A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model

Study Overview
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Brief Summary
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Early diagnosis of NSTEMI and UA patients is mainly through the construction of machine learning model.

Detailed Description
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The patients with NSTEMI and UA were included. After manual labeling, the admiss- ion record characteristics of patients were selected. 75% of the data is used to build the model, and 25% of the data is used to verify the validity of the model. Five classification models of one-dimensional convolution (CNN), naive Bayesian (NB), support vector machine (SVM), random forest (RF) and ensemble learning were constructed to identify and diagnose NSTEMI and UA patients. Multi-fold cross-validation and ROC-AUC curve are used to measure the advantages and disadvantages of the models.

Official Title
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A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model

Conditions
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NSTEMI - Non-ST Segment Elevation MI, Unstable Angina

Intervention / Treatment
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* Diagnostic Test: The model of machine learning


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients were included and excluded strictly according to the diagnostic criteria of Chinese guidelines for diagnosis and treatment of Non-STsegment elevation acute coronary syndrome (2016). The patients were admitted to the hospital with chest pain as the main complaint, and were admitted to the first affiliated Hospital of Xinjiang Medical University and the first affiliated Hospital of Medical College of Shihezi Univ- ersity. the patients were diagnosed as NSTEMI and UA by coronary angiography (age range from 30 to 75 years old). Exclusion Criteria: - 1. Patients with STEMI, aortic dissecting aneurysm, pneumothorax and other non-cardiogenic chest pain. 2.Severe hepatorenal failure, primary tumor without surgical treatment, non-severe infection complicated with shock and pregnant women. 3.Previous severe valvular disease, viral myocarditis, pericardial effusion, cardiac pacemaker implantation, cardiogenic shock with serious complications, hypertensive heart disease, various cardiomyopathy, congenital heart disease, etc. 4.Patients with heart disease, AECOPD, lung tumor and hyperthyroidism were diagnosed in the past.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| CNN model<br>Electronic health information of NSTEMI and UA patients in two chest pain centers from 2017 to 2019 was collected，After manual labeling, the characteristics of patient admission records were selected, and through the construction of one-dimensional convolution (CNN) model. Taking the multi-fold cross-validation and ROC-AUC curve as the measurement index, 75% of the data are modeled and 25% of the data are used to verify the effect of the model. | Diagnostic Test: The model of machine learning<br>* Early diagnosis of NTEMI patients by machine learning model<br>|
| XG boost<br>Through the construction of XG boost model,taking the multi-fold cross-validation and ROC-AUC curve as the measurement index, 75% of the data are modeled and 25% of the data are used to verify the effect of the model. | Diagnostic Test: The model of machine learning<br>* Early diagnosis of NTEMI patients by machine learning model<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accurate diagnosis of NSTEMI from patients with acute chest pain | NSTEMI patients are accurately diagnosed from patients with acute chest pain through a trained machine learning algorithm. Our model uses multi-fold cross-validation and ROC-AUC curve as the measurement index, 75% of the data are modeled, and 25% of the data verify the effect of the model. For this reason, we will calculate the accuracy, specificity and likelihood ratio when the sensitivity cutoff value is 0.9. | Within 1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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NSTEMI, UA, Machine Learning