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In this retrospective study based on a prospectively acquired database, chest pain patients who were treated at Beijing Anzhen Emergency Chest Pain Center, Capital Medical University from September 2014 to February 2015 and at Beijing Bo’ai Hospital, China Rehabilitation Research Center from September 2014 to May 2018, were consecutively enrolled. Beijing Anzhen Hospital and Beijing Bo’ai Hospital, as two of the best cardiovascular hospitals in China, have more concentrated patients with chest pain, more accurate diagnosis of non-ST-ASC with less bias, therefore the chest pain patients with non-ST-ACT are more representative. The patients who were 18 years or older, presented to ED as a result of chest pain with from onset to arrival at the emergency room greater than 2 hours, were included. Patients were excluded if their chest pain caused by with STE-ACS, aortic dissection, pulmonary embolism, trauma or arrhythmia, or they were in end-stage disease, or they were pregnant women or unable or unwilling to provide informed consent. A 3-month follow up was carried out by telephone interview to collect data. Patients were also excluded if their data were incomplete. The study was approved by the Hospital Ethics Committee. All patients had signed informed consent. All procedures performed in studies involving human participants followed the ethical standards of the institutional or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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Patients’ data were collected once they arrived at the ED, including sex, age, medical history, risk factors (any risk factor, including but not limited to hypertension, hypercholesterolemia, diabetes, family history of coronary artery disease, smoking and obesity, body mass index, atherosclerotic disease) and electrocardiogram. As shown in Table 1, the 14 categorical variables were selected for further constructing radar plots and ML models.
Table 1. The 14 features selected in classification
Variables Features Sex [1.2], (Female = 1, Male = 2) Coronary artery disease (CAD) CAD history [0, 1], (Y = 1, N = 0) Ischemic stroke [0, 1], (Y = 1, N = 0) Peripheral arterial disease [0, 1], (Y = 1, N = 0) Hypertension [0, 1], (Y = 1, N = 0) Hypercholesterolemia [0, 1], (Y = 1, N = 0) Diabetes mellitus [0, 1], (Y = 1, N = 0) Current Smoking [0, 1], (Y = 1, N = 0) Obesity [0, 1], (Y = 1, N = 0) Family History of Premature CAD [0, 1], (Y = 1, N = 0) History of Chest Pain with suspected ACS (H) [0, 1, 2] ECG (E) [0, 1, 2] Age (A) [0, 1, 2] Troponin levels (T) [0, 1, 2] -
Endpoint The endpoint was the occurrence of any MACE including acute myocardial infarction (AMI), percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG) or all-cause death within 3 months.
Complete Approach As shown in Supplementary Figures S1–S2 (available in www.besjournal.com), the study cohort was randomly split into 2 datasets, 67% of data was used to train ML models and 33% of data was used to test the developed ML models. A total of 14 features were selected. Five ML models developed by Decision tree, Naïve Bayes, Linear SVC, Logistic Regression and Random Forest predicted the occurrence of MACEs of chest patients with suspected NSTE-ACS. The prediction for MACEs included any event of MACEs, AMI, revascularization and all-cause death in our study. The learning metrics, including Accuracy, Precision, Recall, F-Measure and AUC were used to assess performance of each model. The prediction effect of the ML models and HEART risk scoring system was compared. Ultimately, Naïve Bayes was selected to predict risk factors of occurrence of MACEs (any event, AMI, Revascularization, and all-cause death).
Classification Methods The classification was conducted by using the standard classifiers in ML including Decision Tree, Naïve Bayes, Linear SVC, Logistic regression and Radom Forest. The development of ML models consisted of two stages: training and testing. Prior to the training, the preprocessed dataset was split into training set (67% of the data) and testing set (33% of the data). In the training stage, the labels and features were used to define feature vectors. After that, classifiers were trained on the feature vectors extracted from the training set. In the testing stage, the previously trained classifiers predicted the label of each feature in the testing set. Each classifier was implemented to analyze the testing dataset, and the corresponding scores of the 14 features was obtained.
