The American Heart Association is a leading entity in the healthcare industry, dedicated to promoting cardiovascular health and studying the root causes of heart disease. With a rich history spanning over a century, the organization has made significant contributions to the field of cardiology, publishing groundbreaking research and providing vital health information to the public.

The American Heart Association's online presence is a treasure trove of information, offering insights into the organization's research and initiatives. The data extracted from their website provides valuable details about heart health, including statistics on heart disease, tips for healthy living, and stories of patients who have benefited from the organization's work. From researchers to the general public, anyone interested in learning more about heart health and the American Heart Association's efforts can find valuable information on their website.

What is heart disease?

The term “heart disease” refers to several types of heart conditions. The most common type of heart disease in the United States is coronary artery disease (CAD), which affects the blood flow to the heart. Decreased blood flow can cause a heart attack. What are the symptoms of heart disease?

Sometimes heart disease may be “silent” and not diagnosed until a person experiences signs or symptoms of a heart attack, heart failure, or an arrhythmia. When these events happen, symptoms may include1

Heart attack: Chest pain or discomfort, upper back or neck pain, indigestion, heartburn, nausea or vomiting, extreme fatigue, upper body discomfort, dizziness, and shortness of breath.
Arrhythmia: Fluttering feelings in the chest (palpitations).
Heart failure: Shortness of breath, fatigue, or swelling of the feet, ankles, legs, abdomen, or neck veins.

Learn the Facts About Heart Disease

About 697,000 people in the United States died from heart disease in 2020—that’s 1 in every 5 deaths.1,2

Learn more facts. What are the risk factors for heart disease?

High blood pressure, high blood cholesterol, and smoking are key risk factors for heart disease. About half of people in the United States (47%) have at least one of these three risk factors.2 Several other medical conditions and lifestyle choices can also put people at a higher risk for heart disease, including

Diabetes
Overweight and obesity
Unhealthy diet
Physical inactivity
Excessive alcohol use

Learn about how heart disease and mental health disorders are related.

Learn more about heart disease, heart attack, and related conditions:

Coronary Artery Disease
Heart Attack
Men and Heart Disease
Women and Heart Disease
Other Related Conditions

What is cardiac rehabilitation?

Cardiac rehabilitation (rehab) is an important program for anyone recovering from a heart attack, heart failure, or some types of heart surgery. Cardiac rehab is a supervised program that includes

Physical activity
Education about healthy living, including healthy eating, taking medicine as prescribed, and ways to help you quit smoking
Counseling to find ways to relieve stress and improve mental health

A team of people may help you through cardiac rehab, including your health care team, exercise and nutrition specialists, physical therapists, and counselors or mental health professionals. Heart Disease Quiz

Test your knowledge of heart disease!

CDC’s Public Health Efforts Related to Heart Disease

State Public Health Actions to Prevent and Control Chronic Diseases
Million Hearts®
WISEWOMAN

More Information

American Heart Association
National Heart, Lung, and Blood Institute

References

Centers for Disease Control and Prevention, National Center for Health Statistics. About Multiple Cause of Death, 1999–2020. CDC WONDER Online Database website. Atlanta, GA: Centers for Disease Control and Prevention; 2022. Accessed February 21, 2022.
Tsao CW, Aday AW, Almarzooq ZI, Beaton AZ, Bittencourt MS, Boehme AK, et al. Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association. Circulation. 2022;145(8):e153–e639.
Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart disease and stroke statistics—2021 update: a report from the American Heart Association. Circulation. 2021;143:e254–e743.

Comprehensive dataset containing 135 verified American Heart Association locations in United States with complete contact information, ratings, reviews, and location data.

Heart Disease Risk Prediction Dataset

Overview

This synthetic dataset is designed to predict the risk of heart disease based on a combination of symptoms, lifestyle factors, and medical history. Each row in the dataset represents a patient, with binary (Yes/No) indicators for symptoms and risk factors, along with a computed risk label indicating whether the patient is at high or low risk of developing heart disease.

The dataset contains 70,000 samples, making it suitable for training machine learning models for classification tasks. The goal is to provide researchers, data scientists, and healthcare professionals with a clean and structured dataset to explore predictive modeling for cardiovascular health.

This dataset is a side project of EarlyMed, developed by students of Vellore Institute of Technology (VIT-AP). EarlyMed aims to leverage data science and machine learning for early detection and prevention of chronic diseases.

Dataset Features

Input Features

Symptoms (Binary - Yes/No)

1. Chest Pain (chest_pain): Presence of chest pain, a common symptom of heart disease.
2. Shortness of Breath (shortness_of_breath): Difficulty breathing, often associated with heart conditions.
3. Unexplained Fatigue (fatigue): Persistent tiredness without an obvious cause.
4. Palpitations (palpitations): Irregular or rapid heartbeat.
5. Dizziness/Fainting (dizziness): Episodes of lightheadedness or fainting.
6. Swelling in Legs/Ankles (swelling): Swelling due to fluid retention, often linked to heart failure.
7. Pain in Arm/Jaw/Neck/Back (radiating_pain): Radiating pain, a hallmark of angina or heart attacks.
8. Cold Sweats & Nausea (cold_sweats): Symptoms commonly associated with acute cardiac events.

