Decoding Hidden Pathways: A Comprehensive Exploration of Anomalous Coronary Arteries

TL;DR

This study explores the prevalence, imaging features, and outcomes of anomalous coronary arteries, highlighting risks and management strategies for these congenital heart conditions.

Contribution

The study provides a detailed characterization of coronary artery anomalies in a single-center cohort, emphasizing high-risk variants and outcomes.

Findings

Most patients with anomalous coronary arteries had benign variants, with chest pain as the primary symptom.

High-risk anomalies like ALCAPA and interarterial left main were associated with severe outcomes, including sudden cardiac death.

Surgical correction showed the highest symptom resolution rate compared to medical therapy or PCI.

Abstract

Introduction

Coronary artery anomalies (CAA) are congenital anomalies affecting the origin, course, structure, or number of coronary arteries; certain high-risk configurations, particularly those involving interarterial or intramural segments, can predispose individuals to ischemia, arrhythmias, or sudden cardiac death (SCD). Populations at particular risk include young athletes and individuals with anomalies exhibiting interarterial or intramural courses. This study aimed to characterize the prevalence, imaging features, management strategies, and outcomes of CAAs, including rare variants such as anomalous left coronary artery from the pulmonary artery (ALCAPA), and interarterial left main in a single tertiary care center cohort.

Methods

We retrospectively analyzed 8,000 patients who underwent coronary angiography between January 2012 and December 2021 and identified 133 patients with CAAs. Diagnostic modalities included invasive coronary angiography (CAG) for typical ischemic or acute presentations and coronary CT angiography (CTA) for atypical or lower-risk cases; MRI was unavailable. Demographic data, clinical features, anomaly subtypes, and management strategies [medical therapy, percutaneous coronary intervention (PCI), or surgery] were documented. Outcomes at a median 36-month follow-up included symptom resolution and adverse events clearly defined as myocardial infarction (Fourth Universal Definition), revascularization [PCI/coronary artery bypass grafting (CABG) for stenosis >60%], and cardiac death.

Results

A total of 133 patients (mean age: 52.3 ± 10.5 years; 62% male) were included. Chest pain (60%) was the most common symptom, with 10% asymptomatic. The most frequent anomalies were classified as benign variants, including anomalous right coronary artery (RCA) from the left sinus (37.6%) and left circumflex artery (LCx) from the right sinus (30.1%). Malignant variants included five cases of ALCAPA (3.8%), two interarterial left main origins (1.5%), and two single coronary artery cases (1.5%). One patient with an interarterial left main died of SCD during follow-up. Other less common forms (e.g., high takeoff, fistulas, significant myocardial bridging) were grouped under “Other” (7.5%). Significant concomitant coronary artery disease (CAD) (>50% stenosis) was present in 17.5%. Management strategies included medical therapy (60%), PCI (22.5%), and surgical correction (17.5%). Symptom resolution occurred in 70% of medically treated, 80% of PCI-treated, and 100% of surgically treated patients. Adverse events (overall 10%) - defined as myocardial infarction, revascularization, or cardiac death - occurred in 5% of the medical group, 8.9% of PCI, and 4.3% of surgical cases. Outcome data for rare variants were limited due to low sample size, but suggest higher risk with malignant anatomies.

Conclusions

Anomalous coronary arteries show wide clinical variability; high-risk types like interarterial left main and ALCAPA warrant careful follow-up. While most patients achieve good outcomes, SCD in one case in our cohort highlights the need for timely intervention. Current challenges include limited awareness, lack of standardized screening, inconsistent risk assessment, and absence of personalized treatment protocols. Advancements in imaging, genetics, and artificial intelligence (AI)-driven risk models may enhance early detection and tailored management of CAAs.