Validation of the Framingham Heart Study and CHARGE-AF Risk Scores for Atrial Fibrillation in Hispanics, African-Americans, and Non-Hispanic Whites

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

The mC2HEST Score for Incident Atrial Fibrillation: MESA (Multi-Ethnic Study of Atherosclerosis)

Background

Assessing individuals' risk of developing incident atrial fibrillation (AF) is important for making preventive and screening strategies.

Objectives

The performance of the mC2HEST score for predicting incident AF has scarcely been evaluated, especially in a multi-ethnic population.

Methods

Participants from the MESA (Multi-Ethnic Study of Atherosclerosis were enrolled in the present study, which involved population of different ethnicities (Caucasian, African-American, Chinese-American, and Hispanic) aged between 45 and 84 from 6 communities in the United States. The discriminative and calibration performance of the mC2HEST score was compared with other risk models.

Results

A total of 4,524 subjects (mean age 60.2 ± 9.5 years; 53.0% female) were included; 565 (mean age 67.0 ± 7.9 years; 46.5% female) developed AF during 13.6 ± 2.5 years of follow-up, with an incidence of 0.93%/year. The mC2HEST score had good prediction at 10 years (C-index, 0.72; 95% CI: 0.701 to 0.753), and 15 years (0.773, 95% CI: 0.749 to 0.798). The risk of incident AF increased with higher mC2HEST score points and risk groups (log-rank P < 0.001). The mC2HEST score showed positive net reclassification indexes (0.057, 0.090, 0.128, and 0.143) and integrated discriminative improvement (3.2%, 3.9%, 5.7%, and 4.9%) compared with C2HEST, HAVOC, HATCH, and CHA2DS2-VASc scores, respectively. Optimal calibration was seen in the mC2HEST score (P = 0.41).

Conclusions

The mC2HEST score is a practical model for predicting individuals' risk of incident AF that may be used for guiding AF surveillance, resource allocation, and screening strategies.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

Prevalence of frequent premature ventricular contractions and nonsustained ventricular tachycardia in older women screened for atrial fibrillation in the Women's Health Initiative

BACKGROUND

Frequent premature ventricular contractions (PVCs) and nonsustained ventricular tachycardia (NSVT) have been associated with cardiovascular disease and mortality. Their prevalence, especially in ambulatory populations, is understudied and limited by few female participants and the use of short-duration (24- to 48-hour) monitoring.

OBJECTIVE

The objective of this study was to report the prevalence of frequent PVCs and NSVT in a community-based population of women likely to undergo electrocardiogram (ECG) screening by sequential patch monitoring.

METHODS

Participants from the Women's Health Initiative Strong and Healthy (WHISH) trial with no history of atrial fibrillation (AF) but 5-year predicted risk of incident AF ≥5% by CHARGE-AF score were randomly selected to undergo screening with 7-day ECG patch monitors at baseline, 6 months, and 12 months. Recordings were reviewed for PVCs and NSVT (>5 beats); data were analyzed with multivariate regression models.

RESULTS

There were 1067 participants who underwent ECG screening at baseline, 866 at 6 months, and 777 at 12 months. Frequent PVCs were found on at least 1 patch from 4.3% of participants, and 1 or more episodes of NSVT were found in 12 (1.1%) women. PVC frequency directly correlated with CHARGE-AF score and NSVT on any patch. Detection of frequent PVCs increased with sequential monitoring.

CONCLUSION

In postmenopausal women at high risk for AF, frequent PVCs were relatively common (4.3%) and correlated with higher CHARGE-AF score. As strategies for AF screening continue to evolve, particularly in those individuals at high risk of AF, the prevalence of incidental ventricular arrhythmias is an important benchmark to guide clinical decision-making.

Role of the Electrocardiogram for Identifying the Development of Atrial Fibrillation

Atrial fibrillation (AF), a prevalent cardiac arrhythmia, is associated with increased morbidity and mortality worldwide. Stroke, the leading cause of serious disability in the United States, is among the important complications of this arrhythmia. Recent studies have demonstrated that certain clinical variables can be useful in the prediction of AF development in the future. The electrocardiogram (ECG) is a simple and cost-effective technology that is widely available in various healthcare settings. An emerging body of evidence has suggested that ECG tracings preceding the development of AF can be useful in predicting this arrhythmia in the future. Various variables on ECG especially different P wave parameters have been investigated in the prediction of new-onset AF and found to be useful. Several risk models were also introduced using these variables along with the patient's clinical data. However, current guidelines do not provide a clear consensus regarding implementing these prediction models in clinical practice for identifying patients at risk of AF. Also, the role of intensive screening via ECG or implantable devices based on this scoring system is unclear. The purpose of this review is to summarize AF and various related terminologies and explain the pathophysiology and electrocardiographic features of this tachyarrhythmia. We also discuss the predictive electrocardiographic features of AF, review some of the existing risk models and scoring system, and shed light on the role of monitoring device for screening purposes.

Even mild mitral regurgitation is associated with incident atrial fibrillation in the general population

AIMS

Mitral regurgitation (MR) can be difficult to quantify. We sought to investigate whether the MR jet area to left atrial (LA) area ratio (MR/LA ratio) method for quantifying MRs can be used to predict incident atrial fibrillation (AF) in the general population.

METHODS AND RESULTS

The study included 4466 participants from the 5th Copenhagen City Heart Study, a prospective general population study, who underwent transthoracic echocardiography. MR jet area was measured and indexed to LA area. The endpoint was incident AF. MR was quantified in 4042 participants (mean age: 57 years, 43% men). Of these, 198 (4.9%) developed AF during a median follow-up period of 5.3 years (interquartile range: 4.4-6.1 years). MR was present in 1938 participants (48%) including 1593 (39%) trace/mild MRs (MR/LA ratio ≤ 20% and ≤4 cm2). In unadjusted analysis, MR/LA ratio was associated with incident AF [HR: 1.06 (1.00-1.13), P = 0.042 per 5% increase] but not after adjusting for CHARGE-AF score. However, the association was modified by age (P for interaction = 0.034), such that MR/LA ratio was associated with AF only in participants ≤ 73 years. In these participants, MR/LA ratio 'was' independently associated with AF after adjusting for CHARGE-AF score [HR: 1.14 (1.06-1.24), P = 0.001, per 5% increase]. This finding persisted when restricting the analysis to participants without moderate or severe MR and normal LA size [HR: 1.35 (1.09-1.68), P = 0.005, per 5% increase].

