Characterization of Repolarization Alternans in the Coronary Artery Disease

Burattini, Laura; Burattini, Roberto

doi:10.5772/28766

Cited by 7 publications

(7 citation statements)

References 51 publications

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“…The database enrollment protocol was conducted according to Title 45, U.S. Code of Federal Regulations, Part 46, protection of human subjects (revised: November 13, 2001–effective: December 13, 2001) and in accordance with the Declaration of Helsinki. Furthermore, the research subject review board of the University of Rochester approved the IDEAL protocol ( 36 ). All patients provided a signed consent before participating in the study.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Alkhodari

Jelinek

Karlas

et al. 2021

Front. Cardiovasc. Med.

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Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF.Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges.Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories.Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98.Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.

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Section: Methodsmentioning

confidence: 99%

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Alkhodari

Jelinek

Karlas

et al. 2021

Front. Cardiovasc. Med.

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show abstract

“…The enrolment protocol for the database adhered to the principles of the Declaration of Helsinki and Title 45, U.S. Code of Federal Regulations, Part 46, Protection of Human Subjects (revised: November 13, 2001, effective: December 13, 2001). Moreover, the IDEAL protocol received approval at the University of Rochester from the research subject review board [ 29 ]. Prior to participation in the trial, each patient provided written consent.…”

Section: Methodsmentioning

confidence: 99%

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

Al Younis,

Hadjileontiadis,

Khandoker

et al. 2024

PLoS ONE

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Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcare expenditures, with anticipated escalation in the future. It is essential to classify HF patients into three groups based on their ejection fraction: reduced (HFrEF), mid-range (HFmEF), and preserved (HFpEF), such as for diagnosis, risk assessment, treatment choice, and the ongoing monitoring of heart failure. Nevertheless, obtaining a definitive prediction poses challenges, requiring the reliance on echocardiography. On the contrary, an electrocardiogram (ECG) provides a straightforward, quick, continuous assessment of the patient’s cardiac rhythm, serving as a cost-effective adjunct to echocardiography. In this research, we evaluate several machine learning (ML)-based classification models, such as K-nearest neighbors (KNN), neural networks (NN), support vector machines (SVM), and decision trees (TREE), to classify left ventricular ejection fraction (LVEF) for three categories of HF patients at hourly intervals, using 24-hour ECG recordings. Information from heterogeneous group of 303 heart failure patients, encompassing HFpEF, HFmEF, or HFrEF classes, was acquired from a multicenter dataset involving both American and Greek populations. Features extracted from ECG data were employed to train the aforementioned ML classification models, with the training occurring in one-hour intervals. To optimize the classification of LVEF levels in coronary artery disease (CAD) patients, a nested cross-validation approach was employed for hyperparameter tuning. HF patients were best classified using TREE and KNN models, with an overall accuracy of 91.2% and 90.9%, and average area under the curve of the receiver operating characteristics (AUROC) of 0.98, and 0.99, respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8–9 am, and 10–11 pm were the ones that contributed to the highest classification accuracy. The results pave the way for creating an automated screening system tailored for patients with CAD, utilizing optimal measurement timings aligned with their circadian cycles.

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“…In fact, it is possible to target mitochondria by perimitochondrial ENS, as shown in a recent serendipitous observation. 367 As shown in Figure 94, a branched peptide (295), consisting of a well-established protein tag (i.e., FLAG-tag: DYKDDDDK) 370 as the branch, Nap-ff as the self-assembling motif, [531][532][533] and NBD as the fluorophore, 253 is a substrate of enterokinase (ENTK). 369 295 self-assembles in water to form micelles, which turn into nanofibers after ENTK cuts off the FLAG motif (DDDDK).…”

Section: Subcellular Ensmentioning

confidence: 99%

Enzymatic Noncovalent Synthesis

et al. 2020

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Enzymatic reactions and noncovalent (i.e., supramolecular) interactions are two fundamental nongenetic attributes of life. Enzymatic noncovalent synthesis (ENS) refers to a process where enzymatic reactions control intermolecular noncovalent interactions for spatial organization of higher-order molecular assemblies that exhibit emergent properties and functions. Like enzymatic covalent synthesis (ECS), in which an enzyme catalyzes the formation of covalent bonds to generate individual molecules, ENS is a unifying theme for understanding the functions, morphologies, and locations of molecular ensembles in cellular environments. This review intends to provide a summary of the works of ENS within the last decade and emphasize ENS for functions. After comparing ECS and ENS, we describe a few representative examples where nature uses ENS, as a rule of life, to create the ensembles of biomacromolecules for emergent properties/functions in a myriad of cellular processes. Then, we focus on ENS of man-made (synthetic) molecules in cell-free conditions, classified by the types of enzymes. After that, we introduce the exploration of ENS of man-made molecules in the context of cells by discussing intercellular, peri/intracellular, and subcellular ENS for cell morphogenesis, molecular imaging, cancer therapy, and other applications. Finally, we provide a perspective on the promises of ENS for developing molecular assemblies/processes for functions. This review aims to be an updated introduction for researchers who are interested in exploring noncovalent synthesis for developing molecular science and technologies to address societal needs.

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Characterization of Repolarization Alternans in the Coronary Artery Disease

Cited by 7 publications

References 51 publications

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

Enzymatic Noncovalent Synthesis

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