Clinical trial NCT06939738: Artificial Intelligence Scalable Solution for ST Myocardial Infarction
Official title: Artificial Intelligence Scalable Solution for ST Myocardial Infarction (ASSIST): Cross-sectional Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Acute Myocardial Infarction (AMI)
Keywords: Acute Myocardial Infarction (AMI)
STEMI
NSTEMI
Occlusion Myocardial Infarction (OMI)
AI-based ECG analysis
Lead sponsor: Idoven 1903 S.L.
Sponsor class: INDUSTRY
First posted: 2025-04-23
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients with suspected Acute Coronary Syndrome (ACS) presenting to the emergency department and subsequently referred for angiography at the Catheterization Laboratory of the participating sites.

Brief summary: The ASSIST clinical study is an observational, multicenter study to assess the performance of a cloud-based and AI-powered electrocardiogram (ECG) analysis platform, named Willem™, developed to detect Acute Myocardial Infarction (AMI).

The main objectives are to compare Willem™ performance to detect and triage ECG patterns associated with AMI compared with human ECG interpretation, and to assess the time periods for both approaches.

Detailed description: Delays in triage and diagnosis of patients presented with chest pain or other symptoms suggestive of Acute coronary syndrome (ACS) can be fatal. This study aims to improve those two aspects in acute ischemic disease care: reducing the delays to intervention and improving the accuracy of initial diagnosis, which are of paramount importance in cases of ACS, especially in ST-elevation myocardial infarction (STEMI). The former plays a critical role in minimizing in-hospital mortality rates, which have been shown to decrease proportionally with reduction of times to intervention. The latter relies on a correct interpretation of the ECG, first-line diagnostic tool in the assessment of patients with suspected ACS.

The current standard of care for ACS includes a 12-lead ECG that should be performed within the first 10 minutes from the first medical contact. The ECG must be interpreted by a qualified physician, who will alert the on-call cardiologist to confirm or not the activation of the "infarction code", based on the ECG and clinical presentation. Such activation will mainly entail immediate transfer of the patient to the nearest hospital with the possibility of emergency coronary angiography (if not present in the initial institution), and eventual percutaneous coronary intervention (PCI). Regarding the diagnosis of Acute Myocardial Infarction (AMI), an accurate and rapid interpretation of the first ECG is critical for the differential diagnosis between STEMI, NSTEMI or unstable angina; and follow the proper standard of care guidelines. The largest delays occurs between the first ECG and the transportation for the cardiac catheterization laboratory, which has prognostic implications.

In recent years, automatic digital tools based on artificial intelligence (AI) have been proposed as a solution to support physicians in the ECG interpretation, reducing their workload and time-to-diagnosis, suggesting the beneficial impact of AI-platforms for accurate diagnosis of AMI. In this setting, the AI-platforms should be able to automatically detect ECG patterns linked to unfavorable coronary anatomy and poor outcomes. It is also essential to have the capacity to identify more subtle ECG patterns, not obvious during physicians' interpretation, but indicating high-risk coronary anatomy. Additionally, the platform should assist the prediction of most severe coronary lesions, especially obstructive stenosis. This ability to detect coronary lesions could be useful in preventing unnecessary or premature activation of the catheterization laboratory, mainly in NSTEMI setting.

The ASSIST clinical study is a cross-sectional, multicenter study aiming to collect data to develop the Willem™ platform, an AI-based tool for ECG analysis. This plataform could improve the accuracy for AMI diagnosis, particularly the differentiation between STEMI and NSTEMI, and early identification of patients with Occlusion Myocardial Infarction (OMI).

Eligibility criteria: Inclusion Criteria:

* Age ≥ 18 years;
* Available digitally stored 12-lead ECG traces prior to invasive coronary angiography;
* Available angiographic and clinical data.

Exclusion Criteria:

* ECGs with poor signal quality;
* Lack of digitally stored 12-lead ECG traces prior to coronary angiography;
* Previous coronary events (AMI, coronary revascularizations);
* Non-available clinical or angiographic data.

Primary outcomes:
measure: Device performance
timeFrame: At the time of enrolment and throughout the baseline visit (single study visit)
Secondary outcomes:
measure: Time assessment
timeFrame: At the time of enrolment and throughout the baseline visit (single study visit)
Other outcomes:
Locations:
city: Lisboa
country: Portugal
facility: Unidade Local de Saúde de São José
city: Lisboa
country: Portugal
facility: Unidade Local de Saúde de Lisboa Ocidental
city: Barcelona
country: Spain
facility: Germans Trias i Pujol University Hospital
city: Madrid
country: Spain
facility: Hospital General Universitario Gregorio Marañón
city: Madrid
country: Spain
facility: La Paz University Hospital
Study officials:
affiliation: La Paz University Hospital
name: Alfonso Jurado, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: