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:
- 金额:$ 13.92万
- 依托单位:
- 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:
- 关键词:AddressAffectAgeAlgorithmsArrhythmiaArtificial IntelligenceCardiacCardiac DeathCharacteristicsChildChildhoodClinicalComputational algorithmDataDatabasesDependenceDetectionDevelopmentDiagnosisDiagnosticDiseaseElectrocardiogramElectrophysiology (science)EvaluationGenderGenetic DiseasesGoalsHeartHeart ArrestHuman ResourcesIndividualInheritedInterobserver VariabilityMachine LearningMeasuresMemoryMethodsModalityModelingNomogramsNormal RangeNormalcyObserver VariationPatientsPatternPredictive ValuePrevalenceReproducibilityRiskScreening procedureSensitivity and SpecificityStandardizationStatistical Data InterpretationSyndromeSystemTechniquesTestingaccurate diagnosisage groupartificial intelligence algorithmcohortdiagnostic toolempowermentfollow-upgenerative adversarial networkheart electrical activityimprovedinnovationmachine learning algorithmmachine learning methodmachine learning modelnovelpoint of careportabilitypredictive modelingpredictive toolspreventreading abilityscreeningscreening programskillssudden cardiac deathtooltraittrustworthinessunsupervised learningyoung 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,所有这些都会导致根本性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,将这些值表示为 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心电图变量。
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。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
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ANDRAS BRATINCSAK其他文献
ANDRAS BRATINCSAK的其他文献
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{{ truncateString('ANDRAS BRATINCSAK', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
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9184279 - 财政年份:2016
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