CONCLUSION

MR, including even trace regurgitations quantified by MR/LA ratio, is independently associated with incident AF in individuals ≤ 73 years of age.

Simple risk scoring using sinus rhythm electrocardiograms predicts the incidence of atrial fibrillation in the general population

Atrial fibrillation (AF) is an arrhythmic disease. Prediction of AF development in healthy individuals is important before serious complications occur. We aimed to develop a risk prediction score for future AF using participants' data, including electrocardiogram (ECG) measurements and information such as age and sex. We included 88,907 Japanese participants, aged 30-69 years, who were randomly assigned to derivation and validation cohorts in a ratio of 1:1. We performed multivariate logistic regression analysis and obtained the standardised beta coefficient of relevant factors and assigned scores to them. We created a score based on prognostic factors for AF to predict its occurrence after five years and applied it to validation cohorts to assess its reproducibility. The risk score ranged from 0 to 17, consisting of age, sex, PR prolongation, QT corrected for heart rate prolongation, left ventricular hypertrophy, premature atrial contraction, and left axis deviation. The area under the curve was 0.75 for the derivation cohort and 0.73 for the validation cohort. The incidence of new-onset AF reached over 2% at 10 points of the risk score in both cohorts. Thus, in this study, we showed the possibility of predicting new-onset AF using ECG findings and simple information.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Atrial fibrillation in young hospitalized patients: Clinical characteristics, predictors of new onset, and outcomes

BACKGROUND

Atrial fibrillation (AF) in young adults is an uncommon and not well studied entity.

METHODS

Consecutive patients aged 18-45 years admitted to internal or cardiology services in a large tertiary medical center (January 1, 2009 through December 31, 2019) were included. Clinical, electrocardiographic, and echocardiographic data were compared between patients with and without AF at baseline. Predictors of new-onset AF in the young were identified using multivariate Cox regression model among patients free of baseline AF.

RESULTS

Final cohort included 16,432 patients with median age of 34 (IQR 26-41) years of whom 8914 (56 %) were men. Patients with AF at baseline (N = 366; 2 %) were older, more likely to be men, and had higher proportion of comorbidities and electrocardiographic conduction disorders. Male sex, increased age, obesity, heart failure, congenital heart disease (CHD) and the presence of left or right bundle branch block were all independently associated with baseline AF in a multivariate model (p < 0.001 for all). Sub-analysis of 10,691 (98 %) patients free of baseline AF identified 85 cases of new-onset AF during a median follow up of 3.5 (IQR 1.5-6.5) years. Multivariate model identified increased age, heart failure, and CHD as independent predictors of new-onset AF. Finally, the CHARGE-AF risk score outperformed the CHA2DS2-VASc score in AF prediction [AUC of ROC 0.75 (0.7-0.8) vs. 0.56 (0.48-0.65)].

CONCLUSIONS

AF among hospitalized young adults is not rare. Screening for new-onset AF in young post hospitalization patients may be guided by specific clinical predictors and the CHARGE-AF risk score.

Association Between Insomnia, Stress Events, and Other Psychosocial Factors and Incident Atrial Fibrillation in Postmenopausal Women: Insights From the Women's Health Initiative

Background The association between psychosocial factors and atrial fibrillation (AF) is poorly understood. Methods and Results Postmenopausal women from the Women's Health Initiative were retrospectively analyzed to identify incident AF in relation to a panel of validated psychosocial exposure variables, as assessed by multivariable Cox proportional hazard regression and hierarchical cluster analysis. Among the 83 736 women included, the average age was 63.9±7.0 years. Over an average of 10.5±6.2 years follow-up, there were 23 954 cases of incident AF. Hierarchical cluster analysis generated 2 clusters of highly correlated psychosocial variables: the Stress Cluster included stressful life events, depressive symptoms, and insomnia, and the Strain Cluster included optimism, social support, social strain, cynical hostility, and emotional expressiveness. Incident AF was associated with higher values in the Stress Cluster (hazard ratio [HR], 1.07 per unit cluster score [95% CI, 1.05-1.09]) and the Strain Cluster (HR, 1.03 per unit cluster score [95% CI, 1.00-1.05]). Of the 8 individual psychosocial predictors that were tested, insomnia (HR, 1.04 [95% CI, 1.03-1.06]) and stressful life events (HR, 1.02 [95% CI, 1.01-1.04]) were most strongly associated with increased incidence of AF in Cox regression analysis after multivariate adjustment. Subgroup analyses showed that the Strain Cluster was more strongly associated with incident AF in those with lower traditional AF risks (P for interaction=0.02) as determined by the cohorts for heart and aging research in genomic epidemiology for atrial fibrillation score. Conclusions Among postmenopausal women, 2 clusters of psychosocial stressors were found to be significantly associated with incident AF. Further research is needed to validate these associations.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

Development of a clinical score to estimate pancreatitis-related hospital admissions in patients with a new diagnosis of chronic pancreatitis: the trinity score

BACKGROUND

The clinical course of chronic pancreatitis is unpredictable and there is no globally accepted score to predict the disease course. We developed a clinical score to estimate pancreatitis-related hospitalisation in patients with newly diagnosed chronic pancreatitis.

METHODS

We conducted a retrospective cohort study using two clinical chronic pancreatitis databases held in tertiary referral centres in Dublin, Ireland, and in Tarragona, Spain. Individuals diagnosed with chronic pancreatitis between 2007 and 2014 were eligible for inclusion. Candidate predictors included aetiology, body mass index, exocrine dysfunction, smoking and alcohol history. We used multivariable logistic regression to develop the model.

