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The 2022 American College of Cardiology/American Heart Association/Heart Failure Society of America (ACC/AHA/HFSA) and the 2021 European Society of Cardiology (ESC) both provide evidence‐based guides for the diagnosis and treatment of heart failure (HF). In this review, we aimed to compare recommendations suggested by these guidelines highlighting the differences and latest evidence mentioned in each of the guidelines. While the staging of HF depends on left ventricular ejection fraction, the Universal Definition of HF, suggested in 2021, is described in 2022 ACC/AHA/HFSA guidelines. Both guidelines recommend invasive and non‐invasive tests to diagnose. Despite being identical in the backbone, some differences exist in medical therapy and devices, which can be partially attributed to the recent trials published that are presented in the American guidelines. The recommendation of implantable cardioverter defibrillator for prevention in HF with reduced ejection fraction (HFrEF) patients, made by ACC/AHA/HFSA guidelines, is among the bold differences. It seems that ACC/AHA/HFSA guidelines emphasize the quality of life, cost‐effectiveness, and optimization of care given to patients. On the other hand, the ESC guidelines provide recommendations for certain comorbidities. This comparison can guide clinicians in choosing the proper approach for their own settings and the writing committees in addressing the differences in order to have better consistency in future guidelines.

BackgroundAs 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 methodsVarious 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.ResultsA 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.ConclusionVarious 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.

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Since the start of the coronavirus disease 2019 (COVID-19) pandemic, several biomarkers have been proposed to assess the diagnosis and prognosis of this disease. The present systematic review evaluated endocan (a marker of endothelial cell damage) as a potential diagnostic and prognostic biomarker for COVID-19. PubMed, Scopus, Web of Science, and Embase were searched for studies comparing circulating endocan levels between COVID-19 cases and controls, and/or different severities/complications of COVID-19. Eight studies (686 individuals) were included, from which four reported significantly higher levels of endocan in COVID-19 cases compared with healthy controls. More severe disease was also associated with higher endocan levels in some of the studies. Studies reported higher endocan levels in patients who died from COVID-19, were admitted to an intensive care unit, and had COVID-19-related complications. Endocan also acted as a diagnostic and prognostic biomarker with different cut-offs. In conclusion, endocan could be a novel diagnostic and prognostic biomarker for COVID-19. Further studies with larger sample sizes are warranted to evaluate this role of endocan.

Flavonoids are found in natural health products and plant-based foods. The flavonoid molecules contain a 15-carbon skeleton with the particular structural construction of subclasses. The most flavonoid’s critical subclasses with improved health properties are the catechins or flavonols (e.g., epigallocatechin 3-gallate from green tea), the flavones (e.g., apigenin from celery), the flavanones (e.g., naringenin from citrus), the flavanols (e.g., quercetin glycosides from berries, onion, and apples), the isoflavones (e.g., genistein from soya beans) and the anthocyanins (e.g., cyanidin-3-O-glucoside from berries). Scientific data conclusively demonstrates that frequent intake of efficient amounts of dietary flavonoids decreases chronic inflammation and the chance of oxidative stress expressing the pathogenesis of human diseases like cardiovascular diseases (CVDs). The endoplasmic reticulum (ER) is a critical organelle that plays a role in protein folding, post-transcriptional conversion, and transportation, which plays a critical part in maintaining cell homeostasis. Various stimuli can lead to the creation of unfolded or misfolded proteins in the endoplasmic reticulum and then arise in endoplasmic reticulum stress. Constant endoplasmic reticulum stress triggers unfolded protein response (UPR), which ultimately causes apoptosis. Research has shown that endoplasmic reticulum stress plays a critical part in the pathogenesis of several cardiovascular diseases, including diabetic cardiomyopathy, ischemic heart disease, heart failure, aortic aneurysm, and hypertension. Endoplasmic reticulum stress could be one of the crucial points in treating multiple cardiovascular diseases. In this review, we summarized findings on flavonoids’ effects on the endoplasmic reticulum and their role in the prevention and treatment of cardiovascular diseases.

Background: Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1-year mortality among hypertensive patients who underwent CABG.Hyothesis: ML algorithms can significantly improve mortality prediction after CABG.Methods: Tehran Heart Center's CABG data registry was used to extract several baseline and peri-procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1-year mortality: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB). The area under the curve (AUC), sensitivity, and specificity were used to evaluate the models.Results: Among the 8,493 hypertensive patients who underwent CABG (mean age of 68.27 ± 9.27 years), 303 died in the first year. Eleven features were selected as the best predictors, among which total ventilation hours and ejection fraction were the leading ones. LR showed the best prediction ability with an AUC of 0.82, while the least AUC was for the NB model (0.79). Among the subgroups, the highest AUC for LR model was for two age range groups (50-59 and 80-89 years), overweight, diabetic, and smoker subgroups of hypertensive patients.Conclusions: All ML models had excellent performance in predicting 1-year mortality among CABG hypertension patients, while LR was the best regarding AUC. These