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1.
Nat Commun ; 12(1): 350, 2021 01 13.
Artigo em Inglês | MEDLINE | ID: mdl-33441555

RESUMO

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

2.
Artigo em Inglês | MEDLINE | ID: mdl-33284039

RESUMO

Background - The impact of sex on phenotypic expression in hypertrophic cardiomyopathy (HCM) has not been well characterized in genotyped cohorts. Methods - Retrospective cohort study from an international registry of patients receiving care at experienced HCM centers. Sex-based differences in baseline characteristics and clinical outcomes were assessed. Results - Of 5,873 patients (3,788 genotyped), 2,226 (37.9%) were women. At baseline, women were older (49.0±19.9 vs. 42.9±18.4 years, p<0.001) and more likely to have pathogenic/likely-pathogenic sarcomeric variants (SARC+; 51% vs 43%, p<0.001) despite equivalent utilization of genetic testing. Age at diagnosis varied by sex and genotype despite similar distribution of causal genes. Women were 3.6 to 7.1 years older at diagnosis (p<0.02) except for patients with MYH7 variants where age at diagnosis was comparable for women and men (n=492; 34.8±19.2 vs 33.3±16.8 years, p=0.39). Over 7.7 median years of follow up, NYHA III-IV heart failure (HF) was more common in women (HR 1.87, CI 1.48-2.36, p<0.001), after controlling for their higher burden of symptoms and outflow tract obstruction at baseline, reduced ejection fraction, SARC+, age and hypertension. All-cause mortality was increased in women (HR 1.50, CI 1.13-1.99, p<0.01), but neither ICD utilization nor ventricular arrhythmia varied by sex. Conclusions - In HCM, women are older at diagnosis, partly modified by genetic substrate. Regardless of genotype, women were at higher risk of mortality and developing severe HF symptoms. This points to a sex-effect on long-term myocardial performance in HCM, which should be investigated further.

3.
Artigo em Inglês | MEDLINE | ID: mdl-33125279

RESUMO

Background - The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods - From a sample of 34,287 white British-ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac MRI sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening to identify genetic comorbidities. Results - A genome-wide association study of aortic valve area in these UK Biobank participants showed three significant associations, indexed by rs71190365 (chr13:50764607, DLEU1, p=1.8×10-9), rs35991305 (chr12:94191968, CRADD, p=3.4×10-8) and chr17:45013271:C:T (GOSR2, p=5.6×10-8). Replication on an independent set of 8,145 unrelated European-ancestry participants showed consistent effect sizes in all three loci, although rs35991305 did not meet nominal significance. We constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311,728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (Odds Ratio 1.14, p=2.3×10-6). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=308,683 individuals), phenome-wide association of >10,000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve area and birthweight along with other cardiovascular conditions. Conclusions - These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.

4.
Genes (Basel) ; 11(11)2020 Oct 26.
Artigo em Inglês | MEDLINE | ID: mdl-33114567

RESUMO

Digital health (DH) is the use of digital technologies and data analytics to understand health-related behaviors and enhance personalized clinical care. DH is increasingly being used in clinical trials, and an important field that could potentially benefit from incorporating DH into trial design is pharmacogenetics. Prospective pharmacogenetic trials typically compare a standard care arm to a pharmacogenetic-guided therapeutic arm. These trials often require large sample sizes, are challenging to recruit into, lack patient diversity, and can have complicated workflows to deliver therapeutic interventions to both investigators and patients. Importantly, the use of DH technologies could mitigate these challenges and improve pharmacogenetic trial design and operation. Some DH use cases include (1) automatic electronic health record-based patient screening and recruitment; (2) interactive websites for participant engagement; (3) home- and tele-health visits for patient convenience (e.g., samples for lab tests, physical exams, medication administration); (4) healthcare apps to collect patient-reported outcomes, adverse events and concomitant medications, and to deliver therapeutic information to patients; and (5) wearable devices to collect vital signs, electrocardiograms, sleep quality, and other discrete clinical variables. Given that pharmacogenetic trials are inherently challenging to conduct, future pharmacogenetic utility studies should consider implementing DH technologies and trial methodologies into their design and operation.

