Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.
Circ Res
; 128(2): 172-184, 2021 01 22.
Article
em En
| MEDLINE
| ID: mdl-33167779
ABSTRACT
RATIONALE Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside. OBJECTIVE:
To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes. METHODS ANDRESULTS:
We recorded 5706 ventricular MAPs in 42 patients with coronary artery disease and left ventricular ejection fraction ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 7030 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points (1) sustained VT/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI, 0.76-1.00) and 0.91 for mortality (95% CI, 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT/VF.CONCLUSIONS:
Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fibrilação Ventricular
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Processamento de Sinais Assistido por Computador
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Diagnóstico por Computador
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Redes Neurais de Computação
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Morte Súbita Cardíaca
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Taquicardia Ventricular
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Técnicas Eletrofisiológicas Cardíacas
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Máquina de Vetores de Suporte
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Cardiomiopatias
Tipo de estudo:
Diagnostic_studies
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Etiology_studies
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Observational_studies
/
Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Aged80
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Female
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Humans
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Male
/
Middle aged
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article