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

NCT ID: NCT04682756

Last Updated: 2020-12-31

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

2500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-12-20

Study Completion Date

2022-06-01

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.

Conditions

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NSTEMI - Non-ST Segment Elevation MI Unstable Angina

Keywords

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NSTEMI UA Machine Learning

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

OTHER

Study Groups

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CNN model

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.

The model of machine learning

Intervention Type DIAGNOSTIC_TEST

Early diagnosis of NTEMI patients by machine learning model

XG boost

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.

The model of machine learning

Intervention Type DIAGNOSTIC_TEST

Early diagnosis of NTEMI patients by machine learning model

Interventions

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The model of machine learning

Early diagnosis of NTEMI patients by machine learning model

Intervention Type DIAGNOSTIC_TEST

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.
Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Shihezi University

OTHER

Sponsor Role collaborator

First Affiliated Hospital of Xinjiang Medical University

OTHER

Sponsor Role lead

Responsible Party

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Xiang Ma

Department of Cardiology

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Aikeliyaer Ainiwaer, M.D

Role: PRINCIPAL_INVESTIGATOR

First Affiliated Hospital of Xinjiang Medical University

Quan Qi, Ph.D

Role: STUDY_DIRECTOR

College of Information and Technology, Shihezi University

Yi Ying Du, M.D

Role: PRINCIPAL_INVESTIGATOR

First Affiliated Hospital of Xinjiang Medical University

Locations

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The first affiliated Hospital of Xinjiang Medical University

Ürümqi, Xinjiang, China

Site Status

Countries

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China

References

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Other Identifiers

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XMa

Identifier Type: -

Identifier Source: org_study_id