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Cardiología , Sistema Cardiovascular , Humanos , Inteligencia Artificial , Aprendizaje AutomáticoAsunto(s)
Reserva del Flujo Fraccional Miocárdico , Tomografía Computarizada por Tomografía de Emisión de Positrones , Tomografía de Coherencia Óptica , Humanos , Tomografía de Coherencia Óptica/métodos , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Masculino , Reserva del Flujo Fraccional Miocárdico/fisiología , Femenino , Persona de Mediana Edad , Anciano , Imagen de Perfusión Miocárdica/métodos , Vasos Retinianos/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/fisiopatologíaRESUMEN
Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model's insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73-4.10; log-rank p < 0.001). This remained consistent after adjusting for risk factors (adjusted HR, 2.57; 95% CI 1.62-4.10; p < 0.001). AI-CTRCD score enhanced prediction beyond known factors (time-dependent AUC for 2 years: 0.78 with AI-CTRCD score vs. 0.74 without; p = 0.005). In conclusion, the AI model robustly stratified CTRCD risk from baseline ECG.
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Antineoplásicos , Cardiopatías , Disfunción Ventricular Izquierda , Humanos , Antineoplásicos/efectos adversos , Cardiotoxicidad/diagnóstico , Cardiotoxicidad/etiología , Volumen Sistólico , Inteligencia Artificial , Función Ventricular Izquierda , Antibióticos Antineoplásicos/farmacología , Antraciclinas/efectos adversos , ElectrocardiografíaRESUMEN
Although genome-wide association studies (GWAS) have successfully linked genetic risk loci to various disorders, identifying underlying cellular biological mechanisms remains challenging due to the complex nature of common diseases. We established a framework using human peripheral blood cells, physical, chemical and pharmacological perturbations, and flow cytometry-based functional readouts to reveal latent cellular processes and performed GWAS based on these evoked traits in up to 2,600 individuals. We identified 119 genomic loci implicating 96 genes associated with these cellular responses and discovered associations between evoked blood phenotypes and subsets of common diseases. We found a population of pro-inflammatory anti-apoptotic neutrophils prevalent in individuals with specific subsets of cardiometabolic disease. Multigenic models based on this trait predicted the risk of developing chronic kidney disease in type 2 diabetes patients. By expanding the phenotypic space for human genetic studies, we could identify variants associated with large effect response differences, stratify patients and efficiently characterize the underlying biology.
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Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/genética , Estudio de Asociación del Genoma Completo , Sitios de Carácter Cuantitativo/genética , Predisposición Genética a la Enfermedad , Fenotipo , Células Sanguíneas , Polimorfismo de Nucleótido Simple/genéticaRESUMEN
Background: Atrial septal defect (ASD) increases the risk of adverse cardiovascular outcomes. Despite the potential for risk mitigation through minimally invasive percutaneous closure, ASD remains underdiagnosed due to subtle symptoms and examination findings. To bridge this diagnostic gap, we propose a novel screening strategy aimed at early detection and enhanced diagnosis through the implementation of a convolutional neural network (CNN) to identify ASD from 12-lead electrocardiography (ECG). Methods: ECGs were collected from patients with at least one recorded echocardiogram at 3 hospitals from 2 continents (Keio University Hospital from July 2011 to December 2020, Brigham and Women's Hospital from January 2015 to December 2020, and Dokkyo Medical University Saitama Medical Center from January 2010 and December 2021). ECGs from patients with a diagnosis of ASD were labeled as positive cases while the remainder were labeled as negative. ECGs after the closure of ASD were excluded. After randomly splitting the ECGs into 3 datasets (50% derivation, 20% validation, and 30% test) with no patient overlap, a CNN-based model was trained using the derivation datasets from 2 hospitals and was tested on held-out datasets along with an external validation on the 3rd hospital. All eligible ECGs were used for derivation and validation whereas the earliest ECG for each patient was used for the test and external validation. The discrimination of ASD was assessed by the area under the receiver operating characteristic curve (AUROC). Multiple subgroups were examined to identify any heterogeneity. Findings: A total of 671,201 ECGs from 80,947 patients were collected from the 3 institutions. The AUROC for detecting ASD was 0.85-0.90 across the 3 hospitals. The subgroup analysis showed excellent performance across various characteristics Screening simulation using the model greatly increased sensitivity from 80.6% to 93.7% at specificity 33.6% when compared to using overt ECG abnormalities. Interpretation: A CNN-based model using 12-lead ECG successfully identified the presence of ASD with excellent generalizability across institutions from 2 separate continents. Funding: This work was supported by research grants from JST (JPMJPF2101), JSR corporation, Taiju Life Social Welfare Foundation, Kondou Kinen Medical Foundation, Research fund of Mitsukoshi health and welfare foundation, Tokai University School of Medicine Project Research and Internal Medicine Project Research, Secom Science and Technology Foundation, and Grants from AMED (JP23hma922012 and JP23ym0126813). This work was partially supported by One Brave Idea, co-funded by the American Heart Association and Verily with significant support from AstraZeneca and pillar support from Quest Diagnostics.
