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: 10526388
• 负责人: ANDRAS BRATINCSAK
• 金额: $ 16.7万
• 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/10526388
• 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; Foundations; Gender; Genetic Diseases; Goals; Heart; Heart Arrest; Human Resources; Individual; Inherited; Interobserver Variability; Machine Learning; Measures; 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; base; cohort; diagnostic tool; empowered; 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; 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.

抽象的 心电图（ECG）已使用一个多世纪来检测 心。 ECG用于筛查和诊断遗传心律失常综合征患者，可以 导致心律不齐和心脏猝死。心电图被认为是 IAS的筛查和诊断武器库，因为它便宜，便携式，可提供护理点 结果，不需要高技能的人员表演。但是，从解释的角度来看 ECG不会产生敏感和特定的结果，因此它无法作为准确的筛选或 IAS的诊断工具。这种不准确的一部分来自对太少个人而产生的评估 正常参考ECG值，具有超过100岁的年龄和性别依赖性变量和截止值 为了记住，所有这些都导致基本缺陷和非常高的观察者解释变异性。 我们已经从最大的历史人群中创建了一个新颖的ECG数据库，而不是 27,000名受试者。然后，我们将102个ECG变量的数据转换为表示为z评分的值。 z- 根据定义，得分促进了所有人的正常和异常的直接和客观区分 措施。在z得分中表达ECG值可以消除对解释的观察者间变异性 ECG值。此外，我们开发了通过人工智能增强的复杂计算机算法 （AI）检测可归因于一组受试者的ECG变量的特征性状。 在这项研究中，我们将从IAS患者那里收集ECG。接下来，我们将将这些心电图与我们的心电图进行比较 利用基于Z分数的列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ECG变量的差异。我们将 确定具有IAS疾病实体最有希望的区别特征的心电图变量。 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 在数字化的ECG数据上应用三个不同的模型。首先，我们将量化ECG之间的依赖性 主成分回归和图形套索算法的变量。这 方法将自动确定ECG变量的最佳组合以区分受影响 和普通个人，并将开发一组可用于提供最敏感和最敏感的变量 迄今为止特定IAS的特定疾病-ECG关联。然后，我们将使用两个不同的机器学习 在具有IAS的受试者的心电图中检测新颖性异常的模型。结合 传统的统计分析和基于AI的算法，我们将能够确定特定的ECG变量或 ECG变量组及其Z分数值是作为IAS诊断的预测工具。我们的 长期目标是利用该模型进行大规模筛查工作，以检测年轻人的IAS，从而检测 预防灾难性并发症，例如猝死。