Utility of GRACE and ACUITY-HORIZONS risk scores to guide dual antiplatelet therapy in Korean patients with acute myocardial infarction undergoing drug-eluting stenting

Development and validation of a novel bleeding risk prediction tool for aspirin users with a low body mass index

Aspirin is commonly utilized in the management and prevention of various diseases. However, in specific individuals, particularly those with low body mass index (BMI), aspirin can elevate the risk of bleeding. Achieving a delicate equilibrium between the desirable antiplatelet effects and potential bleeding complications is a notable consideration. The objective of this study was to create a novel bleeding risk prediction tool for aspirin users with a low BMI. A total of 2436 aspirin users with a low BMI were included in this study conducted at the Affiliated Dongyang Hospital of Wenzhou Medical University. Patient data, comprising demographics, clinical characteristics, comorbidities, medical history, and laboratory tests, were collected. The patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation purposes. The identification of clinically significant features associated with bleeding was achieved through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression and boruta analysis. Subsequently, these important features underwent multivariate logistic regression analysis. Based on independent bleeding risk factors, a logistic regression model was constructed and presented as a nomogram. Model performance was evaluated using metrics such as the area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC) in both the training and testing sets. LASSO analysis identified two clinical features, while Boruta analysis identified nine clinical features out of a total of 21 features. Subsequent multivariate logistic regression analysis selected significant independent risk factors. The boruta model, which demonstrated the highest AUC, consisted of six clinical variables: hemoglobin, platelet count, previous bleeding, tumor, smoke, and diabetes mellitus. These variables were integrated into a visually represented nomogram. The model exhibited an AUC of 0.832 (95% CI: 0.788-0.875) in the training dataset and 0.775 (95% CI: 0.698-0.853) in the test dataset, indicating excellent discriminatory performance. Calibration curve analysis revealed close alignment with the ideal curve. Furthermore, DCA, CIC, and NRC demonstrated favorable clinical net benefit for the model. This study has successfully created a novel risk prediction tool specifically designed for aspirin users with a low BMI. This tool enables the stratification of low BMI patients based on their anticipated bleeding risk.

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.

A Practical Nomogram for Predicting the Bleeding Risk in Patients with a History of Myocardial Infarction Treating with Aspirin

BACKGROUND

Aspirin is a widely used antiplatelet medication to prevent blood clots, reducing the risk of cardiovascular event. Healthcare providers need to be mindful of the risk of aspirin-induced bleeding and carefully balancing its benefits against potential risks. The objective of this study was to create a practical nomogram for predicting bleeding risk in patients with a history of myocardial infarction treating with aspirin.

METHODS

A total of 2099 myocardial infarction patients with aspirin were enrolled. The patients were randomly divided into two groups, with a 7:3 ratio, for model development and internal validation. Boruta analysis was utilized to identify clinically significant features associated with bleeding. Logistic regression model based on independent bleeding risk factors was constructed and presented as a nomogram. Model performance was assessed from three aspects: identification, calibration, and clinical utility.

RESULTS

Boruta analysis identified eight clinical features from 25, and further multivariate logistic regression analysis selected four independent risk factors: hemoglobin, platelet count, previous bleeding, and sex. A visual nomogram was created based on these variables. The model achieved an area under the curve of 0.888 (95% CI: 0.845-0.931) in the training dataset and 0.888 (95% CI: 0.808-0.968) in the test dataset. Calibration curve analysis showed close approximation to the ideal curve. Decision curve analysis demonstrated favorable clinical net benefit for the model.

CONCLUSIONS

Our study focused on creating and validating a model to evaluate bleeding risk in patients with a history of myocardial infarction treated with aspirin, which demonstrated outstanding performance in discrimination, calibration, and net clinical benefit.

Safety and efficacy of different P2Y12 inhibitors in patients with acute coronary syndromes stratified by the PRAISE risk score: a multicentre study

AIMS

To establish the safety and efficacy of different dual antiplatelet therapy (DAPT) combinations in patients with acute coronary syndrome (ACS) according to their baseline ischaemic and bleeding risk estimated with a machine learning derived model [machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE) score].

METHODS AND RESULTS

Incidences of death, re-acute myocardial infarction (re-AMI), and Bleeding Academic Research Consortium 3-5 bleeding with aspirin plus different P2Y12 inhibitors (clopidogrel or potent P2Y12 inhibitors: ticagrelor or prasugrel) were appraised among patients of the PRAISE data set grouped in four subcohorts: low-to-moderate ischaemic and bleeding risk; low-to-moderate ischaemic risk and high bleeding risk; high ischaemic risk and low-to-moderate bleeding risk; and high ischaemic and bleeding risk. Hazard ratios (HRs) for the outcome measures were derived with inverse probability of treatment weighting adjustment. Among patients with low-to-moderate bleeding risk, clopidogrel was associated with higher rates of re-AMI in those at low-to-moderate ischaemic risk [HR 1.69, 95% confidence interval (CI) 1.16-2.51; P = 0.006] and increased risk of death (HR 3.2, 1.45-4.21; P = 0.003) and re-AMI (HR 2.23, 1.45-3.41; P < 0.001) in those at high ischaemic risk compared with prasugrel or ticagrelor, without a difference in the risk of major bleeding. Among patients with high bleeding risk, clopidogrel showed comparable risk of death, re-AMI, and major bleeding vs. potent P2Y12 inhibitors, regardless of the baseline ischaemic risk.

CONCLUSION

Among ACS patients with non-high risk of bleeding, the use of potent P2Y12 inhibitors is associated with a lower risk of death and recurrent ischaemic events, without bleeding excess. Patients deemed at high bleeding risk may instead be safely addressed to a less intensive DAPT strategy with clopidogrel.

