Background: Atherogenic index of plasma (AIP), a novel logarithmic index that combines fasting triglyceride and high-density lipoprotein cholesterol concentrations, is associated with the burden of atherosclerosis. This study aimed to evaluate the relationship between AIP and coronary artery disease (CAD) risk, severity, and prognosis in populations with and without established CAD. Methods: PubMed, Embase, and Web of Science were systematically searched from the inception of each database to August 13, 2024. Cross-sectional studies, case-control studies, and prospective or retrospective cohort studies using multivariate analysis were included. Given that the true effect size may differ across studies, a random-effects model for all analyses was applied. Results: Fifty-one observational studies were included in this study. Patients with higher AIP were more likely to have CAD (odds ratio (OR): 2.79, 95% CI 1.75–4.45, P < 0.00001). Furthermore, these patients were more likely to have coronary artery calcification (OR: 2.28, 95% CI 1.74–3.00, P < 0.00001), multivessel CAD (OR: 2.04, 95% CI 1.50–2.77, P < 0.00001), and an increased risk of plaque progression (OR: 1.49, 95% CI 1.17–1.91, P = 0.001). In populations without established CAD, higher AIP levels were associated with an increased risk of Major adverse cardiovascular events (MACE) (hazard ratio (HR): 1.28, 95% CI 1.22–1.35, P < 0.00001). Interestingly, this finding was consistent in patients presenting with acute coronary syndrome (HR: 1.59, 95% CI 1.33–1.89, P < 0.00001) and patients with chronic coronary syndrome or stable CAD (HR: 1.65, 95% CI 1.15–2.37, P = 0.007). Conclusions: This meta-analysis demonstrates that elevated AIP is strongly associated with increased CAD risk, greater severity, and poorer prognosis in populations with and without established CAD. However, more studies are needed to evaluate the predictive performance and determine the optimal cut-off for AIP in different populations. Graphical

Background: As the era of big data analytics unfolds, machine learning (ML) might be a promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive ability of ML models for estimating mortality after coronary artery bypass grafting (CABG).Materials and methods: Various baseline and follow-up features were obtained from the CABG data registry, established in 2005 at Tehran Heart Center. After selecting key variables using the random forest method, prediction models were developed using: Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms. Area Under the Curve (AUC) and other indices were used to assess the performance.Results: A total of 16,850 patients with isolated CABG (mean age: 67.34 ± 9.67 years) were included. Among them, 16,620 had one-year follow-up, from which 468 died. Eleven features were chosen to train the models. Total ventilation hours and left ventricular ejection fraction were by far the most predictive factors of mortality. All the models had AUC > 0.7 (acceptable performance) for 1-year mortality. Nonetheless, LR (AUC = 0.811) and XGBoost (AUC = 0.792) outperformed NB (AUC = 0.783), RF (AUC = 0.783), SVM (AUC = 0.738), and KNN (AUC = 0.715). The trend was similar for two-to-five-year mortality, with LR demonstrating the highest predictive ability.Conclusion: Various ML models showed acceptable performance for estimating CABG mortality, with LR illustrating the highest prediction performance. These models can help clinicians make decisions according to the risk of mortality in patients undergoing CABG.

The potential benefits and risks of combination therapy with sodium-glucose co-transporter-2 inhibitors (SGLT-2is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) versus monotherapy remain a subject of debate to optimize metabolic and cardiovascular outcomes in patients with type 2 diabetes mellitus. This study aims to systematically review and meta-analyze the available evidence from randomized controlled trials. A comprehensive search identified relevant randomized controlled trials comparing combination therapy with SGLT-2i and GLP-1RA to monotherapy or treatment as usual (TAU). The main outcome was the incidence of hospitalization for heart failure. Other outcomes included major adverse cardiovascular events (MACE) (cardiovascular mortality, all-cause mortality, stroke, and myocardial infarction), changes in metabolic parameters, and adverse events. Random-effects meta-analysis estimated risk ratios (RRs), mean difference (MD), and 95% confidence intervals (CIs). We assessed the risk of bias in included studies using the Cochrane ROB 2.0 tool. The meta-analysis included 10 randomized controlled trials with 42,651 participants, of which 2,820 were on combination therapy and the rest on SGLT-2i (37.1%), GLP-1RA (20.1%) monotherapies or TAU (42.8%). Combination therapy had a lower risk of hospitalization for heart failure versus GLP-1RA monotherapy (RR = 0.37, 95% CI 0.22; 0.65), SGLT-2i monotherapy (RR = 0.37, 95% CI 0.19; 0.75), and TAU (RR = 0.43, 95% CI 0.24; 0.75), respectively. Combination therapy also had a significantly lower risk of MACE versus TAU (RR = 0.73, 95% CI 0.61; 0.88). Combination therapy showed greater weight loss and hemoglobin A1c reduction versus SGLT-2i monotherapy (MD = -2.20, 95% CI −3.09; −1.31 and MD = −0.74, 95% CI −1.21; −0.27), respectively, while no difference was noted versus GLP-1RA monotherapy. The incidence of nausea and diarrhea was higher with combination therapy versus SGLT-2i monotherapy (MD = 3.34, 95% CI 1.74; 6.43 and MD = 1.75, 95% CI 1.10; 2.77), respectively. Combination therapy with SGLT-2i and GLP-1RA may provide superior cardiovascular, weight, and Hemoglobin A1c outcomes versus monotherapy despite higher gastrointestinal adverse events. These results may impact the management of patients with metabolic and cardiovascular diseases and highlight the need for further research on combination therapy to optimize outcomes.

