A Risk Score Derived from the Analysis of a Cluster of 27 Serum Inflammatory Cytokines to Predict Long Term Outcome in Patients with Acute Myocardial Infarction: a Pilot Study

摘要

Objective. The aim of our study was to evaluate the clinical utility and prognostic significance of a cluster of 27 serum cytokines for risk stratification after myocardial infarction. Materials and Methods. We enrolled 33 consecutive patients admitted to our institution for acute myocardial infarction and prospectively followed. We evaluated traditional cardiovascular risk factors and assayed, during the acute phase, 27 serum cytokines (IL-1, IL-lra, IL-2, IL-4, IL-5, IL-6, IL -7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-15, IL-17, EOTAXIN, FGE, G-CSF, GM-CSF, IFN-gamma, IP-10, MCP-1, MIP-1 alpha, MIP-1 beta, PDGE, RANTES, TNF-alpha, VEGF) potentially associated with cardiovascular risk. Patients were divided into two groups during follow-up according to the occurrence or absence of adverse cardiovascular events (recurrence of angina, re-infarction, death, need of new revascularization, occurrence of heart failure). We developed an additive risk score by assigning one point for each cytokine that had a value greater than the median value (range 0-27). Cytokines alone and the cytokines score were related to cardiovascular events. Results. Patients with and without major adverse cardiovascular events (MACEs) at follow up had a homogenous distribution of the main cardiovascular risk factors; differences were detected only for sex and age. Patients who experienced MACE had a significantly different distribution of I troponin (p=0.036), IL-8 (p=0.006), IL-13 (p=0.06), IL-10 (p=0.02), IL-17 (p=0.015), IP-10 (p=0.02), MIP-1 beta (p=0.05). At univariate analysis, IL -8 (p=0.046 OR 1.13), IL-10 (p=0.05 OR 1.14) and MIP-1f3 (p=0.016, OR 1.02) were significantly associated with the occurrence of MACE. This association was not confirmed at multivariate analysis. At the analysis of variance., a higher score was significantly associated with the occurrence of adverse events at follow up (F=5.07, p=0.03). At ROC curve analysis, a score greater than 13 better predicted the occurrence of adverse events at follow-up ( AUC 0.72, p=0.03, sensibility 59.1%, specificity 81.8%). Conclusions. In our study we did not identify a single inflammatory cytokine able to predict adverse events in a long term follow up, whereas the presence of more than 13 cytokines above the median value was useful for risk stratification.