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.

项目概要/摘要 晕厥（或短暂意识丧失）是向急诊科就诊的常见原因，代表超过 130 万人 每年访问美国。虽然晕厥通常是良性的，但偶尔也可能由严重的原因引起 心脏病，例如心律失常、瓣膜性心脏病或其他结构性心脏病。尽管彻底 在急诊室的评估中，超过 50% 的病例晕厥的原因仍然未知。该项目的目标是使用 人工智能心电图 (ECG) 模型可改善以下患者的心脏病诊断 通过更好地描述哪些患者需要进一步心脏检查，将晕厥送往急诊科 (ED) 测试，例如超声心动图或长时间心脏监测。 使用机器学习方法的人工智能 (AI) 模型是使用回顾性心电图开发的 预测瓣膜性心脏病以及更广泛的任何结构性心脏病的数据。第一个模型被称为 ValveNet 在预测二尖瓣关闭不全、主动脉瓣狭窄和主动脉关闭不全方面非常准确。第二个 称为 EchoNext 的模型在预测各种形式的结构性心脏病方面非常准确，如诊断为 超声心动图，包括瓣膜性心脏病、心室收缩功能障碍、左心室肥厚和 明显心包积液。虽然前景广阔，但这两种人工智能模型在临床前需要外部验证 执行。在本提案的目标 1 中，我们前瞻性地收集了大约 1,012 名晕厥/预发性 ED 患者的数据。 晕厥来验证这两个人工智能模型在检测瓣膜和结构性热病方面的预测准确性， 包括二尖瓣脱垂，使用超声心动图作为我们的金标准。在目标 2 中，我们将评估是否 基线瓣膜性心脏病是严重心脏事件（例如急性心脏病）的独立危险因素 心律失常，30 天时出现晕厥的 ED 患者。 如果经过验证并显示可以准确预测瓣膜和结构性心脏病，这些人工智能模型 通过快速识别需要的患者，可以在改善晕厥急救护理方面发挥重要作用 超声心动图和/或长时间心脏监测。反过来，这将导致加快医疗和手术 降低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临床护理 晕厥患者并推进晕厥研究领域。