Clinical characteristics and diagnostic challenges of non-ST-segment elevation acute coronary syndrome patients with normal electrocardiograms: a review.

Authors

Yiwen Zhang, Youlu Shen

Published in

Frontiers in cardiovascular medicine. Volume 13. Pages 1835982. Epub Aug 06, 2026.

Abstract

Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) represents a major subtype of acute coronary syndrome (ACS). A clinically relevant but often overlooked subgroup of patients presents with typical ischemic symptoms despite a normal initial 12-lead electrocardiogram (ECG), creating real diagnostic difficulty. This narrative review synthesizes current evidence across several domains: epidemiology, pathophysiology, clinical presentation, diagnostic evaluation, and management of NSTE-ACS patients with normal ECGs. A structured literature search of PubMed, Web of Science, and the China National Knowledge Infrastructure (CNKI) databases, with emphasis on prospective cohort studies, registries, systematic reviews, meta-analyses, and current European Society of Cardiology (ESC) and American College of Cardiology/American Heart Association (ACC/AHA) guideline recommendations. Depending on the definition of "normal ECG" and study population, approximately 1%-8% of confirmed NSTE-ACS patients present with a normal initial ECG. These patients frequently exhibit single-vessel disease, non-obstructive coronary artery disease, or alternative mechanisms such as coronary vasospasm and microvascular dysfunction. High-sensitivity cardiac troponin (hs-cTn) assays using rapid 0/1-h or 0/2-h algorithms constitute the diagnostic cornerstone, while coronary computed tomography angiography (CCTA) provides valuable anatomical assessment, particularly in low-to-intermediate-risk patients. This review presents an integrated diagnostic algorithm, critically evaluates the complementary roles of multimodality imaging techniques, and discusses future directions including artificial intelligence-assisted ECG interpretation and wearable monitoring technologies.

PMID:
42626118
Bibliographic data and abstract were imported from PubMed on 21 Aug 2026.

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