Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE)

Marina-Breysse, Manuel; Quintanilla, Jorge G.; Gil-Perdomo, José Miguel; Juárez-Fernández, Miriam; García-González, Inés; Rial-Bastón, Verónica; Corcobado, María Carmen; Espinosa, M; Ruiz, Francisco; Pérez, Francisco Gómez-Mascaraque; Bringas-Bollada, María; Lillo-Castellano, José María; Pérez‐Castellano, Nicasio; Martínez‐Sellés, Manuel; Sá, Esteban López de; Martín-Benítez, Juan Carlos; Pérez-Villacastı́n, Julián; Filgueiras-Rama, David

doi:10.1016/j.acmx.2018.05.003

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…AI analyses of ECG of patients with ventricular fibrillation have been able to correlate spectral changes with acute cerebral injury, early prediction of mortality, and cerebral performance in comatose survivors after cardiac arrest [68], currently being validated in a multicenter study [69].…”

Section: Risk Prediction and Integration With Clinical Variablesmentioning

confidence: 99%

Current and Future Use of Artificial Intelligence in Electrocardiography

Sellés

Marina-Breysse

2023

JCDD

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Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.

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Section: Risk Prediction and Integration With Clinical Variablesmentioning

confidence: 99%

Current and Future Use of Artificial Intelligence in Electrocardiography

Sellés

Marina-Breysse

2023

JCDD

Self Cite

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Section: Performance Evaluation Metrics For Multiclass Multi-label An...mentioning

confidence: 99%

“…Hamming Loss has opted only for the ensemble-based CVD risk stratification [181][182][183][184]. The PE metrics used in the stress test-based (ECG) [185][186][187] techniques are area-under-the-curve (AUC), sensitivity, specificity, PPV, and NPV [188][189][190][191][192]. As seen from the above discussion, the most important characteristic of the multiclass paradigm is the selection of gold standards having greater than two classes.…”

Section: Performance Evaluation Metrics For Multiclass Multi-label An...mentioning

confidence: 99%

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Suri¹,

Bhagawati

Sauseng

et al. 2022

Diagnostics

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Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

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Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE)

Abstract: A model based on spectral analysis of VF tracings is a promising tool to obtain early prognostic data after SCD.

Cited by 2 publications

References 29 publications

Current and Future Use of Artificial Intelligence in Electrocardiography

Current and Future Use of Artificial Intelligence in Electrocardiography

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

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