Background: Although several studies are available regarding baseline Electrocardiographic (ECG) parameters and major and minor ECG abnormalities, there is considerable controversy regarding their age and gender differences in the literature. Methods: Data from 7630 adults aged ≥ 35 from the Tehran Cohort Study registered between March 2016 and March 2019 were collected. Basic ECG parameters values and abnormalities related to arrhythmia, defined according to the American Heart Association definitions, were analyzed and compared between genders and four distinct age groups. The odds ratio of having any major ECG abnormality between men and women, stratified by age, was calculated. Results: The average age was 53.6 (± 12.66), and women made up 54.2% (n = 4132) of subjects. The average heart rate (HR) was higher among women(p < 0.0001), while the average values of QRS duration, P wave duration, and RR intervals were higher among men(p < 0.0001). Major ECG abnormalities were observed in 2.9% of the study population (right bundle branch block, left bundle branch block, and Atrial Fibrillation were the most common) and were more prevalent among men compared to women but without statistical significance (3.1% vs. 2.7% p = 0.188). Moreover, minor abnormalities were observed in 25.9% of the study population and again were more prevalent among men (36.4% vs. 17% p < 0.001). The prevalence of major ECG abnormalities was significantly higher in participants older than 65. Conclusion: Major and minor ECG abnormalities were roughly more prevalent in male subjects. In both genders, the odds of having major ECG abnormalities surge with an increase in age.

Chimeric antigen receptor (CAR) T-cell therapy is a new revolutionary method for treating refractory or relapsed hematologic malignancies, CAR T-cell therapy has been associated with cytokine release syndrome (CRS) and cardiotoxicity. We directed a systematic review and meta-analysis to determine the incidence and predictors of cardiovascular events (CVE) with CAR T-cell therapy. We investigated PubMed, Embase, Cochrane Library, and ClinicalTrials.gov for studies reporting cardiovascular outcomes in CAR-T cell recipients. The study protocol was listed in the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42023478602). Twenty-three studies were included in this study. The pooled incidence of CVE was 54% for arrhythmias, 30% for heart failure, 20% for cardiomyopathy, 10% for acute coronary syndrome, and 7% for cardiac arrest. Patients with CVE had a higher incidence of cytokine release syndrome grade ≥ 2 (RR 2.36, 95% CI 1.86–2.99). The incidence of cardiac mortality in our meta-analysis was 2% (95% CI: 1%–3%). Left ventricular ejection fraction decline was greater in the CVE group (-9.4% versus -1.5%, p < 0.001). Cardiac biomarkers like BNP, CRP, creatinine, and ferritin were also elevated. CAR T-cell therapy commonly leads to cardiotoxicity, mediated by cytokine release syndrome. Vigilant monitoring and tailored treatments are crucial to mitigate these effects. Importantly, there's no significant difference in cardiac mortality between groups, suggesting insights for optimizing preventive interventions and reducing risks after CAR T-cell therapy.

Background: Acute kidney injury (AKI) is one of the preventable complications of percutaneous coronary intervention (PCI). This study aimed to develop machine learning (ML) models to predict AKI after PCI in patients with acute coronary syndrome (ACS). Methods: This study was conducted at Tehran Heart Center from 2015 to 2020. Several variables were used to design five ML models: Naïve Bayes (NB), Logistic Regression (LR), CatBoost (CB), Multi-layer Perception (MLP), and Random Forest (RF). Feature importance was evaluated with the RF model, CB model, and LR coefficients while SHAP beeswarm plots based on the CB model were also used for deriving the importance of variables in the population using pre-procedural variables and all variables. Sensitivity, specificity, and the area under the receiver operating characteristics curve (ROC-AUC) were used as the evaluation measures. Results: A total of 4592 patients were included, and 646 (14.1%) experienced AKI. The train data consisted of 3672 and the test data included 920 cases. The patient population had a mean age of 65.6 ± 11.2 years and 73.1% male predominance. Notably, left ventricular ejection fraction (LVEF) and fasting plasma glucose (FPG) had the highest feature importance when training the RF model on only pre-procedural features. SHAP plots for all features demonstrated LVEF and age as the top features. With pre-procedural variables only, CB had the highest AUC for the prediction of AKI (AUC 0.755, 95% CI 0.713 to 0.797), while RF had the highest sensitivity (75.9%) and MLP had the highest specificity (64.35%). However, when considering pre-procedural, procedural, and post-procedural features, RF outperformed other models (AUC: 0.775). In this analysis, CB achieved the highest sensitivity (82.95%) and NB had the highest specificity (82.93%). Conclusion: Our analyses showed that ML models can predict AKI with acceptable performance. This has potential clinical utility for assessing the individualized risk of AKI in ACS patients undergoing PCI. Additionally, the identified features in the models may aid in mitigating these risk factors. Graphical