Coronary slow flow phenomenon: a meta-analysis of clinical risk predictors

As coronary angiography becomes more common, more cases of coronary slow flow phenomenon (CSFP) are detected. To identify key clinical predictors of CSFP, we conducted a meta-analysis. This aims to improve treatment decisions for affected patients.

We searched multiple databases (Embase, Web of Science, PubMed) up to May 2025 for studies on the association between clinical risk factors and CSFP. Study heterogeneity was assessed using Cochran's Q test and I² statistics, followed by meta-analysis to pool effect estimates. Publication bias was evaluated with funnel plots and Egger's test. All analyses were conducted using R.

We identified 23 eligible studies, comprising a total of 2,309 patients with CSFP and 3,377 controls. The pooled analysis identified several clinically independent risk factors for CSFP: Triglycerides (TG)[odds ratio (OR) = 1.01, 95% confidence interval (CI): 1.01-1.02)], Totol cholesterol (TC)[OR = 1.008, CI: 1.001-1.015],White blood cell (WBC) counts[OR = 1.07, CI: 1.04-1.10], Platelets/lymphocytes ratio (PLR)[OR = 1.01, CI: 1.01-1.02], Body mass index (BMI)[OR = 1.09, CI: 1.05-1.13], and platelet count (PC) [OR = 1.009, CI: 1.006-1.011], Current smoke (OR = 1.09, 95% CI = 1.07-1.10) were significantly associated with an increased risk of CSFP.

This comprehensive meta-analysis identifies seven key modifiable risk factors for CSFP. These findings not only enhance risk prediction models but also suggest potential therapeutic targets through lipid optimization, anti-inflammatory, antiplatelet strategies, weight control and smoking cessation interventions in CSFP management.

PROSPERO CRD420251057679.