Development of a Novel Inflammatory Index to Predict Coronary Artery Disease Severity in Patients With Acute Coronary Syndrome

The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) have previously demonstrated predictive value in coronary artery disease (CAD). We developed on an expanded, novel systemic immune-inflammation response index (SIIRI), calculated as peripheral neutrophil × monocyte × platelet ÷ lymphocyte count. We assessed 240 patients with an acute coronary syndrome that subsequently underwent percutaneous coronary intervention. CAD severity was measured using the SYNTAX score. Laboratory measurements, including cell counts, were obtained on admission. On multivariate analysis, the SIIRI was an independent predictor of severe CAD with an adjusted odds ratio (OR) of 1.666 [1.376-2.017] per 10 5 -unit increase. The SIIRI had the highest area under the receiver operator curve of .771 [.709-.833] compared to the SII, SIRI neutrophil-lymphocyte ratio, monocyte-lymphocyte ratio, and platelet-lymphocyte ratio. The optimal cut-off for SIIRI was 4.3 × 10 5 , with sensitivity = 69.9% and specificity = 75.8%. Increment in model performance resulting from adding SIIRI versus other inflammatory indices was assessed using discrimination, calibration, and goodness-of-fit measures. When added to a baseline model, the SIIRI resulted in a significant increase in the c-statistic and significant net reclassification index (.808, P < .0001) and integrated discrimination index (.129, P < .0001), and a decrease in Akaike and Bayesian information criteria.