An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.

BACKGROUND: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12­lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke. METHODS: We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF. Incidence of AF within 1 year and AF-related strokes within 3 years of the encounter were identified. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. The efficiency of five methods was evaluated for selecting a cohort for AF screening. The methods were selected from four clinical trials (mSToPS, GUARD-AF, SCREEN-AF and STROKESTOP) and the ECG-based ML model. We simulated patient selection for the five methods between the years 2011 and 2014 and evaluated outcomes for 1 year intervals between 2012 and 2015, resulting in a total of twenty 1-year periods. Patients were considered eligible if they met the criteria before the start of the given 1-year period or within that period. The primary outcomes were numbers needed to screen (NNS) for AF and AF-associated stroke. RESULTS: The clinical trial models indicated large proportions of the population with a prior ECG for AF screening (up to 31%), coinciding with NNS ranging from 14 to 18 for AF and 249-359 for AF-associated stroke. At comparable sensitivity, the ECG ML model indicated a modest number of patients for screening (14%) and had the highest efficiency in NNS for AF (7.3; up to 60% reduction) and AF-associated stroke (223; up to 38% reduction). CONCLUSIONS: An ECG-based ML risk prediction model is more efficient than contemporary AF-screening criteria based on age alone or age and clinical features at indicating a population for AF screening to potentially prevent AF-related strokes.

Genomic Screening for Pathogenic Transthyretin Variants Finds Evidence of Underdiagnosed Amyloid Cardiomyopathy From Health Records.

BACKGROUND: New treatments for transthyretin amyloidosis improve survival, but diagnosis remains challenging. Pathogenic or likely pathogenic (P/LP) variants in the transthyretin (TTR) gene are one cause of transthyretin amyloidosis, and genomic screening has been proposed to identify at-risk individuals. However, data on disease features and penetrance are lacking to inform the utility of such population-based genomic screening for TTR. OBJECTIVES: This study characterized the prevalence of P/LP variants in TTR identified through exome sequencing and the burden of associated disease from electronic health records for individuals with these variants from a large (N = 134,753), primarily European-ancestry cohort. METHODS: We compared frequencies of common disease features and cardiac imaging findings between individuals with and without P/LP TTR variants. RESULTS: We identified 157 of 134,753 (0.12%) individuals with P/LP TTR variants (43% male, median age 52 [Q1-Q3: 37-61] years). Seven P/LP variants accounted for all observations, the majority being V122I (p.V142I; 113, 0.08%). Approximately 60% (n = 91) of individuals with P/LP TTR variants (all V122I) had African ancestry. Diagnoses of amyloidosis were limited (2 of 157 patients), although related heart disease diagnoses, including cardiomyopathy and heart failure, were significantly increased in individuals with P/LP TTR variants who were aged >60 years. Fourteen percent (7 of 49) of individuals aged ≥60 or older with a P/LP TTR variant had heart disease and ventricular septal thickness >1.2 cm, only one of whom was diagnosed with amyloidosis. CONCLUSIONS: Individuals with P/LP TTR variants identified by genomic screening have increased odds of heart disease after age 60 years, although amyloidosis is likely underdiagnosed without knowledge of the genetic variant.

Clinical Findings and Diagnostic Yield of Arrhythmogenic Cardiomyopathy Through Genomic Screening of Pathogenic or Likely Pathogenic Desmosome Gene Variants.

BACKGROUND: Genomic screening holds great promise for presymptomatic identification of hidden disease, and prevention of dramatic events, including sudden cardiac death associated with arrhythmogenic cardiomyopathy (ACM). Herein, we present findings from clinical follow-up of carriers of ACM-associated pathogenic/likely pathogenic desmosome variants ascertained through genomic screening. METHODS: Of 64 548 eligible participants in Geisinger MyCode Genomic Screening and Counseling program (2015-present), 92 individuals (0.14%) identified with pathogenic/likely pathogenic desmosome variants by clinical laboratory testing were referred for evaluation. We reviewed preresult medical history, patient-reported family history, and diagnostic testing results to assess both arrhythmogenic right ventricular cardiomyopathy and left-dominant ACM. RESULTS: One carrier had a prior diagnosis of dilated cardiomyopathy with arrhythmia; no other related diagnoses or diagnostic family history criteria were reported. Fifty-nine carriers (64%) had diagnostic testing in follow-up. Excluding the variant, 21/59 carriers satisfied at least one arrhythmogenic right ventricular cardiomyopathy task force criterion, 11 (52%) of whom harbored DSP variants, but only 5 exhibited multiple criteria. Six (10%) carriers demonstrated evidence of left-dominant ACM, including high rates of atypical late gadolinium enhancement by magnetic resonance imaging and nonsustained ventricular tachycardia. Two individuals received new cardiomyopathy diagnoses and received defibrillators for primary prevention. CONCLUSIONS: Genomic screening for pathogenic/likely pathogenic variants in desmosome genes can uncover both left- and right-dominant ACM. Findings of overt cardiomyopathy were limited but were most common in DSP-variant carriers and notably absent in PKP2-variant carriers. Consideration of the pathogenic/likely pathogenic variant as a major criterion for diagnosis is inappropriate in the setting of genomic screening.