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Unsupervised machine learning (phenomapping) has been used successfully to identify novel subgroups (phenogroups) of heart failure with preserved ejection fraction (HFpEF). However, further investigation of pathophysiological differences between HFpEF phenogroups is necessary to help determine potential treatment options. We performed speckle-tracking echocardiography and cardiopulmonary exercise testing (CPET) in 301 and 150 patients with HFpEF, respectively, as part of a prospective phenomapping study (median age 65 [25th to 75th percentile 56 to 73] years, 39% Black individuals, 65% female). Linear regression was used to compare strain and CPET parameters by phenogroup. All indicies of cardiac mechanics except for left ventricular global circumferential strain worsened in a stepwise fashion from phenogroups 1 to 3 after adjustment for demographic and clinical factors. After further adjustment for conventional echocardiographic parameters, phenogroup 3 had the worst left ventricular global longitudinal, right ventricular free wall, and left atrial booster and reservoir strain. On CPET, phenogroup 2 had the lowest exercise time and absolute peak oxygen consumption (VO2), driven primarily by obesity, whereas phenogroup 3 achieved the lowest workload, relative peak oxygen consumption (VO2), and heart rate reserve on multivariable-adjusted analyses. In conclusion, HFpEF phenogroups identified by unsupervised machine learning analysis differ in the indicies of cardiac mechanics and exercise physiology.
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Insuficiencia Cardíaca , Humanos , Femenino , Anciano , Masculino , Insuficiencia Cardíaca/diagnóstico por imagen , Volumen Sistólico/fisiología , Estudios Prospectivos , Ecocardiografía , Ventrículos Cardíacos/diagnóstico por imagen , Función Ventricular IzquierdaRESUMEN
BACKGROUND: View classification is a key step toward building a fully automated system for interpretation of echocardiograms. However, compared with adult echocardiograms, creating a view classification model for pediatric echocardiograms poses additional challenges, such as greater variation in anatomy, structure size, and views. The aim of this study was to develop a computer vision model to autonomously perform view classification on pediatric echocardiographic images. METHODS: Using a training set of 12,067 echocardiographic images from patients aged 0 to 19 years, a convolutional neural network model was trained to identify 27 preselected standard pediatric echocardiographic views which included anatomic sweeps, color Doppler, and Doppler tracings. A validation set of 6,197 images was used for parameter tuning and model selection. A test set of 9,684 images from 100 different patients was then used to evaluate model accuracy. The model was also evaluated on a per study basis using a second test set consisting of 524 echocardiograms from children with leukemia to identify six preselected views pertinent to cardiac dysfunction surveillance. RESULTS: The model identified the 27 preselected views with 90.3% accuracy. Accuracy was similar across age groups (89.3% for 0-4 years, 90.8% for 4-9 years, 90.0% for 9-14 years, and 91.2% for 14-19 years; P = .12). Examining the view subtypes, accuracy was 78.3% for the cine one location, 90.5% for sweeps with color Doppler, 82.2% for sweeps without color Doppler, and 91.1% for Doppler tracings. Among the leukemia cohort, the model identified the six preselected views on a per study basis with a positive predictive value of 98.7% to 99.2% and sensitivity of 76.9% to 94.8%. CONCLUSIONS: A convolutional neural network model was constructed for view classification of pediatric echocardiograms that was accurate across the spectrum of ages and view types. This work lays the foundation for automated quantitative analysis and diagnostic support to promote efficient, accurate, and scalable analysis of pediatric echocardiograms.
