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

Background: 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. Methods: 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. Results: 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. Conclusion: 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. Systematic Review Registration: PROSPERO CRD420251057679.