Background: The TyG index is an indicator of insulin resistance (IR), which is associated with the development and prognosis of cardiovascular disease. This study aimed to summarize the relationship between the TyG index and the risk, severity, and prognosis of coronary artery disease (CAD) by performing a systematic review and meta-analysis. Methods: The PubMed, EMBASE, The Cochrane Library, and Web of Science databases were searched for articles published from inception until May 1, 2023. Cross-sectional studies, retrospective or prospective cohort studies recruiting patients with CAD were included. For the analysis of CAD severity, the outcomes were coronary artery calcification, coronary artery stenosis, coronary plaque progression, multi-vessel CAD, and in-stent re-stenosis. For the analysis of CAD prognosis, the primary outcome was major adverse cardiovascular events (MACE). Results: Forty-one studies were included in this study. Compared to patients with the lowest TyG index, those with the highest TyG index had a higher CAD risk [odds ratio (OR): 1.94, 95% confidence interval (CI) 1.20–3.14, I² = 91%, P = 0.007]. Additionally, these patients were more likely to have stenotic coronary arteries (OR: 3.49, 95% CI 1.71–7.12, I² = 0%, P = 0.0006), progressed plaques (OR: 1.67, 95% CI 1.28–2.19, I² = 0%, P = 0.002), and with more vessels involved (OR: 2.33, 95% CI 1.59–3.42, I² = 0%, P < 0.0001). When calculated as a categorized variable, it appears that acute coronary syndrome (ACS) patients with higher TyG index levels may have a higher incidence rate of MACE [hazard ratio (HR): 2.09, 95% CI 1.68–2.62, I² = 87%, P < 0.00001], whereas chronic coronary syndrome (CCS) or stable CAD patients with higher TyG index levels showed a trend towards an increased incidence rate of MACE (HR: 1.24, 95% CI 0.96–1.60, I² = 85%, P = 0.09). When calculated as a continuous variable, ACS patients had an HR of 2.28 per 1-unit/1-standard deviation increment of the TyG index (95% CI 1.44–3.63, I² = 95%, P = 0.0005). Similarly, CCS or stable CAD patients had an HR of 1.49 per 1-unit/1-standard deviation increment of the TyG index (95% CI 1.21–1.83, I² = 75%, P = 0.0001). Myocardial infarction with non-obstructive coronary arteries patients had an HR of 1.85 per 1-unit increment of the TyG index (95% CI 1.17–2.93, P = 0.008). Conclusions: The TyG index is a simple new synthetic index that has been proven to be a valuable tool in the whole-course management of CAD patients. Patients with higher TyG index levels are at a higher risk of CAD, more severe coronary artery lesions, and worse prognosis compared to those with lower TyG index levels.

Background: Myocardial infarction can lead to malignant arrhythmia, heart failure, and sudden death. Clinical studies have shown that early identification of and timely intervention for acute MI can significantly reduce mortality. The traditional MI risk assessment models are subjective, and the data that go into them are difficult to obtain. Generally, the assessment is only conducted among high-risk patient groups.Objective: To construct an artificial intelligence–based risk prediction model of myocardial infarction (MI) for continuous and active monitoring of inpatients, especially those in noncardiovascular departments, and early warning of MI.Methods: The imbalanced data contain 59 features, which were constructed into a specific dataset through proportional division, upsampling, downsampling, easy ensemble, and w-easy ensemble. Then, the dataset was traversed using supervised machine learning, with recursive feature elimination as the top-layer algorithm and random forest, gradient boosting decision tree (GBDT), logistic regression, and support vector machine as the bottom-layer algorithms, to select the best model out of many through a variety of evaluation indices.Results: GBDT was the best bottom-layer algorithm, and downsampling was the best dataset construction method. In the validation set, the F1 score and accuracy of the 24-feature downsampling GBDT model were both 0.84. In the test set, the F1 score and accuracy of the 24-feature downsampling GBDT model were both 0.83, and the area under the curve was 0.91.Conclusion: Compared with traditional models, artificial intelligence–based machine learning models have better accuracy and real-time performance and can reduce the occurrence of in-hospital MI from a data-driven perspective, thereby increasing the cure rate of patients and improving their prognosis.

The IL-33/ST2 L signaling pathway is involved in the pathophysiological processes of several diseases and mainly exerts anti-inflammatory and antifibrotic effects. Soluble suppression of tumorigenicity 2 (sST2), which serves as a competitive inhibitory molecule of this pathway, is a member of the interleukin (IL)-1 family, a decoy receptor for IL33, thought to play a role in cardiac remodeling and the inflammatory process. However, the association between sST2 and coronary artery disease (CAD), one of the most common causes of heart failure, is still being explored. We therefore reviewed the research on sST2 in the field of CAD, including reflecting the atherosclerosis burden, predicting no-reflow, predicting prognosis, responding to myocardial remodeling, and guiding management, hoping to provide cardiologists with new perspectives.

Background:Bivalirudin is a direct thrombin inhibitor (DTI) that can be an alternative to unfractionated heparin (UFH). The efficacy and safety of bivalirudin in anticoagulation therapy in extracorporeal membrane oxygenation (ECMO) remain unknown.Methods:This study followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A systematic literature search was performed in PubMed, EMBASE, and The Cochrane Library databases to identify all relevant original studies estimating bivalirudin’s efficacy and safety versus UFH as anticoagulation therapy in ECMO. The time limit for searching is from the search beginning to June 2021. Two researchers independently screened the literature, extracted data and evaluated the risk of bias of the included studies. The meta-analysis (CRD42020214713) was performedviathe RevMan version 5.3.5 Software and STATA version 15.1 Software.Results:Ten articles with 847 patients were included for the quantitative analysis. Bivalirudin can significantly reduce the incidence of major bleeding in children (I²= 48%,p= 0.01, odd ratio (OR) = 0.17, 95% confidence interval (CI): 0.04–0.66), patient thrombosis (I²= 0%,p= 0.02, OR = 0.58, 95% CI: 0.37–0.93), in-circuit thrombosis/interventions (I²= 0%,p= 0.0005, OR = 0.40, 95% CI: 0.24–0.68), and in-hospital mortality (I²= 0%,p= 0.007, OR = 0.64, 95% CI: 0.46–0.88). Also, comparable clinical outcomes were observed in the incidence of major bleeding in adults (I²= 48%,p= 0.65, OR = 0.87, 95% CI: 0.46–1.62), 30-day mortality (I²= 0%,p= 0.61, OR = 0.83, 95% CI: 0.41–1.68), and ECMO duration in adults (I²= 41%,p= 0.75, mean difference (MD) = −3.19, 95% CI: −23.01–16.63) and children (I²= 76%,p= 0.65, MD = 40.33, 95% CI:−135.45–216.12).Conclusions:Compared with UFH, bivalirudin can be a safe and feasible alternative anticoagulant option to UFH as anticoagulation therapy in ECMO, especially for heparin resistance (HR) and heparin-induced thrombocytopenia (HIT) cases.