Predictive Value of Admission Platelet Volume Indices for In-hospital Major Adverse Cardiovascular Events in Acute ST-Segment Elevation Myocardial Infarction

Angiology ◽  
2013 ◽  
Vol 66 (2) ◽  
pp. 155-162 ◽  
Author(s):  
Turgay Celik ◽  
Mehmet G. Kaya ◽  
Mahmut Akpek ◽  
Ozgur Gunebakmaz ◽  
Sevket Balta ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Predictive Value ◽  
Cardiovascular Events ◽  
Major Adverse Cardiovascular Events ◽  
Platelet Volume ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment

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.

scholarly journals Thrombus aspirated from patients with ST-elevation myocardial infarction: Clinical and angiographic outcomes

2016 ◽  
Vol 44 (6) ◽  
pp. 1514-1523 ◽  
Author(s):  
Yapan Yang ◽  
Jingchao Li ◽  
Wenke Xu ◽  
Shujuan Dong ◽  
Haijia Yu ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Events ◽  
Major Adverse Cardiovascular Events ◽  
Percutaneous Coronary Interventions ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
Logistic Analysis ◽  
St Segment ◽  
Percutaneous Coronary ◽  
St Elevation

Objective To investigate differences in clinical and angiographic outcomes between patients with acute myocardial infarction with red and white thrombi. Methods A total of 137 patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary interventions were included. Thrombus material was classified as white or red based on its pathology. Information on characteristics of thrombi was available for 97 (70.8%) patients. Results The total ischaemic time was significantly longer in the red thrombus group compared with the white thrombus group. The incidence of major adverse cardiovascular events in hospital was higher in the red thrombus group than in the white thrombus group (15.6% vs 0%). Multivariable logistic analysis showed that the total ischaemic time was the only predictor of thrombus composition (odds ratio 1.353; 95% confidence interval 1.003, 1.826). Conclusion Red thrombi were present in nearly two-thirds of cases, and were associated with a longer ischaemic time and higher incidence of major adverse cardiovascular events in hospital.

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