6.
Circ Heart Fail ; 13(9): e007230, 2020 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-32894986

RESUMO

BACKGROUND: Over the last 50 years, the epidemiology of hypertrophic cardiomyopathy (HCM) has changed because of increased awareness and availability of advanced diagnostic tools. We aim to describe the temporal trends in age, sex, and clinical characteristics at HCM diagnosis over >4 decades. METHODS: We retrospectively analyzed records from the ongoing multinational Sarcomeric Human Cardiomyopathy Registry. Overall, 7286 patients with HCM diagnosed at an age ≥18 years between 1961 and 2019 were included in the analysis and divided into 3 eras of diagnosis (<2000, 2000-2010, >2010). RESULTS: Age at diagnosis increased markedly over time (40±14 versus 47±15 versus 51±16 years, P<0.001), both in US and non-US sites, with a stable male-to-female ratio of about 3:2. Frequency of familial HCM declined over time (38.8% versus 34.3% versus 32.7%, P<0.001), as well as heart failure symptoms at presentation (New York Heart Association III/IV: 18.1% versus 15.8% versus 12.6%, P<0.001). Left ventricular hypertrophy became less marked over time (maximum wall thickness: 20±6 versus 18±5 versus 17±5 mm, P<0.001), while prevalence of obstructive HCM was greater in recent cohorts (peak gradient >30 mm Hg: 31.9% versus 39.3% versus 39.0%, P=0.001). Consistent with decreasing phenotypic severity, yield of pathogenic/likely pathogenic variants at genetic testing decreased over time (57.7% versus 45.6% versus 38.4%, P<0.001). CONCLUSIONS: Evolving HCM populations include progressively greater representation of older patients with sporadic disease, mild phenotypes, and genotype-negative status. Such trend suggests a prominent role of imaging over genetic testing in promoting HCM diagnoses and urges efforts to understand genotype-negative disease eluding the classic monogenic paradigm.

7.
JCI Insight ; 5(17)2020 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-32879139

RESUMO

Previous studies have shown an association between elevated atrial NADPH-dependent oxidative stress and decreased plasma apelin in patients with atrial fibrillation (AF), though the basis for this relationship is unclear. In the current study, RT-PCR and immunofluorescence studies of human right atrial appendages (RAAs) showed expression of the apelin receptor, APJ, and reduced apelin content in the atria, but not in plasma, of patients with AF versus normal sinus rhythm. Disruption of the apelin gene in mice increased (2.4-fold) NADPH-stimulated superoxide levels and slowed atrial conduction velocities in optical mapping of a Langendorff-perfused isolated heart model, suggesting that apelin levels may influence AF vulnerability. Indeed, in mice with increased AF vulnerability (induced by chronic intense exercise), apelin administration reduced the incidence and duration of induced atrial arrhythmias in association with prolonged atrial refractory periods. Moreover, apelin decreased AF induction in isolated atria from exercised mice while accelerating conduction velocity and increasing action potential durations. At the cellular level, these changes were associated with increased atrial cardiomyocyte sodium currents. These findings support the conclusion that reduced atrial apelin is maladaptive in fibrillating human atrial myocardium and that increasing apelin bioavailability may be a worthwhile therapeutic strategy for treating and preventing AF.

8.
NPJ Digit Med ; 3: 116, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32964139

RESUMO

Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, a multitask deep learning method to jointly assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation. The model is trained on approximately one million simulated unlabeled physiological signals and fine-tuned on a curated dataset of over 500 K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that, in comparison with a single-task model, our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of atrial fibrillation detection from a F1 score of 0.54 to 0.96. We also include in our evaluation a prospectively derived replication cohort of ambulatory participants where the algorithm performed with high sensitivity (0.98), specificity (0.99), and F1 score (0.93). We show that two-stage training can help address the unbalanced data problem common to biomedical applications, where large-scale well-annotated datasets are hard to generate due to the expense of manual annotation, data acquisition, and participant privacy.

