NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial: A Pragmatic, Real-world Study of an Artificial-intelligence Enabled Electrocardiogram Algorithms to Improve the Diagnosis of Cardiovascular Disease
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 1,000
- 试验地点
- 2
- 主要终点
- Rate of new CV diagnoses at 6 months
研究概览
简要总结
The goal of this clinical trial is to determine if a machine learning/artificial intelligence (AI)-based electrocardiogram (ECG) algorithm (rECHOmmend and ECG-AF) can identify undiagnosed cardiovascular disease in patients. It will also examine the safety and effectiveness of using this AI-based tool in a clinical setting. The main questions it aims to answer are:
- Can the AI-based ECG algorithm improve the detection of atrial fibrillation and structural heart disease?
- How does the use of this algorithm affect clinical decision-making and patient outcomes?
Researchers will compare the outcomes of healthcare providers who receive the AI-based ECG results to those who do not. Participants (healthcare providers) will:
Be randomized into two groups: one that receives AI-based ECG results and one that does not.
In the intervention group, receive an assessment of their patient's risk of atrial fibrillation or structural heart disease with each ordered ECG.
Decide whether to perform further clinical evaluation based on the AI-generated risk assessment as part of routine clinical care.
详细描述
There is a large burden of undiagnosed, treatable cardiovascular disease (CVD), encompassing various heart conditions such as arrhythmias (e.g., atrial fibrillation) and structural heart diseases (e.g., valvular disease). Early detection and accurate diagnosis can significantly improve patient outcomes by enabling timely, guideline-based interventions or therapies.
The goal of this study is to leverage machine learning approaches to enhance the detection and diagnosis of CVD. By identifying patients at risk of undiagnosed CVD and referring them for further clinical evaluation, the study aims to improve health outcomes.
Study Overview:
The NOTABLE study will compare the rates of new disease diagnoses, therapeutic interventions, and cardiovascular outcomes between two groups of patients managed by clinicians at Northwestern Medicine:
Patients whose clinicians use ECG predictive models. Patients whose clinicians do not use ECG predictive models.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Screening
- 盲法
- None
入排标准
- 年龄范围
- 40 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Atrial fibrillation algorithm
- •Age 65 or over
- •ECG obtained as part of routine clinical care
- •Structural heart disease algorithm
- •Age 40 or over
- •ECG obtained as part of routine clinical care
排除标准
- •Atrial fibrillation algorithm
- •No history of AF
- •No permanent pacemaker (PPM) or implantable cardioverter defibrillator (ICD)
- •No recent cardiac surgery (within the preceding 30 days)
- •Structural heart disease algorithm
- •No history of SHD
- •No echocardiogram within the past 1 year
研究组 & 干预措施
Intervention
Care teams randomized to the intervention will have access to the AI-enabled ECG-based screening tool.
干预措施: Risk-Based Assessment for Cardiac Dysfunction (Device)
Control
Care teams randomized to control will continue routine practice without access to the AI-enabled ECG-based screening tool.
结局指标
主要结局
Rate of new CV diagnoses at 6 months
时间窗: 6 months
Rate of new CV diagnoses will be defined for each predictive model and a composite of all models, and comparisons will be made between intervention and control groups. AF: New AF diagnosis SHD: New diagnosis of moderate or severe aortic stenosis, aortic regurgitation, or mitral stenosis, new diagnosis of severe mitral regurgitation or tricuspid regurgitation, new diagnosis of LVEF ≤40%, new diagnosis of significant left ventricular hypertrophy (IVSd \>15 mm).
Incidence of New Atrial Fibrillation Diagnosis
时间窗: 6 months from index ECG
Number of participants with a new diagnosis of atrial fibrillation, identified by ICD-10-CM diagnosis code entry in the electronic health record (EHR), among patients ≥65 years old without a prior AF diagnosis who received a 12-lead ECG as part of routine clinical care.
Incidence of New Structural Heart Disease Diagnosis (Composite)
时间窗: 6 months from index ECG
Number of participants with a new diagnosis of one or more of the following, identified by ICD-10-CM diagnosis code and/or echocardiographic report in the EHR: moderate or severe aortic stenosis, moderate or severe aortic regurgitation, moderate or severe mitral stenosis, severe mitral regurgitation, severe tricuspid regurgitation, left ventricular ejection fraction ≤40%, or interventricular septal thickness (IVSd) \>15 mm - among patients ≥40 years old without prior SHD diagnosis who received a 12-lead ECG as part of routine clinical care.
Incidence of New Cardiovascular Diagnosis (Overall Composite: AF + SHD)
时间窗: 6 months from index ECG
Number of participants with a new diagnosis of atrial fibrillation and/or any structural heart disease component listed in Primary Outcome Measure 2, identified by ICD-10-CM diagnosis code and/or echocardiographic report in the EHR.
次要结局
- Rate of new CV therapies at 6 months(6 months)
- Rate of CV outcomes at 6 months(6 months)
- Incidence of New Atrial Fibrillation-Related Therapy (Composite)(6 months from index ECG)
- Incidence of New Structural Heart Disease-Related Therapy (Composite)(6 months from index ECG)
- Incidence of Cardiovascular Death(6 months from index ECG)
- Incidence of Myocardial Infarction(6 months from index ECG)
- Incidence of Hospitalization for a Cardiovascular Cause(6 months from index ECG)
研究者
Sanjiv Shah
Director, Institute for Artificial Intelligence in Medicine - Center for Deep Phenotyping and Precision Therapeutics
Northwestern University