Creating Devices for Personalized Health Monitoring: Cardiovascular Monitoring Case Studies

As part of the mini-symposium entitled Creating Devices for Personalized Health Monitoring, Dr. McManus presents recent work related to cardiac monitoring using smartphone and bioimpedance sensors, including arrhythmia, blood volume, and heart failure monitoring

Incidence, prognosis, and factors associated with cardiac arrest in patients hospitalized with acute coronary syndromes (the GRACE Registry): A master\u27s thesis

Objectives: Contemporary data are lacking with respect to the incidence rates of, factors associated with, and impact of cardiac arrest from ventricular fibrillation or tachycardia (VF-CA) on hospital survival in patients admitted with an acute coronary syndrome (ACS). The objectives of this multinational study were to characterize trends in the magnitude of in-hospital VF-CA complicating an ACS and describe its impact over time on hospital prognosis. Methods: The study population consisted of 59,161 patients enrolled in the Global Registry of Acute Coronary Events Study between 2000 and 2007. Overall, 3,618 patients (6.2%) developed VF-CA during their hospitalization for an ACS. Incidence rates of VF-CA declined over time, albeit in an inconsistent manner. Patients who experienced VF-CA were on average older and had a greater burden of cardiovascular disease, yet were less likely to receive evidence-based cardiac therapies than patients in whom VF-CA did not occur. Hospital death rates were 55.3% and 1.5% in patients with and without VF-CA, respectively. There was a greater than 50% decline in the hospital death rates associated with VF-CA during the years under study. Patients with a VF-CA occurring after 48 hours were at especially high risk for dying during hospitalization (82.8%). Conclusions: Despite reductions in the magnitude of, and short-term mortality from, VF-CA between 2000 and 2007, VF-CA continues to exert a significant adverse effect on survival among patients hospitalized with an ACS. Opportunities exist to improve the identification and treatment of ACS patients at risk for VF-CA to reduce the incidence of, and mortality from, this serious arrhythmic disturbance

Association of Lipid-Related Genetic Variants with the Incidence of Atrial Fibrillation: The AFGen Consortium

BACKGROUND: Several studies have shown associations between blood lipid levels and the risk of atrial fibrillation (AF). To test the potential effect of blood lipids with AF risk, we assessed whether previously developed lipid gene scores, used as instrumental variables, are associated with the incidence of AF in 7 large cohorts. METHODS: We analyzed 64,901 individuals of European ancestry without previous AF at baseline and with lipid gene scores. Lipid-specific gene scores, based on loci significantly associated with lipid levels, were calculated. Additionally, non-pleiotropic gene scores for high-density lipoprotein cholesterol (HDLc) and low-density lipoprotein cholesterol (LDLc) were calculated using SNPs that were only associated with the specific lipid fraction. Cox models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CI) of AF per 1-standard deviation (SD) increase of each lipid gene score. RESULTS: During a mean follow-up of 12.0 years, 5434 (8.4%) incident AF cases were identified. After meta-analysis, the HDLc, LDLc, total cholesterol, and triglyceride gene scores were not associated with incidence of AF. Multivariable-adjusted HR (95% CI) were 1.01 (0.98-1.03); 0.98 (0.96-1.01); 0.98 (0.95-1.02); 0.99 (0.97-1.02), respectively. Similarly, non-pleiotropic HDLc and LDLc gene scores showed no association with incident AF: HR (95% CI) = 1.00 (0.97-1.03); 1.01 (0.99-1.04). CONCLUSIONS: In this large cohort study of individuals of European ancestry, gene scores for lipid fractions were not associated with incident AF

Estimated stroke risk, yield, and number needed to screen for atrial fibrillation detected through single time screening: a multicountry patient-level meta-analysis of 141,220 screened individuals

