Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery

The electrocardiogram or ECG has been in use for over 100 years and remains the most widely performed diagnostic test to characterize cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, innovations in machine learning algorithms, and availability of large-scale digitized ECG data would enable extending the utility of the ECG beyond its current limitations, while at the same time preserving interpretability, which is fundamental to medical decision-making. We identified 36,186 ECGs from the UCSF database that were 1) in normal sinus rhythm and 2) would enable training of specific models for estimation of cardiac structure or function or detection of disease. We derived a novel model for ECG segmentation using convolutional neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output by comparing electrical interval estimates to 141,864 measurements from the clinical workflow. We built a 725-element patient-level ECG profile using downsampled segmentation data and trained machine learning models to estimate left ventricular mass, left atrial volume, mitral annulus e' and to detect and track four diseases: pulmonary arterial hypertension (PAH), hypertrophic cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP). CNN-HMM derived ECG segmentation agreed with clinical estimates, with median absolute deviations (MAD) as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative estimates of left ventricular and mitral annulus e' velocity with good discrimination in binary classification models of left ventricular hypertrophy and diastolic function. Models for disease detection ranged from AUROC of 0.94 to 0.77 for MVP. Top-ranked variables for all models included known ECG characteristics along with novel predictors of these traits/diseases.Comment: 13 pages, 6 figures, 1 Table + Supplemen

Genome-wide association study reveals novel genetic loci:a new polygenic risk score for mitral valve prolapse

AIMS: Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder. METHODS AND RESULTS: We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors. CONCLUSION: We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-beta signalling molecules and spectrin beta. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention. KEY QUESTION: Expand our understanding of the genetic basis for mitral valve prolapse (MVP). Uncover relevant pathways and target genes for MVP pathophysiology. Leverage genetic data for MVP risk prediction. KEY FINDING: Sixteen genetic loci were significantly associated with MVP, including 13 novel loci. Interesting target genes at these loci included LTBP2, TGFB2, ALKP3, BAG3, RBM20, and SPTBN1. A risk score including clinical factors and a polygenic risk score, performed best at predicting MVP, with an area under the receiver operating characteristics curve of 0.677. TAKE-HOME MESSAGE: Mitral valve prolapse has a polygenic basis: many genetic variants cumulatively influence pre-disposition for disease. Disease risk may be modulated via changes to transforming growth factor-beta signalling, the cytoskeleton, as well as cardiomyopathy pathways. Polygenic risk scores could enhance the MVP risk prediction

Mutations in DCHS1 Cause Mitral Valve Prolapse

SUMMARY Mitral valve prolapse (MVP) is a common cardiac valve disease that affects nearly 1 in 40 individuals1–3. It can manifest as mitral regurgitation and is the leading indication for mitral valve surgery4,5. Despite a clear heritable component, the genetic etiology leading to non-syndromic MVP has remained elusive. Four affected individuals from a large multigenerational family segregating non-syndromic MVP underwent capture sequencing of the linked interval on chromosome 11. We report a missense mutation in the DCHS1 gene, the human homologue of the Drosophila cell polarity gene dachsous (ds) that segregates with MVP in the family. Morpholino knockdown of the zebrafish homolog dachsous1b resulted in a cardiac atrioventricular canal defect that could be rescued by wild-type human DCHS1, but not by DCHS1 mRNA with the familial mutation. Further genetic studies identified two additional families in which a second deleterious DCHS1 mutation segregates with MVP. Both DCHS1 mutations reduce protein stability as demonstrated in zebrafish, cultured cells, and, notably, in mitral valve interstitial cells (MVICs) obtained during mitral valve repair surgery of a proband. Dchs1+/− mice had prolapse of thickened mitral leaflets, which could be traced back to developmental errors in valve morphogenesis. DCHS1 deficiency in MVP patient MVICs as well as in Dchs1+/− mouse MVICs result in altered migration and cellular patterning, supporting these processes as etiological underpinnings for the disease. Understanding the role of DCHS1 in mitral valve development and MVP pathogenesis holds potential for therapeutic insights for this very common disease

