Search

Introduction: Pathogenic DNA variants associated with inherited cardiomyopathies are recognized as clinically important and actionable when identified, leading some clinicians to recommend population-wide genomic screening. The prevalence and clinical importance of such variants within the context of contemporary clinical care warrant further study. Methods: Using whole exome sequencing data from participants in Atherosclerosis Risk in Communities (ARIC) and the UK Biobank (UKBB), observed DNA sequence variants in any of 12 genes ( ACTC1, GLA, LMNA, MYBPC3, MYH7, MYL2, MYL3, PRKAG2, TNNI3, TNNT2, TPM1, and TTN ) known to be causative of inherited cardiomyopathies with accepted preventative or therapeutic interventions were classified as pathogenic or likely pathogenic by a clinical laboratory geneticist blinded to case status per recommendations from the American College of Medical Genetics. The relationship of carrier status to risk of death or cardiovascular disease was assessed. Results: Among 9,667 ARIC participants (mean [SD] age 54 [5.7] years; 43.8% female) and 49,744 UKBB participants (mean [SD] age 57.1 [8.0] years; 54.6% female), 68 (0.70%) and 362 (0.73%) harbored an actionable pathogenic or likely pathogenic variant associated with cardiomyopathy, respectively. Carriers of these variants were not reliably identifiable by imaging or electrocardiography signatures. Presence of these variants was associated with 1.7- to 1.8-fold increased risk of heart failure, 2.1- to 2.9-fold increased risk of atrial fibrillation, and 1.6- to 1.8-fold increased risk of all-cause mortality across studies. Conclusions: The findings suggest that approximately 0.7% of middle-aged adult population in ARIC and the UKBB harbored a pathogenic variant associated with cardiomyopathy. Carriers of these variants would be difficult to identify within routine clinical practice but suffer from increased risk of cardiovascular disease and all-cause mortality.

Background: Individuals of South Asian ancestry represent 23% of the global population, corresponding to 1.8 billion people, and have substantially higher risk of atherosclerotic cardiovascular disease compared with most other ethnicities. US practice guidelines now recognize South Asian ancestry as an important risk-enhancing factor. The magnitude of enhanced risk within the context of contemporary clinical care, the extent to which it is captured by existing risk estimators, and its potential mechanisms warrant additional study. Methods: Within the UK Biobank prospective cohort study, 8124 middle-aged participants of South Asian ancestry and 449 349 participants of European ancestry who were free of atherosclerotic cardiovascular disease at the time of enrollment were examined. The relationship of ancestry to risk of incident atherosclerotic cardiovascular disease—defined as myocardial infarction, coronary revascularization, or ischemic stroke—was assessed with Cox proportional hazards regression, along with examination of a broad range of clinical, anthropometric, and lifestyle mediators. Results: The mean age at study enrollment was 57 years, and 202 405 (44%) were male. Over a median follow-up of 11 years, 554 of 8124 (6.8%) individuals of South Asian ancestry experienced an atherosclerotic cardiovascular disease event compared with 19 756 of 449 349 (4.4%) individuals of European ancestry, corresponding to an adjusted hazard ratio of 2.03 (95% CI, 1.86–2.22; P 2-fold higher observed risk, the predicted 10-year risk of cardiovascular disease according to the American Heart Association/American College of Cardiology Pooled Cohort equations and QRISK3 equations was nearly identical for individuals of South Asian and European ancestry. Adjustment for a broad range of clinical, anthropometric, and lifestyle risk factors led to only modest attenuation of the observed hazard ratio to 1.45 (95% CI, 1.28–1.65, P Conclusions: Within a large prospective study, South Asian individuals had substantially higher risk of atherosclerotic cardiovascular disease compared with individuals of European ancestry, and this risk was not captured by the Pooled Cohort Equations.

Introduction: Electronic health records (EHRs) are a potentially rich source of data for developing predictive models for early detection of heart failure (HF). But, EHR documentation can vary both because of patient health and variation in clinical practice and behavior among physicians. Hypothesis: The “noise” contributed by variable physician behaviors, such as differences in the frequency and detail of documentation, could potentially confound predictive models for detection of HF. In this study, we characterized the documentation behaviors of primary care physicians (PCPs) in an effort to better understand this potential source of noise. Methods: We used longitudinal EHR data of a stratified random sample of 5,187 patients who were: 1) ≥50 years of age, and without a history of HF. PCPs (n=144) were identified and paired with the patients that they had treated for a minimum of 6 months. We derived 28 measures to characterize PCP behaviors - documentation frequencies of assertions and denials of selected Framingham HF signs and symptoms (FHFSS) in office visit encounter notes adjusted for patient comorbidities. Hierarchical clustering analyses (HCA) were performed on PCP documentation behaviors. Results: Based on HCA analyses, PCPs were clustered into 3 groups with distinct documentation behaviors. Group 1 PCPs (n=63) documented 10 out of 15 assertions, and 11 out of 13 denials of FHFSS significantly more frequently than Group 3 (n=20); while Group 2 PCPs (n=61) have significantly more frequent denial documentation behaviors than the other two (see Figure 1). No significant differences were found among patients’ chronic, episodic and cardio metabolic chronic disease counts (comorbidities) in each of the groups (p Conclusions: This study identified PCP groups with distinct documentation behaviors unrelated to patient complexity. This source of noise and potential confounder should be taken into account for predictive modeling.

Introduction: Electronic health records (EHRs) represent an opportunity for real-time early risk prediction. EHR records were used to determine the extent to which machine learning tools and a purely data driven approach to modeling (DDM) could detect heart failure subtypes, i.e., preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF), 12 months before a clinical diagnosis. Methods: Incident HF cases were identified from Geisinger Clinic primary care patients age 50-85, diagnosed between 2001 and 2010 and further defined as HFrEF if LVEF≤40 and HFpEF if LVEF>50. Controls were chosen to match HF cases by age, gender, location and primary care physician. EHR data were extracted on demographics, ICD-9 codes, medication orders, clinical and behavioral measures. Modeling was completed to detect HF using data from a 24-month observation window 12 months before the HF diagnosis. Patient feature vectors were generated from the data and summarized by one or more aggregation functions (e.g., counts, means). For the HF endpoint, modeling was done with and without patients who had an acute coronary syndrome (ACS) event within 12 months of the diagnosis. Regularized logistic regression was applied using information gain feature selection and 10-fold cross validation. Model performance was assessed by the area under the ROC curve (AUC) and complexity by the number of selected features. Results: Performance for HFpEF is better than for HFrEF. The HFpEF model is more complex than the HFrEF model as indicated by more EHR information that was needed to discriminate the HFpEF cases from controls. Performance with and without ACS cases is similar though models including ACS cases are more complex than models excluding them (Table). Conclusions: Purely data driven approaches to modeling can be used to detect HF 12 months before clinical diagnosis. Model performance and complexity varies with the HF subtypes indicating differences in the complexity of modeling the HF subtypes.