RESULTS

We analysed data from 154 patients with newly diagnosed chronic pancreatitis. Of these, 105 patients (68%) had at least one hospital admission for pancreatitis-related reasons in the 6 years following diagnosis. Aetiology of chronic pancreatitis, body mass index, use of pain medications and gender were found to be predictive of more pancreatic-related hospital admissions. These predictors were used to develop a clinical score which showed acceptable discrimination (area under the ROC curve = 0.70).

DISCUSSION

We developed a clinical score based on easily accessible clinical parameters to predict pancreatitis-related hospitalisation in patients with newly diagnosed chronic pancreatitis.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Serial 7-Day Electrocardiogram Patch Screening for AF in High-Risk Older Women by the CHARGE-AF Score

BACKGROUND

Asymptomatic atrial fibrillation (AF) is associated with an increased risk of stroke. The yield of serial electrocardiographic (ECG) screening for AF is unknown.

OBJECTIVES

The aim of this study was to determine the frequency of AF detected by serial, 7-day ECG patch screenings in older women identified as having an elevated risk of AF according to the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology)-AF clinical prediction score.

METHODS

Postmenopausal women with a 5-year predicted risk of new-onset AF ≥5% according to CHARGE-AF were recruited from the ongoing WHISH (Women's Health Initiative Strong and Healthy) randomized trial of a physical activity intervention. Participants with AF at baseline by self-report or medical records review were excluded. Screening with 7-day ECG patch monitors was performed at baseline, 6 months, and 12 months from study enrollment.

RESULTS

On baseline monitoring, 2.5% of the cohort had AF detected, increasing to 3.7% by 6 months and 4.9% cumulatively by 12 months. Yield of patch screening was higher among participants with a higher (≥10%) CHARGE-AF score: 4.2% had AF detected at baseline, 5.9% at 6 months, and 7.2% at 12 months. Most participants with patch-identified AF never had a clinical diagnosis of AF (36 of 46 [78%]).

CONCLUSIONS

Older women with an elevated CHARGE-AF score had a high prevalence of AF on 7-day ECG patch screening. Serial screening over 12 months substantially increased the detection of AF. These data can be useful in helping identify high-risk participants for enrollment in future studies of the management of asymptomatic AF.(Women's Health Initiative Silent Atrial Fibrillation Recording Study [WHISH STAR]; NCT05366803.).

Incidence and impact of atrial fibrillation in heart failure patients: real-world data in a large community

AIMS

The objective of the present study is to assess the bidirectional association between heart failure (HF) and atrial fibrillation (AF) using real-world data.

METHODS AND RESULTS

From an electronic health recording with a population of 3 799 885 adult subjects, those with prevalent or incident HF were selected and followed throughout a study period of 5 years. Prevalence and incidence of AF, and their impact in the risk for acute HF hospitalization, worsening renal function, ischaemic and haemorrhagic stroke, and all-cause mortality were identified. We analysed all incident and prevalent patients with HF and AF, 128 086 patients (S1), and subsequently analysed a subset of patients with incident HF and AF, 57 354 patients (S2). We analysed all incident and prevalent patients with HF and AF, 128 086 patients (S1), and subsequently a subset of patients with incident HF and AF, 57 354 patients (S2). The prevalence of AF was 59 906 (46.7%) of the HF patients, while incidence in the S2 was 231/1000 patients/year. In both cohorts, S1 and S2, AF significantly increases the risk of acute heart failure hospitalization [incidence 79.1/1000 and 97.5/1000 patients/year; HR 1.53 (1.48-1.59 95% CI) and HR 1.32 (1.24-1.41 95% CI), respectively], risk of decreased renal function (eGFR reduced by >20%) [66.2/1000 and 94.0/1000 patients/year; HR 1.13 (1.09-1.18 95% CI) and HR 1.22 (1.14-1.31 95% CI), respectively] and all-cause mortality [203/1000 and 294/1000 patients/year; HR 1.62 (1.58-1.65 95% CI) and HR 1.65 (1.59-1.70 95% CI), respectively]. The number of episodes of hospitalization for acute heart failure was also significantly higher in the AF patients (27 623 vs. 10 036, P < 0.001). However, the risk for ischaemic stroke was reduced in the AF subjects [HR 0.66 (0.63-0.74 95% CI)], probably due to the anticoagulant treatment.

CONCLUSIONS

AF is associated with an increment in the risk of episodes of acute heart failure as well as decline of renal function and increment of all-cause mortality.

Prediction performance and fairness heterogeneity in cardiovascular risk models

Prediction models are commonly used to estimate risk for cardiovascular diseases, to inform diagnosis and management. However, performance may vary substantially across relevant subgroups of the population. Here we investigated heterogeneity of accuracy and fairness metrics across a variety of subgroups for risk prediction of two common diseases: atrial fibrillation (AF) and atherosclerotic cardiovascular disease (ASCVD). We calculated the Cohorts for Heart and Aging in Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) score for AF and the Pooled Cohort Equations (PCE) score for ASCVD in three large datasets: Explorys Life Sciences Dataset (Explorys, n = 21,809,334), Mass General Brigham (MGB, n = 520,868), and the UK Biobank (UKBB, n = 502,521). Our results demonstrate important performance heterogeneity across subpopulations defined by age, sex, and presence of preexisting disease, with fairly consistent patterns across both scores. For example, using CHARGE-AF, discrimination declined with increasing age, with a concordance index of 0.72 [95% CI 0.72-0.73] for the youngest (45-54 years) subgroup to 0.57 [0.56-0.58] for the oldest (85-90 years) subgroup in Explorys. Even though sex is not included in CHARGE-AF, the statistical parity difference (i.e., likelihood of being classified as high risk) was considerable between males and females within the 65-74 years subgroup with a value of - 0.33 [95% CI - 0.33 to - 0.33]. We also observed weak discrimination (i.e., < 0.7) and suboptimal calibration (i.e., calibration slope outside of 0.7-1.3) in large subsets of the population; for example, all individuals aged 75 years or older in Explorys (17.4%). Our findings highlight the need to characterize and quantify the behavior of clinical risk models within specific subpopulations so they can be used appropriately to facilitate more accurate, consistent, and equitable assessment of disease risk.

