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TheWe identified rare, putativeSixty individuals (0.03%; median age 58 years [47-70 interquartile range], 43% male) harbored 27 unique

PURPOSE: To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI. MATERIALS AND METHODS: In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (E(cc)) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models. RESULTS: The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global E(cc) and 0.75 and 0.48, respectively, for segmental E(cc). Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental E(cc). CONCLUSION: StrainNet outperformed FT for global and segmental E(cc) analysis of cine MRI. Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023

Background: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography. Methods: Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction 15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010. Results: Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year. Conclusions: An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.

Background: Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population. Methods: We performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed. Results: Fifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc /decile of PRS =1.4 ms [95% CI, 1.3 to 1.5]; P =1.1×10 -196 ). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS). Conclusions: QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

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., Central Illustration

Bayesian Optimization (BO) is a well-studied hyperparameter tuning technique that is more efficient than grid search for high-cost, high-parameter machine learning problems. Echocardiography is a ubiquitous modality for evaluating heart structure and function in cardiology. In this work, we use BO to optimize the architectural and training-related hyperparameters of a previously published deep fully convolutional neural network model for multi-structure segmentation in echocardiography. In a fair comparison, the resulting model outperforms this recent state-of-the-art on the annotated CAMUS dataset in both apical two- and four-chamber echo views. We report mean Dice overlaps of 0.95, 0.96, and 0.93 on left ventricular (LV) endocardium, LV epicardium, and left atrium respectively. We also observe significant improvement in derived clinical indices, including smaller median absolute errors for LV end-diastolic volume (4.9mL vs. 6.7), end-systolic volume (3.1mL vs. 5.2), and ejection fraction (2.6% vs. 3.7); and much tighter limits of agreement, which were already within inter-rater variability for non-contrast echo. These results demonstrate the benefits of BO for echocardiography segmentation over a recent state-of-the-art framework, although validation using large-scale independent clinical data is required.

PURPOSE: Right ventricular (RV) function is increasingly recognized for its prognostic value in many disease states. As with the left ventricle (LV), strain-based measurements may have better prognostic value than typical chamber volumes or ejection fraction. Complete functional characterization of the RV requires high-resolution, 3D displacement tracking methods, which have been prohibitively challenging to implement. Zonal excitation during Displacement ENcoding with Stimulated Echoes (DENSE) magnetic resonance imaging (MRI) has helped reduce scan time for 2D LV strain quantification. We hypothesized that zonal excitation could alternatively be used to reproducibly acquire higher resolution, 3D-encoded DENSE images for quantification of bi-ventricular strain within a single breath-hold. METHODS: We modified sequence parameters for a 3D zonal excitation DENSE sequence to achieve in-plane resolution

BackgroundInherited cardiomyopathies can present with broad variation of phenotype. Data are limited regarding genetic screening strategies and outcomes associated with putative pathogenic variants (PuPV) in cardiomyopathy-associated genes in the general population.ObjectiveWe aimed to determine the risk of mortality and cardiomyopathy-related outcomes associated with PuPV in cardiomyopathy-associated genes in UK Biobank.MethodsUsing whole exome sequencing data, variants in dilated, hypertrophic and arrhythmogenic cardiomyopathy-associated genes with at least limited evidence of disease causality according to ClinGen Expert Panel curations, were annotated using REVEL (≥0.65) and ANNOVAR (predicted loss of function) to identify PuPVs. Individuals with PuPV comprised the genotype-positive (G+) and those without PuPV the genotype-negative (G-) cohorts. Group comparisons were made using time-to-event analyses for the primary (all-cause mortality) and secondary outcomes (diagnosis of cardiomyopathy; composite outcome of diagnosis of cardiomyopathy, heart failure, arrhythmia, stroke, and death).ResultsAmong 200,619 participants, 22,401 (11.2%) were found to host ≥1 PuPV in cardiomyopathy-associated genes (G+). After adjusting for age and sex, G+ individuals had increased all-cause mortality [HR 1.07 (95%CI 1.02-1.13; p=0.011)] and increased rates of diagnosis of cardiomyopathy later in life [HR 2.37 (95%CI 1.98-2.85; pConclusionsAdults with PuPV in cardiomyopathy-associated genes have higher all-cause mortality and increased risk of developing cardiomyopathy-associated features and complications, compared to genotype-negative controls.Condensed AbstractLeveraging the UK Biobank prospective cohort, we analyzed whole exome sequencing data in dilated, hypertrophic and arrhythmogenic cardiomyopathy-associated genes using a population screening ‘genotype-first’ approach. Individuals with putative pathogenic variants in genes implicated in cardiomyopathies showed an increased risk of all-cause mortality, higher risk of developing clinical cardiomyopathy later in life, and higher risk of a composite outcome (cardiomyopathy, heart failure, arrhythmia, stroke, and death) compared to genotype-negative controls. These findings highlight the potential role of ‘genotype-first’ approach in elevating personalized medicine into population level precision health in the future.

