Background: For patients with heart failure (HF), the effect of angiotensin receptor-neprilysin inhibitors (ARNIs, sacubitril/valsartan) on cardiac remodeling has been found to be superior to angiotensin-converting enzyme inhibitors (ACEI). However, little data have described the impact of early-initiation ARNI in patients with acute anterior ST-segment elevation myocardial infarction (STEMI).Methods: In this prospective, randomized, double-blind, parallel-group trial, we enrolled 131 anterior STEMI patients who were treated with primary percutaneous coronary intervention (PCI) between February 2019 and December 2019. All patients received standard STEMI management and were divided into 2 groups (ARNI/enalapril). Primary efficacy outcomes were the between-group difference in change (from baseline to 4-, 12-, and 24-week) in N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentration, left ventricular ejection fraction (LVEF), and left ventricular end-systolic volumes and end-diastolic volumes (LVESV and LVEDV). Secondary outcomes were determined by a composite of death, reinfarction, outpatient HF or HF hospitalization, malignant arrhythmia, and stroke. Safety outcomes included worsening renal function, hypotension, hyperkalemia, angioedema and cough.Results: We found that NT-proBNP concentration decreased more in the ARNI group than in the enalapril group [4 weeks: ratio of ARNI vs.

Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.

BackgroundCompared with conventional medicines, angiotensin receptor-neprilysin inhibitor (ARNI) could further improve the prognosis for multiple cardiovascular diseases, such as heart failure, hypertension, and myocardial infarction. However, the relationship between ARNI therapy and the recurrence of atrial fibrillation (AF) after radiofrequency catheter ablation is currently unknown.MethodsThis study is a retrospective cohort study. Patients with consecutive persistent or paroxysmal AF undergoing first-time radiofrequency ablation were enrolled from February 2018 to October 2021. We compared the risk of AF recurrence in patients with catheter ablation who received ARNI with the risk of AF recurrence in those who received the angiotensin-converting enzyme inhibitor (ACEI). The propensity-score matched analysis was conducted to examine the effectiveness of ARNI. We used a Cox regression model to evaluate AF recurrence events.ResultsAmong 679 eligible patients, 155 patients with ARNI treatment and 155 patients with ACEI treatment were included in the analyses. At a median follow-up of 228 (196–322) days, ARNI as compared with ACEI was associated with a lower risk of AF recurrence [adjusted hazard ratio (HR), 0.39; 95% confidence interval (CI), 0.24–0.63; p < 0.001]. In addition, no interaction was found in the subgroup analysis.ConclusionAngiotensin receptor-neprilysin inhibitor treatment was associated with a decreased risk of AF recurrence after first-time radiofrequency catheter ablation.

Background This study aimed to establish and assess a prediction model for patients with persistent atrial fibrillation (AF) treated with nifekalant during the first radiofrequency catheter ablation (RFCA). Methods In this study, 244 patients with persistent AF from January 17, 2017 to December 14, 2017, formed the derivation cohort, and 205 patients with persistent AF from December 15, 2017 to October 28, 2018, constituted the validation cohort. The least absolute shrinkage and selection operator regression was used for variable screening and the multivariable Cox survival model for nomogram development. The accuracy and discriminative capability of this predictive model were assessed according to discrimination (area under the curve [AUC]) and calibration. Clinical practical value was evaluated using decision curve analysis. Results Body mass index, AF duration, sex, left atrial diameter, and the different responses after nifekalant administration were identified as AF recurrence-associated factors, all of which were selected for the nomogram. In the development and validation cohorts, the AUC for predicting 1-year AF-free survival was 0.863 (95% confidence interval (CI) 0.801–0.926) and 0.855 (95% CI 0.782–0.929), respectively. The calibration curves showed satisfactory agreement between the actual AF-free survival and the nomogram prediction in the derivation and validation cohorts. In both groups, the prognostic score enabled stratifying the patients into different AF recurrence risk groups. Conclusions This predictive nomogram can serve as a quantitative tool for estimating the 1-year AF recurrence risk for patients with persistent AF treated with nifekalant during the first RFCA.

ObjectiveThis study aimed to identify risk factors for coronary heart disease (CHD) in patients with type 2 diabetes mellitus (T2DM), build a clinical prediction model, and draw a nomogram.Study design and methodsCoronary angiography was performed for 1,808 diabetic patients who were recruited at the department of cardiology in The Second Affiliated Hospital of Nanchang University from June 2020 to June 2022. After applying exclusion criteria, 560 patients were finally enrolled in this study and randomly divided into training cohorts (n = 392) and validation cohorts (n = 168). The least absolute shrinkage and selection operator (LASSO) is used to filter features in the training dataset. Finally, we use logical regression to establish a prediction model for the selected features and draw a nomogram.ResultsThe discrimination, calibration, and clinical usefulness of the prediction model were evaluated using the c-index, receiver operating characteristic (ROC) curve, calibration chart, and decision curve. The effects of gender, diabetes duration, non-high-density lipoprotein cholesterol, apolipoprotein A1, lipoprotein (a), homocysteine, atherogenic index of plasma (AIP), nerve conduction velocity, and carotid plaque merit further study. The C-index was 0.803 (0.759–0.847) in the training cohort and 0.775 (0.705–0.845) in the validation cohort. In the ROC curve, the Area Under Curve (AUC) of the training set is 0.802, and the AUC of the validation set is 0.753. The calibration curve showed no overfitting of the model. The decision curve analysis (DCA) demonstrated that the nomogram is effective in clinical practice.ConclusionBased on clinical information, we established a prediction model for CHD in patients with T2DM.