Mortality in the United States During 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 < .001), July and August (odds ratio, 0.58; 95% CI, 0.49-0.69; P < .001), and September through November (odds ratio, 0.59; 95% CI, 0.47-0.73). Conclusions and Relevance: In this cohort study, high rates of in-hospital COVID-19 mortality among registry patients in March and April 2020 decreased by more than one-third by June and remained near that rate through November. This difference in mortality rates between the months of March and April and later months persisted even after adjusting for age, sex, medical history, and COVID-19 disease severity and did not appear to be associated with changes in the characteristics of patients being admitted.

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment and patient adherence to prevention guidelines. We investigated the diet domain in FHS (Framingham Heart Study), CHS (Cardiovascular Heart Study), and the ARIC study (Atherosclerosis Risk in Communities) to identify potential lifestyle and behavioral factors associated with coronary heart disease, HF, and stroke. METHODS: We used machine learning feature selection based on random forest analysis to identify potential risk factors associated with coronary heart disease, stroke, and HF in FHS. We evaluated the significance of selected variables using univariable and multivariable Cox proportional hazards analysis adjusted for known cardiovascular risks. Findings from FHS were then validated using CHS and ARIC. RESULTS: We identified multiple dietary and behavioral risk factors for cardiovascular disease outcomes including marital status, red meat consumption, whole milk consumption, and coffee consumption. Among these dietary variables, increasing coffee consumption was associated with decreasing long-term risk of HF congruently in FHS, ARIC, and CHS. CONCLUSIONS: Higher coffee intake was found to be associated with reduced risk of HF in all three studies. Further study is warranted to better define the role, possible causality, and potential mechanism of coffee consumption as a potential modifiable risk factor for HF.

American Heart Association COVID-19 CVD Registry Powered by Get With The Guidelines.

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 <4 weeks after it was first announced on April 3, 2020, AHA's COVID-19 CVD Registry powered by Get With The Guidelines received its first clinical records. 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.

Subchondroplasty of the Foot: Two Case Reports.

Early avascular necrosis of metatarsal heads and cuboid injuries are uncommon conditions encountered by foot and ankle specialists. Treatment options are limited and typically include long periods of offloading or non-weightbearing. There is limited published information on alternative treatment approaches for such pathologies when conservative therapies fail. Presented are 2 patient cases treated with a percutaneous calcium phosphate injection after failure of standard therapy, persistent pain, and bone marrow edema in the foot.