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Inteligencia Artificial , Leucemia , Humanos , Niño , Ecocardiografía/métodos , Valor Predictivo de las Pruebas , Simulación por ComputadorRESUMEN
BACKGROUND: Novel targeted treatments increase the need for prompt hypertrophic cardiomyopathy (HCM) detection. However, its low prevalence (0.5%) and resemblance to common diseases present challenges that may benefit from automated machine learning-based approaches. We aimed to develop machine learning models to detect HCM and to differentiate it from other cardiac conditions using ECGs and echocardiograms, with robust generalizability across multiple cohorts. METHODS: Single-institution HCM ECG models were trained and validated on external data. Multi-institution models for ECG and echocardiogram were trained on data from 3 academic medical centers in the United States and Japan using a federated learning approach, which enables training on distributed data without data sharing. Models were validated on held-out test sets for each institution and from a fourth academic medical center and were further evaluated for discrimination of HCM from aortic stenosis, hypertension, and cardiac amyloidosis. Last, automated detection was compared with manual interpretation by 3 cardiologists on a data set with a realistic HCM prevalence. RESULTS: We identified 74 376 ECGs for 56 129 patients and 8392 echocardiograms for 6825 patients at the 4 academic medical centers. Although ECG models trained on data from each institution displayed excellent discrimination of HCM on internal test data (C statistics, 0.88-0.93), the generalizability was limited, most notably for a model trained in Japan and tested in the United States (C statistic, 0.79-0.82). When trained in a federated manner, discrimination of HCM was excellent across all institutions (C statistics, 0.90-0.96 and 0.90-0.96 for ECG and echocardiogram model, respectively), including for phenotypic subgroups. The models further discriminated HCM from hypertension, aortic stenosis, and cardiac amyloidosis (C statistics, 0.84, 0.83, and 0.88, respectively, for ECG and 0.93, 0.94, 0.85, respectively, for echocardiogram). Analysis of electrocardiography-echocardiography paired data from 11 823 patients from an external institution indicated a higher sensitivity of automated HCM detection at a given positive predictive value compared with cardiologists (0.98 versus 0.81 at a positive predictive value of 0.01 for ECG and 0.78 versus 0.59 at a positive predictive value of 0.24 for echocardiogram). CONCLUSIONS: Federated learning improved the generalizability of models that use ECGs and echocardiograms to detect and differentiate HCM from other causes of hypertrophy compared with training within a single institution.
