Predictive risk stratification using HEART (history, electrocardiogram, age, risk factors, and initial troponin) and TIMI (thrombolysis in myocardial infarction) scores in non-high risk chest pain patients

Bhattacharya, Priyanka; Golamari, Reshma; Vunnam, Sandhya; Moparthi, Smitha; Venkatappa, Neethi; Dollard, Denis J.; Missri, José; Yang, Wei; Kimmel, Stephen E.

doi:10.1097/md.0000000000016370

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…All medical findings available when physicians suspected ACS were investigated: demographics and past history; characteristics of chest pain and dyspnea and physical examinations [ 7 ]; vital signs and baseline laboratory findings; electrocardiographic and echocardiographic data; and the heart score which consisted of the categories of history (1 point for patients who did not have any clear documentation) [ 17 ]. Numerous clinical data were used in our study.…”

Section: Methodsmentioning

confidence: 99%

Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom

Choi

Lee²,

Youngik³

et al. 2022

PLoS ONE

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Background The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimicking disease. We aimed to determine which of all clinical variables were more important using conventional logistic regression (cLR) and machine learning (ML). Materials Of 3382 patients with chest pain/discomfort or dyspnea in whom CAG was performed, 1893 were included. All clinical data were divided as follows (i): Demographics, history, and physical examination; (ii): (i) plus electrocardiography; and (iii): (ii) plus echocardiography, and analyzed by cLR and ML. Results In multivariable analysis via cLR, the AUC and accuracy of the model using the final 20 variables were 0.795 and 72.62%, respectively. In multivariable analysis via ML, the best AUCs in the internal validation were 0.8 with (i), 0.81 with (ii), 0.83 with (iii), and in external validation, the best AUCs were 0.71 with (i), 0.74 with (ii), and 0.79 with (iii). The best AUCs and accuracy of the fittest model including 21 importance variables by ML were 0.81 and 72.48% in internal validation; and 0.75 and 70.5% in external validation, respectively. The importance variables in ML and cLR were similar, but slightly different and the additional discriminators via ML were found. Conclusion The assessment using the fittest importance variables can assist physicians in differentiating mimicking diseases in which coronary angiography may not be required in patients suspected of having acute coronary syndrome in emergency department.

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Section: Methodsmentioning

confidence: 99%

Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom

Choi

Lee²,

Youngik³

et al. 2022

PLoS ONE

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“…According to the Framingham heart study [ 7 ], the prediction models for prediction of heart diseases have mainly been categorized into two groups, such as regression-based risk assessment methods and machine learning-based classification methods. The previous risk assessment methods have been the Framingham Risk Score (FRS) [ 8 , 9 , 10 ], QRISK [ 11 , 12 ], Thrombolysis in Myocardial Infarction (TIMI) [ 13 , 14 ], Global Registry of Acute Coronary Events (GRACE) [ 15 , 16 ], and History, Electrocardiogram, Age, and Risk factors and Troponin (HEART) [ 17 , 18 , 19 ] models, whereas the machine learning-based classification methods for heart disease [ 20 ] are Random Forest (RF) [ 21 , 22 , 23 ], Extra Tree (ET) [ 20 , 24 ], Support Vector Machine (SVM) [ 25 ], Gradient Boosting Machine (GBM) [ 26 , 27 ], Neural Networks (NN) [ 28 , 29 , 30 ], and other ensemble models [ 6 , 31 ]. However, there are some drawbacks and limitations of previous models, as follows.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay

Sherazi

Zheng

Lee

2023

Sensors

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Nowadays, machine learning (ML) is a revolutionary and cutting-edge technology widely used in the medical domain and health informatics in the diagnosis and prognosis of cardiovascular diseases especially. Therefore, we propose a ML-based soft-voting ensemble classifier (SVEC) for the predictive modeling of acute coronary syndrome (ACS) outcomes such as STEMI and NSTEMI, discharge reasons for the patients admitted in the hospitals, and death types for the affected patients during the hospital stay. We used the Korea Acute Myocardial Infarction Registry (KAMIR-NIH) dataset, which has 13,104 patients’ data containing 551 features. After data extraction and preprocessing, we used the 125 useful features and applied the SMOTETomek hybrid sampling technique to oversample the data imbalance of minority classes. Our proposed SVEC applied three ML algorithms, such as random forest, extra tree, and the gradient-boosting machine for predictive modeling of our target variables, and compared with the performances of all base classifiers. The experiments showed that the SVEC outperformed other ML-based predictive models in accuracy (99.0733%), precision (99.0742%), recall (99.0734%), F1-score (99.9719%), and the area under the ROC curve (AUC) (99.9702%). Overall, the performance of the SVEC was better than other applied models, but the AUC was slightly lower than the extra tree classifier for the predictive modeling of ACS outcomes. The proposed predictive model outperformed other ML-based models; hence it can be used practically in hospitals for the diagnosis and prediction of heart problems so that timely detection of proper treatments can be chosen, and the occurrence of disease predicted more accurately.

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The value of using the HEART score among cocaine associated chest pain patients in the emergency department - A closer look

Holmes

Posey

Wang

2021

The American Journal of Emergency Medicine

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Predictive risk stratification using HEART (history, electrocardiogram, age, risk factors, and initial troponin) and TIMI (thrombolysis in myocardial infarction) scores in non-high risk chest pain patients

Abstract: Supplemental Digital Content is available in the text

Cited by 4 publications

References 30 publications

Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom

Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom

A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay

The value of using the HEART score among cocaine associated chest pain patients in the emergency department - A closer look

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