ECG Methods for the Prompt Identification of Coronary Events

NCT ID: NCT04237688

Last Updated: 2024-08-16

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

6000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2013-05-01

Study Completion Date

2023-12-31

Brief Summary

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There is a clear need to develop improved tools to stratify risk in patients who seek emergency care for chest pain, one of the most common and potentially deadliest conditions encountered in acute care settings. The ECG has been the mainstay of initial evaluation of chest pain patients, yet is currently only diagnostic for a small subset of patients with ST-elevation myocardial infarction. Prior studies have identified candidate markers of ECG characteristics and preliminary algorithms that can identify patients with non-ST elevation myocardial infarction as well as those with very low risk of coronary artery disease. This study will enroll a cohort of consecutive chest pain patients needed to confirm the accuracy of these ECG markers and determine their maximal clinical utility as part of a risk stratification tool. With these improved tools, emergency providers (physicians, nurses, and paramedics) will be able to streamline the care provided to these patients beyond the costly and time-consuming overnight observation for serial cardiac enzymes and provocative testing.

Detailed Description

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The disease burden of coronary artery diseases (CAD) remains one of the most serious and persistent health problems for the investigator's nation. One out of every six deaths in the United States is solely attributed to CAD. Every year, nearly 7 million adults visit the emergency department (ED), reporting chest pain associated with a possible heart attack. Being able to immediately differentiate between cardiac (ischemic) and non-cardiac chest pain is crucial and improves survival and reduces disability for thousands of people. The electrocardiogram (ECG) is an indispensable tool to detect ischemia and is the first tool available for initial chest pain evaluation in EDs and, even earlier, by paramedics in the field. Current guidelines focus on ST elevation (STE) to identify patients most in need of immediate treatment. Unfortunately, STE only occurs in one-third of all patients with acute myocardial infarction, and missed myocardial infarction remains one of the highest sources of medical malpractice in emergency medicine. This translates to delayed treatment for nearly 1 million heart attacks until costly, time-consuming, and more invasive diagnostics are conducted and interpreted. Prior work by the investigators has explored novel ECG markers, other than STE, and noted that the dynamic ECG changes in these markers can improve diagnosis of acute coronary syndromes (ACS). This prior data showed that myocardial ischemia disturbs the ventricular repolarization process and results in quantifiable distortions of T waves on the surface 12-lead ECG prior to any displacement of the ST segment. Markers of T wave abnormalities, therefore, can identify patients with ischemic chest pain in the absence of acute ST changes very early in the triage process. The current study plans to evaluate how these novel ischemic ECG changes evolve over time during the acute phase of the coronary syndromes. This approach has a high potential for improving the speed and accuracy of diagnosing ACS and is potentially deployable in any ED or ambulance to greatly accelerate definitive treatment, thereby reducing morbidity and mortality for thousands of patients each year.

Conditions

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Chest Pain Myocardial Infarction Myocardial Ischemia Acute Coronary Syndrome

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* (1) 18 years of age or over; (2) present with a chief complaint of non-traumatic chest pain or other atypical, suspicious symptoms requiring ECG evaluation (e.g., shortness of breath); and (3) arrives at ED by EMS transport with 12-lead ECG already obtained.

Exclusion Criteria

* (1) those with traumatic chest pain; (2) those arriving at the ED by private means without prehospital ECG.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Heart, Lung, and Blood Institute (NHLBI)

NIH

Sponsor Role collaborator

University of Pittsburgh

OTHER

Sponsor Role lead

Responsible Party

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Salah Shafiq Al-Zaiti

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Salah s Al-Zaiti, RN, PhD

Role: PRINCIPAL_INVESTIGATOR

Univ of Pittsburgh

Locations

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University of Pittsburgh Medical Center

Pittsburgh, Pennsylvania, United States

Site Status

Countries

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United States

References

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Bouzid Z, Faramand Z, Martin-Gill C, Sereika SM, Callaway CW, Saba S, Gregg R, Badilini F, Sejdic E, Al-Zaiti SS. Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome. Ann Emerg Med. 2023 Jan;81(1):57-69. doi: 10.1016/j.annemergmed.2022.08.005. Epub 2022 Oct 15.

Reference Type DERIVED
PMID: 36253296 (View on PubMed)

Faramand Z, Helman S, Ahmad A, Martin-Gill C, Callaway C, Saba S, Gregg RE, Wang J, Al-Zaiti S. Performance and limitations of automated ECG interpretation statements in patients with suspected acute coronary syndrome. J Electrocardiol. 2021 Nov-Dec;69S:45-50. doi: 10.1016/j.jelectrocard.2021.08.014. Epub 2021 Aug 18.

Reference Type DERIVED
PMID: 34465465 (View on PubMed)

Faramand Z, Martin-Gill C, Callaway C, Al-Zaiti S. Modified HEART score to optimize risk stratification in cocaine-associated chest pain. Am J Emerg Med. 2021 Sep;47:307-308. doi: 10.1016/j.ajem.2021.01.023. Epub 2021 Jan 15. No abstract available.

Reference Type DERIVED
PMID: 33494961 (View on PubMed)

Bouzid Z, Faramand Z, Gregg RE, Frisch SO, Martin-Gill C, Saba S, Callaway C, Sejdic E, Al-Zaiti S. In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department. J Am Heart Assoc. 2021 Feb 2;10(3):e017871. doi: 10.1161/JAHA.120.017871. Epub 2021 Jan 17.

Reference Type DERIVED
PMID: 33459029 (View on PubMed)

Other Identifiers

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R01HL137761

Identifier Type: NIH

Identifier Source: secondary_id

View Link

STUDY19060239

Identifier Type: -

Identifier Source: org_study_id

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