Unique Cardiovascular Disease Risk Factors in Hispanic Individuals

Purpose of Review

This review summarizes contemporary data on unique cardiovascular disease (CVD) risk factors in Hispanic individuals in the USA, and how addressing these factors is important in addressing health equity.

Recent Findings

Recent studies have shown high rates of traditional CVD risk factors in Hispanic individuals such as obesity, hypertension, diabetes, hyperlipidemia, and emerging CVD risk factors like hypertensive disorders of pregnancy, psychological stress, and occupational exposures. However, most studies fail to consider the significant heterogeneity in risk factor burden and outcomes in atherosclerotic CVD by Hispanic subgroup. Heart failure and rhythm disorders are less well studied in Hispanic adults, making risk assessment for these conditions difficult. High levels of CVD risk factors in Hispanic youth given an aging Hispanic population overall highlight the importance of risk mitigation among these individuals.

Summary

In brief, these data highlight the significant, unique burden of CVD risk among Hispanic individuals in the USA and predict a rising burden of disease among this growing and aging population. Future CVD research should focus on including robust, diverse Hispanic cohorts as well as specifically delineating results for disaggregated Hispanic groups across CVDs. This will allow for better risk assessment, prevention, and treatment decisions to promote health equity for Hispanic patients.

Is machine learning the future for atrial fibrillation screening?

Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.

Association between CHARGE-AF risk score and LA mechanics: LA reservoir strain can be a single parameter for predicting AF risk

AIMS

Atrial fibrillation (AF) is a prevalent arrhythmia and the leading preventable cause of cardioembolic stroke. Scoring systems for predicting AF risk do not use imaging modalities. We sought to determine whether LA longitudinal strain could be used as a single parameter for predicting the risk of AF.

METHODS AND RESULTS

Consecutive patients diagnosed with diastolic dysfunction between December 2019 and March 2020 were included. Two-dimensional, colour flow, continuous pulse-wave, and tissue Doppler transthoracic echocardiography (TTE) were performed using a Vivid E9 imaging system (GE Medical Systems, Chicago, USA). Measurements were obtained in the standard manner recommended by the American Society of Echocardiography. Moreover, LA longitudinal strain was measured using 2D speckle tracking echocardiography in the four-chamber view to evaluate left atrial function. The CHARGE-AF scoring system was used to predict AF risk.A total of 148 patients (mean age: 57.6 ± 11.9; male: 53%) with feasible views for LA strain measurement were divided into two groups based on a 10% CHARGE-AF cut-off score. The >10% group (48 patients; 32%) was defined as having a predicted 5-year AF risk >10%, and the ≤10% group (100 patients; 68%) was defined as having a predicted risk <10%. In the multivariate analysis, LA reservoir strain (LASr) was independently associated with CHARGE-AF score. Furthermore, using the Pearson correlation method, LASr was found to be highly correlated with CHARGE-AF score (r = -0.74, p < 0.0001).

CONCLUSIONS

LASr was highly correlated with CHARGE-AF risk score and may be used as a parameter to predict atrial myopathy and hence AF risk.

Cohort design and natural language processing to reduce bias in electronic health records research

Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95-0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012-0.030 in C3PO vs. 0.028-0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.

Will Apple devices' passive atrial fibrillation detection prevent strokes? Estimating the proportion of high-risk actionable patients with real-world user data

OBJECTIVE

Utilizing integrated electronic health record (EHR) and consumer-grade wearable device data, we sought to provide real-world estimates for the proportion of wearers that would likely benefit from anticoagulation if an atrial fibrillation (AFib) diagnosis was made based on wearable device data.

MATERIALS AND METHODS

This study utilized EHR and Apple Watch data from an observational cohort of 1802 patients at Cedars-Sinai Medical Center who linked devices to the EHR between April 25, 2015 and November 16, 2018. Using these data, we estimated the number of high-risk patients who would be actionable for anticoagulation based on (1) medical history, (2) Apple Watch wear patterns, and (3) AFib risk, as determined by an existing validated model.

RESULTS

Based on the characteristics of this cohort, a mean of 0.25% (n = 4.58, 95% CI, 2.0-8.0) of patients would be candidates for new anticoagulation based on AFib identified by their Apple Watch. Using EHR data alone, we find that only approximately 36% of the 1802 patients (n = 665.93, 95% CI, 626.0-706.0) would have anticoagulation recommended even after a new AFib diagnosis.

DISCUSSION AND CONCLUSION

These data suggest that there is limited benefit to detect and treat AFib with anticoagulation among this cohort, but that accessing clinical and demographic data from the EHR could help target devices to the patients with the highest potential for benefit. Future research may analyze this relationship at other sites and among other wearable users, including among those who have not linked devices to their EHR.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Racial and Ethnic Considerations in Patients With Atrial Fibrillation: JACC Focus Seminar 5/9

Atrial fibrillation (AF) affects at least 60 million individuals globally and is associated with substantial impacts on morbidity, mortality, and health care expenditures. This review focuses on how race and ethnicity influence AF epidemiology, risk prediction, treatment, and outcomes; knowledge gaps in these areas are identified. Most AF studies have predominantly included White populations, with an underrepresentation of racial and ethnic groups, including but not limited to Black, Hispanic, and Indigenous individuals. Enhancement and implementation of AF risk prediction, prevention, and management call for studies that will gather accurate race-based epidemiologic data and evaluate social determinants and genetic factors in the context of multiple races and ethnicities. Available studies highlight inequities in access to treatment as well as outcomes between White individuals and persons of other races/ethnicities. These inequities will need to be addressed by a renewed emphasis on structural and social determinants of health that contribute to AF.