BACKGROUND: The precise age distribution and calculated stroke risk of screen-detected atrial fibrillation (AF) is not known. Therefore, it is not possible to determine the number needed to screen (NNS) to identify one treatable new AF case (NNS-Rx) (i.e., Class-1 oral anticoagulation [OAC] treatment recommendation) in each age stratum. If the NNS-Rx is known for each age stratum, precise cost-effectiveness and sensitivity simulations can be performed based on the age distribution of the population/region to be screened. Such calculations are required by national authorities and organisations responsible for health system budgets to determine the best age cutoffs for screening programs and decide whether programs of screening should be funded. Therefore, we aimed to determine the exact yield and calculated stroke-risk profile of screen-detected AF and NNS-Rx in 5-year age strata. METHODS AND FINDINGS: A systematic review of Medline, Pubmed, and Embase was performed (January 2007 to February 2018), and AF-SCREEN international collaboration members were contacted to identify additional studies. Twenty-four eligible studies were identified that performed a single time point screen for AF in a general ambulant population, including people \u3e /=65 years. Authors from eligible studies were invited to collaborate and share patient-level data. Statistical analysis was performed using random effects logistic regression for AF detection rate, and Poisson regression modelling for CHA2DS2-VASc scores. Nineteen studies (14 countries from a mix of low- to middle- and high-income countries) collaborated, with 141,220 participants screened and 1,539 new AF cases. Pooled yield of screening was greater in males across all age strata. The age/sex-adjusted detection rate for screen-detected AF in \u3e /=65-year-olds was 1.44% (95% CI, 1.13%-1.82%) and 0.41% (95% CI, 0.31%-0.53%) for \u3c 65-year-olds. New AF detection rate increased progressively with age from 0.34% ( \u3c 60 years) to 2.73% ( \u3e /=85 years). Neither the choice of screening methodology or device, the geographical region, nor the screening setting influenced the detection rate of AF. Mean CHA2DS2-VASc scores (n = 1,369) increased with age from 1.1 ( \u3c 60 years) to 3.9 ( \u3e /=85 years); 72% of \u3e /=65 years had \u3e /=1 additional stroke risk factor other than age/sex. All new AF \u3e /=75 years and 66% between 65 and 74 years had a Class-1 OAC recommendation. The NNS-Rx is 83 for \u3e /=65 years, 926 for 60-64 years; and 1,089 for \u3c 60 years. The main limitation of this study is there are insufficient data on sociodemographic variables of the populations and possible ascertainment biases to explain the variance in the samples. CONCLUSIONS: People with screen-detected AF are at elevated calculated stroke risk: above age 65, the majority have a Class-1 OAC recommendation for stroke prevention, and \u3e 70% have \u3e /=1 additional stroke risk factor other than age/sex. Our data, based on the largest number of screen-detected AF collected to date, show the precise relationship between yield and estimated stroke risk profile with age, and strong dependence for NNS-RX on the age distribution of the population to be screened: essential information for precise cost-effectiveness calculations

Metabolomic Profiling in Relation to New-Onset Atrial Fibrillation (from the Framingham Heart Study)

Previous studies have shown several metabolic biomarkers to be associated with prevalent and incident atrial fibrillation (AF), but the results have not been replicated. We investigated metabolite profiles of 2,458 European ancestry participants from the Framingham Heart Study without AF at the index examination and followed them for 10 years for new-onset AF. Amino acids, organic acids, lipids, and other plasma metabolites were profiled by liquid chromatography-tandem mass spectrometry using fasting plasma samples. We conducted Cox proportional hazard analyses for association between metabolites and new-onset AF. We performed hypothesis-generating analysis to identify novel metabolites and hypothesis-testing analysis to confirm the previously reported associations between metabolites and AF. Mean age was 55.1 +/- 9.9 years, and 53% were women. Incident AF developed in 156 participants (6.3%) in 10 years of follow-up. A total of 217 metabolites were examined, consisting of 54 positively charged metabolites, 59 negatively charged metabolites, and 104 lipids. None of the 217 metabolites met our a priori specified Bonferroni corrected level of significance in the multivariate analyses. We were unable to replicate previous results demonstrating associations between metabolites that we had measured and AF. In conclusion, in our metabolomics approach, none of the metabolites we tested were significantly associated with the risk of future AF