Risk Factors (Binary - Yes/No or Continuous)

1. Age (age): Patient's age in years (continuous variable).
2. High Blood Pressure (hypertension): History of hypertension (Yes/No).
3. High Cholesterol (cholesterol_high): Elevated cholesterol levels (Yes/No).
4. Diabetes (diabetes): Diagnosis of diabetes (Yes/No).
5. Smoking History (smoker): Whether the patient is a smoker (Yes/No).
6. Obesity (obesity): Obesity status (Yes/No).
7. Family History of Heart Disease (family_history): Family history of cardiovascular conditions (Yes/No).

Output Label

• Heart Disease Risk (risk_label): Binary label indicating the risk of heart disease:
  • 0: Low risk
  • 1: High risk

Data Generation Process

This dataset was synthetically generated using Python libraries such as numpy and pandas. The generation process ensured a balanced distribution of high-risk and low-risk cases while maintaining realistic correlations between features. For example: - Patients with multiple risk factors (e.g., smoking, hypertension, and diabetes) were more likely to be labeled as high risk. - Symptom patterns were modeled after clinical guidelines and research studies on heart disease.

Sources of Inspiration

The design of this dataset was inspired by the following resources:

Books

• "Harrison's Principles of Internal Medicine" by J. Larry Jameson et al.: A comprehensive resource on cardiovascular diseases and their symptoms.
• "Mayo Clinic Cardiology" by Joseph G. Murphy et al.: Provides insights into heart disease risk factors and diagnostic criteria.

Research Papers

• Framingham Heart Study: A landmark study identifying key risk factors for cardiovascular disease.
• American Heart Association (AHA) Guidelines: Recommendations for diagnosing and managing heart disease.

Existing Datasets

• UCI Heart Disease Dataset: A widely used dataset for heart disease prediction.
• Kaggle’s Heart Disease datasets: Various datasets contributed by the community.

Clinical Guidelines

• Centers for Disease Control and Prevention (CDC): Information on heart disease symptoms and risk factors.
• World Health Organization (WHO): Global statistics and risk factor analysis for cardiovascular diseases.

Applications

This dataset can be used for a variety of purposes:

1. Machine Learning Research:

   • Train classification models (e.g., Logistic Regression, Random Forest, XGBoost) to predict heart disease risk.
   • Experiment with feature engineering, model tuning, and evaluation metrics like Accuracy, Precision, Recall, and ROC-AUC.

2. Healthcare Analytics:

   • Identify key risk factors contributing to heart disease.
   • Develop decision support systems for early detection of cardiovascular risks.

3. Educational Purposes:

   • Teach students and practitioners about predictive modeling in healthcare.
   • Demonstrate the importance of feature selection...

Comprehensive dataset containing 8 verified American Heart Association locations in Florida, United States with complete contact information, ratings, reviews, and location data.

Statistical comparison of the lumped parameter variables for each American Heart Association/American College of Cardiology heart failure stage model, using 2-tailed Student's T-test.

Comprehensive dataset containing 8 verified American Heart Association locations in Texas, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 3 verified American Heart Association locations in Massachusetts, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 3 verified American Heart Association locations in Wisconsin, United States with complete contact information, ratings, reviews, and location data.

Abstract Background There are substantial opportunities to improve the quality of cardiovascular care in developing countries through the implementation of a quality program. Objective To evaluate the effect of a Best Practice in Cardiology (BPC) program on performance measures and patient outcomes related to heart failure, atrial fibrillation and acute coronary syndromes in a subset of Brazilian public hospitals. Methods The Boas Práticas em Cardiologia (BPC) program was adapted from the American Heart Association’s (AHA) Get With The Guidelines (GWTG) Program for use in Brazil. The program is being started simultaneously in three care domains (acute coronary syndrome, atrial fibrillation and heart failure), which is an approach that has never been tested within the GWTG. There are six axes of interventions borrowed from knowledge translation literature that will address local barriers identified through structured interviews and regular audit and feedback meetings. The intervention is planned to include at least 10 hospitals and 1,500 patients per heart condition. The primary endpoint includes the rates of overall adherence to care measures recommended by the guidelines. Secondary endpoints include the effect of the program on length of stay, overall and specific mortality, readmission rates, quality of life, patients’ health perception and patients’ adherence to prescribed interventions. Results It is expected that participating hospitals will improve and sustain their overall adherence rates to evidence-based recommendations and patient outcomes. This is the first such cardiovascular quality improvement (QI) program in South America and will provide important information on how successful programs from developed countries like the United States can be adapted to meet the needs of countries with developing economies like Brazil. Also, a successful program will give valuable information for the development of QI programs in other developing countries. Conclusions This real-world study provides information for assessing and increasing adherence to cardiology guidelines in Brazil, as well as improvements in care processes. (Arq Bras Cardiol. 2019; xx(x):xxx-xxx)

The American Heart Association/American College of Cardiology Heart Failure classification From Jessup et al. (2009) [4].

