Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients
Background: Risk stratification is an integral component of ST-segment-elevation myocardial infarction (STEMI) management practices. This study aimed to derive a machine learning (ML) model for risk stratification and identification of factors associated with in-hospital and 30-day mortality in pati...
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Elsevier
2025-05-01
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| Series: | Indian Heart Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0019483225000562 |
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| author | Mohit D. Gupta Dixit Goyal Shekhar Kunal Manu Kumar Shetty M.P. Girish Vishal Batra Ankit Bansal Prashant Mishra Mansavi Shukla Vanshika Kohli Akul Chadha Arisha Fatima Subrat Muduli Anubha Gupta Jamal Yusuf |
| author_facet | Mohit D. Gupta Dixit Goyal Shekhar Kunal Manu Kumar Shetty M.P. Girish Vishal Batra Ankit Bansal Prashant Mishra Mansavi Shukla Vanshika Kohli Akul Chadha Arisha Fatima Subrat Muduli Anubha Gupta Jamal Yusuf |
| author_sort | Mohit D. Gupta |
| collection | DOAJ |
| description | Background: Risk stratification is an integral component of ST-segment-elevation myocardial infarction (STEMI) management practices. This study aimed to derive a machine learning (ML) model for risk stratification and identification of factors associated with in-hospital and 30-day mortality in patients with STEMI and compare it with traditional TIMI score. Methods: This was a single center prospective study wherein subjects >18 years with STEMI (n = 1700) were enrolled. Patients were divided into two groups: training (n = 1360) and validation dataset (n = 340). Six ML algorithms (Extra Tree, Random Forest, Multiple Perceptron, CatBoost, Logistic Regression and XGBoost) were used to train and tune the ML model and to determine the predictors of worse outcomes using feature selection. Additionally, the performance of ML models both for in-hospital and 30-day outcomes was compared to that of TIMI score. Results: Of the 1700 patients, 168 (9.88 %) had in-hospital mortality while 30-day mortality was reported in 210 (12.35 %) subjects. In terms of in-hospital mortality, Random Forest ML model (sensitivity: 80 %; specificity: 74 %; AUC: 80.83 %) outperformed the TIMI score (sensitivity: 70 %; specificity: 64 %; AUC:70.7 %). Similarly, Random Forest ML model (sensitivity: 81.63 %; specificity: 78.35 %; AUC: 78.29 %) had better performance as compared to TIMI score (sensitivity: 63.26 %; specificity: 63.91 %; AUC: 63.59 %) for 30-day mortality. Key predictors for worse outcomes at 30-days included mitral regurgitation on presentation, smoking, cardiogenic shock, diabetes, ventricular septal rupture, Killip class, age, female gender, low blood pressure and low ejection fraction. Conclusions: ML model outperformed the traditional regression based TIMI score as a risk stratification tool in patients with STEMI. |
| format | Article |
| id | doaj-art-0168fc7855c54d7b99698767f8c7ff6e |
| institution | OA Journals |
| issn | 0019-4832 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| series | Indian Heart Journal |
| spelling | doaj-art-0168fc7855c54d7b99698767f8c7ff6e2025-08-20T02:07:58ZengElsevierIndian Heart Journal0019-48322025-05-0177313314110.1016/j.ihj.2025.03.010Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patientsMohit D. Gupta0Dixit Goyal1Shekhar Kunal2Manu Kumar Shetty3M.P. Girish4Vishal Batra5Ankit Bansal6Prashant Mishra7Mansavi Shukla8Vanshika Kohli9Akul Chadha10Arisha Fatima11Subrat Muduli12Anubha Gupta13Jamal Yusuf14Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India; Corresponding author. Room no. 125, First floor, Academic block, Department of Cardiology, Govind Ballabh Pant Institute of Postgraduate Medical Education & Research, New Delhi, 110002, India.Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, ESIC Medical College and Hospital, Faridabad, Haryana, IndiaDepartment of Pharmacology, Maulana Azad Medical College, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaDepartment of Electronics and Communications Engineering, Indraprastha Institute of Information Technology, Delhi, IndiaDepartment of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, IndiaBackground: Risk stratification is an integral component of ST-segment-elevation myocardial infarction (STEMI) management practices. This study aimed to derive a machine learning (ML) model for risk stratification and identification of factors associated with in-hospital and 30-day mortality in patients with STEMI and compare it with traditional TIMI score. Methods: This was a single center prospective study wherein subjects >18 years with STEMI (n = 1700) were enrolled. Patients were divided into two groups: training (n = 1360) and validation dataset (n = 340). Six ML algorithms (Extra Tree, Random Forest, Multiple Perceptron, CatBoost, Logistic Regression and XGBoost) were used to train and tune the ML model and to determine the predictors of worse outcomes using feature selection. Additionally, the performance of ML models both for in-hospital and 30-day outcomes was compared to that of TIMI score. Results: Of the 1700 patients, 168 (9.88 %) had in-hospital mortality while 30-day mortality was reported in 210 (12.35 %) subjects. In terms of in-hospital mortality, Random Forest ML model (sensitivity: 80 %; specificity: 74 %; AUC: 80.83 %) outperformed the TIMI score (sensitivity: 70 %; specificity: 64 %; AUC:70.7 %). Similarly, Random Forest ML model (sensitivity: 81.63 %; specificity: 78.35 %; AUC: 78.29 %) had better performance as compared to TIMI score (sensitivity: 63.26 %; specificity: 63.91 %; AUC: 63.59 %) for 30-day mortality. Key predictors for worse outcomes at 30-days included mitral regurgitation on presentation, smoking, cardiogenic shock, diabetes, ventricular septal rupture, Killip class, age, female gender, low blood pressure and low ejection fraction. Conclusions: ML model outperformed the traditional regression based TIMI score as a risk stratification tool in patients with STEMI.http://www.sciencedirect.com/science/article/pii/S0019483225000562Artificial intelligenceAcute coronary syndromeRisk stratification |
| spellingShingle | Mohit D. Gupta Dixit Goyal Shekhar Kunal Manu Kumar Shetty M.P. Girish Vishal Batra Ankit Bansal Prashant Mishra Mansavi Shukla Vanshika Kohli Akul Chadha Arisha Fatima Subrat Muduli Anubha Gupta Jamal Yusuf Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients Indian Heart Journal Artificial intelligence Acute coronary syndrome Risk stratification |
| title | Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients |
| title_full | Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients |
| title_fullStr | Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients |
| title_full_unstemmed | Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients |
| title_short | Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients |
| title_sort | comparative evaluation of machine learning models versus timi score in st segment elevation myocardial infarction patients |
| topic | Artificial intelligence Acute coronary syndrome Risk stratification |
| url | http://www.sciencedirect.com/science/article/pii/S0019483225000562 |
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