Practical Approaches to Care in Emergency Syncope (PACES)

紧急晕厥的实用护理方法 (PACES)

• 批准号: 10854193
• 负责人: Marc Probst
• 金额: $ 50.28万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10854193
• 关键词: Acceleration; Accident and Emergency department; Acute; Administrative Supplement; Age; Aortic Valve Insufficiency; Aortic Valve Stenosis; Area Under Curve; Artificial Intelligence; Benign; Cardiac; Cardiovascular system; Caring; Cessation of life; Characteristics; Clinical; Dangerousness; Data; Data Science; Data Scientist; Data Set; Detection; Diagnosis; Disease; Echocardiography; Electrocardiogram; Emergency Department Physician; Emergency Department patient; Emergency Situation; Emergency department visit; Enrollment; Ethnic Origin; Evaluation; Event; Functional disorder; Future; Gender; Goals; Health Care Costs; Heart Diseases; Heart Valve Diseases; Hospitalization; Hospitals; Image; Investigation; Ischemia; Lead; Left; Left Ventricular Hypertrophy; Link; Machine Learning; Medical; Methods; Mitral Valve; Mitral Valve Insufficiency; Mitral Valve Prolapse; Modeling; Monitor; Morbidity - disease rate; New York; Operative Surgical Procedures; Participant; Patient Care; Patient-Focused Outcomes; Patients; Pericardial effusion; Play; Population; Quality of Care; Race; Research; Research Technics; Risk; Risk Factors; Role; Sensitivity and Specificity; Syncope; System; Testing; Time; Training; Unconscious State; United States; Validation; Ventricular; Visit; aged; artificial intelligence method; clinical care; clinical implementation; cohort; experience; follow-up; high risk; improved; innovation; large datasets; model development; mortality; multidisciplinary; parent grant; participant enrollment; patient population; prospective; risk stratification; structural heart disease; sudden cardiac death; tool

Project Summary/Abstract Syncope (or transient loss of consciousness) is a common reason to present to the ED, representing over 1.3 million visits per year in the United States. Although syncope is most often benign, it can occasionally be caused by serious cardiac diseases such as cardiac dysrhythmia, valvular heart disease, or other structural heart disease. Despite thorough evaluation in the ED, the cause of syncope remains unknown in over 50% of cases. The goal of this project is to use artificial intelligence-electrocardiogram (ECG) models to improve the diagnosis of cardiac disease for patients who present to the emergency department (ED) with syncope, by better delineating which patients require further cardiac testing, such as echocardiography or prolonged cardiac monitoring. Artificial intelligence (AI) models, using machine learning approaches, have been developed using retrospective ECG data to predict valvular heart disease and, more broadly, any structural heart disease. The first model, known as ValveNet, is highly accurate at predicting mitral regurgitation, aortic stenosis, and aortic regurgitation. The second model, known as EchoNext, is highly accurate at predicting all forms of structural heart disease as diagnosed by echocardiography, including valvular heart disease, ventricular systolic dysfunction, left ventricular hypertrophy, and significant pericardial effusions. While promising, these two AI models require external validation prior to clinical implementation. In Aim 1 of this proposal, we use prospectively collect data on ~1,012 ED patients with syncope/pre- syncope to validate the predictive accuracy of these two AI models in detecting valvular and structural heat disease, including mitral valve prolapse, using echocardiography as our gold standard. In Aim 2, we will assess the whether baseline valvular heart disease is an independent risk factor for serious cardiac events, such as acute cardiac dysrhythmias, at 30 days among ED patients with syncope. If validated and shown to accurately predict valvular and structural heart disease, these artificial intelligence models could play a major role in improving emergency syncope care by rapidly identifying patients who require echocardiography and/or prolonged cardiac monitoring. This would, in turn, lead to expedited medical and surgical therapy to reduce cardiac morbidity and mortality. This study, entitled SyncopeNet, will help improve clinical care for patients with syncope and advance the field of syncope research.

项目摘要/摘要 晕厥(或一过性意识丧失)是向急诊室报告的常见原因，超过130万人 在美国每年的访问量。虽然晕厥通常是良性的，但偶尔也会由严重的 心脏疾病，如心律失常、心脏瓣膜病或其他结构性心脏病。尽管彻底地 在ED的评估中，超过50%的病例晕厥的原因仍不清楚。该项目的目标是使用 人工智能-心电图模型可提高以下患者对心脏病的诊断 通过更好地描述哪些患者需要进一步的心脏检查，向急诊科(ED)介绍晕厥 测试，如超声心动图或延长心脏监测。 使用机器学习方法开发的人工智能(AI)模型使用回溯性心电 数据来预测瓣膜心脏病，更广泛地说，任何结构性心脏病。第一种模式，被称为 ValveNet在预测二尖瓣关闭不全、主动脉瓣狭窄和主动脉瓣关闭不全方面具有很高的准确性。第二 被称为EchoNext的模型在预测所有形式的结构性心脏病方面具有很高的准确性， 超声心动图，包括心脏瓣膜病，心脏收缩功能不全，左心室肥厚，以及 大量心包积液。虽然前景看好，但这两个人工智能模型需要在临床之前进行外部验证 实施。在本提案的目标1中，我们使用前瞻性收集~1,012名患有晕厥/晕厥前期的ED患者的数据。 为了验证这两个人工智能模型在检测瓣膜和结构性中暑方面的预测准确性， 包括二尖瓣脱垂，以超声心动图为金标准。在目标2中，我们将评估是否 基线心脏瓣膜病是严重心脏事件的独立危险因素，如急性心脏事件 有晕厥的ED患者在30天时出现节律紊乱。 如果经过验证并被证明可以准确预测瓣膜和结构性心脏病，这些人工智能模型 可通过快速识别需要治疗晕厥的患者，在改善急诊晕厥护理方面发挥重要作用 超声心动图和/或延长心脏监测。这反过来又会加快医疗和外科手术的速度 减少心脏病发病率和死亡率的治疗。这项名为SyncopeNet的研究将有助于改善对 促进了晕厥患者和晕厥领域的研究。