Plaque characteristics and inflammatory markers for the prediction of major cardiovascular events in patients with ST-segment elevation myocardial infarction

2017 ◽  
Vol 33 (10) ◽  
pp. 1445-1454 ◽  
Author(s):  
Ae-Young Her ◽  
Kyoung Im Cho ◽  
Gillian Balbir Singh ◽  
Dae Seong An ◽  
Young-Hoon Jeong ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Inflammatory Markers ◽  
Cardiovascular Events ◽  
St Segment Elevation ◽  
Segment Elevation Myocardial Infarction ◽  
Major Cardiovascular Events ◽  
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 Long-term risk of death and recurrent cardiovascular events following acute coronary syndromes

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254008
Author(s):  
Pishoy Gouda ◽  
Anamaria Savu ◽  
Kevin R. Bainey ◽  
Padma Kaul ◽  
Robert C. Welsh
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Acute Coronary Syndromes ◽  
Cardiovascular Events ◽  
Residual Risk ◽  
St Segment Elevation ◽  
Coronary Syndrome ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment ◽  
Coronary Syndromes

Estimates of the risk of recurrent cardiovascular events (residual risk) among patients with acute coronary syndromes have largely been based on clinical trial populations. Our objective was to estimate the residual risk associated with common comorbidities in a large, unselected, population-based cohort of acute coronary syndrome patients. 31,056 ACS patients (49.5%—non-ST segment elevation myocardial infarction [NSTEMI], 34.0%—ST segment elevation myocardial infarction [STEMI] and 16.5%—unstable angina [UA]) hospitalised in Alberta between April 2010 and March 2016 were included. The primary composite outcome was major adverse cardiovascular events (MACE) including: death, stroke or recurrent myocardial infarction. The secondary outcome was death from any cause. Cox-proportional hazard models were used to identify the impact of ACS type and commonly observed comorbidities (heart failure, hypertension, peripheral vascular disease, renal disease, cerebrovascular disease and diabetes). At 3.0 +/- 3.7 years, rates of MACE were highest in the NSTEMI population followed by STEMI and UA (3.58, 2.41 and 1.68 per 10,000 person years respectively). Mortality was also highest in the NSTEMI population followed by STEMI and UA (2.23, 1.38 and 0.95 per 10,000 person years respectively). Increased burden of comorbidities was associated with an increased risk of MACE, most prominently seen with heart failure (adjusted HR 1.83; 95% CI 1.73–1.93), renal disease (adjusted HR 1.52; 95% CI 1.40–1.65) and diabetes (adjusted HR 1.51; 95% CI 1.44–1.59). The cumulative presence of each of examined comorbidities was associated with an incremental increase in the rate of MACE ranging from 1.7 to 9.98 per 10,000 person years. Rates of secondary prevention medications at discharge were high including: statin (89.5%), angiotensin converting enzyme inhibitor/angiotensin receptor blocker (84.1%) and beta-blockers (85.9%). Residual cardiovascular risk following an acute coronary syndrome remains high despite advances in secondary prevention. A higher burden of comorbidities is associated with increased residual risk that may benefit from aggressive or novel therapies.

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