Abstract
BackgroundEarly identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI).MethodsA total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git). The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set.ResultsThree ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42–7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01–4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98–4.56) are significant factors related to arrhythmia after AMI.ConclusionsWe used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research.Trial registration Clinical Trial Registry No.: ChiCTR2100041960.
Highlights
acute myocardial infarction (AMI) is a clinically critical disease [1]
machine learning (ML) analysis Variable selection ML extracted top-15 feature-ranking with the random forest for further modeling
Model evaluation and comparison We use three ML algorithms to build a predictive model of tachyarrhythmia after AMI
Summary
AMI is a clinically critical disease [1]. Recent studies have emphasized that percutaneous coronary intervention (PCI) can reduce acute and long-term mortality [2]. MI indicates myocardial infarction; CI, cerebral infarction; HF, heart failure; CHD, coronary heart disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; pro-BNP, pro-B-type natriuretic peptide; CRP, C-reactive protein; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; DDP, D dimer; Cr, creatinine; TNI, Troponin I; CK-MB, creatine Kinase Isoenzyme; UGLU, urine glucose; P-R, PR interval; QTc, QTc interval; BBB, bundle-branch-block; LAFB, left anterior branch block; LPFB, left posterior branch block; LBBB, left bundle branch block; RBBB, right bundle branch block; LVEF, left ventricular ejection fraction; FS, fraction shortening; E/A, mitral valve peak velocity early diastolic filling (E wave) to peak velocity of late diastolic filling (A wave) ratio; Dt, E deceleration time; LVEDD, left ventricular end-diastolic diameter; IVST, interventricular septum thickness; LVPWT, left ventricular posterior wall thickness; LA, left atrium diameter; RA (up and down), right atrium up and down diameter; RA (right and left), right atrium right and left diameter; PA, pulmonary artery internal dimension; Vpa, Pulmonary peak flow rate; Vao, Peak aortic velocity; LAD, left anterior descending; LCX, left circumflex artery; RCA, right coronary artery; LM, left main coronary artery Criterion Gini Random state. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