A novel risk stratification system "Angiographic GRACE Score" for predicting in-hospital mortality of patients with acute myocardial infarction: Data from the K-ACTIVE Registry

BACKGROUND

The Global Registry of Acute Coronary Events (GRACE) score is the most accurate risk assessment system for acute myocardial infarction (AMI), which was proposed in Western countries. However, it is unclear whether GRACE score is applicable to the present Japanese patients with a high prevalence of emergent percutaneous coronary intervention (PCI) and vasospasm. This study aimed to clarify the usefulness of GRACE risk score for risk stratification of Japanese AMI patients treated with early PCI and to evaluate a novel risk stratification system, "angiographic GRACE score," which is the GRACE risk score adjusted by the information of the culprit coronary artery and its flow at pre- and post-PCI, to improve its predicting availability.

METHODS

The subjects were 1817 AMI patients who underwent PCI within 24 h of onset between October 2015 and August 2017 and were registered in Kanagawa Acute Cardiovascular (K-ACTIVE) Registry via survey form. The association between the clinical parameters and in-hospital mortality was investigated.

RESULTS

A total of 79 (4.3%) in-hospital deaths were identified. The C-statistics for the in-hospital mortality of the GRACE score was 0.86, which was higher than that of the other conventional risk factors, including age (0.65), systolic blood pressure (0.70), heart rate (0.62), Killip classification (0.77), and serum levels of creatinine (0.68) and peak creatine kinase (0.74). The angiographic GRACE score improved the C-statistics from 0.86 of the original GRACE score to 0.89 (p < 0.05). In the setting of the cut-off value at 200, in-hospital mortality in the patients with the angiographic GRACE score <200 was 0.6%, which was relatively lower than those with ≥200, 9.4%.

CONCLUSIONS

The GRACE score is a useful predictor of in-hospital mortality among Japanese AMI patients in the PCI era. Moreover, the angiographic GRACE score could improve the predicting availability.

Comorbidity and low use of new antiplatelets in acute coronary syndrome

INTRODUCTION

Despite the use of the new generation P2Y12 inhibitors (Ticagrelor and Prasugrel) with aspirin is the recommended therapy in acute NSTE-ACS patients, their current use in clinical practice remains quite low and might be related, among several variables, with increased comorbidity burden. We aimed to assess the prevalence of these treatments and whether their use could be associated with comorbidity.

METHOD

A multicentric prospective registry was conducted at 8 Cardiac Intensive Care Units (October 2017-April 2018) in patients admitted with non ST elevation myocardial infarction. Antithrombotic treatment was recorded and the comorbidity risk was assessed using the Charlson Comorbidity Index. We created a multivariate model to identify the independent predictors of the use of new inhibitors of P2Y12.

RESULTS

A total of 629 patients were included, median age 67 years, 23.2% women, 359 patients (57.1%) treated with clopidogrel and 40.6% with new P2Y12 inhibitors: ticagrelor (228 patients, 36.2%) and prasugrel (30 patients, 4.8%). Among the patients with very high comorbidity (Charlson Score > 6) clopidogrel was the drug of choice (82.6%), meanwhile in patients with low comorbility (Charlson Score 0-1) was the ticagrelor or prasugrel (63.6%). Independent predictors of the use of ticagrelor or prasugrel were a low Charlson Comorbidity Index, a low CRUSADE score and the absence of prior bleeding.

CONCLUSION

Antiplatelet treatment with Ticagrelor or Pasugrel was low in patients admitted with NSTE-ACS. Comorbidity calculated with Charlson Comorbidity Index was a powerful predictor of the use of new generation P2Y12 inhibitors in this population.

Ticagrelor for Asian patients with acute coronary syndrome in real-world practice: A systematic review and meta-analysis of observational studies

OBJECTIVE

We aimed to assess the efficacy and safety of ticagrelor compared to clopidogrel in Asian patients with acute coronary syndrome (ACS) in real-world practice.

METHODS

PubMed, Web of Science and Scopus databases were searched systematically to obtain relevant Asian observational studies.

RESULTS

The meta-analysis included six studies with 27959 participants. Compared with clopidogrel, ticagrelor was significantly beneficial in prevention of major adverse cardiac events (MACCEs) (OR=0.62; 95% CI: 0.46-0.83, I2=69%, p=0.001) mainly driven by reducing stroke (OR=0.62; 95% CI: 0.49-0.78, I2=0%, p<0.001). No differences were found between ticagrelor and clopidogrel in the risk of cardiovascular mortality (OR=0.66; 95% CI: 0.41-1.06, I2=0%, p=0.09), target vessel revascularization (OR=0.53; 95% CI: 0.21-1.35, I2=82%, p=0.18), major bleeding (OR=1.11; 95% CI: 0.62-2.00, I2=75%, p=0.73), and net adverse clinical and cerebral events (OR=0.76; 95% CI: 0.55-1.04, I2=78%, p=0.09). However, ticagrelor significantly increased the incidence of major/minor (OR=1.73; 95% CI: 1.36-2.21, I2=0%, p<0.001) and minor bleeding (OR=1.73; 95% CI: 1.29-2.32, I2=0%, p<0.001). Sensitivity analyses did not find consistent effect of ticagrelor on prevention of all-cause death and myocardial infarction.

CONCLUSION

This meta-analysis suggested that ticagrelor might reduce the risk of MACCEs mainly by reducing stroke in Asian patients with ACS without increasing the rates of major bleeding. Ticagrelor did not show a significant effect on other parts of MACCEs. Considerable increase in the risk of major/minor and minor bleeding was observed in ticagrelor compared with clopidogrel users. Further high-quality studies are required to support these findings.