Holter features to detect coronary artery spasm in ANOCA patients: A pilot study

Diantha Jm Schipaanboord1, Caïa Crooijmans2, Nicole S Erler3

  • 1Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.

Insights

Holter monitoring may identify coronary artery spasm (CAS) in angina patients with non-obstructive coronary artery disease (ANOCA). This non-invasive method showed potential for detecting CAS features, including ST depression and heart rate changes, especially overnight.

Area of Science:

  • Cardiology
  • Medical Diagnostics
  • Electrocardiography

Background:

  • Coronary artery spasm (CAS) is common in patients with angina and non-obstructive coronary artery disease (ANOCA).
  • Current diagnostic methods like coronary function testing (CFT) are invasive and limit further research.
  • Identifying non-invasive diagnostic tools for CAS is crucial in ANOCA patients.

Purpose of the Study:

  • To investigate if Holter monitoring with symptom tracking can identify features of CAS in ANOCA patients.
  • To assess the diagnostic potential of Holter-based CAS features.

Main Methods:

  • A pilot study involving 42 ANOCA patients who underwent 12-lead Holter monitoring for 2-7 days with simultaneous symptom tracking.
  • Comparison of Holter-ECG characteristics and symptoms between patients with and without CAS.
  • Calculation of diagnostic measures for CAS using various thresholds for ischemia-related parameters.

Main Results:

  • Coronary artery spasm (CAS) was diagnosed in 79% of patients.
  • Patients with CAS showed significantly more ST depression (≥1 min/day) compared to those without CAS (p=0.013 for ≥0.035 mV).
  • CAS patients exhibited lower heart rates and longer PQ/QT times, particularly during nighttime and early morning.

Conclusions:

  • Holter monitoring can reveal potential signals for coronary artery spasm (CAS) in ANOCA patients.
  • ST depression, lower heart rates, and prolonged PQ times during night/early morning are indicative of CAS.
  • Further large-scale, potentially AI-driven studies are warranted to validate these findings.
Abstract

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