scholarly journals GW25-e2515 Macrophage migration inhibitory factor in predicting short- and long-term major adverse cardiovascular events in patients with ST-segment elevation myocardial infarction

2014 ◽  
Vol 64 (16) ◽  
pp. C61
Author(s):  
Men Li ◽  
Ma Yi-tong ◽  
Yang Yi-ning
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Events ◽  
Macrophage Migration Inhibitory Factor ◽  
Major Adverse Cardiovascular Events ◽  
Macrophage Migration ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment ◽  
Short And Long Term

scholarly journals The predictive power of biomarker macrophage migration inhibitory factor in patients with st- segment elevation myocardial infarction

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
I Vishnevskaya ◽  
M P Kopytsya ◽  
T Y E Storozhenko
Keyword(s):  
Myocardial Infarction ◽  
Adverse Events ◽  
Macrophage Migration Inhibitory Factor ◽  
Adverse Outcome ◽  
Adverse Outcomes ◽  
Macrophage Migration ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment

Abstract   Biomarkers have been taken one of the first places as diagnostic and prognostic tools in acute myocardial infarction (AMI). They are used both in the acute and in the long-term periods of the disease to predict various adverse events Their especially important property is the ability to predict the long-term adverse events of the disease, which can significantly improve the outcome. One of the promising biomarkers for the early adverse outcome prediction is the proinflammatory cytokine macrophage migration inhibitory factor (MIF). Purpose To determine the MIF significance in 1-year adverse outcomes prognosis after AMI. Methods 130 ST-segment elevation myocardial infarction (STEMI) patients (72.6% male and 27.4% female) were enrolled, mean age was 58.25±1.22 years. Control group of 12 healthy volunteers included. All patients underwent a baseline investigation which includes standard electrocardiography, echocardiography with strain, angiography, and determination of marker of myocardial necrosis – cardiac troponin T. Also, the level of MIF, soluble suppression of tumorigenicity-2 (sST2), C-reactive protein determined during the first 12 hours after the STEMI, before the percutaneous coronary intervention (PCI), 6 hours, and 24 hours after the PCI. The endpoint was composite and included all-cause mortality, nonfatal myocardial infarction, nonfatal stroke, and hospitalization for unstable angina, acute decompensated heart failure. During 1-year follow-up 18% of patients reached the endpoint. Results The effect of several variables of clinical, instrumental and laboratory status were assessed on reaching the endpoint by patients. We have found that MIF level determined before PCI (AUC 0.73; p=0.003; 95% Cl: 0.613 – 0.826) might be a significant independent predictor of mortality with sensitivity (Se) 70% and specificity (Sp) 80%. MIF level 6 hours after PCI showed even better result (AUC 0.8; p=0.002; 95% Cl: 0.64 – 0.9; Se 74%, Sp 82%). MIF >3934 pg/ml associated with the highest risk of adverse events. For identification of the main risk factors for adverse outcome, we have used logistic regression method. The MIF level determined before the PCI was the most important to predict adverse outcomes (odds ratios is 1.0006, p=0,0038; x2=4.58). Areas under the ROC for the model was equal to 0.8; 95% Cl: 0.58 to 0.89). Neither sST2 nor CRP have not shown any significant results (p<0.05). According to the data of the Kaplan-Meier survival analysis, long-term survival after STEMI was significantly lower in patient with the level of MIF determined during the first 12 hours after the event more than 2988 pg/ml (Log-rank = −4,891, p=0.014). Conclusions Biomarker MIF has showed as an independent tool associated with the risk of adverse outcome 1 year after STEMI. MIF could be used in routine clinical practice to improve risk stratification of patients with STEMI. FUNDunding Acknowledgement Type of funding sources: None.

A macrophage migration inhibitory factor as a predictor of reperfusion myocardial injury in patients with st-segment elevation myocardial infarction

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
I Vishnevskaya ◽  
T.Y.E Storozhenko ◽  
M.P Kopytsya
Keyword(s):  
Myocardial Infarction ◽  
Blood Flow ◽  
Macrophage Migration Inhibitory Factor ◽  
Myocardial Injury ◽  
Macrophage Migration ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
No Reflow ◽  
St Segment ◽  
No Reflow Phenomenon

