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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.

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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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