An AED is an electronic device that analyzes the heart’s rhythm and tells the user to deliver a defibrillation shock when it is required. An AED is portable and easy to use. Anyone who has successfully completed a nationally-recognized CPR/AED course can use an AED. The AED program will train the public to recognize early warning signs of cardiac arrest, perform cardio-pulmonary resuscitation (CPR), and promote the placement of AEDs in places where people work, exercise, and congregate. DC Fire/EMS partners in this effort are the American Heart Association, the DC Chamber of Commerce, the National Capital Chapter of the American Red Cross and Medtronic Physio-Control. Together we are promoting healthy eating and proper exercise, public education programs encouraging people to learn CPR and how to operate an AED, and increasing the availability of AEDs in the District. For further information email: dcpad.program@dc.gov.The Registry serves as a repository of important data about AEDs and related AED program personnel and infrastructure, including information about:Individuals responsible for managing AED program sites and inspecting AEDsThe identity of AEDs by the manufacturer, make/model and serial numberThe whereabouts of AEDs by physical address and specific placement locationSpecific days and times of AED availabilityThe type and expiration dates of AED batteries and AED electrodes

Comprehensive dataset containing 1 verified American Heart Association locations in Nebraska, United States with complete contact information, ratings, reviews, and location data.

BackgroundThe ability to apply standard and interoperable solutions for implementing and managing medical registries as well as aggregate, reproduce, and access data sets from legacy formats and platforms to advanced standard formats and operating systems are crucial for both clinical healthcare and biomedical research settings.PurposeOur study describes a reproducible, highly scalable, standard framework for a device registry implementation addressing both local data quality components and global linking problems.Methods and ResultsWe developed a device registry framework involving the following steps: (1) Data standards definition and representation of the research workflow, (2) Development of electronic case report forms using REDCap (Research Electronic Data Capture), (3) Data collection according to the clinical research workflow and, (4) Data augmentation by enriching the registry database with local electronic health records, governmental database and linked open data collections, (5) Data quality control and (6) Data dissemination through the registry Web site. Our registry adopted all applicable standardized data elements proposed by American College Cardiology / American Heart Association Clinical Data Standards, as well as variables derived from cardiac devices randomized trials and Clinical Data Interchange Standards Consortium. Local interoperability was performed between REDCap and data derived from Electronic Health Record system. The original data set was also augmented by incorporating the reimbursed values paid by the Brazilian government during a hospitalization for pacemaker implantation. By linking our registry to the open data collection repository Linked Clinical Trials (LinkedCT) we found 130 clinical trials which are potentially correlated with our pacemaker registry.ConclusionThis study demonstrates how standard and reproducible solutions can be applied in the implementation of medical registries to constitute a re-usable framework. Such approach has the potential to facilitate data integration between healthcare and research settings, also being a useful framework to be used in other biomedical registries.

We present a proteomics dataset relying on nanoLC-MS/MS-based shotgun proteomics and using data-independent acquisitions on an Orbitrap hybrid instrument to enable comparisons of protein levels in human carotid endarterectomies with atheromatous plaques, complicated lesions, and healthy vasculature. Study approval, as well as collection and selection of study specimens were described in our previous publication (https://doi.org/https://doi.org/10.1089/ars.2020.8234). Briefly, atherosclerotic samples were obtained from patients by carotid endarterectomies, and healthy carotid arteries (H) were harvested from cadavers of suicide or fatal trauma victims without cardiovascular diseases. All samples were classified by a pathologist according to guidelines by the American Heart Association as atheromatous (A), having complicated lesions (C) and were identical with those having met specified study criteria and included in high-throughput global transcriptomic analyses by RNA-seq earlier. Specifically, inclusion criteria were as follows: samples received within 1 h after endarterectomy, no RNA and protein degradation occurred, blood clot in the artery was absent, and collection of samples was done appropriately. This is a follow up proteomics dataset that used the same samples and the same sample processing protocol but relied on data-dependent acquisition (dataset identifier PXD038922, doi:10.6019/PXD038922). For further comparison, RNA-seq data are also available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under the accession number PRJNA594843.

ACC/AHA =  American College of Cardiology/American Heart Association; ATS =  American Thoracic Society; CDISC =  Clinical Data Interchange Standards Consortium; NCI =  National Cancer Institute; SF-36 =  Short-form 36 questionnaire.