Abstract Introduction Major adverse cardiovascular events in patients with ST-segment elevation myocardial infarction (STEMI) are still common despite the modern treatment approaches. It may be caused by the “no-reflow” phenomenon. One of the promising biomarkers for the coronary “no-reflow” phenomenon prediction is proinflammatory cytokine macrophage migration inhibitory factor (MIF). Purpose To estimate the role of MIF in the prediction of early reperfusion myocardial injury in patients with STEMI. Methods The study involved 341 STEMI patients (78.6% male and 21.4% female) with an average age of 59.08±9.65 years. Control group of 12 healthy volunteers included. All patients were made to undergo a baseline investigation. In addition, the level of MIF determined twice during the first 12 hours of STEMI, before the percutaneous coronary intervention (PCI) and after the procedure. Coronary blood flow evaluated using TIMI flow grade and myocardial blush grade (MBG). All patients had epicardial blood flow TIMI 3. The criteria for “no-reflow” diagnosis were myocardial perfusion at MBG 0 or MBG 1 level with complete recovery of epicardial blood flow or ST-segment resolution (rST) of less than 70% from baseline within 2 hours after PCI. All patients were divided into two groups according to MBG and rST after PCI more and less than 70%: 147 patients in the first group with MBG stage 0–1, 182 patients with MBG stage 2–3 Results 64% of STEMI patients had elevated MIF levels above the highest value in healthy controls (2778±217 ng/ml; 225±6,7 ng/ml; p=0,0003). The level of MIF biomarker, determined before PCI was significantly higher in the group of patients with MBG 0–1 in comparison to MBG 2–3. (4708±471 ng/ml vs 2914±347ng/ml; p=0,004). Using the multivariate regression analysis, the dependencies of the biomarker MIF on the parameters of the reperfusion myocardial injuries were obtained. MIF measured before revascularization as well as the patient's gender, was an independent predictor of MBG 0–1 and rST less than 70% (coefficients Beta 0,1; odd ratio 1,1; 95%confidential interval (CI) 1,0–1,2; p=0,037 and coefficient Beta 2,9; odd ratio 17.7; 95% CI 0,96–32; p=0,05, respectively). Conclusions The study revealed that MIF predicts reperfusion myocardial injury in patients with STEMI. Future investigations of the MIF biological effects are the perspective direction in the field of modern cardiology. Funding Acknowledgement Type of funding source: None

scholarly journals A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0249338
Author(s):  
Syed Waseem Abbas Sherazi ◽  
Jang-Whan Bae ◽  
Jong Yun Lee
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Events ◽  
Ensemble Classifier ◽  
Major Adverse Cardiovascular Events ◽  
Test Dataset ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
Complete Dataset ◽  
St Segment

Objective Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms. Methods We used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score. Results The accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models. Conclusions The performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.

scholarly journals A Nomogram Based on Apelin-12 for the Prediction of Major Adverse Cardiovascular Events after Percutaneous Coronary Intervention among Patients with ST-Segment Elevation Myocardial Infarction

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Enfa Zhao ◽  
Hang Xie ◽  
Yushun Zhang
Keyword(s):  
Myocardial Infarction ◽  
Percutaneous Coronary Intervention ◽  
Cardiovascular Events ◽  
Coronary Intervention ◽  
Cox Proportional Hazards ◽  
Major Adverse Cardiovascular Events ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment ◽  
Percutaneous Coronary

Objective. This study aimed to establish a clinical prognostic nomogram for predicting major adverse cardiovascular events (MACEs) after primary percutaneous coronary intervention (PCI) among patients with ST-segment elevation myocardial infarction (STEMI). Methods. Information on 464 patients with STEMI who performed PCI procedures was included. After removing patients with incomplete clinical information, a total of 460 patients followed for 2.5 years were randomly divided into evaluation (n = 324) and validation (n = 136) cohorts. A multivariate Cox proportional hazards regression model was used to identify the significant factors associated with MACEs in the evaluation cohort, and then they were incorporated into the nomogram. The performance of the nomogram was evaluated by the discrimination, calibration, and clinical usefulness. Results. Apelin-12 change rate, apelin-12 level, age, pathological Q wave, myocardial infarction history, anterior wall myocardial infarction, Killip’s classification > I, uric acid, total cholesterol, cTnI, and the left atrial diameter were independently associated with MACEs (all P<0.05). After incorporating these 11 factors, the nomogram achieved good concordance indexes of 0.758 (95%CI = 0.707–0.809) and 0.763 (95%CI = 0.689–0.837) in predicting MACEs in the evaluation and validation cohorts, respectively, and had well-fitted calibration curves. The decision curve analysis (DCA) revealed that the nomogram was clinically useful. Conclusions. We established and validated a novel nomogram that can provide individual prediction of MACEs for patients with STEMI after PCI procedures in a Chinese population. This practical prognostic nomogram may help clinicians in decision making and enable a more accurate risk assessment.

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