Prevalence and Electronic Health Record-Based Phenotype of Loss-of-Function Genetic Variants in Arrhythmogenic Right Ventricular Cardiomyopathy-Associated Genes.

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is associated with variants in desmosome genes. Secondary findings of pathogenic/likely pathogenic variants, primarily loss-of-function (LOF) variants, are recommended for clinical reporting; however, their prevalence and associated phenotype in a general clinical population are not fully characterized. METHODS: From whole-exome sequencing of 61 019 individuals in the DiscovEHR cohort, we screened for putative loss-of-function variants in PKP2, DSC2, DSG2, and DSP. We evaluated measures from prior clinical ECG and echocardiograms, manually over-read to evaluate ARVC diagnostic criteria, and performed a PheWAS (phenome-wide association study). Finally, we estimated expected penetrance using Bayesian inference. RESULTS: One hundred forty individuals (0.23%; 59±18 years old at last encounter; 33% male) had an ARVC variant (G+). None had an existing diagnosis of ARVC in the electronic health record, nor significant differences in prior ECG or echocardiogram findings compared with matched controls without variants. Several G+ individuals satisfied major repolarization (n=4) and ventricular function (n=5) criteria, but this prevalence matched controls. PheWAS showed no significant associations of other heart disease diagnoses. Combining our best genetic and disease prevalence estimates yields an estimated penetrance of 6.0%. CONCLUSIONS: The prevalence of ARVC loss-of-function variants is ≈1:435 in a general clinical population of predominantly European descent, but with limited electronic health record-based evidence of phenotypic association in our population, consistent with a low penetrance estimate. Prospective deep phenotyping and longitudinal follow-up of a large sequenced cohort is needed to determine the true clinical relevance of an incidentally identified ARVC loss-of-function variant.

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning.

OBJECTIVES: The goal of this study was to use machine learning to more accurately predict survival after echocardiography. BACKGROUND: Predicting patient outcomes (e.g., survival) following echocardiography is primarily based on ejection fraction (EF) and comorbidities. However, there may be significant predictive information within additional echocardiography-derived measurements combined with clinical electronic health record data. METHODS: Mortality was studied in 171,510 unselected patients who underwent 331,317 echocardiograms in a large regional health system. The authors investigated the predictive performance of nonlinear machine learning models compared with that of linear logistic regression models using 3 different inputs: 1) clinical variables, including 90 cardiovascular-relevant International Classification of Diseases, Tenth Revision, codes, and age, sex, height, weight, heart rate, blood pressures, low-density lipoprotein, high-density lipoprotein, and smoking; 2) clinical variables plus physician-reported EF; and 3) clinical variables and EF, plus 57 additional echocardiographic measurements. Missing data were imputed with a multivariate imputation by using a chained equations algorithm (MICE). The authors compared models versus each other and baseline clinical scoring systems by using a mean area under the curve (AUC) over 10 cross-validation folds and across 10 survival durations (6 to 60 months). RESULTS: Machine learning models achieved significantly higher prediction accuracy (all AUC >0.82) over common clinical risk scores (AUC = 0.61 to 0.79), with the nonlinear random forest models outperforming logistic regression (p < 0.01). The random forest model including all echocardiographic measurements yielded the highest prediction accuracy (p < 0.01 across all models and survival durations). Only 10 variables were needed to achieve 96% of the maximum prediction accuracy, with 6 of these variables being derived from echocardiography. Tricuspid regurgitation velocity was more predictive of survival than LVEF. In a subset of studies with complete data for the top 10 variables, multivariate imputation by chained equations yielded slightly reduced predictive accuracies (difference in AUC of 0.003) compared with the original data. CONCLUSIONS: Machine learning can fully utilize large combinations of disparate input variables to predict survival after echocardiography with superior accuracy.

Impact of the cone operation on left ventricular size, function, and dyssynchrony in Ebstein anomaly: a cardiovascular magnetic resonance study.

