Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients.

IF 1.4 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS
Mohit D Gupta, Dixit Goyal, Shekhar Kunal, Manu Kumar Shetty, Girish Mp, Vishal Batra, Ankit Bansal, Prashant Mishra, Mansavi Shukla, Vanshika Kohli, Akul Chadha, Arisha Fatima, Subrat Muduli, Anubha Gupta, Jamal Yusuf
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引用次数: 0

Abstract

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.

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来源期刊
Indian heart journal
Indian heart journal CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
2.60
自引率
6.70%
发文量
82
审稿时长
52 days
期刊介绍: Indian Heart Journal (IHJ) is the official peer-reviewed open access journal of Cardiological Society of India and accepts articles for publication from across the globe. The journal aims to promote high quality research and serve as a platform for dissemination of scientific information in cardiology with particular focus on South Asia. The journal aims to publish cutting edge research in the field of clinical as well as non-clinical cardiology - including cardiovascular medicine and surgery. Some of the topics covered are Heart Failure, Coronary Artery Disease, Hypertension, Interventional Cardiology, Cardiac Surgery, Valvular Heart Disease, Pulmonary Hypertension and Infective Endocarditis. IHJ open access invites original research articles, research briefs, perspective, case reports, case vignette, cardiovascular images, cardiovascular graphics, research letters, correspondence, reader forum, and interesting photographs, for publication. IHJ open access also publishes theme-based special issues and abstracts of papers presented at the annual conference of the Cardiological Society of India.
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