Prediction of Atrial Fibrillation Using Machine Learning: A Review

There has been recent immense interest in the use of machine learning techniques in the prediction and screening of atrial fibrillation, a common rhythm disorder present with significant clinical implications primarily related to the risk of ischemic cerebrovascular events and heart failure. Prior to the advent of the application of artificial intelligence in clinical medicine, previous studies have enumerated multiple clinical risk factors that can predict the development of atrial fibrillation. These clinical parameters include previous diagnoses, laboratory data (e.g., cardiac and inflammatory biomarkers, etc.), imaging data (e.g., cardiac computed tomography, cardiac magnetic resonance imaging, echocardiography, etc.), and electrophysiological data. These data are readily available in the electronic health record and can be automatically queried by artificial intelligence algorithms. With the modern computational capabilities afforded by technological advancements in computing and artificial intelligence, we present the current state of machine learning methodologies in the prediction and screening of atrial fibrillation as well as the implications and future direction of this rapidly evolving field.

Ethnic and Gender Differences in 10-Year Coronary Heart Disease Risk: a Cross-Sectional Study in Hawai'i

BACKGROUND

Cardiovascular disease (CVD) is the leading cause of death in the US. In Hawai'i, Filipinos and Native Hawaiians have the highest rates of CVD-related risk factors. CVD risk across these ethnic groups has not been examined. This cross-sectional study examines 10-year CVD risk as determined by the Framingham Risk Score (FRS) across ethnic groups in Hawai'i, controlling for clinical, demographic, and psychosocial factors.

METHODS

This study includes secondary data analysis of the Kohala Health Research Project dataset. All non-pregnant adults (≥ 18 years of age) who resided in the community of interest during the study period were eligible to participate with 1462 participants completing the clinical examination and surveys. This analysis included clinical, demographic, and psychosocial variables. Ethnic differences were examined using the chi-squared test and one-way ANOVA. Multiple linear regression on FRS was conducted and least square means of FRS were calculated.

RESULTS

Data from 1146 individuals were analyzed. Participants were 44.4% Native Hawaiian, 15.4% Filipino, 15.3% Japanese, and 25% non-Hispanic White; 55.4% were female and had a mean age of 48.8 years. For males, the unadjusted Japanese mean FRS was significantly higher compared with the other ethnic groups. For females, Filipino and Japanese mean FRS were significantly higher compared with Native Hawaiians and non-Hispanic Whites. In the fully adjusted model, there were no ethnic group differences in FRS among males and Filipinos had significantly higher FRS compared with non-Hispanic White among females.

CONCLUSIONS

This cross-sectional community-based epidemiological study examined ethnic differences in CVD risk after adjusting for age, depression, social support, and acculturation. The results suggest that some ethnic differences in CVD risk persist even after controlling for confounders but that recalibration of risk assessment is necessary.

Usefulness of Rhythm Monitoring Following Acute Ischemic Stroke

We characterized monitor utilization in stroke survivors and assessed associations with underlying clinical atrial fibrillation (AF) risk. We retrospectively analyzed consecutive patients with acute ischemic stroke 10/2018-6/2019 without prevalent AF and assessed the 6-month incidence of monitor utilization (Holter/ECG, event/patch, implantable loop recorder [ILR]) using Fine-Gray models accounting for the competing risk of death. We assessed for predictors of monitor utilization using cause-specific hazards regression adjusted for the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) score, stroke subtype, and discharge disposition. Of 493 patients with acute ischemic stroke (age 65±16; 47% women), the 6-month incidence of monitor utilization was 36.5% (95% CI 31.7, 41.3), and 6-month mortality was 13.6% (10.4, 16.8). Monitoring was performed with Holter/event (n = 107; 72.3%), ILR (n = 34; 23.0%) or both (n = 7; 4.7%). Monitoring was more likely after cryptogenic (hazard ratio [HR] 4.53 [3.22, 6.39]; 6-month monitor incidence 70.6%) and cardioembolic (HR 2.43 [1.28, 4.62]; incidence 47.7%) stroke, versus other/undocumented (incidence 22.7%). Among patients with cryptogenic stroke, the 6-month incidence of ILR was 27.5% [18.5, 36.5]. Monitoring was more likely after discharge home (HR 1.80 [1.29, 2.52]; incidence 46.1%) versus facility (incidence 24.9%). Monitoring was not associated with CHARGE-AF score (HR 1.08 per 1-SD increase [0.91, 1.27]), even though CHARGE-AF was associated with incident AF (HR 1.56 [1.03, 2.35]). In conclusion, rhythm monitors are utilized after one-third of ischemic strokes. Monitoring is more frequent after cryptogenic strokes, though ILR use is low. Monitor utilization is not associated with AF risk.

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

RESULTS

Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Performance of Atrial Fibrillation Risk Prediction Models in Over 4 Million Individuals

BACKGROUND

Atrial fibrillation (AF) is associated with increased risks of stroke and heart failure. Electronic health record (EHR)-based AF risk prediction may facilitate efficient deployment of interventions to diagnose or prevent AF altogether.

METHODS

We externally validated an electronic health record AF (EHR-AF) score in IBM Explorys Life Sciences, a multi-institutional dataset containing statistically deidentified EHR data for over 21 million individuals (Explorys Dataset). We included individuals with complete AF risk data, ≥2 office visits within 2 years, and no prevalent AF. We compared EHR-AF to existing scores including CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation), C2HEST (coronary artery disease or chronic obstructive pulmonary disease, hypertension, elderly, systolic heart failure, thyroid disease), and CHA2DS2-VASc. We assessed association between AF risk scores and 5-year incident AF, stroke, and heart failure using Cox proportional hazards modeling, 5-year AF discrimination using C indices, and calibration of predicted AF risk to observed AF incidence.