BACKGROUND: In addition to tricuspid regurgitation (TR) and right ventricular (RV) enlargement, patients with Ebstein anomaly are at risk for left ventricular (LV) dysfunction and dyssynchrony. We studied the impact of the cone tricuspid valve reconstruction operation on LV size, function, and dyssynchrony. METHODS: All Ebstein anomaly patients who had both pre- and postoperative cardiovascular magnetic resonance (CMR) studies were retrospectively identified. From cine images, RV and LV volumes and ejection fractions (EF) were calculated, and LV circumferential and longitudinal strain were measured by feature tracking. To quantify LV dyssynchrony, temporal offsets (TOs) were computed among segmental circumferential strain versus time curves using cross-correlation analysis and patient-specific reference curves. An LV dyssynchrony index was calculated as the standard deviation of the TOs. RESULTS: Twenty patients (65% female) were included with a median age at cone operation of 16 years, and a median time between pre- and postoperative CMR of 2.8 years. Postoperatively, there was a decline in the TR fraction (56 ± 19% vs. 5 ± 4%, p < 0.001), RV end-diastolic volume (EDV) (242 ± 110 ml/m2 vs. 137 ± 82 ml/m2, p < 0.001), and RV stroke volume (SV) (101 ± 35 vs. 51 ± 7 ml/m2, p < 0.001). RV EF was unchanged. Conversely, there was an increase in both LV EDV (68 ± 13 vs. 85 ± 13 ml/m2, p < 0.001) and LV stroke volume (37 ± 8 vs. 48 ± 6 ml/m2, p < 0.001). There was no change in LV EF, or global circumferential and longitudinal strain but basal septal circumferential strain improved (16 ± 7% vs. 22 ± 5%, p = 0.04). LV contraction become more synchronous (dyssynchrony index: 32 ± 17 vs. 21 ± 9 msec, p = 0.02), and the extent correlated with the reduction in RV EDV and TR. CONCLUSIONS: In patients with the Ebstein anomaly, the cone operation led to reduced TR and RV stroke volume, increased LV stroke volume, improved LV basal septal strain, and improved LV synchrony. Our data demonstrates that the detrimental effect of the RV on LV function can be mitigated by the cone operation.

Electronic health record phenotype in subjects with genetic variants associated with arrhythmogenic right ventricular cardiomyopathy: a study of 30,716 subjects with exome sequencing.

PurposeArrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart disease. Clinical follow-up of incidental findings in ARVC-associated genes is recommended. We aimed to determine the prevalence of disease thus ascertained.MethodsIndividuals (n = 30,716) underwent exome sequencing. Variants in PKP2, DSG2, DSC2, DSP, JUP, TMEM43, or TGFß3 that were database-listed as pathogenic or likely pathogenic were identified and evidence-reviewed. For subjects with putative loss-of-function (pLOF) variants or variants of uncertain significance (VUS), electronic health records (EHR) were reviewed for ARVC diagnosis, diagnostic criteria, and International Classification of Diseases (ICD-9) codes.ResultsEighteen subjects had pLOF variants; none of these had an EHR diagnosis of ARVC. Of 14 patients with an electrocardiogram, one had a minor diagnostic criterion; the rest were normal. A total of 184 subjects had VUS, none of whom had an ARVC diagnosis. The proportion of subjects with VUS with major (4%) or minor (13%) electrocardiogram diagnostic criteria did not differ from that of variant-negative controls. ICD-9 codes showed no difference in defibrillator use, electrophysiologic abnormalities or nonischemic cardiomyopathies in patients with pLOF or VUSs compared with controls.ConclusionpLOF variants in an unselected cohort were not associated with ARVC phenotypes based on EHR review. The negative predictive value of EHR review remains uncertain.

Smooth muscle cell deletion of low-density lipoprotein receptor-related protein 1 augments angiotensin II-induced superior mesenteric arterial and ascending aortic aneurysms.

OBJECTIVE: Low-density lipoprotein receptor-related protein 1 (LRP1), a multifunctional protein involved in endocytosis and cell signaling pathways, leads to several vascular pathologies when deleted in vascular smooth muscle cells (SMCs). The purpose of this study was to determine whether LRP1 deletion in SMCs influenced angiotensin II-induced arterial pathologies. APPROACH AND RESULTS: LRP1 protein abundance was equivalent in selected arterial regions, but SMC-specific LRP1 depletion had no effect on abdominal and ascending aortic diameters in young mice. To determine the effects of LRP1 deficiency on angiotensin II vascular responses, SMC-specific LRP1 (smLRP1(+/+)) and smLRP1-deficient (smLRP1(-/-)) mice were infused with saline, angiotensin II, or norepinephrine. Several smLRP(-/-) mice died of superior mesenteric arterial (SMA) rupture during angiotensin II infusion. In surviving mice, angiotensin II profoundly augmented SMA dilation in smLRP1(-/-) mice. SMA dilation was blood pressure dependent as demonstrated by a similar response during norepinephrine infusion. SMA dilation was also associated with profound macrophage accumulation, but minimal elastin fragmentation. Angiotensin II infusion led to no significant differences in abdominal aorta diameters between smLRP1(+/+) and smLRP1(-/-) mice. In contrast, ascending aortic dilation was exacerbated markedly in angiotensin II-infused smLRP1(-/-) mice, but norepinephrine had no significant effect on either aortic region. Ascending aortas of smLRP1(-/-) mice infused with angiotensin II had minimal macrophage accumulation but significantly increased elastin fragmentation and mRNA abundance of several LRP1 ligands including MMP-2 (matrix metalloproteinase-2) and uPA (urokinase plasminogen activator). CONCLUSIONS: smLRP1 deficiency had no effect on angiotensin II-induced abdominal aortic aneurysm formation. Conversely, angiotensin II infusion in smLRP1(-/-) mice exacerbated SMA and ascending aorta dilation. Dilation in these 2 regions had differential association with blood pressure and divergent pathological characteristics.

