Social vulnerability index (SVI) estimates the vulnerability of communities to disasters, encompassing 4 separate domains (socioeconomic, household composition and disability, minority status and language, and housing and transportation). The SVI has been linked with risk and outcomes of cardiovascular disease (CVD). This scoping review explored the literature between the SVI and CVD continuum, with a goal to identify gaps in understanding the impact of the SVI on CVD and to elucidate future research opportunities. We systematically searched 7 databases from inception to May 19, 2023, for articles that explored the relationship between the SVI and CVD care continuum, including prevention, diagnosis and prevalence, treatment, and health outcomes. Extracted data included SVI ranking type, populations, outcomes, and quality of studies. Twelve studies evaluated the impact of SVI on the CVD continuum. Five studies explored mortality outcomes, 3 studies explored CVD risk factor prevalence, 4 studies explored CVD prevalence, 1 study explored access to health care in those with CVD, 1 study explored the use of cardiac rehabilitation services, and 1 study explored heart failure readmission rates, all of which revealed statistically significant associations with SVI. All studies included the SVI aggregate percentile ranking, while 5 studies focused on individual thematic components. We identified gaps in understanding the SVI's impact on CVD care continuum, particularly regarding CVD prevention and early detection. This review provides a comprehensive understanding of the SVI's application in assessing various aspects of the CVD care continuum and highlights potential avenues for future research.

Background: Hyperlipidemia is a major cardiovascular disease (CVD) risk factor, but there are limited data on its mortality trends in CVD over time. We assessed annual hyperlipidemia‐related CVD mortality trends in the United States, including the COVID‐19 pandemic's impact. Methods and Results: Mortality data were obtained from the Centers for Disease Control and Prevention repository between 1999 and 2020 among patients ≥15 years old, using International Classification of Diseases, Tenth Revision ( ICD‐10 ) codes for hyperlipidemia (E78.0–E78.5) and CVD (I00–I99). Age‐adjusted mortality rates (AAMRs) per 1 000 000 population were standardized to the 2000 US population. Log‐linear regression models were used to evaluate mortality shifts. Average annual percentage change from 1999 to 2019 was used to project 2020 AAMRs, estimating pandemic‐attributed excess deaths. From 1999 to 2020, 483 155 hyperlipidemia‐related CVD deaths occurred. Despite a general CVD mortality decline, hyperlipidemia‐related CVD AAMRs rose from 36.33 in 1999 to 99.77 in 2019. Ischemic heart diseases (AAMR 49.39) were the leading cause, whereas hypertension had the highest mortality increase (average annual percentage change +10.23%). Mortality rates were higher in men (AAMR 104.87) and non‐Hispanic (AAMR 82.49), and rural populations (AAMR 89.98). Highest mortality was observed in Black populations (AAMR 84.35), those ≥75 years old (AAMR 646.45), and Western US regions (AAMR 96.88). During the first pandemic year, deaths exceeded projections by 10.55%, with notable increases among ages 35 to 75 (14.23%), Hispanic (17.96%), Black (14.82%), and urban (11.68%) groups. Conclusions: Hyperlipidemia‐related CVD mortality has risen over the past 2 decades, further heightened by the COVID‐19 pandemic, with higher impact on men, Black Americans, the older population, and rural residents. Further study is needed to understand contributing factors and mitigate disparities.

Cardiovascular disease is a leading cause of morbidity and mortality, largely dominated by ischemic heart diseases (IHDs). Social determinants of health, including geographic, psychosocial, and socioeconomic factors, influence the development of IHD. This study aimed to evaluate yearly trends and disparities in IHD mortality and to assess the impact of social vulnerability. We performed cross-sectional analyses using United States county-level mortality data and social vulnerability index (SVI) obtained from the Centers for Disease Control and Prevention databases. Age-adjusted mortality rates (AAMRs) per 100,000 population were compared between aggregated U.S. county groups, stratified by demographic information and SVI quartiles. Log-linear regression models were used to identify mortality trends from 1999 to 2020, with inflection points determined through the Monte-Carlo permutation test. We identified a total of 9,108,644 deaths related to IHD between 1999 and 2020. Overall AAMR decreased from 194.6 in 1999 to 91.8 in 2020. Males (AAMR: 161.51) and Black (AAMR: 141.49) populations exhibited higher AAMR compared to females (AAMR: 93.16) and White (AAMR: 123.34) populations, respectively. Disproportionate AAMRs were observed among nonmetropolitan (AAMR: 136.17) and Northeastern (AAMR: 132.96) regions. Counties with a higher SVI experienced a greater AAMR, with a cumulative excess of 20.91 deaths per 100,000 person-years associated with increased social vulnerability. Despite a decline in IHD mortality from 1999 to 2020, disparities persisted among racial, gender, and geographic subgroups. A higher SVI was linked to increased IHD mortality. Policy interventions should prioritize integrating the SVI into health care delivery systems to effectively address these disparities.

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.