Development and validation of a clinical prediction model for coronary microcirculatory impairment post-PCI in STEMI patients based on lactate, creatinine, and hs-CRP

Microvascular dysfunction following primary percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI) remains a significant determinant of poor prognosis, despite successful epicardial reperfusion. This study aimed to establish a clinically applicable prediction model to assess the risk of coronary microcirculatory disturbances (CMD) using arterial lactate, serum creatinine, and high-sensitivity C-reactive protein levels. A retrospective analysis was conducted on 104 STEMI patients treated with emergency PCI. Patients were categorized into CMD and non-CMD groups based on echocardiographic microvascular perfusion assessment within 24 hours post-intervention. Multivariate logistic regression was applied to identify independent predictors and build a diagnostic model, which was validated using a separate cohort (n = 80). Model performance was evaluated using receiver operating characteristic curves and the Hosmer-Lemeshow goodness-of-fit test. Elevated levels of lactate (>2.620 mmol/L), serum creatinine (>113.760 μmol/L), and high-sensitivity C-reactive protein (>9.310 mg/L), along with older age, hypertension, diabetes, prolonged ischemic time, and coronary slow-flow were identified as independent predictors of CMD. The derived logistic model demonstrated high predictive accuracy (AUC = 0.903), with robust performance in the external validation cohort (AUC = 0.868). A multivariable prediction model incorporating biochemical and clinical indicators offers reliable early identification of CMD risk in STEMI patients after PCI. This tool may guide tailored post-revascularization management strategies aimed at improving myocardial perfusion and patient outcomes.