Measures of dyssynchrony in the left ventricle of healthy children and young patients with dilated cardiomyopathy.

BACKGROUND: Doppler tissue imaging may help identify children with dyssynchrony who could benefit from resynchronization therapy. However, few studies have quantified dyssynchrony measures in children; no study has investigated the relationship among age, heart rate, and dyssynchrony measures in children; and no study has quantified cross-correlation delay in children. The aim of this study was to test the hypotheses that measures of left ventricular dyssynchrony would correlate with age, primarily because of the correlation between heart rate and age, and that children with cardiomyopathy would have left ventricular dyssynchrony. METHODS: Sixty healthy children and 11 children with dilated cardiomyopathy were prospectively enrolled. Seven dyssynchrony measures were quantified: septal-to-lateral delay, peak velocity difference, the standard deviations of times to peak in 12 segments in systole and diastole, and cross-correlation delay in systole, diastole, and the whole cycle. RESULTS: The seven dyssynchrony measures were either not correlated with age or only weakly correlated with age after correcting for heart rate using Bazett's formula. Septal-to-lateral delay, peak velocity difference, and the standard deviation of times to peak in 12 segments in systole showed dyssynchrony in 57% to 85% of normal controls, compared with 20% for cross-correlation delay in the whole cycle and 3% for the standard deviation of times to peak in 12 segments in diastole. Cross-correlation delay in systole, cross-correlation delay in diastole, cross-correlation delay in the whole cycle, and the standard deviation of times to peak in 12 segments in diastole were elevated in children with dilated cardiomyopathy compared with controls. CONCLUSIONS: Echocardiographic dyssynchrony measures should be corrected for heart rate using Bazett's formula in children. Time-to-peak Doppler tissue imaging dyssynchrony measures classify many healthy children as having abnormalities with the timing of left ventricular contraction, which suggests that the methodology is not accurate in children. In preliminary studies, cross-correlation dyssynchrony measures show elevated systolic and diastolic measures of dyssynchrony in children with dilated cardiomyopathy compared with controls, which deserves further investigation to help identify children who may benefit from resynchronization therapy.

Variability in tissue Doppler echocardiographic measures of dyssynchrony is reduced with use of a larger region of interest.

BACKGROUND: Doppler tissue imaging (DTI)-based dyssynchrony parameters failed to predict response to cardiac resynchronization therapy (CRT) in the multicenter Predictors of Response to Cardiac Resynchronization Therapy (PROSPECT) trial. Large variability during the interpretation of DTI data was one of several factors thought to contribute to this failure. In this study, the authors hypothesized that using larger regions of interest (ROIs) to generate velocity curves from Doppler tissue images would significantly reduce the variability of DTI dyssynchrony parameters. METHODS: The variability of 3 ROI sizes (6 x 6, 18 x 6, and 30 x 6 mm) was compared in 30 patients undergoing CRT. Variability due to manual ROI placement was determined for each ROI size by placing 3 ROIs in each myocardial segment, 6mm apart from one another. Thus, 3 velocity curves were generated for each segment and each ROI size. Four published dyssynchrony parameters were calculated from all permutations of the 3 ROI positions per segment. A mean modified coefficient of variation was calculated for each parameter and ROI size. RESULTS: The 6 x 6 mm ROI had a mean coefficient of variation of 27%. The 18 x 6 and 30 x 6 mm ROIs had significantly lower coefficients of variation (17% and 14%, respectively) than the 6 x 6 mm ROI (P < .01 for both). The 30 x 6 mm ROI also reduced the diagnostic inconsistency of dyssynchrony parameters by 44% (P = .024) compared with the 6 x 6 mm ROI. CONCLUSION: Using a 30 x 6 mm ROI instead of a 6 x 6 mm ROI to quantify tissue Doppler dyssynchrony reduces variability by 47% and diagnostic inconsistency by 44%. The authors recommend using a 30 x 6 mm ROI in future trials to minimize variability.