Emerging genetic testing delivery models have enabled individuals to receive testing without a medical indication. This article will highlight key considerations for patient care in the setting of adult patients with positive results for monogenic disease identified through genomic screening. Suggestions for how to adapt genetic counseling to a genomic screening population will encompass topics such as phenotyping, risk assessments, and the use of existing guidelines and resources. Case examples will demonstrate principles of genotype-first patient care.

Supplemental Digital Content is available in the text., Background: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. Methods: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. Results: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9–7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. Conclusions: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.

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 12‑lead 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.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.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).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.

Background Heart failure is a prevalent, costly disease for which new value-based payment models demand optimized population management strategies. Objectives This study sought to generate a strategy for managing populations of patients with heart failure by leveraging large clinical datasets and machine learning. Methods Geisinger electronic health record data were used to train machine learning models to predict 1-year all-cause mortality in 26,971 patients with heart failure who underwent 276,819 clinical episodes. There were 26 clinical variables (demographics, laboratory test results, medications), 90 diagnostic codes, 41 electrocardiogram measurements and patterns, 44 echocardiographic measurements, and 8 evidence-based “care gaps”: flu vaccine, blood pressure of Results Machine learning models achieved areas under the receiver-operating characteristic curve (AUCs) of 0.74 to 0.77 in a split-by-year training/test scheme, with the nonlinear XGBoost model (AUC: 0.77) outperforming linear logistic regression (AUC: 0.74). Out of 13,238 currently living patients, 2,844 were predicted to die within a year, and closing all care gaps was predicted to save 231 of these lives. Prioritizing patients for intervention by using the predicted reduction in 1-year mortality risk outperformed all other priority rankings (e.g., random selection or Seattle Heart Failure risk score). Conclusions Machine learning can be used to priority-rank patients most likely to benefit from interventions to optimize evidence-based therapies. This approach may prove useful for optimizing heart failure population health management teams within value-based payment models.

Use of machine learning for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to 1) assess the generalizability of five state-of-the-art machine learning-based echocardiography segmentation models within a large clinical dataset, and 2) test the hypothesis that a quality control (QC) method based on segmentation uncertainty can further improve segmentation results. Five models were applied to 47,431 echocardiography studies that were independent from any training samples. Chamber volume and mass from model segmentations were compared to clinically-reported values. The median absolute errors (MAE) in left ventricular (LV) volumes and ejection fraction exhibited by all five models were comparable to reported inter-observer errors (IOE). MAE for left atrial volume and LV mass were similarly favorable to respective IOE for models trained for those tasks. A single model consistently exhibited the lowest MAE in all five clinically-reported measures. We leveraged the 10-fold cross-validation training scheme of this best-performing model to quantify segmentation uncertainty for potential application as QC. We observed that filtering segmentations with high uncertainty improved segmentation results, leading to decreased volume/mass estimation errors. The addition of contour-convexity filters further improved QC efficiency. In conclusion, five previously published echocardiography segmentation models generalized to a large, independent clinical dataset—segmenting one or multiple cardiac structures with overall accuracy comparable to manual analyses—with variable performance. Convexity-reinforced uncertainty QC efficiently improved segmentation performance and may further facilitate the translation of such models.

Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to (1) assess the generalizability of five state-of-the-art ML-based echocardiography segmentation models within a large Geisinger clinical dataset, and (2) test the hypothesis that a quality control (QC) method based on segmentation uncertainty can further improve segmentation results. Five models were applied to 47,431 echocardiography studies that were independent from any training samples. Chamber volume and mass from model segmentations were compared to clinically-reported values. The median absolute errors (MAE) in left ventricular (LV) volumes and ejection fraction exhibited by all five models were comparable to reported inter-observer errors (IOE). MAE for left atrial volume and LV mass were similarly favorable to respective IOE for models trained for those tasks. A single model consistently exhibited the lowest MAE in all five clinically-reported measures. We leveraged the tenfold cross-validation training scheme of this best-performing model to quantify segmentation uncertainty. We observed that removing segmentations with high uncertainty from 14 to 71% studies reduced volume/mass MAE by 6-10%. The addition of convexity filters improved specificity, efficiently removing 10% studies with large MAE (16-40%). In conclusion, five previously published echocardiography segmentation models generalized to a large, independent clinical dataset-segmenting one or multiple cardiac structures with overall accuracy comparable to manual analyses-with variable performance. Convexity-reinforced uncertainty QC efficiently improved segmentation performance and may further facilitate the translation of such models.

Introduction: With expanding genomic sequencing, incidental identification of patients with disease-causing variants who do not have overt disease is increasingly common. For example, secondary findings of desmosome gene variants associated with arrhythmogenic cardiomyopathy (ACM) are recommended for clinical return but exhibit low penetrance. Additional biomarkers, such as myocardial tissue characteristics, are needed to uncover early disease in patients with genetic risk. Hypothesis: Patients with desmosome variants (G + ) but no clinical phenotype have abnormal ventricular volumes, left ventricular (LV) mass, native T1, T2, post-contrast T1, synthetic extracellular volume (sECV), and late gadolinium enhancement (LGE) via cardiac MRI. Methods: G + individuals were ascertained after clinical confirmation of likely pathogenic/pathogenic desmosome variants from 64548 sequenced Geisinger MyCode patients. MRI studies with myocardial mapping from a single scanner were included. Controls were identified from clinical MRI studies read as having normal biventricular structure and function. Patients who met diagnostic criteria for any cardiomyopathy were excluded from both groups. Ventricular volumes, mass, and tissue properties were defined by manual segmentation. Results: Qualifying studies were identified for 18 G + and 19 genotype unknown (G + ) controls, with similar age and sex. Biventricular volumes and function, as well as LV mass, were similar (Figure). Native T1 was elevated (mean±SEM 1027±9 ms G + vs. 979±15 ms G + ; p = 0.04). No significant differences were found in post-contrast T1, sECV, or native T2. Atypical LGE patterns were noted in 5/18 G + vs. 0/19 G + (p = 0.02). Conclusion: Elevated native T1 and atypical LGE, perhaps due to subclinical fibrosis, were detected in G + patients with normal mass and volume and no overt clinical cardiomyopathy. Longitudinal follow up with larger cohorts will help assess the prognostic significance of these findings.