9.
Circ Genom Precis Med ; 13(5): 396-405, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32841044

RESUMO

BACKGROUND: Pathogenic variants in MYBPC3, encoding cardiac MyBP-C (myosin binding protein C), are the most common cause of familial hypertrophic cardiomyopathy. A large number of unique MYBPC3 variants and relatively small genotyped hypertrophic cardiomyopathy cohorts have precluded detailed genotype-phenotype correlations. METHODS: Patients with hypertrophic cardiomyopathy and MYBPC3 variants were identified from the Sarcomeric Human Cardiomyopathy Registry. Variant types and locations were analyzed, morphological severity was assessed, and time-event analysis was performed (composite clinical outcome of sudden death, class III/IV heart failure, left ventricular assist device/transplant, atrial fibrillation). For selected missense variants falling in enriched domains, myofilament localization and degradation rates were measured in vitro. RESULTS: Among 4756 genotyped patients with hypertrophic cardiomyopathy in Sarcomeric Human Cardiomyopathy Registry, 1316 patients were identified with adjudicated pathogenic truncating (N=234 unique variants, 1047 patients) or nontruncating (N=22 unique variants, 191 patients) variants in MYBPC3. Truncating variants were evenly dispersed throughout the gene, and hypertrophy severity and outcomes were not associated with variant location (grouped by 5'-3' quartiles or by founder variant subgroup). Nontruncating pathogenic variants clustered in the C3, C6, and C10 domains (18 of 22, 82%, P<0.001 versus Genome Aggregation Database common variants) and were associated with similar hypertrophy severity and adverse event rates as observed with truncating variants. MyBP-C with variants in the C3, C6, and C10 domains was expressed in rat ventricular myocytes. C10 mutant MyBP-C failed to incorporate into myofilaments and degradation rates were accelerated by ≈90%, while C3 and C6 mutant MyBP-C incorporated normally with degradation rate similar to wild-type. CONCLUSIONS: Truncating variants account for 91% of MYBPC3 pathogenic variants and cause similar clinical severity and outcomes regardless of location, consistent with locus-independent loss-of-function. Nontruncating MYBPC3 pathogenic variants are regionally clustered, and a subset also cause loss of function through failure of myofilament incorporation and rapid degradation. Cardiac morphology and clinical outcomes are similar in patients with truncating versus nontruncating variants.

10.
medRxiv ; 2020 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-32766602

RESUMO

During COVID19 and other viral pandemics, rapid generation of host and pathogen genomic data is critical to tracking infection and informing therapies. There is an urgent need for efficient approaches to this data generation at scale. We have developed a scalable, high throughput approach to generate high fidelity low pass whole genome and HLA sequencing, viral genomes, and representation of human transcriptome from single nasopharyngeal swabs of COVID19 patients.

11.
Cell ; 181(5): 1112-1130.e16, 2020 05 28.
Artigo em Inglês | MEDLINE | ID: mdl-32470399

RESUMO

Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.

12.
Front Physiol ; 11: 181, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32231588

RESUMO

The myocardium has an intrinsic ability to sense and respond to mechanical load in order to adapt to physiological demands. Primary examples are the augmentation of myocardial contractility in response to increased ventricular filling caused by either increased venous return (Frank-Starling law) or aortic resistance to ejection (the Anrep effect). Sustained mechanical overload, however, can induce pathological hypertrophy and dysfunction, resulting in heart failure and arrhythmias. It has been proposed that angiotensin II type 1 receptor (AT1R) and apelin receptor (APJ) are primary upstream actors in this acute myocardial autoregulation as well as the chronic maladaptive signaling program. These receptors are thought to have mechanosensing capacity through activation of intracellular signaling via G proteins and/or the multifunctional transducer protein, ß-arrestin. Importantly, ligand and mechanical stimuli can selectively activate different downstream signaling pathways to promote inotropic, cardioprotective or cardiotoxic signaling. Studies to understand how AT1R and APJ integrate ligand and mechanical stimuli to bias downstream signaling are an important and novel area for the discovery of new therapeutics for heart failure. In this review, we provide an up-to-date understanding of AT1R and APJ signaling pathways activated by ligand versus mechanical stimuli, and their effects on inotropy and adaptive/maladaptive hypertrophy. We also discuss the possibility of targeting these signaling pathways for the development of novel heart failure therapeutics.