Valvular disease burden in the modern era of percutaneous and surgical interventions: the UK Biobank

BackgroundThe burden of valvular heart disease (VHD) has increased significantly among ageing populations, yet remains poorly understood in the present-day context of percutaneous and surgical interventions.ObjectiveTo define the incidence, clinical correlates and associated mortality of VHD in the UK Biobank cohort.MethodsWe interrogated data collected in the UK Biobank between 1 January 2000 and 30 June 2020. VHD incidence was determined using International Classification of Disease-10 codes for aortic stenosis (AS), aortic regurgitation (AR), mitral stenosis, mitral regurgitation (MR) and mitral valve prolapse. We calculated HRs for incident VHD and all-cause mortality. Clinical correlates of VHD included demographics, coronary artery disease, heart failure and atrial fibrillation. Surgical and percutaneous interventions for mitral and aortic VHD were considered time-dependent variables.ResultsAmong 486 187 participants, the incidence of any VHD was 16 per 10 000 person-years, with highest rates for MR (8.2), AS (7.2) and AR (5.0). Age, heart failure, coronary artery disease and atrial fibrillation were significantly associated with all types of VHD. In our adjusted model, aortic and mitral VHD had an increased risk of all-cause death compared with no VHD (HR 1.62, 95% CI 1.44 to 1.82, p<0.001 and HR 1.25, 95% CI 1.09 to 1.44, p=0.002 for aortic and mitral VHD, respectively).ConclusionVHD continues to constitute a significant public health burden, with MR and AS being the most common. Age and cardiac comorbidities remain strong risk factors for VHD. In the modern era of percutaneous and surgical interventions, mortality associated with VHD remains high

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

BackgroundThe ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG while at the same time preserving interpretability for medical decision-making.Methods and resultsWe identified 36 186 ECGs from the University of California, San Francisco database that would enable training of models for estimation of cardiac structure or function or detection of disease. We segmented the ECG into standard component waveforms and intervals using a novel combination of convolutional neural networks and hidden Markov models and evaluated this segmentation by comparing resulting electrical intervals against 141 864 measurements produced during the clinical workflow. We then built a patient-level ECG profile, a 725-element feature vector and used this profile to train and interpret machine learning models for examples of cardiac structure (left ventricular mass, left atrial volume, and mitral annulus e-prime) and disease (pulmonary arterial hypertension, hypertrophic cardiomyopathy, cardiac amyloid, and mitral valve prolapse). ECG measurements derived from the convolutional neural network-hidden Markov model segmentation agreed with clinical estimates, with median absolute deviations as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Models trained using patient-level ECG profiles enabled surprising quantitative estimates of left ventricular mass and mitral annulus e' velocity (median absolute deviation of 16% and 19%, respectively) with good discrimination for left ventricular hypertrophy and diastolic dysfunction as binary traits. Model performance using our approach for disease detection demonstrated areas under the receiver operating characteristic curve of 0.94 for pulmonary arterial hypertension, 0.91 for hypertrophic cardiomyopathy, 0.86 for cardiac amyloid, and 0.77 for mitral valve prolapse.ConclusionsModern machine learning methods can extend the 12-lead ECG to quantitative applications well beyond its current uses while preserving the transparency that is so fundamental to clinical care

Sex Differences and Similarities in Valvular Heart Disease

As populations age worldwide, the burden of valvular heart disease has grown exponentially, and so has the proportion of affected women. Although rheumatic valve disease is declining in high-income countries, degenerative age-related causes are rising. Calcific aortic stenosis and degenerative mitral regurgitation affect a significant proportion of elderly women, particularly those with comorbidities. Women with valvular heart disease have been underrepresented in many of the landmark studies which form the basis for guideline recommendations. As a consequence, surgical referrals in women have often been delayed, with worse postoperative outcomes compared with men. As described in this review, a more recent effort to include women in research studies and clinical trials has increased our knowledge about sex-based differences in epidemiology, pathophysiology, diagnostic criteria, treatment options, outcomes, and prognosis