Introduction: Timely diagnosis of structural heart disease improves patient outcomes, yet millions remain undiagnosed. ECG-based prediction models can help identify high-risk patients for targeted screening, but existing individual disease models often have low positive predictive values (PPV) and limited clinical utility. Hypothesis: An ECG-based composite model can predict one of multiple, actionable structural heart conditions and yield higher prevalence and PPVs than individual models. Methods: Using 2,141,366 ECGs linked to echocardiography and EHR records from 461,466 adults from 1984-2021, we trained machine learning models to predict any of 7 echocardiography-confirmed diseases within 1 year. This composite label included: moderate or severe valvular disease (aortic stenosis or regurgitation, mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction of 15mm. We tested various combinations of inputs and evaluated model performance with 1) cross-validation and 2) a simulated retrospective deployment. We measured area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC), and PPV at 90% sensitivity. Results: Our composite "rECHOmmend" model using age, sex and ECG traces had an AUROC of 0.91, AUPRC of 0.78, and PPV of 52% at 90% sensitivity and 23% disease prevalence. Individual disease models had similar AUROCs (0.88-0.93), but lower AUPRCs (0.07-0.71) and PPVs (2%-41%; Figure). Across inputs, model AUROCs ranged from 0.85 to 0.93. Our simulated deployment model classified 22% of at-risk patients in 2010 as high-risk, of whom 40% developed true, echo-confirmed disease within 1 year. Conclusions: An ECG-based machine learning model using a composite endpoint can predict undiagnosed structural heart disease, outperforming single disease models with higher PPVs to facilitate targeted screening with echocardiography.

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 deep neural network (DNN) model risk prediction based on ECG would be superior to age and clinical variables at selecting a population at high risk for AF and AF-related stroke. Methods: We retrospectively included all patients with an ECG at Geisinger without a prior history of AF. Incidence of AF and AF-related strokes were identified as outcomes within 1 and 3 years after the ECG, respectively. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. We selected a high-risk cohort for AF screening based on five risk stratification methods - criteria from four clinical trials (mSToPS, STROKESTOP, GUARD-AF and SCREEN-AF) and the DNN model at the qualifying ECG. We simulated patient selection and evaluated outcomes for twenty 1-year periods between 2010-2014 centered around the ECG encounter. For the clinical trials, the patients were considered eligible if they met the criteria before or within the period unless they satisfied exclusion criteria at the time of ECG. Results: The DNN model achieved optimal sensitivity (65%), PPV (10%), NNS for AF (10) within this population compared with all other risk models with a NNS for AF-related stroke of 160. Total screening number, sensitivity, positive predictive value (PPV) and number needed to screen (NNS) to capture AF and AF-related stroke are summarized in Table 1. The number of additional screens for the DNN model was slightly higher for two of the other models (SCREEN-AF and STROKESTOP) but lower than the other two (mSToPS and GUARD-AF). Conclusions: A DNN ECG-based risk prediction model is superior to contemporary AF-screening criteria based on age alone or age and clinical features in selecting a population for additional screening due to high risk for future AF and potential AF-related strokes.

Introduction: Initiation of QTc-prolonging medications may lead to the rare but potentially catastrophic event, torsades de pointes (TDP). At present, no adequate, generalizable tools exist to predict drug-induced long QTc (LQT); machine learning from ECG data is a promising approach. Hypothesis: Prediction of drug-induced LQT using an ECG-based machine learning model is feasible, and outperforms a model trained on baseline QTc, age, and sex alone. Methods: We identified baseline 12-lead ECGs with QTc values < 500 ms for patients who had not received any known, conditional, or possible QTc prolonging medication per CredibleMeds at the time of ECG or within the past 90 days. We matched these with ECGs from the same patients while they were taking at least one CredibleMeds drug (“on-drug” ECGs). Using 5-fold cross-validation, we trained and tested two machine learning models using the baseline ECGs of the 92,848 resulting pairs to predict drug-induced LQT (≥500 ms) in the on-drug ECGs: a deep neural network using ECG voltage data, and a gradient-boosted tree using the baseline QTc. Age and sex were also inputs to both models. Results: On-drug LQT prevalence was 16%. The ECG model demonstrated superior performance in predicting on-drug LQT (area under the receiver operating characteristic curve (AUC) = 0.756) compared to the QTc model (0.710). At a potential operating point (Figure), the ECG model had 89% sensitivity and 95% negative predictive value. Even in the subset of patients with baseline QTc < 470/480 ms (male/female; post-drug LQT prevalence = 14%), the ECG model demonstrated good performance (AUC = 0.736). Conclusions: An ECG-based machine learning model can stratify patients by risk of developing drug-induced LQT better than a model using baseline QTc alone. This model may have clinical value to identify high-risk drug starts that would benefit from closer monitoring and others who are at low risk of drug-induced LQT.