RESULTS

Of 21 825 853 individuals in the Explorys Dataset, 4 508 180 comprised the analysis (age 62.5, 56.3% female). AF risk scores were strongly associated with 5-year incident AF (hazard ratio per SD increase 1.85 using CHA2DS2-VASc to 2.88 using EHR-AF), stroke (1.61 using C2HEST to 1.92 using CHARGE-AF), and heart failure (1.91 using CHA2DS2-VASc to 2.58 using EHR-AF). EHR-AF (C index, 0.808 [95% CI, 0.807-0.809]) demonstrated favorable AF discrimination compared to CHARGE-AF (0.806 [95% CI, 0.805-0.807]), C2HEST (0.683 [95% CI, 0.682-0.684]), and CHA2DS2-VASc (0.720 [95% CI, 0.719-0.722]). Of the scores, EHR-AF demonstrated the best calibration to incident AF (calibration slope, 1.002 [95% CI, 0.997-1.007]). In subgroup analyses, AF discrimination using EHR-AF was lower in individuals with stroke (C index, 0.696 [95% CI, 0.692-0.700]) and heart failure (0.621 [95% CI, 0.617-0.625]).

CONCLUSIONS

EHR-AF demonstrates predictive accuracy for incident AF using readily ascertained EHR data. AF risk is associated with incident stroke and heart failure. Use of such risk scores may facilitate decision support and population health management efforts focused on minimizing AF-related morbidity.

How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals.

RESULTS

Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Evaluation of Risk Prediction Models of Atrial Fibrillation (from the Multi-Ethnic Study of Atherosclerosis [MESA])

Atrial fibrillation (AF) is prevalent and strongly associated with higher cardiovascular disease (CVD) risk. Machine learning is increasingly used to identify novel predictors of CVD risk, but prediction improvements beyond established risk scores are uncertain. We evaluated improvements in predicting 5-year AF risk when adding novel candidate variables identified by machine learning to the CHARGE-AF Enriched score, which includes age, race/ethnicity, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and NT-proBNP. We included 3,534 participants (mean age, 61.3 years; 52.0% female) with complete data from the prospective Multi-Ethnic Study of Atherosclerosis. Incident AF was defined based on study electrocardiograms and hospital discharge diagnosis ICD-9 codes, supplemented by Medicare claims. Prediction performance was evaluated using Cox regression and a parsimonious model was selected using LASSO. Within 5 years of baseline, 124 participants had incident AF. Compared with the CHARGE-AF Enriched model (c-statistic, 0.804), variables identified by machine learning, including biomarkers, cardiac magnetic resonance imaging variables, electrocardiogram variables, and subclinical CVD variables, did not significantly improve prediction. A 23-item score derived by machine learning achieved a c-statistic of 0.806, whereas a parsimonious model including the clinical risk factors age, weight, current smoking, NT-proBNP, coronary artery calcium score, and cardiac troponin-T achieved a c-statistic of 0.802. This analysis confirms that the CHARGE-AF Enriched model and a parsimonious 6-item model performed similarly to a more extensive model derived by machine learning. In conclusion, these simple models remain the gold standard for risk prediction of AF, although addition of the coronary artery calcium score should be considered.

Should This Patient Be Screened for Atrial Fibrillation?: Grand Rounds Discussion From Beth Israel Deaconess Medical Center

Atrial fibrillation (AFib) is the most common type of cardiac arrhythmia, affecting 2.7 million to 6.1 million persons in the United States. Although some persons with AFib have no symptoms, others do. For those without symptoms, AFib may be detected by 12-lead electrocardiogram (ECG), single-lead monitors (such as ambulatory blood pressure monitors and pulse oximeters), or consumer devices (such as wearable monitors and smartphones). Pulse palpation and heart auscultation also may detect AFib. In a systematic review, screening with ECG identified more new cases of AFib than no screening. Atrial fibrillation is an important cause of stroke, and without anticoagulant treatment, patients with AFib have approximately a 5-fold increased risk for stroke. The U.S. Preventive Services Task Force reviewed the benefits and harms of ECG screening for AFib in adults aged 65 years or older and found inadequate evidence that ECG identifies AFib more effectively than usual care. This conclusion is in contrast to guidelines from the European Society of Cardiology and the National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand, which found that active screening for AFib in patients older than 65 years may be useful. Here, 2 cardiologists discuss the risks and benefits of screening for AFib, if and when they would recommend screening, and whether they would recommend anticoagulation for a patient with screen-detected AFib.

Risk Factors for Symptomatic Atrial Fibrillation-Analysis of an Outpatient Database

Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in practice and is the leading cause of debilitating strokes with significant economic burden. It is currently not known whether asymptomatic undiagnosed AF should be treated if detected by various screening methods. Currently, United States guidelines have no recommendations for identifying patients with asymptomatic undiagnosed AF due to lack of evidence. The American Heart Association Center for Health Technology & Innovation undertook a plan to identify tools in 3 phases that may be useful in improving outcomes in patients with undiagnosed AF. In phase I we sought to identify AF risk factors that can be used to develop a risk score to identify high-risk patients using a large commercial insurance dataset. The principal findings of this study show that individuals at high risk for AF are those with advanced age, the presence of heart failure, coronary artery disease, hypertension, metabolic disorders, and hyperlipidemia. Our analysis also found that chronic respiratory failure was a significant risk factor for those over 65 years of age and chronic kidney disease for those less than 65 years of age.

An exploratory model for the non-fatal drowning risks in children in Guangdong, China

Background

Drowning is a leading cause of accidental death in children under 14 years of age in Guangdong, China. We developed a statistical model to classify the risk of drowning among children based on the risk factors.

Methods

A multiple-stage cluster random sampling was employed to select the students in Grades 3 to 9 in two townships in Qingyuan, Guangdong. Questionnaire was a self-reported measure consisting of general information, knowledge, attitudes and activities. A univariate logistic regression model was used to preliminarily select the independent variables at a P value of 0.1 for multivariable model. Three-quarters of the participants were randomly selected as a training sample to establish the model, and the remaining were treated as a testing sample to validate the model.

Results

A total of 8390 children were included in this study, about 12.18% (1013) experienced drowning during the past one year. In the univariate logistic regression model, introvert personality, unclear distributions of water areas on the way to school, and bad relationships with their classmates and families were positively associated with drowning. However, females, older age and lower swimming skills were negatively associated with drowning. After employing the prediction model with these factors to estimate drowning risk of the students in the testing samples, the results of Hosmer-Lemeshow tests showed non-significant differences between the predictive results and actual risk (χ2 = 5.97, P = 0.65).