ML Models Validation The performances of ML models constructed by those five classifiers were evaluated by calculating standard metrics, which were Accuracy, Precision, Recall and F-measure. Accuracy is the ratio of the number of correct predictions to total number of predictions. Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. Recall is the ratio of correctly predicted positive observations to the all observations in actual class. F-measure is the weighted average of Precision and Recall. The standard metrics were calculated by following equations:
$$ \text{Accuracy}=\frac{\left({{\rm{TP}}+{\rm{TN}}}\right)}{{{\rm{TP}}+{\rm{TN}}+{\rm{FP}}+{\rm{FN}}}} $$ (1) $$ \text{Precision}=\frac{\text{TP}}{{{\rm{TP}}+{\rm{FP}}}} $$ (2) $$ \text{Recall}=\frac{\text{TP}}{{{\rm{TP}}+{\rm{FN}}}} $$ (3) $$ \text{F-measure}=2\times \frac{{\text{Recall}} \times {\rm{Precision}}}{{\text{Recall}}+{\rm{Precision}}} $$ (4) where TP is true positive, TN is true negative, FP is false positive and FN is false negative.
Both precision and recall were based on a consideration and measure of weighting function. F-measure was the harmonic mean of precision and recall, and balances in between, in the range of 0 to 1. The ML techniques were implemented in the open-source Python 3.6 environment in Anaconda 3.0 platform with Jupyter Notebook.
Statistical Analysis The continuous variables were described as mean ± SD or as Medians (Interquartile Range). The categorical variables were reported as counts and percentages. Parametric continuous variables were compared between groups using Student’s t test and Man-Whitney U-test for non-parametric variables. Categorical data were compared using the χ2 test for 2 × 2 tables. All statistical tests were two-tailed with P < 0.05 considered as statistically significant. The performances of the models were evaluated by Accuracy, Precision, Recall and F-measure. The comparisons between HEART prediction model and the ML prediction model above were performed in the aspects of those standard metrics.
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Creening process for the study population was shown in Supplementary Figure S2. A total of 1,499 patients were enrolled in this study via screening 1,907 patients with chest pain presenting to the emergency clinic according to exclusion and inclusion criteria. In addition, 27 patients were lost to follow-up. Ultimately, 1,472 patients were enrolled. Most of the participants were male and has underlying diseases. There were 617 patients (41.9%) experiencing at least one MACE (including AMI, PCI, GABG and death) during the 3 months follow-up. PCI was the most common MACE which occurred in 473 patients (32.1%). AMI was ranked second in MACEs, with 206 patients (14.0%). CABG was needed for 121 patients (8.2%). There were only 10 deaths (0.7%). In addition, some chest pain patients experienced more than one MACEs. Specifically, 150 patients concurrently experienced PCI and AMI, 33 patients concurrently experienced AMI and CABG, 2 patients experienced PCI and CABG. Of the 10-death case, AMI, CABG and PCI concurrently occurred in 6, 4 and 3 patients, respectively. Few patients concurrently experienced 3 or 4 MACEs. In general, 617 patients underwent 810 events, an average of 1.31 events per patient.
Detailed baseline characteristics of the study population were shown in Table 2. Most of subjects were elderly, with an average age of 59.1 years. Males and females accounted for 64.8% and 35.2% of the population, respectively. Most of enrolled patients were accompanied by hypertension, with a proportion of 63.9%, while some were accompanied by hypercholesterolemia, diabetes, family history of premature CAD, MI, CABG, ischemic stroke, peripheral artery disease, carotid artery disease, elevated hs-cTn I, but all less than 35.0%. In addition, 13.3% and 29.6% of enrolled patients were suffering from obesity and current smoking, respectively.