13.
JAMA Netw Open ; 3(4): e202064, 2020 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-32242908

RESUMO

Importance: Atrial fibrillation (AF) affects more than 6 million people in the United States; however, much AF remains undiagnosed. Given that more than 265 million people in the United States own smartphones (>80% of the population), smartphone applications have been proposed for detecting AF, but the accuracy of these applications remains unclear. Objective: To determine the accuracy of smartphone camera applications that diagnose AF. Data Sources and Study Selection: MEDLINE and Embase were searched until January 2019 for studies that assessed the accuracy of any smartphone applications that use the smartphone's camera to measure the amplitude and frequency of the user's fingertip pulse to diagnose AF. Data Extraction and Synthesis: Bivariate random-effects meta-analyses were constructed to synthesize data. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) of Diagnostic Test Accuracy Studies reporting guideline. Main Outcomes and Measures: Sensitivity and specificity were measured with bivariate random-effects meta-analysis. To simulate the use of these applications as a screening tool, the positive predictive value (PPV) and negative predictive value (NPV) for different population groups (ie, age ≥65 years and age ≥65 years with hypertension) were modeled. Lastly, the association of methodological limitations with outcomes were analyzed with sensitivity analyses and metaregressions. Results: A total of 10 primary diagnostic accuracy studies, with 3852 participants and 4 applications, were included. The oldest studies were published in 2016 (2 studies [20.0%]), while most studies (4 [40.0%]) were published in 2018. The applications analyzed the pulsewave signal for a mean (range) of 2 (1-5) minutes. The meta-analyzed sensitivity and specificity for all applications combined were 94.2% (95% CI, 92.2%-95.7%) and 95.8% (95% CI, 92.4%-97.7%), respectively. The PPV for smartphone camera applications detecting AF in an asymptomatic population aged 65 years and older was between 19.3% (95% CI, 19.2%-19.4%) and 37.5% (95% CI, 37.4%-37.6%), and the NPV was between 99.8% (95% CI, 99.83%-99.84%) and 99.9% (95% CI, 99.94%-99.95%). The PPV and NPV increased for individuals aged 65 years and older with hypertension (PPV, 20.5% [95% CI, 20.4%-20.6%] to 39.2% [95% CI, 39.1%-39.3%]; NPV, 99.8% [95% CI, 99.8%-99.8%] to 99.9% [95% CI, 99.9%-99.9%]). There were methodological limitations in a number of studies that did not appear to be associated with diagnostic performance, but this could not be definitively excluded given the sparsity of the data. Conclusions and Relevance: In this study, all smartphone camera applications had relatively high sensitivity and specificity. The modeled NPV was high for all analyses, but the PPV was modest, suggesting that using these applications in an asymptomatic population may generate a higher number of false-positive than true-positive results. Future research should address the accuracy of these applications when screening other high-risk population groups, their ability to help monitor chronic AF, and, ultimately, their associations with patient-important outcomes.


Assuntos
Fibrilação Atrial/diagnóstico , Confiabilidade dos Dados , Determinação da Frequência Cardíaca/instrumentação , Smartphone/instrumentação , Adulto , Idoso , Idoso de 80 Anos ou mais , Fibrilação Atrial/epidemiologia , Coleta de Dados/métodos , Feminino , Dedos/fisiologia , Humanos , Hipertensão/epidemiologia , Hipertensão/fisiopatologia , Masculino , Programas de Rastreamento/métodos , Pessoa de Meia-Idade , Aplicativos Móveis/estatística & dados numéricos , Valor Preditivo dos Testes , Sensibilidade e Especificidade , Estados Unidos/epidemiologia
14.
Nature ; 580(7802): 252-256, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32269341

RESUMO

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease1, screening for cardiotoxicity2 and decisions regarding the clinical management of patients with a critical illness3. However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training4,5. Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.


