Novel disease-electrocardiogram associations in inherited arrhythmia syndromes

遗传性心律失常综合征中的新疾病-心电图关联

基本信息

批准号：
10684855
负责人：
ANDRAS BRATINCSAK
金额：
$ 13.92万
依托单位：
KAPIOLANI MEDICAL CENTER WOMEN/CHILDREN
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-16 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10684855
关键词：
Address Affect Age Algorithms Arrhythmia Artificial Intelligence Cardiac Cardiac Death Characteristics Child Childhood Clinical Computational algorithm Data Databases Dependence Detection Development Diagnosis Diagnostic Disease Electrocardiogram Electrophysiology (science)Evaluation Gender Genetic Diseases Goals Heart Heart Arrest Human Resources Individual Inherited Interobserver Variability Machine Learning Measures Memory Methods Modality Modeling Nomograms Normal Range Normalcy Observer Variation Patients Pattern Predictive Value Prevalence Reproducibility Risk Screening procedure Sensitivity and Specificity Standardization Statistical Data Interpretation Syndrome System Techniques Testing accurate diagnosis age group artificial intelligence algorithm cohort diagnostic tool empowerment follow-up generative adversarial network heart electrical activity improved innovation machine learning algorithm machine learning method machine learning model novel point of care portability predictive modeling predictive tools prevent reading ability screening screening program skills sudden cardiac death tool trait trustworthiness unsupervised learning young adult

项目摘要

ABSTRACT Electrocardiograms (ECGs) have been used for more than a century to detect the electrical activity of the heart. ECGs are used to screen and diagnose patients with inherited arrhythmia syndromes, diseases that can result in cardiac arrhythmias and sudden cardiac death. The ECG is considered an important part of the screening and diagnostic armamentarium of IAS, because it is inexpensive, portable, provides point-of-care results and does not require highly skilled personnel to perform. However, from the standpoint of interpretation, the ECG does not yield a sensitive and specific result and therefore it fails to serve as an accurate screening or diagnostic tool for IAS. Part of this inaccuracy derives from the assessment of too few individuals to generate the normal reference ECG values, with more than 100 age and gender-dependent variables and cut-off values to memorize, all resulting in foundational deficiencies and a very high inter-observer interpretation variability. We have created a novel ECG database from the largest historical cohort of normal individuals of more than 27,000 subjects. We then transformed the data of 102 ECG variables to express the values as Z-scores. Z- scores by definition facilitate an immediate and objective distinction of normality and abnormality across all measures. Expressing the ECG values in Z-scores eliminates inter-observer variability in the interpretation of ECG values. In addition, we developed sophisticated computer algorithms enhanced by artificial intelligence (AI) to detect characteristic traits of ECG variables attributable to a group of subjects. In this study we will collect ECGs from patients with IAS. Next, we will compare these ECGs to our ECG database of normal individuals utilizing the Z-score based nomograms. We will use statistical analysis to detect differences in the 102 ECG variables between the affected (IAS) and unaffected (normal) subjects. We will identify the ECG variables that show the most promising distinction characteristics for an IAS disease entity. Next, we will use AI algorithms to detect highly sensitive and specific combinations of ECG variables. We will apply three different models on the digitized ECG data. First, we will quantify dependencies between ECG variables with a combination of principal components regression and graphical LASSO algorithms. This approach will automatically identify the best combination of ECG variables to differentiate between affected and normal individuals, and will develop a set of variables that can be used to provide the most sensitive and specific disease-ECG associations for specific IAS to date. We will then use two distinct machine learning models to detect anomalies and pattern of novelties in the ECG of subjects with IAS. With the combination of traditional statistical analysis and the AI based algorithms, we will be able to identify specific ECG variables or groups of ECG variables and their Z-score values to serve as predictive tools for the diagnosis of IAS. Our long-term goal is to utilize this model for large scale screening efforts to detect IAS in the young and thereby prevent catastrophic complications, such as sudden cardiac death.

抽象的心电图 (ECG) 已被用于检测心电活动一个多世纪了心。心电图用于筛查和诊断患有遗传性心律失常综合征的患者，这些疾病可以导致心律失常和心源性猝死。心电图被认为是心电图的重要组成部分 IAS 的筛查和诊断设备，因为它便宜、便携，可提供即时护理结果并且不需要高技能人员来执行。然而，从解释的角度来看，心电图不能产生敏感和具体的结果，因此它不能作为准确的筛查或 IAS 诊断工具。这种不准确的部分原因在于评估的个人太少而无法产生正常参考心电图值，具有 100 多个年龄和性别相关变量和截止值记住，所有这些都导致了根本性的缺陷和观察者间解释的高度可变性。我们从最大的正常个体历史队列中创建了一个新颖的心电图数据库 27,000 名受试者。然后，我们转换 102 个心电图变量的数据，将值表示为 Z 分数。 Z- 根据定义，分数有助于立即客观地区分所有的正常和异常情况措施。以 Z 分数表达心电图值消除了观察者之间解释的差异心电图值。此外，我们还开发了由人工智能增强的复杂计算机算法（AI）检测一组受试者的心电图变量的特征。在这项研究中，我们将收集 IAS 患者的心电图。接下来，我们将这些心电图与我们的心电图进行比较使用基于 Z 分数的列线图的正常个体数据库。我们将使用统计分析来检测受影响（IAS）和未受影响（正常）受试者之间 102 个心电图变量的差异。我们将识别显示 IAS 疾病实体最有希望区分特征的 ECG 变量。接下来，我们将使用人工智能算法来检测高度敏感且特定的心电图变量组合。我们将对数字化心电图数据应用三种不同的模型。首先，我们将量化心电图之间的依赖性结合主成分回归和图形 LASSO 算法的变量。这方法将自动识别心电图变量的最佳组合，以区分受影响的患者和正常个体，并将开发一组变量，可用于提供最敏感和迄今为止，特定 IAS 的特定疾病与心电图关联。然后我们将使用两种不同的机器学习检测 IAS 受试者心电图异常和新模式的模型。与传统的统计分析和基于人工智能的算法，我们将能够识别特定的心电图变量或心电图变量组及其 Z 分数值可作为诊断 IAS 的预测工具。我们的长期目标是利用该模型进行大规模筛查工作，以检测年轻人中的 IAS，从而预防灾难性并发症，例如心源性猝死。