Objective:To compare the value of HEART and TIMI scores in predicting major adverse cardiovascular events(MACEs)of patients with chest pain in the emergency department at a tertiary care hospital in Ahmedabad,a city i...Objective:To compare the value of HEART and TIMI scores in predicting major adverse cardiovascular events(MACEs)of patients with chest pain in the emergency department at a tertiary care hospital in Ahmedabad,a city in western India.Methods:A prospective study was conducted on chest pain patients from January to December 2019.All adult patients with non-traumatic chest pain presenting to the emergency department were included,and their HEART and TIMI scores were evaluated.The patients were followed up within 4 weeks for monitoring any major adverse cardiac events or death.The receiver-operating characteristics(ROC)curve was used to determine the value of HEART and TIMI scores in predicting MACEs.Besides,the specificity,sensitivity,positive predictive value(PPV),and negative predictive value(NPV)of the two scores were assessed and compared.Results:A total of 350 patients were evaluated[mean age(55.03±16.6)years,56.6%of males].HEART score had the highest predictive value of MACEs with an area under the curve(AUC)of 0.98,followed by the TIMI score with an AUC of 0.92.HEART score had the highest specificity of 98.0%(95%CI:96.4%-99.6%),the sensitivity of 75.0%(95%CI:70.7%-79.3%),and PPV of 97.0%(95%CI:94.1%-99.9%)and NPV of 82.5%(95%CI:74.6%-90.4%)for low-risk patients.TIMI score had a specificity of 95.0%(95%CI:92.4%-97.6%),sensitivity of 75.0%(95%CI:69.4%-80.6%),PPV of 92.3%(95%CI:88.1%-96.5%)and NPV of 82.3%(95%CI:73.8%-90.8%)for low-risk patients.Conclusions:HEART score is an easier and more practical triage instrument to identify chest pain patients with low-risk for MACEs compared to TIMI score.Patients with high HEART scores have a higher risk of MACEs and require early therapeutic intervention and aggressive management.展开更多
Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest ...Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research Center.Five classifiers were used to develop ML models.Accuracy,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system.Ultimately,ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.Results According to learning metrics,ML models constructed by different classifiers were superior over HEART(History,ECG,Age,Risk factors,&Troponin)scoring system when predicting acute myocardial infarction(AMI)and all-cause death.However,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI.Among the five ML algorithms,Linear support vector machine(SVC),Naïve Bayes and Logistic regression classifiers stood out with all Accuracy,Precision,Recall and F-Measure from 0.8 to 1.0 for predicting any event,AMI,revascularization and all-cause death(vs.HEART≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(vs.HEART≤0.85).ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome(ACS),abnormal electrocardiogram(ECG),elevated hs-cTn I,sex and smoking were risk factors of MACEs.Conclusion Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and Logistic.ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.展开更多
文摘Objective:To compare the value of HEART and TIMI scores in predicting major adverse cardiovascular events(MACEs)of patients with chest pain in the emergency department at a tertiary care hospital in Ahmedabad,a city in western India.Methods:A prospective study was conducted on chest pain patients from January to December 2019.All adult patients with non-traumatic chest pain presenting to the emergency department were included,and their HEART and TIMI scores were evaluated.The patients were followed up within 4 weeks for monitoring any major adverse cardiac events or death.The receiver-operating characteristics(ROC)curve was used to determine the value of HEART and TIMI scores in predicting MACEs.Besides,the specificity,sensitivity,positive predictive value(PPV),and negative predictive value(NPV)of the two scores were assessed and compared.Results:A total of 350 patients were evaluated[mean age(55.03±16.6)years,56.6%of males].HEART score had the highest predictive value of MACEs with an area under the curve(AUC)of 0.98,followed by the TIMI score with an AUC of 0.92.HEART score had the highest specificity of 98.0%(95%CI:96.4%-99.6%),the sensitivity of 75.0%(95%CI:70.7%-79.3%),and PPV of 97.0%(95%CI:94.1%-99.9%)and NPV of 82.5%(95%CI:74.6%-90.4%)for low-risk patients.TIMI score had a specificity of 95.0%(95%CI:92.4%-97.6%),sensitivity of 75.0%(95%CI:69.4%-80.6%),PPV of 92.3%(95%CI:88.1%-96.5%)and NPV of 82.3%(95%CI:73.8%-90.8%)for low-risk patients.Conclusions:HEART score is an easier and more practical triage instrument to identify chest pain patients with low-risk for MACEs compared to TIMI score.Patients with high HEART scores have a higher risk of MACEs and require early therapeutic intervention and aggressive management.
基金supported by Beijing Nova Program[Z201100006820087]National Key R&D Program of China[2020YFC2004800]+2 种基金National Natural Science Foundation of China[81870322]The Capital Health Research and Development of Special Fund[2018-1-2061]The Natural Science Foundation of Beijing,China[7191002].
文摘Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research Center.Five classifiers were used to develop ML models.Accuracy,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system.Ultimately,ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.Results According to learning metrics,ML models constructed by different classifiers were superior over HEART(History,ECG,Age,Risk factors,&Troponin)scoring system when predicting acute myocardial infarction(AMI)and all-cause death.However,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI.Among the five ML algorithms,Linear support vector machine(SVC),Naïve Bayes and Logistic regression classifiers stood out with all Accuracy,Precision,Recall and F-Measure from 0.8 to 1.0 for predicting any event,AMI,revascularization and all-cause death(vs.HEART≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(vs.HEART≤0.85).ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome(ACS),abnormal electrocardiogram(ECG),elevated hs-cTn I,sex and smoking were risk factors of MACEs.Conclusion Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and Logistic.ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.
