ObjectiveAt present, there is no early prediction model of left ventricular reverse remodeling (LVRR) for people who are in cardiac arrest with an ejection fraction (EF) of ≤35% at first diagnosis; thus, the purpose of this article is to provide a supplement to existing research.Materials and methodsA total of 109 patients suffering from heart attack with an EF of ≤35% at first diagnosis were involved in this single-center research study. LVRR was defined as an absolute increase in left ventricular ejection fraction (LVEF) from ≥10% to a final value of >35%, with analysis features including demographic characteristics, diseases, biochemical data, echocardiography, and drug therapy. Extreme gradient boosting (XGBoost), random forest, and logistic regression algorithm models were used to distinguish between LVRR and non-LVRR cases and to obtain the most important features.ResultsThere were 47 cases (42%) of LVRR in patients suffering from heart failure with an EF of ≤35% at first diagnosis after optimal drug therapy. General statistical analysis and machine learning methods were combined to exclude a number of significant feature groups. The median duration of disease in the LVRR group was significantly lower than that in the non-LVRR group (7 vs. 48 months); the mean values of creatine kinase (CK) and MB isoenzyme of creatine kinase (CK-MB) in the LVRR group were lower than those in the non-LVRR group (80.11 vs. 94.23 U/L; 2.61 vs. 2.99 ng/ml; 27.19 vs. 28.54 mm). Moreover, AUC values for our feature combinations ranged from 97 to 94% and to 87% when using the XGBoost, random forest, and logistic regression techniques, respectively. The ablation test revealed that beats per minute (BPM) and disease duration had a greater impact on the model’s ability to accurately forecast outcomes.ConclusionShorter disease duration, slightly lower CK and CK-MB levels, slightly smaller right and left ventricular and left atrial dimensions, and lower mean heart rates were found to be most strongly predictive of LVRR development (BPM).

Objective: To explore the correlation between characteristics of myocardial energy expenditure (MEE) and the degree of diastolic dysfunction in patients of heart failure with preserved ejection fraction (HFpEF) and its clinical significance. Methods: 125 consecutive patients diagnosed with HFpEF in the Department of Cardiology, Affiliated Zhongshan Hospital of Dalian University from January 2018 to October 2018 were enrolled. According to the degree of diastolic dysfunction, patients were divided into group A (8 ≤ E/e' ≤15) and group B (E/e'> 15), and MEE was calculated, patients finished 1-year clinical follow-up. Results: The level of MEE in group A was significantly lower than that in group B (p < 0.05). During 1-year follow up, MEE over 3145.69 kcal/systole was associated with increased risk of death as compared to patients with MEE less 3145.69 kcal/systole, and in patients with MEE over 101.68 kcal/min than in patients with MEE less than 101.68 kcal/min. Conclusion: There is a significant correlation between MEE and diastolic dysfunction and MEE over 3145.69 kcal/systole as well as MEE over 101.68 kcal/min are linked with increased risk of 1-year mortality in HFpEF.

BACKGROUND:The structure and composition of the gut microbiota influence patients' response to therapeutic interventions. It is also known that the response to statin treatment can vary greatly from one patient to another, suggesting a possible connection between microbiome composition and response to statins. In the present study, we aim to explore the influence of the microbiome composition on the response to statin treatment among patients with coronary artery disease (CAD).METHODS:A prospective cohort of 836 CAD patients enrolled from January 2016 to December 2017 was used to perform a nested case-control study. We divided 110 CAD patients into two groups according to their response to statins (good response group and poor response group) and compared their gut microbiota.RESULTS:Our analysis reveals no significant difference in microbiome between the two groups. However, significant differences were found in the relative proportion of numerous genera between GR and PR groups. Most remarkably, we could observe that a poor response to statin treatment correlates to a significant decrease in the abundance of beneficial bacteria for the lipid metabolism (Akkermansia muciniphila (A. muciniphila) and Lactobacillus) and a significant increase in the abundance of bacteria (Holdemanella and Facecallibacterium).CONCLUSIONS:Gut microbiota structure is associated with the response to statin. Our results suggest that manipulation of the gut microbiota composition can be an interesting and effective treatment strategy to blood lipid control among CAD patients.