Table 2. Baseline characteristics of the study cohort
Characteristics Values P Age, years 59.1 ± 10.20 / Sex Male 953 (64.8) < 0.001 Female 519 (35.2) Hypertension No 531 (36.1) < 0.001 Yes 941 (63.9) Hypercholesterolemia No 1,103 (74.9) < 0.001 Yes 369 (25.1) DM No 1,051 (71.4) < 0.001 Yes 421 (28.6) Family history of premature CAD No 1,269 (86.2) < 0.001 Yes 203 (13.8) Smoke No 1,036 (70.4) < 0.001 Yes 436 (29.6) Obesity No 1,276 (86.7) < 0.001 Yes 196 (13.3) AMI No 1,265 (85.9) < 0.001 Yes 207 (14.1) PCI No 998 (67.8) < 0.001 Yes 474 (32.2) CABG No 1,351 (91.8) < 0.001 Yes 121 (8.2) Ischemic stroke No 1,324 (89.9) < 0.001 Yes 148 (10.1) PAD No 1,392 (94.6) < 0.001 Yes 80 (5.4) CAD No 1,025 (69.6) < 0.001 Yes 447 (30.4) History of chest pain with suspected ACS Slightly 152 (10.3) < 0.001 Moderately 841 (57.1) Highly 479 (32.6) ECG Normal 518 (35.2) < 0.001 Non-specific repolarization disturbance 667 (45.3) Significant ST deviation 287 (19.5) Age group (years) ≤ 45 132 (8.9) < 0.001 45– 891 (60.5) ≥ 65 449 (30.6) Troponin ≤ normal limit 1,174 (79.8) < 0.001 1–3× normal limit 103 (7.0) > 3× normal limit 195 (13.2) Note. Data are mean ± SD or n (%). CABG: coronary artery bypass graft; CAD: coronary artery disease; ECG: electrocardiogram; PAD: peripherial arterial disease; DM: diabetes mellitus; hs-cTn I: high-sensitivity cardiac troponin I; AMI: acute myocardial infarction; PCI: percutaneous coronary intervention. -
As shown in Figure 1, the radar plot illustrated the features of patients with any one of MACEs, PCI, AMI, CABG or all-cause death, and identified important predictors of MACEs. Most patients with MACE(s) had history of chest pain with suspected ACS, and underlying diseases, especially hypertension and DM. Compared with the incidence of other MACEs, AMI could be more associated with history of chest pain with suspected ACS and higher troponin levels. Patients undergoing PCI had similar characteristics of AMI, but had lower troponin levels. Patients who experienced CABG were much older (data not shown here). Additionally, CABG were more correlated with hypertension and abnormal electrocardiogram results than other MACEs. Hypertension and hypercholesterolemia could more contribute to all-cause death. Compared with other features, hypertension, history of chest pain with suspected ACS, and smoking were more associated with any one of MACEs.
Figure 1. (A) Radar plot for the 14 most important predictors of any MACE event; (B) Radar plot for the 14 most important predictors of PCI; (C) Radar plot for the 13 most important predictors of AMI; (D) Radar plot for the 14 most important predictors of CABG; (E) Radar plot for the 7 most important predictors of all-cause-death.
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As shown in Table 3, from the perspective of these learning metrics, Linear SVC, Naïve Bayes and Logistic performed better than HEART scoring system in prediction for each of MACEs. Precision is the most important indicator to assess performance of ML models. The Precision scores in each MACE with classifiers of Linear SVC, Naïve Bayes and Logistic were higher than those with HEART scoring system. Especially, the Precision scores were equal or greater than 0.95 in AMI and all-cause death, while those scores were around 0.77 with HEART scoring system.
Table 3. ML prediction for any event, AMI, revascularization and all-cause death
Classifier Any event AMI Revascularization All-cause death A P R F A P R F A P R F A P R F Linear SVC 0.80 0.80 0.80 0.80 0.95 0.95 0.95 0.95 0.80 0.80 0.80 0.80 1.00 0.99 1.00 0.99 Decision Tree 0.75 0.75 0.75 0.75 0.96 0.96 0.96 0.96 0.74 0.74 0.74 0.73 0.99 0.99 0.99 0.99 Naive Bayes 0.80 0.81 0.80 0.80 0.96 0.97 0.96 0.96 0.80 0.80 0.80 0.80 0.99 0.99 0.99 0.99 Logistic 0.80 0.80 0.80 0.80 0.95 0.95 0.95 0.95 0.80 0.80 0.80 0.80 1.00 0.99 1.00 0.99 Random Forest 0.74 0.74 0.74 0.74 0.95 0.96 0.95 0.96 0.71 0.71 0.71 0.71 1.00 0.99 1.00 0.99 HEART 0.77 0.78 0.77 0.77 0.77 0.78 0.77 0.77 0.77 0.78 0.77 0.77 0.77 0.78 0.77 0.77 Note. A, Accuracy; P, Precision; R: Recall; F, F-measure; AMI, acute myocardial infarction cute myocardial infarction. Linear SVC, Naïve Bayes and Logistic prediction models outperformed the other two models. The performance of Decision Tree and Random Forest Classifiers were worse than HEART scoring system when predict any event of MACEs and revascularization owing to all learning metrics by decision tree and random forest classifiers ≤ 0.75. However, decision tree and random forest classifiers performed greater in terms of predicting AMI (all learning metrics ≥ 0.95) and All-cause death (all learning metrics ≥ 0.99). On the whole, machine learning models constructed by any classifier improved the performance compared with HEART scoring system for AMI and all cause death prediction, based on that the Accuracy, Precision, Recall and F-Measure of all classifiers were ≥ 0.95.