BackgroundThe FLNC gene has recently garnered attention as a likely cause of arrhythmogenic cardiomyopathy (ACM), which is considered an actionable genetic condition. However, the association with disease in an unselected clinical population is unknown. We hypothesized that individuals with loss-of-function variants in FLNC (FLNCLOF) would have increased odds for ACM-associated phenotypes versus variant-negative controls in the Geisinger MyCode cohort.MethodsWe identified rare, putative FLNCLOF among 171,948 individuals with exome sequencing linked to health records. Associations with ACM phenotypes from available diagnoses and cardiac evaluations were investigated.ResultsSixty individuals (0.03%; median age 58 years [47–70 IQR], 43% male) harbored 27 unique FLNCLOF. These individuals had significantly increased odds ratios (OR) for dilated cardiomyopathy (OR:4.9, [95% confidence interval: 2.6–7.6]; pFLNCLOF. Overall, at least 9% of FLNCLOF carriers demonstrated evidence of penetrant disease.ConclusionsFLNCLOF variants are associated with increased odds of ventricular arrhythmia and dysfunction in an unselected clinical population. These findings support genomic screening of FLNC for actionable secondary findings.

BackgroundEarly diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, electrocardiogram (ECG)-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values (PPVs) to facilitate meaningful recommendations for echocardiography.MethodsUsing 2,232,130 ECGs linked to electronic health records and echocardiography reports from 484,765 adults between 1984-2021, we trained machine learning models to predict the presence of any of seven echocardiography-confirmed diseases within one year. This composite label included: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction 15mm. We tested various combinations of input features (demographics, labs, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multi-site validation trained on one clinical site and tested on 11 other independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010.FindingsOur composite “rECHOmmend” model using age, sex and ECG traces had an area under the receiver operating characteristic curve (AUROC) of 0.91 and a PPV of 42% at 90% sensitivity at a prevalence of 17.9% for our composite label. Individual disease models had AUROCs ranging from 0.86-0.93 and lower PPVs from 1%-31%. The AUROC for models using different input features ranged from 0.80-0.93, increasing with additional features. Multi-site validation showed similar results to the cross-validation, with an aggregate AUROC of 0.91 across our independent test set of 11 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without pre-existing known structural heart disease in a single year, 2010, 11% were classified as high-risk, of which 41% developed true, echocardiography-confirmed disease within one year.InterpretationAn ECG-based machine learning model using a composite endpoint can predict previously undiagnosed, clinically significant structural heart disease while outperforming single disease models and improving practical utility with higher PPVs. This approach can facilitate targeted screening with echocardiography to improve under-diagnosis of structural heart disease.

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) provide evidence for variant pathogenicity. Effect sizes are associated with pathogenic ClinVar assertions (P

Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of >1.10 for common variants (allele frequency > 0.05) and >1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 x 10(-8) under an additive genetic model.Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.

Background: Atrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years. Methods: We analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration. Results: Among 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720–0.824) and Predict-AF (0.749–0.831) had largely comparable discrimination, both favorable to continuous age (0.675–0.801). Calibration was similar using CHARGE-AF (slope range, 0.67–0.87) and Predict-AF (0.65–0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024–0.057) but more favorable among individuals aged Conclusions: AF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals.