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Amiloidosis , Cardiomiopatía Hipertrófica , Hipertensión , Cardiomiopatía Hipertrófica/diagnóstico por imagen , Cardiomiopatía Hipertrófica/epidemiología , Ecocardiografía , Electrocardiografía , HumanosRESUMEN
BACKGROUND: Dilated cardiomyopathy (DCM) is a final common manifestation of heterogenous etiologies. Adverse outcomes highlight the need for disease stratification beyond ejection fraction. OBJECTIVES: The purpose of this study was to identify novel, reproducible subphenotypes of DCM using multiparametric data for improved patient stratification. METHODS: Longitudinal, observational UK-derivation (n = 426; median age 54 years; 67% men) and Dutch-validation (n = 239; median age 56 years; 64% men) cohorts of DCM patients (enrolled 2009-2016) with clinical, genetic, cardiovascular magnetic resonance, and proteomic assessments. Machine learning with profile regression identified novel disease subtypes. Penalized multinomial logistic regression was used for validation. Nested Cox models compared novel groupings to conventional risk measures. Primary composite outcome was cardiovascular death, heart failure, or arrhythmia events (median follow-up 4 years). RESULTS: In total, 3 novel DCM subtypes were identified: profibrotic metabolic, mild nonfibrotic, and biventricular impairment. Prognosis differed between subtypes in both the derivation (P < 0.0001) and validation cohorts. The novel profibrotic metabolic subtype had more diabetes, universal myocardial fibrosis, preserved right ventricular function, and elevated creatinine. For clinical application, 5 variables were sufficient for classification (left and right ventricular end-systolic volumes, left atrial volume, myocardial fibrosis, and creatinine). Adding the novel DCM subtype improved the C-statistic from 0.60 to 0.76. Interleukin-4 receptor-alpha was identified as a novel prognostic biomarker in derivation (HR: 3.6; 95% CI: 1.9-6.5; P = 0.00002) and validation cohorts (HR: 1.94; 95% CI: 1.3-2.8; P = 0.00005). CONCLUSIONS: Three reproducible, mechanistically distinct DCM subtypes were identified using widely available clinical and biological data, adding prognostic value to traditional risk models. They may improve patient selection for novel interventions, thereby enabling precision medicine.
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Cardiomiopatías , Cardiomiopatía Dilatada , Cardiomiopatía Dilatada/diagnóstico , Cardiomiopatía Dilatada/genética , Creatinina , Femenino , Fibrosis , Humanos , Masculino , Persona de Mediana Edad , Proteómica , Volumen SistólicoRESUMEN
The full range of cell functions is under-determined in most human diseases. The evidence that somatic cell competition and clonal imbalance play a role in non-neoplastic chronic disease reveal a need for a dedicated effort to explore single cell function if we are to understand the mechanisms by which cell population behaviors influence disease. It will be vital to document not only the prevalent pathologic behaviors but also those beneficial functions eliminated or suppressed by competition. An improved mechanistic understanding of the role of somatic cell biology will help to stratify chronic disease, define more precisely at an individual level the role of environmental factors and establish principles for prevention and potential intervention throughout the life course and across the trajectory from wellness to disease.
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BACKGROUND: Researchers routinely evaluate novel biomarkers for incorporation into clinical risk models, weighing tradeoffs between cost, availability, and ease of deployment. For risk assessment in population health initiatives, ideal inputs would be those already available for most patients. We hypothesized that common hematologic markers (eg, hematocrit), available in an outpatient complete blood count without differential, would be useful to develop risk models for cardiovascular events. METHODS: We developed Cox proportional hazards models for predicting heart attack, ischemic stroke, heart failure hospitalization, revascularization, and all-cause mortality. For predictors, we used 10 hematologic indices (eg, hematocrit) from routine laboratory measurements, collected March 2016 to May 2017 along with demographic data and diagnostic codes. As outcomes, we used neural network-based automated event adjudication of 1 028 294 discharge summaries. We trained models on 23 238 patients from one hospital in Boston and evaluated them on 29 671 patients from a second one. We assessed calibration using Brier score and discrimination using Harrell's concordance index. In addition, to determine the utility of high-dimensional interactions, we compared our proportional hazards models to random survival forest models. RESULTS: Event rates in our cohort ranged from 0.0067 to 0.075 per person-year. Models using only hematology indices had concordance index ranging from 0.60 to 0.80 on an external validation set and showed the best discrimination when predicting heart failure (0.80 [95% CI, 0.79-0.82]) and all-cause mortality (0.78 [0.77-0.80]). Compared with models trained only on demographic data and diagnostic codes, models that also used hematology indices had better discrimination and calibration. The concordance index of the resulting models ranged from 0.75 to 0.85 and the improvement in concordance index ranged up to 0.072. Random survival forests had minimal improvement over proportional hazards models. CONCLUSIONS: We conclude that low-cost, ubiquitous inputs, if biologically informative, can provide population-level readouts of risk.
