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

• 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
• 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

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.

摘要 心电图（ECG）已经使用了超过世纪来检测心脏的电活动。 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， 导致心律失常和心脏性猝死。心电图被认为是一个重要的组成部分， IAS的筛查和诊断设备，因为它便宜，便携，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， ECG不能产生敏感和特异的结果，因此不能作为准确的筛查， IAS诊断工具。这种不准确的部分原因是评估的个体太少， 正常参考ECG值，超过100个年龄和性别相关变量和截止值 所有这些都导致基础缺陷和非常高的观察者间解释可变性。 我们已经创建了一个新的心电图数据库，来自历史上最大的正常个体队列， 27,000名受试者然后，我们将102个ECG变量的数据转换为Z分数。Z- 根据定义，评分有助于立即和客观地区分所有患者的正常性和异常性。 措施用Z分数表示ECG值消除了观察者之间在解释 ECG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)以检测可归因于一组受试者的ECG变量的特征。 在本研究中，我们将收集IAS患者的ECG。接下来，我们将这些ECG与我们的ECG进行比较 使用基于Z分数的列线图的正常个体的数据库。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ECG变量的差异。我们将 识别显示IAS疾病实体的最有希望的区别特征的ECG变量。 接下来，我们将使用AI算法来检测ECG变量的高度敏感和特定组合。我们将 对数字化ECG数据应用三种不同的模型。首先，我们将量化ECG之间的依赖关系， 变量的主成分回归和图形LASSO算法的组合。这 该方法将自动识别ECG变量的最佳组合，以区分受影响的 和正常人，并将开发一套变量，可用于提供最敏感， 迄今为止，特定IAS的特定疾病-ECG关联。然后，我们将使用两种不同的机器学习 检测IAS受试者ECG中异常和新奇模式的模型。相结合 传统的统计分析和基于AI的算法，我们将能够识别特定的ECG变量或 ECG变量组及其Z评分值作为IAS诊断的预测工具。我们 长期目标是利用这一模型进行大规模筛查，以检测年轻人的IAS， 预防灾难性并发症，如心脏猝死。