Conclusions

Male, younger children, higher swimming skills, bad relationship with their classmates and families, introvert personality and unclear distributions of water areas on the way to school were important risk factors of non-fatal drowning among children. The prediction model based on these variables has an acceptable predictive ability.

Relative contribution of modifiable risk factors for incident atrial fibrillation in Hispanics, African Americans and non-Hispanic Whites

BACKGROUND

Contribution of modifiable risk factors for the risk of new onset atrial fibrillation (AF) in minority populations is poorly understood. Our objective was to compare the population attributable risk (PAR) of various risk factors for incident AF between Hispanic, African American and non-Hispanic Whites.

METHODS

An ECG/EMR database was interrogated for individuals free of AF for development of subsequent AF from 2000 to 2013. Cox regression analysis controlled for age > 65, male gender, body mass index > 40 kg/m², systolic blood pressure > 140 mm Hg, diabetes mellitus, heart failure, socioeconomic status less than the first percentile in New York State, and race/ethnicity. PAR was calculated as (prevalence of X) ∗ (HR - 1)/HR, where HR is the hazard ratio, and X is the risk factor.

RESULTS

47,722 persons free of AF (43% Hispanic, 37% Black and 20% White) were followed for subsequent incident AF. Hypertension in African Americans and Hispanics had a 7.93% and 7.66% greater PAR compared with non-Hispanics Whites. Similar findings existed for the presence of heart failure, with a higher PAR in non-Whites compared to Whites.

CONCLUSION

In conclusion, modifiable risk factors play an important role in the risk of incident AF. Higher PAR estimates in African Americans and Hispanics were observed for elevated systolic blood pressure and heart failure. Identification of these modifiable risk factors for atrial fibrillation in non-White minorities may assist in targeting better prevention therapies and planning from a public health perspective. No funding sources were used for this study.

Relation of Body Mass Index to Development of Atrial Fibrillation in Hispanics, Blacks, and Non-Hispanic Whites

No previous studies have examined the interaction between body mass index (BMI) and race/ethnicity with the risk of atrial fibrillation (AF). We retrospectively followed 48,323 persons free of AF (43% Hispanic, 37% black, and 20% white; median age 60 years) for subsequent incident AF (ascertained from electrocardiograms). BMI categories included very severely underweight (BMI <15 kg/m²), severely underweight (BMI 15.1 to 15.9 kg/m²), underweight (BMI 16 to 18.4 kg/m²), normal (BMI 18.5 to 24.9 kg/m²), overweight (BMI 25.0 to 29.9 kg/m²), moderately obese (BMI 30 to 34.9 kg/m²), severely obese (BMI 35 to 39.9 kg/m²), and very severely obese (BMI >40 kg/m²). Cox regression analysis controlled for baseline covariates: heart failure, gender, age, treatment for hypertension, diabetes, PR length, systolic blood pressure, left ventricular hypertrophy, socioeconomic status, use of β blockers, calcium channel blockers, and digoxin. Over a follow-up of 13 years, 4,744 AF cases occurred. BMI in units of 10 was associated with the development of AF (adjusted hazard ratio 1.088, 95% confidence interval 1.048 to 1.130, p <0.01). When stratified by race/ethnicity, non-Hispanic whites compared with blacks and Hispanics had a higher risk of developing AF, noted in those whom BMI classes were overweight to severely obese. In conclusion, our study demonstrates that there exists a relation between obesity and race/ethnicity for the development of AF. Non-Hispanic whites had a higher risk of developing AF compared with blacks and Hispanics.

Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation

BACKGROUND

The long-term probability of developing atrial fibrillation (AF) considering genetic predisposition and clinical risk factor burden is unknown.

METHODS

We estimated the lifetime risk of AF in individuals from the community-based Framingham Heart Study. Polygenic risk for AF was derived using a score of ≈1000 AF-associated single-nucleotide polymorphisms. Clinical risk factor burden was calculated for each individual using a validated risk score for incident AF comprised of height, weight, systolic and diastolic blood pressure, current smoking status, antihypertensive medication use, diabetes mellitus, history of myocardial infarction, and history of heart failure. We estimated the lifetime risk of AF within tertiles of polygenic and clinical risk.

RESULTS

Among 4606 participants without AF at 55 years of age, 580 developed incident AF (median follow-up, 9.4 years; 25th-75th percentile, 4.4-14.3 years). The lifetime risk of AF >55 years of age was 37.1% and was substantially influenced by both polygenic and clinical risk factor burden. Among individuals free of AF at 55 years of age, those in low-polygenic and clinical risk tertiles had a lifetime risk of AF of 22.3% (95% confidence interval, 15.4-9.1), whereas those in high-risk tertiles had a risk of 48.2% (95% confidence interval, 41.3-55.1). A lower clinical risk factor burden was associated with later AF onset after adjusting for genetic predisposition (P<0.001).

CONCLUSIONS

In our community-based cohort, the lifetime risk of AF was 37%. Estimation of polygenic AF risk is feasible and together with clinical risk factor burden explains a substantial gradient in long-term AF risk.

Cardiovascular risk status of Afro-origin populations across the spectrum of economic development: findings from the Modeling the Epidemiologic Transition Study

BACKGROUND

Cardiovascular risk factors are increasing in most developing countries. To date, however, very little standardized data has been collected on the primary risk factors across the spectrum of economic development. Data are particularly sparse from Africa.

METHODS

In the Modeling the Epidemiologic Transition Study (METS) we examined population-based samples of men and women, ages 25-45 of African ancestry in metropolitan Chicago, Kingston, Jamaica, rural Ghana, Cape Town, South Africa, and the Seychelles. Key measures of cardiovascular disease risk are described.

RESULTS

The risk factor profile varied widely in both total summary estimates of cardiovascular risk and in the magnitude of component factors. Hypertension ranged from 7% in women from Ghana to 35% in US men. Total cholesterol was well under 200 mg/dl for all groups, with a mean of 155 mg/dl among men in Ghana, South Africa and Jamaica. Among women total cholesterol values varied relatively little by country, following between 160 and 178 mg/dl for all 5 groups. Levels of HDL-C were virtually identical in men and women from all study sites. Obesity ranged from 64% among women in the US to 2% among Ghanaian men, with a roughly corresponding trend in diabetes. Based on the Framingham risk score a clear trend toward higher total risk in association with socioeconomic development was observed among men, while among women there was considerable overlap, with the US participants having only a modestly higher risk score.

CONCLUSIONS

These data provide a comprehensive estimate of cardiovascular risk across a range of countries at differing stages of social and economic development and demonstrate the heterogeneity in the character and degree of emerging cardiovascular risk. Severe hypercholesterolemia, as characteristic in the US and much of Western Europe at the onset of the coronary epidemic, is unlikely to be a feature of the cardiovascular risk profile in these countries in the foreseeable future, suggesting that stroke may remain the dominant cardiovascular event.

Trajectories of Risk Factors and Risk of New-Onset Atrial Fibrillation in the Framingham Heart Study

The associations of long-term patterns of risk factors and the risk of incident atrial fibrillation (AF) are incompletely characterized. Among 4351 Framingham Study participants (mean age 50±11 years at baseline examination, 57% women) from the original and offspring cohorts, we defined longitudinal patterns, referred to as trajectories, of AF risk factors and a composite AF risk score using ≈16 years of data. We used Cox proportional hazards models to examine the association of trajectories to 15-year risk of AF. During follow-up, 719 participants developed AF. Five distinct trajectory groups were identified for systolic blood pressure (BP): groups 1 and 2 (normotensive throughout), group 3 (prehypertensive), group 4 (hypertensive initially with decreasing BP), and group 5 (hypertensive and increasing BP). In multivariable-adjusted analyses, compared with group 1, groups 4 (hazard ratio 2.05; 95% confidence interval 1.24-3.37) and 5 (hazard ratio 1.95; 95% confidence interval 1.08-3.49) were associated with incident AF. Three trajectory groups were identified for antihypertensive treatment. Compared with the group with no treatment throughout, the other 2 groups were associated with increased risk of incident AF. Distinct trajectories for diastolic BP, smoking, diabetes mellitus, and the composite risk score were not associated with increased 15-year risk of AF. Longitudinal trajectories may distinguish how exposures related to AF contribute toward prospective AF risk. Distinct trajectory groups with persistently elevated systolic BP and longer antihypertensive treatment are associated with increased risk of incident AF.

A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study

BACKGROUND

Atrial fibrillation (AF) affects more than 33 million individuals worldwide and increases risks of stroke, heart failure, and death. The CHARGE-AF risk score was developed to predict incident AF in three American cohorts and it was validated in two European cohorts. The CHA2DS2-VASc risk score was derived to predict risk of stroke, peripheral embolism, and pulmonary embolism in individuals with AF, but it has been increasingly used for AF risk prediction. We compared CHARGE-AF risk score versus CHA2DS2-VASc risk score for incident AF risk in a community-based cohort.

METHODS AND RESULTS

We studied Framingham Heart Study participants aged 46 to 94 years without prevalent AF and with complete covariates. We predicted AF risk using Fine-Gray proportional sub-distribution hazards regression. We used the Wald χ(2) statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) χ(2) statistic for calibration. We included 9722 observations (mean age 63.9 ± 10.6 years, 56% women) from 4548 unique individuals: 752 (16.5%) developed incident AF and 793 (17.4%) died. The mean CHARGE-AF score was 12.0 ± 1.2 and the sub-distribution hazard ratio (sHR) for AF per unit increment was 2.15 (95% CI, 99-131%; P < .0001). The mean CHA2DS2-VASc score was 2.0 ± 1.5 and the sHR for AF per unit increment was 1.43 (95% CI, 37%-51%; P < .0001). The CHARGE-AF model had better fit than CHA2DS2-VASc (Wald χ(2) = 403 vs 209, both with 1 df), improved discrimination (C-statistic = 0.75, 95% CI, 0.73-0.76 vs C-statistic = 0.71, 95% CI, 0.69-0.73), and better calibration (HL χ(2) = 5.6, P = .69 vs HL χ(2) = 28.5, P < .0001).

CONCLUSION

The CHARGE-AF risk score performed better than the CHA2DS2-VASc risk score at predicting AF in a community-based cohort.

Asymmetric dimethylarginine, related arginine derivatives, and incident atrial fibrillation

BACKGROUND

Oxidative stress plays an important role in the development of atrial fibrillation (AF). Arginine derivatives including asymmetric dimethylarginine (ADMA) are central to nitric oxide metabolism and nitrosative stress. Whether blood concentrations of arginine derivatives are related to incidence of AF is uncertain.

METHODS AND RESULTS

In 3,310 individuals (mean age 58 ± 10 years, 54% women) from the community-based Framingham Study, we prospectively examined the relations of circulating levels of ADMA, l-arginine, symmetric dimethylarginine (SDMA), and the ratio of l-arginine/ADMA to incidence of AF using proportional hazards regression models. Over a median follow-up time of 10 years, 247 AF cases occurred. Using age- and sex-adjusted regression models, ADMA was associated with a hazard ratio of 1.15 per 1-SD increase in loge-biomarker concentration (95% CI 1.02-1.29, P = .02) for AF, which was no longer significant after further risk factor adjustment (hazard ratio 1.09, 95% CI 0.97-1.23, P = .15). Neither l-arginine nor SDMA was related to new-onset AF. A clinical model comprising clinical risk factors for AF (for age, sex, height, weight, systolic blood pressure, diastolic blood pressure, current smoking, diabetes, hypertension treatment, myocardial infarction, and heart failure; c statistic = 0.781; 95% CI 0.753-0.808) was not improved by the addition of ADMA (0.782; 95% CI 0.755-0.809).

CONCLUSIONS

Asymmetric dimethylarginine and related arginine derivatives were not associated with incident AF in the community after accounting for other clinical risk factors and confounders. Its role in the pathogenesis of AF needs further refinement.