scholarly journals Two-year clinical performance of Absorb BVS compared to Xience EES in ST-segment elevation myocardial infarction: a pooled analysis of AIDA and COMPARE-ABSORB trials

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L.S.M Kerkmeijer ◽  
G Chao ◽  
R Tijssen ◽  
T Gori ◽  
R.P Kraak ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Target Lesion ◽  
Pooled Analysis ◽  
St Segment Elevation ◽  
Patient Level ◽  
Segment Elevation Myocardial Infarction ◽  
St Segment ◽  
Absorb Bvs ◽  
Target Lesion Failure

Abstract Introduction Bioresorbable vascular scaffolds (BVS) use appears theoretically attractive in patients presenting with ST-segment elevation myocardial infarction (STEMI) as acute lesions are generally composed of soft plaques, in which optimal BVS deployment and expansion is easier to achieve. Furthermore, those patients are generally younger and would benefit longer from the promise of vascular restoration therapy. Purpose In this patient level pooled analysis of two clinical trials, we evaluated the clinical outcomes of Absorb BVS versus Xience everolimus-eluting stent (EES) in STEMI patients at 2-year follow-up. Methods We performed an individual patient-level pooled analysis of the AIDA and COMPARE-ABSORB trials in which 3515 patient were randomly assigned to Absorb BVS (n=1772) or Xience EES (n=1743). Clinical outcomes in STEMI patients were analyzed by randomized treatment assignment cumulative through 2 years. The primary efficacy outcomes measure was target lesion failure (cardiac death, target-vessel myocardial infarction or target lesion revascularization), and the primary safety outcome measure was device thrombosis at 2-year follow-up. Results 350 (19.8%) STEMI patients were allocated to Absorb BVS versus 328 (18.8%) to Xience EES. The mean age of patient presenting with STEMI was 60 years old, 76.0% were males and 15.3% had diabetes mellitus. At 2-years target lesion failure occurred in 8.4% of BVS STEMI patients and 6.2% of EES STEMI patients (p=0.253). The 2-year rates of cardiac death (2.6% vs 1.6%, p=0.332), TV-MI (4.7% vs 2.5%) and TLR (6.8% vs 4.1%) were not significantly different. The 2-year incidence of definite device thrombosis was 4.7% in Absorb BVS versus 1.8% in Xience EES (p=0.045). Conclusion In the present patient-level pooled analysis of the AIDA and COMPARE-Absorb trials, BVS was associated with increased rates of device thrombosis in STEMI patients compared to Xience EES. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Abbott

10-Year Follow-Up of Patients With Everolimus-Eluting Versus Bare-Metal Stents After ST-Segment Elevation Myocardial Infarction

2021 ◽  
Vol 77 (9) ◽  
pp. 1165-1178 ◽  
Author(s):  
Salvatore Brugaletta ◽  
Josep Gomez-Lara ◽  
Luis Ortega-Paz ◽  
Victor Jimenez-Diaz ◽  
Marcelo Jimenez ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Bare Metal Stents ◽  
Metal Stents ◽  
Bare Metal ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document