Direct oral anticoagulants versus warfarin for secondary prevention of cerebral infarction and bleeding in older adults with atrial fibrillation

Metabolomic and transcriptomic analyses reveals candidate genes and pathways involved in secondary metabolism in Bergenia purpurascens

Bergenia purpurascens is an important medicinal, edible and ornamental plant. The lack of omics information hinders the study of its metabolic pathways and related genes. In order to investigate candidate genes and pathways involved in secondary metabolism in B. purpurascens, roots, stems and leaves of B. purpurascens were subjected to metabolomic and transcriptomic analyses in this study. A total of 351 differentially accumulated secondary metabolites were identified. We identified 120 candidate genes involved in phenylpropanoid and flavonoid biosynthesis pathway, from which 29 key candidate genes were obtained by WGCNA. Five UDP-Glycosyltransferases and four O-methyltransferases were suggested to be the candidate enzymes involved in synthetic pathway from gallic acid to bergenin by correlation analysis between transcriptional and metabolic levels and phylogenetic analysis. This study provides data resources and new insights for further studies on the biosynthesis of major active components in B. purpurascens.

Machine learning derived model for the prediction of bleeding in dual antiplatelet therapy patients

Objective

This study aimed to develop a predictive model for assessing bleeding risk in dual antiplatelet therapy (DAPT) patients.

Methods

A total of 18,408 DAPT patients were included. Data on patients' demographics, clinical features, underlying diseases, past history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. LASSO regression, multivariate logistic regression, and six machine learning models, including random forest (RF), k-nearest neighbor imputing (KNN), decision tree (DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC).

Results

The XGBoost model demonstrated the highest AUC. The model features were comprised of seven clinical variables, including: HGB, PLT, previous bleeding, cerebral infarction, sex, Surgical history, and hypertension. A nomogram was developed based on seven variables. The AUC of the model was 0.861 (95% CI 0.847-0.875) in the development cohort and 0.877 (95% CI 0.856-0.898) in the validation cohort, indicating that the model had good differential performance. The results of calibration curve analysis showed that the calibration curve of this nomogram model was close to the ideal curve. The clinical decision curve also showed good clinical net benefit of the nomogram model.

Conclusions

This study successfully developed a predictive model for estimating bleeding risk in DAPT patients. It has the potential to optimize treatment planning, improve patient outcomes, and enhance resource utilization.

Predictive Model of Internal Bleeding in Elderly Aspirin Users Using XGBoost Machine Learning

Objective

This study aimed to develop a predictive model for assessing internal bleeding risk in elderly aspirin users using machine learning.

Methods

A total of 26,030 elderly aspirin users (aged over 65) were retrospective included in the study. Data on patient demographics, clinical features, underlying diseases, medical history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. Patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation, respectively. Least absolute shrinkage and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and multivariate logistic regression were employed to develop prediction models. Model performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC).

Results

The XGBoost model exhibited the highest AUC among all models. It consisted of six clinical variables: HGB, PLT, previous bleeding, gastric ulcer, cerebral infarction, and tumor. A visual nomogram was developed based on these six variables. In the training dataset, the model achieved an AUC of 0.842 (95% CI: 0.829-0.855), while in the test dataset, it achieved an AUC of 0.820 (95% CI: 0.800-0.840), demonstrating good discriminatory performance. The calibration curve analysis revealed that the nomogram model closely approximated the ideal curve. Additionally, the DCA curve, CIC, and NRC demonstrated favorable clinical net benefit for the nomogram model.

Conclusion

This study successfully developed a predictive model to estimate the risk of bleeding in elderly aspirin users. This model can serve as a potential useful tool for clinicians to estimate the risk of bleeding in elderly aspirin users and make informed decisions regarding their treatment and management.

LASSO-derived model for the prediction of bleeding in aspirin users

Aspirin is widely used for both primary and secondary prevention of panvascular diseases, such as stroke and coronary heart disease (CHD). The optimal balance between reducing panvascular disease events and the potential increase in bleeding risk remains unclear. This study aimed to develop a predictive model specifically designed to assess bleeding risk in individuals using aspirin. A total of 58,415 individuals treated with aspirin were included in this study. Detailed data regarding patient demographics, clinical characteristics, comorbidities, medical history, and laboratory test results were collected from the Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups at a ratio of 7:3. The larger group was used for model development, while the smaller group was used for internal validation. To develop the prediction model, we employed least absolute shrinkage and selection operator (LASSO) regression followed by multivariate logistic regression. The performance of the model was assessed through metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The LASSO-derived model employed in this study incorporated six variables, namely, sex, operation, previous bleeding, hemoglobin, platelet count, and cerebral infarction. It demonstrated excellent performance at predicting bleeding risk among aspirin users, with a high AUC of 0.866 (95% CI 0.857-0.874) in the training dataset and 0.861 (95% CI 0.848-0.875) in the test dataset. At a cutoff value of 0.047, the model achieved moderate sensitivity (83.0%) and specificity (73.9%). The calibration curve analysis revealed that the nomogram closely approximated the ideal curve, indicating good calibration. The DCA curve demonstrated a favorable clinical net benefit associated with the nomogram model. Our developed LASSO-derived predictive model has potential as an alternative tool for predicting bleeding in clinical settings.