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Enfermedades Cardiovasculares , Insuficiencia Cardíaca , Hematología , Inteligencia Artificial , Biomarcadores , Enfermedades Cardiovasculares/epidemiología , Factores de Riesgo de Enfermedad Cardiaca , Insuficiencia Cardíaca/diagnóstico , Insuficiencia Cardíaca/epidemiología , Insuficiencia Cardíaca/terapia , Humanos , Medición de Riesgo/métodos , Factores de RiesgoRESUMEN
Thrombosis is the leading complication of common human disorders including diabetes, coronary heart disease, and infection and remains a global health burden. Current anticoagulant therapies that target the general clotting cascade are associated with unpredictable adverse bleeding effects, because understanding of hemostasis remains incomplete. Here, using perturbational screening of patient peripheral blood samples for latent phenotypes, we identified dysregulation of the major mechanosensory ion channel Piezo1 in multiple blood lineages in patients with type 2 diabetes mellitus (T2DM). Hyperglycemia activated PIEZO1 transcription in mature blood cells and selected high Piezo1expressing hematopoietic stem cell clones. Elevated Piezo1 activity in platelets, red blood cells, and neutrophils in T2DM triggered discrete prothrombotic cellular responses. Inhibition of Piezo1 protected against thrombosis both in human blood and in zebrafish genetic models, particularly in hyperglycemia. Our findings identify a candidate target to precisely modulate mechanically induced thrombosis in T2DM and a potential screening method to predict patient-specific risk. Ongoing remodeling of cell lineages in hematopoiesis is an integral component of thrombotic risk in T2DM, and related mechanisms may have a broader role in chronic disease.
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Diabetes Mellitus Tipo 2 , Hiperglucemia , Trombosis , Animales , Humanos , Hiperglucemia/complicaciones , Canales Iónicos/metabolismo , Mecanotransducción Celular , Pez Cebra/metabolismo , Proteínas de Pez Cebra/metabolismoRESUMEN
Aim: Left ventricular systolic dysfunction (LVSD) carries an increased risk for overt heart failure and mortality, yet treatable to mitigate disease progression. An artificial intelligence (AI)-enabled 12-lead electrocardiogram (ECG) model demonstrated promise in LVSD screening, but the performance dropped unexpectedly in external validation. We thus sought to train de novo models for LVSD detection and investigated their performance across multiple institutions and across a broader set of patient strata. Methods and results: ECGs taken within 14 days of an echocardiogram were obtained from four academic hospitals (three in the United States and one in Japan). Four AI models were trained to detect patients with ejection fraction (EF) <40% using ECGs from each of the four institutions. All the models were then evaluated on the held-out test data set from the same institution and data from the three external institutions. Subgroup analyses stratified by patient characteristics and common ECG abnormalities were performed. A total of 221 846 ECGs were identified from the 4 institutions. While the Brigham and Women's Hospital (BWH)-trained and Keio-trained models yielded similar accuracy on their internal test data [area under the receiver operating curve (AUROC) 0.913 and 0.914, respectively], external validity was worse for the Keio-trained model (AUROC: 0.905-0.915 for BWH trained and 0.849-0.877 for Keio-trained model). Although ECG abnormalities including atrial fibrillation, left bundle branch block, and paced rhythm-reduced detection, the models performed robustly across patient characteristics and other ECG features. Conclusion: While using the same model architecture, different data sets produced models with different performances for detecting low-EF highlighting the importance of external validation and extensive stratification analysis.
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Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results.
