A Prediction Model Based on Systemic Immune-Inflammatory Index Combined with Other Predictors for Major Adverse Cardiovascular Events in Acute Myocardial Infarction Patients - Li X; Yu C; Liu X; Chen Y; Wang Y; Liang H; Qiu S; Lei L; Xiu J (Dove Medical Press, 2024)
Xiaobo Li,1,2 Chen Yu,1 Xuewei Liu,3 Yejia Chen,1 Yutian Wang,1 Hongbin Liang,1 ShiFeng Qiu,1 Li Lei,1 Jiancheng Xiu1 1Department of Cardiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China; 2Department of Cardiology, Xiangdong Hospital, Hunan Normal University, Liling, Hunan, People's Republic of China; 3The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Southern Medical University, Dongguan, Guangdong, People's Republic of ChinaCorrespondence: Jiancheng Xiu, Tel +8613903064940, Email xiujch@163.comObjective: To evaluate the prognostic value of the systemic immune-inflammatory index (SII) for predicting in-hospital major adverse cardiovascular events (MACEs) in patients with acute myocardial infarction (AMI) and establish a relevant nomogram.Methods: This study included 954 AMI patients. We examined three inflammatory factors (SII, platelet to lymphocyte ratio (PLR) and neutrophil to lymphocyte ratio (NLR)) to see which one predicts in-hospital MACEs better. The predictors were subsequently screened using bidirectional stepwise regression method, and a MACE nomogram was constructed via logistic regression analysis. The predictive value of the model was evaluated using the area under the curve (AUC), sensitivity and specificity. In addition, the clinical utility of the nomogram was evaluated using decision curve analysis. We also compared the nomogram with the Global Registry of Acute Coronary Events (GRACE) scoring system.Results: 334 (35.0%) patients had MACEs. The SII (AUC =0.684) had a greater predictive value for in-hospital MACEs in AMI patients than the PLR (AUC =0.597, P< 0.001) or NLR (AUC=0.654, P=0.01). The area under the curve (AUC) of the SII-based multivariable model for predicting MACEs, which was based on the SII, Killip classification, left ventricular ejection fraction, age, urea nitrogen (BUN) concentration and electrocardiogram-based diagnosis, was 0.862 (95% CI: 0.833- 0.891). Decision curve and calibration curve analysis revealed that SII-based multivariable model demonstrated a good fit and calibration and provided positive net benefits than the model without SII. The predictive value of the SII-based multivariable model was greater than that of the GRACE scoring system (P< 0.001).Conclusion: SII is a promising, reliable biomarker for identifying AMI patients at high risk of in-hospital MACEs, and SII-based multivariable model may serve as a quick and easy tool to identify these patients.Keywords: systemic immune-inflammatory index, major adverse cardiovascular events, inflammation, acute myocardial infarction