Hemorrhage induced by antithrombotic agents: new insights from a real-world pharmacovigilance study

BACKGROUND

Hemorrhage represents the most common and serious side effect of antithrombotic agents. Many studies have compared the risk of bleeding between different antithrombotic agents, but analysis of time-to-onset for hemorrhage induced by these drugs is yet sparse.

METHODS

We conducted a retrospective study based on the adverse drug reaction reports on antithrombotic agents collected by the Henan Adverse Drug Reaction Monitoring Center. We assessed the reporting odds ratio to determine the disproportionate reporting signals for bleeding and the Weibull shape parameter was used to evaluate the time-to-onset data.

RESULTS

In the signal detection, crude low molecular weight heparin-hemorrhage was found as a positive signal. The hemorrhage for most antithrombotic agents was random failure profiles. In particular, the hazard of hemorrhage decreased over time for warfarin and clopidogrel and increased for alteplase, nadroparin, and dipyridamole.

CONCLUSION

We found that the risk of bleeding in patients taking Crude low molecular weight heparins was significantly higher compared to other antithrombotic agents, but with a small magnificence, which may be attributed to the severely irrational use of this medication under improper management. Statistics in days, results showed that the risk of bleeding decreased over time for warfarin and clopidogrel and increased for alteplase, nadroparin, and dipyridamole.

Trend of anticoagulant therapy in elderly patients with atrial fibrillation considering risks of cerebral infarction and bleeding

The introduction of direct oral anticoagulants (DOACs) has greatly changed the use of anticoagulant therapy in patients with non-valvular atrial fibrillation (Af). Therefore, this study aimed to examine changes in the proportions of oral anticoagulant prescriptions in patients with non-valvular Af aged ≥ 65 years, taking into consideration the risk of cerebral infarction and bleeding. Anticoagulant prescriptions in outpatients aged ≥ 65 years with Af were temporally analyzed using the nationwide claims database in Japan. Trends in anticoagulant prescriptions were examined according to cerebral infarction and bleeding risk. The proportion of anticoagulant prescriptions for 12,076 Af patients increased from 41% in 2011 to 56% in 2015. An increase in DOAC prescriptions was accompanied by an increase in the proportion of anticoagulant prescriptions in each group according to the CHA2DS2-VASc and HAS-BLED scores. The proportion of anticoagulant prescriptions for patients with a high risk of developing cerebral infarction and bleeding showed a marked increase. Trends in anticoagulant prescriptions in Af patient with a CHA₂DS₂-VASc score ≥ 2 and HAS-BLED scores ≥ 3 showed a marked increase in DOAC prescriptions. The widespread use of DOACs greatly changes the profile the prescription of anticoagulant therapy in patients with Af.

Benefits and Risks Associated with Long-term Oral Anticoagulation after Successful Atrial Fibrillation Catheter Ablation: Systematic Review and Meta-analysis

Oral anticoagulation (OAC) prevents thromboembolism yet greatly increases the risk of bleeding, inciting concern among clinicians. Current guidelines lack sufficient evidence supporting long-term OAC following successful atrial fibrillation catheter ablation (CA). A literature search was performed in PubMed, Google Scholar, Medline, and Scopus to seek out studies that compare continued and discontinued anticoagulation in post-ablation Atrial fibrillation (AF) patients. Funnel plots and Egger’s test examined potential bias. Via the random-effects model, summary odds ratios (OR) with 95% confidence intervals (CI) were calculated using RevMan (5.4) and STATA (17.0). Twenty studies, including 22 429 patients (13 505 off-OAC) were analyzed. Stratified CHA₂DS₂-VASc score ≥2 examining thromboembolic events (TE) favored OAC continuation (OR 1.86; 95% CI: 1.02-3.40; P = .04). Sensitivity analysis demonstrated this association was attenuated. The on-OAC arm had greater incidence of major bleeding (MB) (OR 0.16; 95% CI: 0.08-0.95; P < .00001), particularly intracranial hemorrhage (ICH) and gastrointestinal bleeding (GI); (OR 0.17; 95% CI: 0.08-0.36; P < .00001) and (OR 0.12; 95% CI: 0.04-0.32; P < .0001), respectively. Our findings support sustained anticoagulation in patients with a CHA₂DS₂-VASc score of ≥2. Due to reduced outcome robustness, physician discretion is still advised.