Assuntos
Aprendizado Profundo , Cardiopatias/diagnóstico , Cardiopatias/fisiopatologia , Coração/fisiologia , Coração/fisiopatologia , Modelos Cardiovasculares , Gravação em Vídeo , Fibrilação Atrial , Conjuntos de Dados como Assunto , Ecocardiografia , Insuficiência Cardíaca/fisiopatologia , Hospitais , Humanos , Estudos Prospectivos , Reprodutibilidade dos Testes , Função Ventricular Esquerda/fisiologia
15.
Circulation ; 141(17): 1371-1383, 2020 Apr 28.
Artigo em Inglês | MEDLINE | ID: mdl-32228044

RESUMO

BACKGROUND: The term "end stage" has been used to describe hypertrophic cardiomyopathy (HCM) with left ventricular systolic dysfunction (LVSD), defined as occurring when left ventricular ejection fraction is <50%. The prognosis of HCM-LVSD has reportedly been poor, but because of its relative rarity, the natural history remains incompletely characterized. METHODS: Data from 11 high-volume HCM specialty centers making up the international SHaRe Registry (Sarcomeric Human Cardiomyopathy Registry) were used to describe the natural history of patients with HCM-LVSD. Cox proportional hazards models were used to identify predictors of prognosis and incident development. RESULTS: From a cohort of 6793 patients with HCM, 553 (8%) met the criteria for HCM-LVSD. Overall, 75% of patients with HCM-LVSD experienced clinically relevant events, and 35% met the composite outcome (all-cause death [n=128], cardiac transplantation [n=55], or left ventricular assist device implantation [n=9]). After recognition of HCM-LVSD, the median time to composite outcome was 8.4 years. However, there was substantial individual variation in natural history. Significant predictors of the composite outcome included the presence of multiple pathogenic/likely pathogenic sarcomeric variants (hazard ratio [HR], 5.6 [95% CI, 2.3-13.5]), atrial fibrillation (HR, 2.6 [95% CI, 1.7-3.5]), and left ventricular ejection fraction <35% (HR, 2.0 [95% CI, 1.3-2.8]). The incidence of new HCM-LVSD was ≈7.5% over 15 years. Significant predictors of developing incident HCM-LVSD included greater left ventricular cavity size (HR, 1.1 [95% CI, 1.0-1.3] and wall thickness (HR, 1.3 [95% CI, 1.1-1.4]), left ventricular ejection fraction of 50% to 60% (HR, 1.8 [95% CI, 1.2, 2.8]-2.8 [95% CI, 1.8-4.2]) at baseline evaluation, the presence of late gadolinium enhancement on cardiac magnetic resonance imaging (HR, 2.3 [95% CI, 1.0-4.9]), and the presence of a pathogenic/likely pathogenic sarcomeric variant, particularly in thin filament genes (HR, 1.5 [95% CI, 1.0-2.1] and 2.5 [95% CI, 1.2-5.1], respectively). CONCLUSIONS: HCM-LVSD affects ≈8% of patients with HCM. Although the natural history of HCM-LVSD was variable, 75% of patients experienced adverse events, including 35% experiencing a death equivalent an estimated median time of 8.4 years after developing systolic dysfunction. In addition to clinical features, genetic substrate appears to play a role in both prognosis (multiple sarcomeric variants) and the risk for incident development of HCM-LVSD (thin filament variants).

16.
NPJ Digit Med ; 3: 10, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31993508

RESUMO

Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures, and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.86), left ventricular hypertrophy (AUC = 0.75), left ventricular end systolic and diastolic volumes ( R 2 = 0.74 and R 2 = 0.70), and ejection fraction ( R 2 = 0.50), as well as predicted systemic phenotypes of age ( R 2 = 0.46), sex (AUC = 0.88), weight ( R 2 = 0.56), and height ( R 2 = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlights hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, provide preliminary interpretation in areas with insufficient qualified cardiologists, and predict phenotypes challenging for human evaluation.

