Abstract
Introduction: Acute coronary syndrome (ACS), encompassing unstable angina, NSTEMI, and STEMI, is a major cause of morbidity and mortality worldwide. Novel inflammatory and nutritional biomarkers may provide incremental value for risk stratification beyond conventional predictors. This work sought to determine whether the Pan-Immune-Inflammatory Value (PIV) and the Hemoglobin-Albumin-Lymphocyte-Platelet (HALP) score could serve as independent prognostic indicators in individuals presenting with acute coronary syndrome. Methods: A retrospective multicenter study included ACS patients hospitalized between January 2020 and May 2024. Demographics, clinical data, and laboratory results were collected. PIV was calculated as follows: neutrophils × platelets × monocytes/lymphocytes. HALP score was calculated as follows: hemoglobin × albumin × lymphocytes/platelets. Correlations with clinical parameters and mortality prediction were analyzed. Results: A total of 1134 patients (mean age 62 ± 12 years) were included. PIV showed positive correlations with WBC (Rho = 0.574), troponin (Rho = 0.381), and CRP (Rho = 0.295), and negative correlations with HDL (Rho = -0.101) and ejection fraction (Rho = -0.316) (all p < 0.01). PIV independently predicted mortality with a cut-off ≥1074.2 (AUC = 0.619, sensitivity 45%, specificity 79.9%). HALP score negatively correlated with age, troponin, CRP, and ICU stay, and predicted mortality with a cut-off ≤3.58 (AUC = 0.722, sensitivity 53.8%, specificity 82%). Comparative ROC analysis showed that HALP demonstrated superior discriminative ability for mortality prediction compared with PIV. Conclusions: PIV and HALP score are independent prognostic markers in ACS, reflecting inflammatory burden and nutritional status. Their integration into clinical workflows may enhance risk stratification and support individualized management strategies. Given their simplicity and universal availability, PIV and HALP may serve as practical adjunctive tools to established risk scores, enabling early identification of high-risk ACS patients at the time of admission.