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Cardiología/estadística & datos numéricos , Enfermedades Cardiovasculares/epidemiología , Interpretación Estadística de Datos , Guías como Asunto , Proyectos de Investigación/normas , American Heart Association , Teorema de Bayes , Cardiología/métodos , Cardiología/organización & administración , Enfermedades Cardiovasculares/diagnóstico , Enfermedades Cardiovasculares/etiología , Manejo de la Enfermedad , Susceptibilidad a Enfermedades , Predisposición Genética a la Enfermedad , Humanos , Metaanálisis como Asunto , Pronóstico , Ensayos Clínicos Controlados Aleatorios como Asunto , Análisis de Supervivencia , Estados UnidosRESUMEN
Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85-0.91 for ECG and 0.89-1.00 for echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3-4% at 52-71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74-77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases.
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Amiloidosis/diagnóstico por imagen , Inteligencia Artificial , Ecocardiografía , ElectrocardiografíaRESUMEN
Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.
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Neuropatías Amiloides Familiares/metabolismo , Cardiomiopatías/metabolismo , Insuficiencia Cardíaca/metabolismo , Aprendizaje Automático , Prealbúmina/metabolismo , Neuropatías Amiloides Familiares/genética , Cardiomiopatías/genética , Registros Electrónicos de Salud , Insuficiencia Cardíaca/genética , Humanos , Prealbúmina/genéticaRESUMEN
Despite increasing ability to understand and correct molecular derangements in disease, genomics and novel phenotypic assays are unevenly deployed in clinical practice. This has hampered translational research and our ability to identify clinically actionable subtypes of disease. Historic examples illustrate how the perspectives of stakeholders across the healthcare ecosystem can influence adoption of innovations in healthcare. Consideration of these factors, from discovery to implementation, can accelerate adoption of new molecular and digital phenotypes in a 'learning' healthcare ecosystem.
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Atención a la Salud/tendencias , Invenciones , Pautas de la Práctica en Medicina/tendencias , Manejo de la Enfermedad , Susceptibilidad a Enfermedades , Humanos , Terapias en Investigación , Investigación Biomédica TraslacionalRESUMEN
Mutations in the calcium-binding protein calsequestrin cause the highly lethal familial arrhythmia catecholaminergic polymorphic ventricular tachycardia (CPVT). In vivo, calsequestrin multimerizes into filaments, but there is not yet an atomic-resolution structure of a calsequestrin filament. We report a crystal structure of a human cardiac calsequestrin filament with supporting mutational analysis and in vitro filamentation assays. We identify and characterize a new disease-associated calsequestrin mutation, S173I, that is located at the filament-forming interface, and further show that a previously reported dominant disease mutation, K180R, maps to the same surface. Both mutations disrupt filamentation, suggesting that disease pathology is due to defects in multimer formation. An ytterbium-derivatized structure pinpoints multiple credible calcium sites at filament-forming interfaces, explaining the atomic basis of calsequestrin filamentation in the presence of calcium. Our study thus provides a unifying molecular mechanism through which dominant-acting calsequestrin mutations provoke lethal arrhythmias.
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Calcio/química , Calsecuestrina/química , Miocardio/metabolismo , Taquicardia Ventricular/genética , Adulto , Sitios de Unión , Calcio/metabolismo , Proteínas de Unión al Calcio/genética , Proteínas de Unión al Calcio/metabolismo , Calsecuestrina/genética , Calsecuestrina/metabolismo , Clonación Molecular , Cristalografía por Rayos X , Escherichia coli/genética , Escherichia coli/metabolismo , Femenino , Expresión Génica , Genes Dominantes , Vectores Genéticos/química , Vectores Genéticos/metabolismo , Humanos , Cinética , Masculino , Persona de Mediana Edad , Proteínas Mitocondriales/genética , Proteínas Mitocondriales/metabolismo , Modelos Moleculares , Mutación , Miocardio/patología , Linaje , Unión Proteica , Conformación Proteica en Hélice alfa , Conformación Proteica en Lámina beta , Dominios y Motivos de Interacción de Proteínas , Multimerización de Proteína , Proteínas Recombinantes/química , Proteínas Recombinantes/genética , Proteínas Recombinantes/metabolismo , Taquicardia Ventricular/metabolismo , Taquicardia Ventricular/patologíaRESUMEN
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.