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Different physical exercise modalities have been widely studied in patients having heart failure with preserved ejection fraction (HFpEF) but with variably reported findings. We, therefore, conducted a systematic review and meta-analysis to evaluate whether the efficacy of physical activity in the management of HFpEF is related to exercise modalities. PubMed and Embase were searched up to July 2021. The eligible studies included randomized controlled trials that identified effects of physical exercise on patients with HFpEF. Sixteen studies were included to evaluate the efficiency of physical exercise in HFpEF. A pooled analysis showed that exercise training significantly improved peak oxygen uptake (VO 2 ), ventilatory anaerobic threshold, distance covered in the 6-minute walking test, the ratio of early diastolic mitral inflow to annular velocities, the Short Form 36 physical component score, and the Minnesota Living with Heart Failure Questionnaire total score. However, the changes in other echocardiographic parameters including the ratio of peak early to late diastolic mitral inflow velocities, early diastolic mitral annular velocity, and left atrial volume index were not significant. Both high-intensity and moderate-intensity training significantly improved exercise capacity (as defined by peak VO 2 ), with moderate-intensity exercise having a superior effect. Furthermore, exercise-induced improvement in peak VO 2 was partially correlated with exercise duration. Physical exercise could substantially improve exercise capacity, quality of life, and some indicators of cardiac diastolic function in patients with HFpEF. A protocol of moderate-intensity exercise training lasting a longer duration might be more beneficial compared with high-intensity training for patients with HFpEF.

(1) Background: Patients with acute myocardial infarction (AMI) still experience many major adverse cardiovascular events (MACEs), including myocardial infarction, heart failure, kidney failure, coronary events, cerebrovascular events, and death. This retrospective study aims to assess the prognostic value of machine learning (ML) for the prediction of MACEs. (2) Methods: Five-hundred patients diagnosed with AMI and who had undergone successful percutaneous coronary intervention were included in the study. Logistic regression (LR) analysis was used to assess the relevance of MACEs and 24 selected clinical variables. Six ML models were developed with five-fold cross-validation in the training dataset and their ability to predict MACEs was compared to LR with the testing dataset. (3) Results: The MACE rate was calculated as 30.6% after a mean follow-up of 1.42 years. Killip classification (Killip IV vs. I class, odds ratio 4.386, 95% confidence interval 1.943–9.904), drug compliance (irregular vs. regular compliance, 3.06, 1.721–5.438), age (per year, 1.025, 1.006–1.044), and creatinine (1 µmol/L, 1.007, 1.002–1.012) and cholesterol levels (1 mmol/L, 0.708, 0.556–0.903) were independent predictors of MACEs. In the training dataset, the best performing model was the random forest (RDF) model with an area under the curve of (0.749, 0.644–0.853) and accuracy of (0.734, 0.647–0.820). In the testing dataset, the RDF showed the most significant survival difference (log-rank p = 0.017) in distinguishing patients with and without MACEs. (4) Conclusions: The RDF model has been identified as superior to other models for MACE prediction in this study. ML methods can be promising for improving optimal predictor selection and clinical outcomes in patients with AMI.

Cardiovascular disease (CVD) is one of the most serious health disorders with increasing prevalence and high morbidity and mortality. Although diagnosis and treatment of CVD have achieved huge breakthrough in recent years, it still needs additional enhancements, which result in the demand for new techniques. Artificial intelligence (AI) is an emerging science field that has been widely used to guide diseases diagnosis, evaluation and treatment. AI techniques are promising in CVD to explore novel pathogenic genes phenotype, guide optimal individualized therapeutic strategy, improve the management and quality of discharged patients, predict disease prognosis, and as adjuvant therapy tool. Thus, we summarize the latest application of AI techniques in clinical diagnosis, evaluation and treatment of CVD, aiming to provide novel beneficial evidence of AI and promote its application in CVD.

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