17.
JAMA Cardiol ; 5(1): 65-72, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31693057

RESUMO

Importance: Patients with hypertrophic cardiomyopathy (HCM) are prone to body weight increase and obesity. Whether this predisposes these individuals to long-term adverse outcomes is still unresolved. Objective: To describe the association of body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) with long-term outcomes in patients with HCM in terms of overall disease progression, heart failure symptoms, and arrhythmias. Design, Setting, and Participants: In this cohort study, retrospective data were analyzed from the ongoing prospective Sarcomeric Human Cardiomyopathy Registry, an international database created by 8 high-volume HCM centers that includes more than 6000 patients who have been observed longitudinally for decades. Records from database inception up to the first quarter of 2018 were analyzed. Patients were divided into 3 groups according to BMI class (normal weight group, <25; preobesity group, 25-30; and obesity group, >30). Patients with 1 or more follow-up visits were included in the analysis. Data were analyzed from April to October 2018. Exposures: Association of baseline BMI with outcome was assessed. Main Outcome and Measures: Outcome was measured against overall and cardiovascular mortality, a heart failure outcome (ejection fraction less than 35%, New York Heart Association class III/IV symptoms, cardiac transplant, or assist device implantation), a ventricular arrhythmic outcome (sudden cardiac death, resuscitated cardiac arrest, or appropriate implantable cardioverter-defibrillator therapy), and an overall composite outcome (first occurrence of any component of the ventricular arrhythmic or heart failure composite end point, all-cause mortality, atrial fibrillation, or stroke). Results: Of the 3282 included patients, 2019 (61.5%) were male, and the mean (SD) age at diagnosis was 47 (15) years. These patients were observed for a median (interquartile range) of 6.8 (3.3-13.3) years. There were 962 patients in the normal weight group (29.3%), 1280 patients in the preobesity group (39.0%), and 1040 patients in the obesity group (31.7%). Patients with obesity were more symptomatic (New York Heart Association class of III/IV: normal weight, 87 [9.0%]; preobesity, 138 [10.8%]; obesity, 215 [20.7%]; P < .001) and more often had obstructive physiology (normal weight, 201 [20.9%]; preobesity, 327 [25.5%]; obesity, 337 [32.4%]; P < .001). At follow-up, obesity was independently associated with the HCM-related overall composite outcome (preobesity vs normal weight: hazard ratio [HR], 1.102; 95% CI, 0.920-1.322; P = .29; obesity vs normal weight: HR, 1.634; 95% CI, 1.332-1.919; P < .001) and the heart failure composite outcome (preobesity vs normal weight: HR, 1.192; 95% CI, 0.930-1.1530; P = .20; obesity vs normal weight: HR, 1.885; 95% CI, 1.485-2.393; P < .001) irrespective of age, sex, left atrium diameter, obstruction, and genetic status. Obesity increased the likelihood of atrial fibrillation but not of life-threatening ventricular arrhythmias. Conclusions and Relevance: Obesity is highly prevalent among patients with HCM and is associated with increased likelihood of obstructive physiology and adverse outcomes. Strategies aimed at preventing obesity and weight increase may play an important role in management and prevention of disease-related complications.