The Charleston Heart Study (CHS) is a prospective cohort study of 2,283 subjects (1,394 whites, 889 blacks) in which risk factors of coronary disease have been examined for the past 43 years. The CHS began enrolling a random selection of community residents who in 1960 were 35 years of age and older ����?? including men and women, black and white. A unique feature of this cohort is the fact that 102 high socio-economic status (SES) black men were purposefully included. The primary hypothesis of the original study was to investigate racial differences in the manifestation and risk factors for coronary disease. Over the ensuing 40+ years, a variety of outcome measurements were incorporated into the re-examination of the participants, including psychosocial, behavioral, aging and functional measures. Subjects were initially interviewed and examined in 1960 and 1963. Subsequent interviews and examinations took place during the following time periods: 1974-1975, 1984-1985, 1987-1989, and 1990-1991. During the most recent questionnaire (1990-1991), the following topics were examined: general health, smoking, functional disability, physical disability, cardiovascular health, sexual dysfunction, cognitive disability, depression, coffee consumption, medication history, medical history, nutrition, and body image. In addition, serum samples and blood pressure measurements were taken, and a physical exam was performed by a physician. A search of the National Death Index was completed through the year 2000, matching individuals with date and cause of death. Vital status of the CHS study participants through 12-31-2000 is presented below. Dead * White Men 539 (82.5%) * White Women 500 (67.5%) * Black Men 281 (84.4%) * High SES Black Men 59 (57.8%) * Black Women 343 (75.6%) Data Availability: Datasets are stored in the National Archive of Computerized Data on Aging (NACDA) in the ICPSR as Study No. 4050. Data are also available from the Medical University of South Carolina Library; contact a PI, Paul J. Nietert, nieterpj (at) musc.edu for further information. * Dates of Study: 1960-2000 * Study Features: Longitudinal, Minority Oversamples, Anthropometric Measures * Sample Size: 1960: 2,283 (baseline) Link ICPSR, http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04050

Recent studies have suggested that high levels of social support can encourage better health behaviours and result in improved cardiovascular health. In this study we evaluated the association between social support and ideal cardiovascular health among urban Jamaicans. We conducted a cross-sectional study among urban residents in Jamaica’s south-east health region. Socio-demographic data and information on cigarette smoking, physical activity, dietary practices, blood pressure, body size, cholesterol, and glucose, were collected by trained personnel. The outcome variable, ideal cardiovascular health, was defined as having optimal levels of ≥5 of these characteristics (ICH-5) according to the American Heart Association definitions. Social support exposure variables included number of friends (network size), number of friends willing to provide loans (instrumental support) and number of friends providing advice (informational support). Principal component analysis was used to create a social support score using these three variables. Survey-weighted logistic regression models were used to evaluate the association between ICH-5 and social support score. Analyses included 841 participants (279 males, 562 females) with mean age of 47.6 ± 18.42 years. ICH-5 prevalence was 26.6% (95%CI 22.3, 31.0) with no significant sex difference (male 27.5%, female 25.7%). In sex-specific, multivariable logistic regression models, social support score, was inversely associated with ICH-5 among males (OR 0.67 [95%CI 0.51, 0.89], p = 0.006) but directly associated among females (OR 1.26 [95%CI 1.04, 1.53], p = 0.020) after adjusting for age and community SES. Living in poorer communities was also significantly associated with higher odds of ICH-5 among males, while living communities with high property value was associated with higher odds of ICH among females. In this study, higher level of social support was associated with better cardiovascular health among women, but poorer cardiovascular health among men in urban Jamaica. Further research should explore these associations and identify appropriate interventions to promote cardiovascular health.

BackgroundThe association between healthy lifestyle and American Heart Association (AHA) Life's Essential 8 (LE8) score and apparent treatment-resistant hypertension(aTRH)remains uncertain. We aimed to explore the association between healthy lifestyle and higher LE8 score and apparent treatment-resistant hypertension in the general population.MethodsUsing NHANES data from 2005 to 2018, we included and analyzed information on 7,474 participants eligible for this study. The association between LE8 and aTRH was explored using logistic regression models, and the association between LE8 and antihypertension drugs uncontrolled hypertension was further explored using logistic regression models.ResultsParticipants with higher LE8 scores tended to be non-Hispanic white and married or living with a partner; have low income and higher education; and be without Chronic kidney disease (CKD)(all p-values

SID: Italian Diabetes Society. ESC/EASD: European Society of Cardiology and European Association for the Study of Diabetes. ESH/ESC: European Society of Hypertension and European Society of Cardiology. ACC/AHA: American Cardiology Association and American Heart Association. A symbol * means that the recommendation only applies when the condition is at a high level of severity. Diagnostic tests are recommended once per year, except HbA1c for T2DM which is recommended twice a year.