Artificial Intelligence in ANOCA ((AI)NOCA)

Artificial Intelligence-assisted Diagnostics In Angina With No Obstructive Coronary Artery Disease

Angina pectoris is diagnosed in >180.000 people in the Netherlands each year. Diagnosis in angina pectoris focuses on epicardial coronary stenosis, the identification of which may lead to guideline-directed medical therapy or revascularization. However, no such stenosis is identified in 40-70% of patients. This condition, angina with no obstructed coronary artery (ANOCA), is more prevalent in women and is related to poor quality of life, high medical expenses, and a higher incidence of adverse events.

The origin of ANOCA can be evaluated during invasive coronary angiography by coronary function testing (CFT) to identify coronary vasomotor disorders. This relates to vasospasm of the coronary artery and microcirculation, or to impaired microvascular vasodilation. For the diagnosis of vasospasm, CFT needs to result in electrocardiographic signs of myocardial ischemia as part of the diagnostic criteria. This is a critical point in the diagnosis of vasospasm, as these signs can be subtle and can vary, and are therefore prone to misinterpretation. Apart from this caveat, the diagnosis approach therefore currently requires an invasive procedure for the diagnosis. This limits the broad application and hampers early identification and treatment of ANOCA.

During CFT, a coronary guide wire is routinely advanced in the coronary artery which also allows obtaining an intracoronary ECG by attaching a sterile alligator clamp to a standard electrocardiogram lead. This allows continuous recording of intracoronary ECG throughout CFT on the same monitor as the routine ECG. This technique can increase sensitivity for myocardial ischemia during CFT. Further, Holter ECG monitoring allows the identification of ischemic changes in the ECG in the outpatient setting. Evidence is lacking on the patterns of myocardial ischemia that occur during spontaneous angina pectoris symptoms in ANOCA patients, and on the sensitivity of Holter ECG for this purpose. Finally, the interpretation of ischemic patterns on ECG tracings can be cumbersome, especially when changes are subtle or change from beat to beat. The use of deep learning techniques allows to automate the interpretation of ECG traces and may improve the standardized diagnosis in ANOCA.

Study Overview

• Status: Not yet recruiting
• Conditions: Angina, Stable
• Intervention / Treatment: Diagnostic test: Holter monitoring, intracoronary ECG

• Study Type: Interventional
• Enrollment (Estimated): 250
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Tim P van de Hoef, MD, PhD; Phone Number: +3188755555; Email: t.p.vandehoef@umcutrecht.nl

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

1. Clinical indication for comprehensive coronary function testing because of persisting chest discomfort at least 2 times per week despite current medical therapy.
2. Absence of obstructive coronary artery disease with an indication for revascularization, documented by means of recent coronary computed tomography angiography (CCTA) or invasive coronary angiography (with invasive coronary pressure measurements if clinically indicated).
3. Patient is willing and able to provide written informed consent.

Exclusion Criteria:

1. Absence of chest discomfort after initiation of medical therapy.
2. Language barrier preventing sufficient understanding and communication in Dutch.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Prevalence of myocardial ischemia in patients with equivocal coronary function test results	The prevalence of ischemic changes on the intracoronary ECG in patients with equivocal coronary function test results.	During the procedure
Diagnostic accuracy of perprocedural Holter ECG monitoring to diagnose coronary vasomotor disorders	Accuracy of the Holter ECG to identify ischemic ECG changes compared with the standard 12-lead ECG during acetylcholine-provoked chest pain.	During the procedure
Diagnostic ccuracy of outpatient Holter monitoring for coronary vasomotor disorders	Diagnostic accuracy of outpatient Holter monitoring to diagnose vasomotor disorders using the coronary function test results as the reference standard	7 days

Collaborators and Investigators

• Sponsor: UMC Utrecht

Study record dates

Study Major Dates

• Study Start (Estimated): August 1, 2024
• Primary Completion (Estimated): August 1, 2028
• Study Completion (Estimated): December 1, 2028

Study Registration Dates

• First Submitted: April 3, 2024
• First Submitted That Met QC Criteria: April 23, 2024
• First Posted (Actual): April 29, 2024

Study Record Updates

• Last Update Posted (Actual): April 29, 2024
• Last Update Submitted That Met QC Criteria: April 23, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Vascular Diseases; Pain; Neurologic Manifestations; Chest Pain; Angina Pectoris; Angina, Stable
• Other Study ID Numbers: 24U-1359

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: IPD will be made available upon reasonable request.
• IPD Sharing Time Frame: A fully anonymized data set containing data from those patients who agreed with data sharing will be available upon reasonable request 12 months after finalization of the study.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No