Transcriptional Regulation of Cardiac Remodeling in a Porcine Model with Validation in Human Subjects

Introduction: The majority of new atrial fibrillation (AF) cases occur in elderly patients with cardiac remodeling (CR) in the setting of structural heart disease and heart failure (HF). We leveraged a unique animal model to identify cardiac microRNAs (miRNAs) and gene regulatory mechanisms that drive this process. Methods: We prospectively quantified atrial expression of 48 miRNAs by high-throughput qRT-PCR in 15 pigs with right-atrial pacing-induced heart disease (5 pigs with AF/severe HF, 5 pigs with AF/mild HF, and 5 control pigs) as well as in 21 patients (11 with AF and CR and 10 controls) undergoing cardiac surgery. CR and HF were defined through a metric of left atrial volume index, BNP and ejection fraction. MiRNA levels were normalized to global mean and expression compared across pig subtypes and between the two human groups. Results: In the porcine model, miR-208b was upregulated at week 1 (ΔCT= -3.9, pT = -5.5, pT = -1.5, pT = -1.5, pT = -1.5, pT = -1.5, p\u3c0.05). Conclusions: Dysregulation of miR-208b is confirmed in our porcine model and is validated in humans. Prior studies have identified miR-208b in both myosin isoform switching and conduction disease. We theorize that dysregulation of miR-208b may play a critical role in atrial structural remodeling and vulnerability to AF

Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium

BACKGROUND: Tools for the prediction of atrial fibrillation (AF) may identify high-risk individuals more likely to benefit from preventive interventions and serve as a benchmark to test novel putative risk factors. METHODS AND RESULTS: Individual-level data from 3 large cohorts in the United States (Atherosclerosis Risk in Communities [ARIC] study, the Cardiovascular Health Study [CHS], and the Framingham Heart Study [FHS]), including 18 556 men and women aged 46 to 94 years (19% African Americans, 81% whites) were pooled to derive predictive models for AF using clinical variables. Validation of the derived models was performed in 7672 participants from the Age, Gene and Environment-Reykjavik study (AGES) and the Rotterdam Study (RS). The analysis included 1186 incident AF cases in the derivation cohorts and 585 in the validation cohorts. A simple 5-year predictive model including the variables age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and history of myocardial infarction and heart failure had good discrimination (C-statistic, 0.765; 95% CI, 0.748 to 0.781). Addition of variables from the electrocardiogram did not improve the overall model discrimination (C-statistic, 0.767; 95% CI, 0.750 to 0.783; categorical net reclassification improvement, -0.0032; 95% CI, -0.0178 to 0.0113). In the validation cohorts, discrimination was acceptable (AGES C-statistic, 0.664; 95% CI, 0.632 to 0.697 and RS C-statistic, 0.705; 95% CI, 0.664 to 0.747) and calibration was adequate. CONCLUSION: A risk model including variables readily available in primary care settings adequately predicted AF in diverse populations from the United States and Europe

Accuracy and Coverage of Using the Assigned International Classification of Diseases, 9th and 10th Revision, Clinical Modification Codes for Detecting Bleeding Events in Electronic Health Record

Background: Hemorrhages are common events that confer significant risk for in-hospital and post-discharge morbidity and mortality among cardiovascular disease (CVD) patients treated with anticoagulation. International Classification of Diseases, 9th and 10th Revision, Clinical Modification (ICD-9-CM, ICD-10-CM) codes have been widely used in CVD research and managements. Objective: To determine the accuracy and coverage of assigned ICD-CM codes for reporting bleeding events. Methods: From the University of Massachusetts Medical School electronic health record (EHR) database we identified 21k patients on anticoagulation with high bleeding risks based on their ICD-9-CM or ICD-10-CM codes. Through manual chart review, we selected one unstructured note (i.e., physical exam findings, historical narratives) from each patient and identified 299 notes with and 102 free from bleeds using convenience sampling. We extracted bleeding events, labeled them as “current” or “historical”, and determined their severity (major/minor, clinically relevant/irrelevant) based on International Society on Thrombosis and Haemostasis (ISTH) criteria. Using the chart extractions as gold standard, we calculated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-9-CM and ICD10-CM for detecting bleeding. Results: In Administrative claims data, ICD-9-CM had a sensitivity of 35.3%, 96.1% specificity, 96.7% PPV, and 31.2% NPV for detecting bleeding, whereas ICD-10-CM codes had a sensitivity of 55.0%, 96.3% specificity, 97. 9% PPV, and 40.6% NPV. Both ICD-CM codes exhibited better sensitivity for detecting current bleeds (41.1%, 73.4%) and major bleeds (50.0%, 62.9%) as compared with historical (32.4%, 43.8%) and minor ones (31.6%, 56.8%). Conclusions: Half of all bleeding events among patients with CVD were not reflected in administrative claims data. Although the code’s precision is acceptable, the low sensitivity suggests bleeding events may be under-reported by claims data. Our findings have important clinical implications and suggest that novel methods are needed to enhance bleeding identification to improve clinical decision making

Device Therapies Among Patients Receiving Primary Prevention Implantable Cardioverter-Defibrillators in the Cardiovascular Research Network

BACKGROUND: Primary prevention implantable cardioverter-defibrillators (ICDs) reduce mortality in selected patients with left ventricular systolic dysfunction by delivering therapies (antitachycardia pacing or shocks) to terminate potentially lethal arrhythmias; inappropriate therapies also occur. We assessed device therapies among adults receiving primary prevention ICDs in 7 healthcare systems. METHODS AND RESULTS: We linked medical record data, adjudicated device therapies, and the National Cardiovascular Data Registry ICD Registry. Survival analysis evaluated therapy probability and predictors after ICD implant from 2006 to 2009, with attention to Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups: left ventricular ejection fraction, 31% to 35%; nonischemic cardiomyopathy \u3c 9 months\u27 duration; and New York Heart Association class IV heart failure with cardiac resynchronization therapy defibrillator. Among 2540 patients, 35% were \u3c 65 years old, 26% were women, and 59% were white. During 27 (median) months, 738 (29%) received \u3e /=1 therapy. Three-year therapy risk was 36% (appropriate, 24%; inappropriate, 12%). Appropriate therapy was more common in men (adjusted hazard ratio [HR], 1.84; 95% confidence interval [CI], 1.43-2.35). Inappropriate therapy was more common in patients with atrial fibrillation (adjusted HR, 2.20; 95% CI, 1.68-2.87), but less common among patients \u3e /=65 years old versus younger (adjusted HR, 0.72; 95% CI, 0.54-0.95) and in recent implants (eg, in 2009 versus 2006; adjusted HR, 0.66; 95% CI, 0.46-0.95). In Centers for Medicare and Medicaid Services Coverage With Evidence Development analysis, inappropriate therapy was less common with cardiac resynchronization therapy defibrillator versus single chamber (adjusted HR, 0.55; 95% CI, 0.36-0.84); therapy risk did not otherwise differ for Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups. CONCLUSIONS: In this community cohort of primary prevention patients receiving ICD, therapy delivery varied across demographic and clinical characteristics, but did not differ meaningfully for Centers for Medicare and Medicaid Services Coverage With Evidence Development subgroups

Adherence of Mobile App-Based Surveys and Comparison With Traditional Surveys: eCohort Study

BACKGROUND: eCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection. OBJECTIVE: The aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center. METHODS: We defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC). RESULTS: Among the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77). CONCLUSIONS: We observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors