Temporal Trends in Cardiovascular Disease Prevalence Among Asian American Subgroups.

BACKGROUND: Asian and multiracial individuals represent the 2 fastest growing racial and ethnic groups in the United States, yet most prior studies report Asian American and Native Hawaiian or Other Pacific Islander as a single racial group, with limited data on cardiovascular disease (CVD) prevalence among subgroups. We sought to evaluate temporal trends in CVD burden among disaggregated Asian subgroups. METHODS AND RESULTS: Patients with CVD based on International Classification of Diseases, Ninth Revision and Tenth Revision (ICD-9 and ICD-10) coding who received care from a mixed-payer health care organization in California between 2008 and 2018 were classified into self-identified racial and ethnic subgroups (non-Hispanic White [NHW], Asian Indian, Chinese, Filipino, Japanese, Korean, Native Hawaiian or Other Pacific Islander, and multiracial groups). Adjusted trends in CVD prevalence over time by subgroup were compared using logistic regression. Among 3 494 071 patient-years, prevalence of CVD increased faster among all subgroups except Japanese and Native Hawaiian or Other Pacific Islander patients (P<0.01 for each, reference: NHW). Filipino patients had the highest overall CVD prevalence, which increased from 34.3% to 45.1% over 11 years (increase from 17.3%-21.9%, P<0.0001, reference: NHW). Asian Indian patients had the fastest increase in CVD prevalence over time (16.9%-23.7%, P<0.0001, reference: NHW). Among subcategories of disease, hypertension increased faster among Asian Indian, Chinese, Filipino, Korean, and multiracial groups (P<0.01 for all, reference: NHW), and coronary artery disease increased faster among Asian Indian, Chinese, Filipino, and Japanese groups (P<0.05 for each, reference: NHW). CONCLUSIONS: The increasing prevalence of CVD among disaggregated Asian, Native Hawaiian or Other Pacific Islander, and multiracial subgroups over time highlights the importance of tailored approaches to addressing CVD in these diverse subpopulations.

Atrial ectopy as a mediator of the association between race and atrial fibrillation.

BACKGROUND: Blacks have a lower risk of atrial fibrillation (AF) despite having more AF risk factors, but the mechanism remains unknown. Premature atrial contraction (PAC) burden is a recently identified risk factor for AF. OBJECTIVE: The purpose of this study was to determine whether the burden of PACs explains racial differences in AF risk. METHODS: PAC burden (number per hour) was assessed by 24-hour ambulatory electrocardiographic (ECG) monitoring in a randomly selected subset of patients in the Cardiovascular Health Study. Participants were followed prospectively for the development of AF, diagnosed by study ECG and hospital admission records. RESULTS: Among 938 participants (median age 73 years; 34% black; 58% female), 206 (22%) developed AF over a median follow-up of 11.0 years (interquartile range 6.1-13.4). After adjusting for age, sex, body mass index, coronary disease, congestive heart failure, diabetes, hypertension, alcohol consumption, smoking status, and study site, black race was associated with a 42% lower risk of AF (hazard ratio 0.58, 95% confidence interval [CI] 0.40-0.85; P = .005). The baseline PAC burden was 2.10 times (95% CI 1.57-2.83; P <.001) higher in whites than blacks. There was no detectable difference in premature ventricular contraction (PVC) burden by race. PAC burden mediated 19.5% (95% CI 6.3-52.5) of the adjusted association between race and AF. CONCLUSION: On average, whites exhibited more PACs than blacks, and this difference statistically explains a modest proportion of the differential risk of AF by race. The differential PAC burden, without differences in PVCs, by race suggests that identifiable common exposures or genetic influences might be important to atrial pathophysiology.

Ectopy on a Single 12-Lead ECG, Incident Cardiac Myopathy, and Death in the Community.

BACKGROUND: Atrial fibrillation and heart failure are 2 of the most common diseases, yet ready means to identify individuals at risk are lacking. The 12-lead ECG is one of the most accessible tests in medicine. Our objective was to determine whether a premature atrial contraction observed on a standard 12-lead ECG would predict atrial fibrillation and mortality and whether a premature ventricular contraction would predict heart failure and mortality. METHODS AND RESULTS: We utilized the CHS (Cardiovascular Health) Study, which followed 5577 participants for a median of 12 years, as the primary cohort. The ARIC (Atherosclerosis Risk in Communities Study), the replication cohort, captured data from 15 792 participants over a median of 22 years. In the CHS, multivariable analyses revealed that a baseline 12-lead ECG premature atrial contraction predicted a 60% increased risk of atrial fibrillation (hazard ratio, 1.6; 95% CI, 1.3-2.0; P<0.001) and a premature ventricular contraction predicted a 30% increased risk of heart failure (hazard ratio, 1.3; 95% CI, 1.0-1.6; P=0.021). In the negative control analyses, neither predicted incident myocardial infarction. A premature atrial contraction was associated with a 30% increased risk of death (hazard ratio, 1.3; 95% CI, 1.1-1.5; P=0.008) and a premature ventricular contraction was associated with a 20% increased risk of death (hazard ratio, 1.2; 95% CI, 1.0-1.3; P=0.044). Similarly statistically significant results for each analysis were also observed in ARIC. CONCLUSIONS: Based on a single standard ECG, a premature atrial contraction predicted incident atrial fibrillation and death and a premature ventricular contraction predicted incident heart failure and death, suggesting that this commonly used test may predict future disease.

Smartphone-Based Geofencing to Ascertain Hospitalizations.

BACKGROUND: Ascertainment of hospitalizations is critical to assess quality of care and the effectiveness and adverse effects of various therapies. Smartphones, mobile geolocators that are ubiquitous, have not been leveraged to ascertain hospitalizations. Therefore, we evaluated the use of smartphone-based geofencing to track hospitalizations. METHODS AND RESULTS: Participants aged ≥18 years installed a mobile application programmed to geofence all hospitals using global positioning systems and cell phone tower triangulation and to trigger a smartphone-based questionnaire when located in a hospital for ≥4 hours. An in-person study included consecutive consenting patients scheduled for electrophysiology and cardiac catheterization procedures. A remote arm invited Health eHeart Study participants who consented and engaged with the study via the internet only. The accuracy of application-detected hospitalizations was confirmed by medical record review as the reference standard. Of 22 eligible in-person patients, 17 hospitalizations were detected (sensitivity 77%; 95% confidence interval, 55%-92%). The length of stay according to the application was positively correlated with the length of stay ascertained via the electronic medical record (r=0.53; P=0.03). In the remote arm, the application was downloaded by 3443 participants residing in all 50 US states; 243 hospital visits at 119 different hospitals were detected through the application. The positive predictive value for an application-reported hospitalization was 65% (95% confidence interval, 57%-72%). CONCLUSIONS: Mobile application-based ascertainment of hospitalizations can be achieved with modest accuracy. This first proof of concept may ultimately be applicable to geofencing other types of prespecified locations to facilitate healthcare research and patient care.

Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep.

BACKGROUND: Smartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality. AIMS: The aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep. METHODS: We performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index-PSQI) were obtained by survey. Linear regression was used to obtain effect estimates. RESULTS: Total screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency. CONCLUSIONS: These findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.

The QT Interval as a Noninvasive Marker of Atrial Refractoriness.

BACKGROUND: Atrial refractoriness may be an important determinant of atrial fibrillation (AF) risk, but its measurement is not clinically accessible. Because the QT interval predicts incident AF and the atrium and ventricle share repolarizing ion currents, we investigated the association between an individual's QT interval and atrial effective refractory period (AERP). METHODS: In paroxysmal AF patients presenting for catheter ablation, the QT interval was measured from the surface 12-lead electrocardiogram. The AERP was defined as the longest S1-S2 coupling interval without atrial capture using a 600-ms drive cycle length. RESULTS: In 28 patients, there was a positive correlation between QTc and mean AERP. After multivariate adjustment, a 1-ms increase in QTc predicted a 0.70-ms increase in AERP. CONCLUSIONS: The QTc interval reflects the AERP, suggesting that the QTc interval may be used as a marker of atrial refractoriness relevant to assessing AF risk and mechanism-specific therapeutic strategies.

Electrocardiographic Predictors of Incident Atrial Fibrillation.

Atrial fibrillation (AF) is likely secondary to multiple different pathophysiological mechanisms that are increasingly but incompletely understood. Motivated by the hypothesis that 3 previously described electrocardiographic predictors of AF identify distinct AF mechanisms, we sought to determine if these electrocardiographic findings independently predict incident disease. Among Cardiovascular Health Study participants without prevalent AF, we determined whether left anterior fascicular block (LAFB), a prolonged QTC, and atrial premature complexes (APCs) each predicted AF after adjusting for each other. We then calculated the attributable risk in the exposed for each electrocardiographic marker. LAFB and QTC intervals were assessed on baseline 12-lead electrocardiogram (n = 4,696). APC count was determined using 24-hour Holter recordings obtained in a random subsample (n = 1,234). After adjusting for potential confounders and each electrocardiographic marker, LAFB (hazard ratio [HR] 2.1, 95% confidence interval [CI] 1.1 to 3.9, p = 0.023), a prolonged QTC (HR 2.5, 95% CI 1.4 to 4.3, p = 0.002), and every doubling of APC count (HR 1.2, 95% CI 1.1 to 1.3, p <0.001) each remained independently predictive of incident AF. The attributable risk of AF in the exposed was 35% (95% CI 13% to 52%) for LAFB, 25% (95% CI 0.6% to 44%) for a prolonged QTC, and 34% (95% CI 26% to 42%) for APCs. In conclusion, in a community-based cohort, 3 previously established electrocardiogram-derived AF predictors were each independently associated with incident AF, suggesting that they may represent distinct mechanisms underlying the disease.