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Published on: January 28, 2020
Systemic immune-inflammation Index is an independent risk factor for Major adverse cardiovascular events in patients
Deguang Wang1, Jingxian Xing1, Zhaoqing Xie1
1Department of Coronary Heart Disease 7, Cangzhou Central Hospital, Cangzhou, China.
Insights
The systemic immune-inflammation index (SII) can predict major adverse cardiovascular events (MACE) in patients with coronary artery ectasia (CAE). This readily available biomarker improves risk assessment for this high-risk group.
Area of Science:
- Cardiology
- Inflammation Biomarkers
- Risk Stratification
Background:
- Coronary artery ectasia (CAE) lacks reliable long-term risk stratification biomarkers.
- The systemic immune-inflammation index (SII) reflects immune-inflammatory balance and has shown prognostic value in cardiovascular diseases.
Purpose of the Study:
- To determine if SII independently predicts major adverse cardiovascular events (MACE) in patients with confirmed coronary artery ectasia (CAE).
Main Methods:
- Retrospective cohort study of 200 CAE patients followed for 30 months.
- Calculated SII (platelet count × neutrophil count / lymphocyte count).
- Assessed MACE prediction using ROC analysis, Kaplan-Meier, and Cox regression, evaluating incremental value.
Main Results:
- 18% of patients experienced MACE; higher baseline SII was linked to adverse events.
- SII demonstrated good discriminatory ability for MACE (AUC 0.81, cut-off 645).
- Elevated SII independently predicted MACE and significantly improved risk model discrimination and reclassification.
Conclusions:
- Elevated SII is an independent predictor of MACE in coronary artery ectasia (CAE) patients.
- Incorporating SII into risk models enhances prognostic accuracy.
- SII is a valuable tool for risk stratification in high-risk CAE populations.
Background:
Reliable biomarkers for long-term risk stratification in coronary artery ectasia (CAE) remain limited. The systemic immune-inflammation index (SII), derived from routine hematological parameters, reflects the balance between inflammation and immune status and has demonstrated prognostic value in various cardiovascular conditions.
Objective:
This study aimed to evaluate whether SII independently predicts major adverse cardiovascular events (MACE) in patients with angiographically confirmed CAE.
Methods:
In this retrospective cohort study at Cangzhou Central Hospital, 200 consecutive patients with CAE were enrolled and followed for a median duration of 30 months. SII was calculated as platelet count×neutrophil count divided by lymphocyte count. The primary endpoint was MACE, defined as a composite of cardiovascular death, nonfatal myocardial infarction, ischemic stroke, and target vessel revascularization. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal SII cut-off value. Kaplan-Meier survival analysis and Cox proportional hazards regression models were used to assess the association between SII and outcomes. Incremental predictive value was evaluated by comparing model discrimination and reclassification indices.
Results:
During follow-up, 18% of patients experienced MACE. Baseline SII levels were significantly higher in patients who developed adverse events. ROC analysis demonstrated good discriminatory ability of SII for predicting MACE (AUC 0.81), with an optimal cut-off value of 645. Kaplan-Meier analysis showed significantly lower event-free survival in patients with high SII levels. In multivariate Cox regression analysis, SII remained independently associated with MACE both as a continuous variable (adjusted HR 1.72 per SD increase) and as a categorical variable (adjusted HR 2.48 for high vs. low SII). Addition of SII to a baseline clinical model significantly improved discrimination (C-statistic increase from 0.72 to 0.83) and enhanced risk reclassification.
Conclusion:
Elevated systemic immune-inflammation index is an independent predictor of major adverse cardiovascular events in patients with coronary artery ectasia. Incorporation of SII into clinical risk assessment models significantly improves prognostic accuracy, suggesting that this readily available biomarker may serve as a valuable tool for risk stratification in this high-risk population.
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