18.
JAMA Cardiol ; 5(1): 83-91, 2020 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-31799990

RESUMO

Importance: Racial differences are recognized in multiple cardiovascular parameters, including left ventricular hypertrophy and heart failure, which are 2 major manifestations of hypertrophic cardiomyopathy. The association of race with disease expression and outcomes among patients with hypertrophic cardiomyopathy is not well characterized. Objective: To assess the association between race, disease expression, care provision, and clinical outcomes among patients with hypertrophic cardiomyopathy. Design, Setting, and Participants: This retrospective cohort study included data on black and white patients with hypertrophic cardiomyopathy from the US-based sites of the Sarcomeric Human Cardiomyopathy Registry from 1989 through 2018. Exposures: Self-identified race. Main Outcomes and Measures: Baseline characteristics; genetic architecture; adverse outcomes, including cardiac arrest, cardiac transplantation or left ventricular assist device implantation, implantable cardioverter-defibrillator therapy, all-cause mortality, atrial fibrillation, stroke, and New York Heart Association (NYHA) functional class III or IV heart failure; and septal reduction therapies. The overall composite outcome consists of the first occurrence of any component of the ventricular arrhythmic composite end point, cardiac transplantation, left ventricular assist device implantation, NYHA class III or IV heart failure, atrial fibrillation, stroke, or all-cause mortality. Results: Of 2467 patients with hypertrophic cardiomyopathy at the time of analysis, 205 (8.3%) were black (130 male [63.4%]; mean [SD] age, 40.0 [18.6] years) and 2262 (91.7%) were white (1351 male [59.7%]; mean [SD] age, 45.5 [20.5] years). Compared with white patients, black patients were younger at the time of diagnosis (mean [SD], 36.5 [18.2] vs 41.9 [20.2] years; P < .001), had higher prevalence of NYHA class III or IV heart failure at presentation (36 of 205 [22.6%] vs 174 of 2262 [15.8%]; P = .001), had lower rates of genetic testing (111 [54.1%] vs 1404 [62.1%]; P = .03), and were less likely to have sarcomeric mutations identified by genetic testing (29 [26.1%] vs 569 [40.5%]; P = .006). Implantation of implantable cardioverter-defibrillators did not vary by race; however, invasive septal reduction was less common among black patients (30 [14.6%] vs 521 [23.0%]; P = .007). Black patients had less incident atrial fibrillation (35 [17.1%] vs 608 [26.9%]; P < .001). Black race was associated with increased development of NYHA class III or IV heart failure (hazard ratio, 1.45; 95% CI, 1.08-1.94) which persisted on multivariable Cox proportional hazards regression (hazard ratio, 1.97; 95% CI, 1.34-2.88). There were no differences in the associations of race with stroke, ventricular arrhythmias, all-cause mortality, or the overall composite outcome. Conclusions and Relevance: The findings suggest that black patients with hypertrophic cardiomyopathy are diagnosed at a younger age, are less likely to carry a sarcomere mutation, have a higher burden of functionally limited heart failure, and experience inequities in care with lower use of invasive septal reduction therapy and genetic testing compared with white patients. Further study is needed to assess whether higher rates of heart failure may be associated with underlying ancestry-based disease pathways, clinical management, or structural inequities.

19.
NPJ Digit Med ; 3(1): 10, 2020 Jan 24.
Artigo em Inglês | MEDLINE | ID: mdl-33483633

RESUMO

Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures, and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.86), left ventricular hypertrophy (AUC = 0.75), left ventricular end systolic and diastolic volumes ([Formula: see text] = 0.74 and [Formula: see text] = 0.70), and ejection fraction ([Formula: see text] = 0.50), as well as predicted systemic phenotypes of age ([Formula: see text] = 0.46), sex (AUC = 0.88), weight ([Formula: see text] = 0.56), and height ([Formula: see text] = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlights hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, provide preliminary interpretation in areas with insufficient qualified cardiologists, and predict phenotypes challenging for human evaluation.

20.
J Genet Couns ; 28(6): 1107-1118, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31478310

RESUMO

BACKGROUND: Despite growing evidence of diagnostic yield and clinical utility of whole exome sequencing (WES) in patients with undiagnosed diseases, there remain significant cost and reimbursement barriers limiting access to such testing. The diagnostic yield and resulting clinical actions of WES for patients who previously faced insurance coverage barriers have not yet been explored. METHODS: We performed a retrospective descriptive analysis of clinical WES outcomes for patients facing insurance coverage barriers prior to clinical WES and who subsequently enrolled in the Undiagnosed Diseases Network (UDN). Clinical WES was completed as a result of participation in the UDN. Payer type, molecular diagnostic yield, and resulting clinical actions were evaluated. RESULTS: Sixty-six patients in the UDN faced insurance coverage barriers to WES at the time of enrollment (67% public payer, 26% private payer). Forty-two of 66 (64%) received insurance denial for clinician-ordered WES, 19/66 (29%) had health insurance through a payer known not to cover WES, and 5/66 (8%) had previous payer denial of other genetic tests. Clinical WES results yielded a molecular diagnosis in 23 of 66 patients (35% [78% pediatric, 65% neurologic indication]). Molecular diagnosis resulted in clinical actions in 14 of 23 patients (61%). CONCLUSIONS: These data demonstrate that a substantial proportion of patients who encountered insurance coverage barriers to WES had a clinically actionable molecular diagnosis, supporting the notion that WES has value as a covered benefit for patients who remain undiagnosed despite objective clinical findings.


Assuntos
Cobertura do Seguro , Doenças não Diagnosticadas/genética , Sequenciamento Completo do Exoma , Criança , Pré-Escolar , Feminino , Testes Genéticos/métodos , Humanos , Masculino , Estudos Retrospectivos , Estados Unidos
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