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

December 29, 2020 updated by: Xiang Ma, First Affiliated Hospital of Xinjiang Medical University
Early diagnosis of NSTEMI and UA patients is mainly through the construction of machine learning model.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Observational

Enrollment (Actual)

2500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Xinjiang
      • Ürümqi, Xinjiang, China, 830000
        • The First Affiliated Hospital of Xinjiang Medical University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 75 years (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

Patients with NSTEMI and UA were included in the Chest Pain Center of the First Affiliated Hospital of Xinjiang Medical University and the First Affiliated Hospital of Medical College of Shihezi University from 2017 to 2019.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
Diagnostic test: The model of machine learning
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.
Diagnostic test: The model of machine learning
Early diagnosis of NTEMI patients by machine learning model

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accurate diagnosis of NSTEMI from patients with acute chest pain
Time Frame: Within 1 year
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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

First Affiliated Hospital of Xinjiang Medical University

Collaborators

Shihezi University

Investigators

  • Principal Investigator: Aikeliyaer Ainiwaer, M.D, First Affiliated Hospital of Xinjiang Medical University
  • Study Director: Quan Qi, Ph.D, College of Information and Technology, Shihezi University
  • Principal Investigator: Yi Ying Du, M.D, First Affiliated Hospital of Xinjiang Medical University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 20, 2020

Primary Completion (Anticipated)

December 20, 2021

Study Completion (Anticipated)

June 1, 2022

Study Registration Dates

First Submitted

December 19, 2020

First Submitted That Met QC Criteria

December 19, 2020

First Posted (Actual)

December 24, 2020

Study Record Updates

Last Update Posted (Actual)

December 31, 2020

Last Update Submitted That Met QC Criteria

December 29, 2020

Last Verified

December 1, 2020

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • XMa

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

IPD Plan Description

Machine learning model to identify patients with UA and NSTMI

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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