However, the ROC curves and AUC in Figure 2 presented not exactly the same trend as the other 4 metrics in Table 3. For AMI prediction, the AUC by Random Forest was 0.96, the AUC by Decision Tree was 0.94, the AUCs by Linear SVC classifier, Naïve Bayes and Logistic were 0.98, while the AUC by HEART scoring system only was 0.85. Therefore, according to ROC curves and AUC, the ML models constructed by different classifiers performed better than HEART scoring system when predicting AMI. Linear SVC classifier, Naïve Bayes and Logistic performed better than HEART scoring system when predicted any event of MACEs, AMI and revascularization. However, the AUC of the ML model constructed by Linear SVC classifier, Naïve Bayes and Logistic was 0.75, lower than HEART (0.85) in prediction for all-cause death. The too low AUC of ML employing different classifiers could be associated with too small of sample size, only 10 death cases. In addition, the AUCs by Random Forest and Decision Tree were ≤ 0.80 to predict any event of MACEs and revascularization, while the AUC by HEART scoring system. Therefore, the performacne of ML models constructed by Random Forest and Decision Tree was worsen than HEART scoring system. Evaluating learning metrics and ROC curves comprehensively, Linear SVC classifier, Naïve Bayes and Logistic performed better than the other 2 classifiers, which was agreenment with the results by learning metric evaluation.
Figure 2. ROC curves for (A) any event, (B) AMI, (C) revascularization and (D) all-cause death of machine learning and HEART. TPR: true positive rate; AMI: acute myocardial infarction.
Ultimately, considering great learning metrics (over 0.8 for Accuracy, Precision, Recall and F-Measure) and high AUC (0.88 for any event, 0.98 for AMI, 0.87 for revascularization), ML model developed by Naïve Bayes was used for predicting the occurrence of MACEs from 14 features. As shown in Figure 3, the history of chest pain with suspected ACS, abnormal ECG and elevated hs-cTn I were risk factors for any event of MACEs, revascularization and AMI, and the 3 risk factors also were taken into consideration for HEART scoring system. In particular, we observed that elevated hs-cTn I remarkably increased the incidence of AMI. Moreover, ML model also predicted that sex and smoking were also risk factors for any event of MACEs, revascularization and AMI, presenting the advantages of ML model when predicting MACEs[20]. This finding couldn’t be achieved by HEART scoring system, which only takes 5 factors into consideration[6]. We didn’t find any correlation between all-cause death and 14 features, which could be explained by that enough sample size is the basis of good performance for prediction.
doi: 10.3967/bes2023.089
Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS
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Abstract:
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. -
Key words:
- Machine learning /
- MACEs /
- Chest pain /
- Suspected NSTE-ACS
This study was reviewed and approved by the Ethics Committee of Beijing Anzhen Hospital affiliated of Capital Medical University; approval number was 2018055X. Written informed consent was obtained from all individual participants included in the study. All procedures performed in studies involving human participants followed the ethical standards of the institutional or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
&These authors contributed equally to this work.
注释:1) COMPETING INTERESTS: 2) ETHICS APPROVAL AND CONSENT TO PARTICIPATE: -
Figure 1. (A) Radar plot for the 14 most important predictors of any MACE event; (B) Radar plot for the 14 most important predictors of PCI; (C) Radar plot for the 13 most important predictors of AMI; (D) Radar plot for the 14 most important predictors of CABG; (E) Radar plot for the 7 most important predictors of all-cause-death.
T: Elevated hs-cTn I, A: age, E: abnormal ECG, H: History of chest pain with suspected ACS. PCI: percutaneous coronary intervention; AMI: acute myocardial infarction; CAGB: coronary artery bypass grafting; PAD: peripherial arterial disease; CAD: coronary artery disease; ECG: electrocardiogram; ACS: acute coronary syndrome.
Figure 3. Prediction for the occurrence of MACEs (Any event, AMI, Revascularization, and all-cause death) from 14 features by ML model constructed by Naïve Bayes.
H, history of chest pain with suspected ACS; E, abnormal ECG; A, age; T, elevated hs-cTn I. AMI: acute myocardial infarction; ACS: acute coronary syndrome; ECG: electrocardiogram.
