This protocol will collect real world EHR data to support the product development life cycle activities associated with developing the Major Adverse Cardiac Events (MACE) Clinical Decision Support (CDS) software. The data will also be utilized in subsequent clinical validation to support an FDA application and/or applications to other regulatory agencies as needed.
The primary objective is to develop a machine learning tool which predicts risk of 30-day MACE (major adverse cardiac event) risk stratification among patients visiting ED with suspicion of ACS (Acute Coronary Syndrome). The data will also be utilized in subsequent clinical validation. In addition to retrospective Electronic Health Record (EHR) data, Health Information Exchange (HIE) data and patient reported outcomes will be collected to capture 30-day MACE outcomes, as applicable.
Study Type
OBSERVATIONAL
Patients with at least one Beckman High Sensitivity Troponin test at the Emergency Department visit
Johns Hopkins University
Baltimore, Maryland, United States
Henry Ford Health System
Detroit, Michigan, United States
Kettering Health
Kettering, Ohio, United States
Performance Characteristics NPV (Negative Predictive Value), Specificity and Sensitivity
Clinical performance of MACE (Major Adverse Cardiac Events) CDS (Clinical Decision Support) tool to identify a patient's risk of for having a MACE (Major Adverse Cardiac Events) within 30 days, with both a single High-Sensitive Troponin and a second High-Sensitive Troponin (if applicable).
Time frame: Within 30-days from the Emergency Department Visit with suspicion of ACS (Acute Coronary Syndrome)
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