Association between platelet mass index and angiographic severity of coronary artery disease: a retrospective observational study

Abstract Background Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide. Platelet-related haematological parameters have been proposed as potential biomarkers associated with the occurrence and severity of CAD. This study aimed to evaluate the association of platelet mass index (PMI) with angiographic findings and clinical outcomes in patients with CAD. Methods This retrospective observational study screened 412 patients who underwent coronary angiography. After application of the predefined eligibility criteria, 350 patients were eligible, of whom 339 had the platelet count and mean platelet volume data required for PMI calculation and were included in the final analyses. Demographic characteristics, cardiovascular risk factors, haematological parameters, angiographic findings, clinical presentation, and treatment strategy were obtained from electronic medical records. Continuous variables were expressed as mean ± standard deviation or median with interquartile range, and categorical variables as frequencies and percentages. Univariable and multivariable binary logistic-regression analyses were performed to evaluate factors associated with angiographic severity. Initial analyses were conducted using IBM SPSS Statistics version 23, while expanded regression analyses, model diagnostics, non-parametric bootstrap analyses, calibration assessment, and receiver operating characteristic analysis were performed using Python. PMI was reported per 100-unit increase for clinical interpretability, and an exploratory quartile analysis was also performed. Model discrimination was assessed using the area under the receiver operating characteristic curve, and statistical significance was defined as p < 0.05. Results The mean age of the analysed study population was 59.5 ± 12.4 years, and the majority of participants were male (80.5%). PMI demonstrated a weak inverse correlation with age ( r = − 0.14, p = 0.008). Troponin level ( r = − 0.09, p = 0.104), diabetes mellitus ( r = 0.02, p = 0.728), and RCA, LAD, and LCX involvement were not significantly associated with PMI; a weak inverse association was observed with LMCA involvement ( r = − 0.11, p = 0.042). PMI values did not differ significantly according to treatment strategy ( p = 0.256), CAD clinical presentation ( p = 0.374), sex ( p = 0.089), or five-category angiographic severity ( p = 0.131). A statistically significant overall difference in PMI was observed among age groups ( p = 0.019), although Bonferroni-adjusted pairwise comparisons were not significant. In univariable analysis, PMI showed a borderline association with angiographic severity (OR = 0.98 per 100-unit increase, 95% CI 0.96–1.00, p = 0.056), whereas age was significantly associated with greater severity (OR = 1.02 per year, 95% CI 1.00–1.04, p = 0.018). In the adjusted model, PMI was not independently associated with angiographic severity (adjusted OR = 0.98 per 100-unit increase, 95% CI 0.96–1.00, p = 0.117), whereas age remained independently associated with greater severity (adjusted OR = 1.02 per year, 95% CI 1.00–1.04, p = 0.031). The final model showed modest discrimination (AUC = 0.607; bootstrap 95% CI 0.546–0.664) and acceptable calibration, with a bootstrap-corrected calibration intercept of 0.046 and slope of 0.744. Conclusion PMI was not independently associated with angiographic severity, clinical presentation, or treatment decisions in patients with coronary artery disease undergoing angiography. These findings indicate that PMI has limited utility as an independent biomarker of angiographic CAD severity. Further large-scale prospective studies are needed to clarify the potential clinical role of platelet-related indices in cardiovascular risk stratification.