Table 1. The 14 features selected in classification
Variables Features Sex [1.2], (Female = 1, Male = 2) Coronary artery disease (CAD) CAD history [0, 1], (Y = 1, N = 0) Ischemic stroke [0, 1], (Y = 1, N = 0) Peripheral arterial disease [0, 1], (Y = 1, N = 0) Hypertension [0, 1], (Y = 1, N = 0) Hypercholesterolemia [0, 1], (Y = 1, N = 0) Diabetes mellitus [0, 1], (Y = 1, N = 0) Current Smoking [0, 1], (Y = 1, N = 0) Obesity [0, 1], (Y = 1, N = 0) Family History of Premature CAD [0, 1], (Y = 1, N = 0) History of Chest Pain with suspected ACS (H) [0, 1, 2] ECG (E) [0, 1, 2] Age (A) [0, 1, 2] Troponin levels (T) [0, 1, 2] Table 2. Baseline characteristics of the study cohort
Characteristics Values P Age, years 59.1 ± 10.20 / Sex Male 953 (64.8) < 0.001 Female 519 (35.2) Hypertension No 531 (36.1) < 0.001 Yes 941 (63.9) Hypercholesterolemia No 1,103 (74.9) < 0.001 Yes 369 (25.1) DM No 1,051 (71.4) < 0.001 Yes 421 (28.6) Family history of premature CAD No 1,269 (86.2) < 0.001 Yes 203 (13.8) Smoke No 1,036 (70.4) < 0.001 Yes 436 (29.6) Obesity No 1,276 (86.7) < 0.001 Yes 196 (13.3) AMI No 1,265 (85.9) < 0.001 Yes 207 (14.1) PCI No 998 (67.8) < 0.001 Yes 474 (32.2) CABG No 1,351 (91.8) < 0.001 Yes 121 (8.2) Ischemic stroke No 1,324 (89.9) < 0.001 Yes 148 (10.1) PAD No 1,392 (94.6) < 0.001 Yes 80 (5.4) CAD No 1,025 (69.6) < 0.001 Yes 447 (30.4) History of chest pain with suspected ACS Slightly 152 (10.3) < 0.001 Moderately 841 (57.1) Highly 479 (32.6) ECG Normal 518 (35.2) < 0.001 Non-specific repolarization disturbance 667 (45.3) Significant ST deviation 287 (19.5) Age group (years) ≤ 45 132 (8.9) < 0.001 45– 891 (60.5) ≥ 65 449 (30.6) Troponin ≤ normal limit 1,174 (79.8) < 0.001 1–3× normal limit 103 (7.0) > 3× normal limit 195 (13.2) Note. Data are mean ± SD or n (%). CABG: coronary artery bypass graft; CAD: coronary artery disease; ECG: electrocardiogram; PAD: peripherial arterial disease; DM: diabetes mellitus; hs-cTn I: high-sensitivity cardiac troponin I; AMI: acute myocardial infarction; PCI: percutaneous coronary intervention. Table 3. ML prediction for any event, AMI, revascularization and all-cause death
Classifier Any event AMI Revascularization All-cause death A P R F A P R F A P R F A P R F Linear SVC 0.80 0.80 0.80 0.80 0.95 0.95 0.95 0.95 0.80 0.80 0.80 0.80 1.00 0.99 1.00 0.99 Decision Tree 0.75 0.75 0.75 0.75 0.96 0.96 0.96 0.96 0.74 0.74 0.74 0.73 0.99 0.99 0.99 0.99 Naive Bayes 0.80 0.81 0.80 0.80 0.96 0.97 0.96 0.96 0.80 0.80 0.80 0.80 0.99 0.99 0.99 0.99 Logistic 0.80 0.80 0.80 0.80 0.95 0.95 0.95 0.95 0.80 0.80 0.80 0.80 1.00 0.99 1.00 0.99 Random Forest 0.74 0.74 0.74 0.74 0.95 0.96 0.95 0.96 0.71 0.71 0.71 0.71 1.00 0.99 1.00 0.99 HEART 0.77 0.78 0.77 0.77 0.77 0.78 0.77 0.77 0.77 0.78 0.77 0.77 0.77 0.78 0.77 0.77 Note. A, Accuracy; P, Precision; R: Recall; F, F-measure; AMI, acute myocardial infarction cute myocardial infarction. -
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22424+Supplementary Materials.pdf