Accurate and efficient classification of variant pathogenicity is critical for research and clinical care. Using data from three large studies, we demonstrate that population-based associations between rare variants and quantitative endophenotypes for three monogenic diseases (low-density-lipoprotein cholesterol for familial hypercholesterolemia, electrocardiographic QTc interval for long QT syndrome, and glycosylated hemoglobin for maturity-onset diabetes of the young) are proxies for variant pathogenicity. Effect sizes were associated with pathogenic ClinVar assertions (P

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. We 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). We 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 Conclusions Machine learning can fully utilize large combinations of disparate input variables to predict survival after echocardiography with superior accuracy.

Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.

Existing deep-learning methods achieve state-of-art segmentation of multiple heart substructures from 2D echocardiography videos, an important step in the diagnosis and management of cardiovascular disease. However, these methods generally perform frame-level segmentation, ignoring the temporal coherence in heart motion between frames, which is a useful signal in clinical protocols. In this work, we implement temporally consistent video segmentation, which has recently been shown to improve performance on the multi-structure annotated CAMUS dataset. We show that data augmentation further improves results, which are consistent with prior state-of-art works. Our 10-fold cross-validation shows that video segmentation improves the automatic comparison to clinical indices including smaller mean absolute errors for left ventricular end-diastolic volume (8.7 mL vs 9.9 mL), end-systolic volume (6.3 mL vs 6.6 mL), and ejection fraction (EF) (4.6% vs 5.3%). In segmenting key cardiac structures, video segmentation achieves mean Dice overlap of 0.93 on left ventricular endocardium, 0.95 on left ventricular epicardium, and 0.88 on left atrium. To assess clinical generalizability, we further apply the CAMUS-trained video segmentation models, without tuning, to a larger, recently published EchoNet-Dynamic clinical dataset. On 1274 patients in the test set, we obtain absolute errors of 6:3% ± 5:4 in EF, confirming the reliability of this scheme. In that the EchoNet-Dynamic videos contain limited annotation only for left ventricle endocardium, this effort extends at little cost generalizable, multi-structure video segmentation to a large clinical dataset.

Background Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved. Methods We present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS. Results Using a test set of 9566 admissions extracted from the MIMIC-III database, we show that OASIS + outperforms nine previously developed severity scoring methods (including OASIS) in predicting in-hospital mortality. Furthermore, our results show that the supervised learning algorithms considered in our experiments demonstrated higher predictive performance when trained using the observed clinical variables as opposed to OASIS subscores. Conclusions Our results suggest that there is room for improving the prognostic accuracy of the OASIS severity scores by replacing the simple linear additive scoring function with more sophisticated non-linear machine learning models such as RF and XGB.

Purpose The genetic architecture of Plakophilin 2 (PKP2) cardiomyopathy can inform our understanding of its variant pathogenicity and protein function. Methods We assess the gene-wide and regional association of truncating and missense variants in PKP2 with arrhythmogenic cardiomyopathy (ACM), and arrhythmogenic right ventricular cardiomyopathy (ARVC) specifically. A discovery data set compares genetic testing requisitions to gnomAD. Validation is performed in a rigorously phenotyped definite ARVC cohort and non-ACM individuals in the Geisinger MyCode cohort. Results The etiologic fraction (EF) of ACM-related diagnoses from truncating variants in PKP2 is significant (0.85 [0.80,0.88], p < 2 x 10(-16)), increases for ARVC specifically (EF = 0.96 [0.94,0.97], p < 2 x 10(-16)), and is highest in definite ARVC versus non-ACM individuals (EF = 1.00 [1.00,1.00], p < 2 x 10(-16)). Regions of missense variation enriched for ACM probands include known functional domains and the C-terminus, which was not previously known to contain a functional domain. No regional enrichment was identified for truncating variants. Conclusion This multicohort evaluation of the genetic architecture of PKP2 demonstrates the specificity of PKP2 truncating variants for ARVC within the ACM disease spectrum. We identify the PKP2 C-terminus as a potential functional domain and find that truncating variants likely cause disease irrespective of transcript position.