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

• Conditions: Unstable Angina; NSTEMI - Non-ST Segment Elevation MI
• Interventions: Diagnostic Test: The model of machine learning

• Registration Number: NCT04682756
• Lead Sponsor: First Affiliated Hospital of Xinjiang Medical University

Brief Summary

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

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 Timeline

• Status Verified: 2020-12
• Study First Submitted: 2020-12-19
• Study First Submitted QC: 2020-12-19
• Study Start: 2020-12-20
• Study First Posted: 2020-12-24
• Last Update Submitted: 2020-12-29
• Last Update Posted: 2020-12-31
• Primary Completion: 2021-12-20
• Study Completion: 2022-06-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 2500

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
CNN model	The model of machine learning	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.
XG boost	The model of machine learning	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.

Primary Outcome Measures

Name	Time	Method
Accurate diagnosis of NSTEMI from patients with acute chest pain	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.

Trial Locations

Locations (1)

The first affiliated Hospital of Xinjiang Medical University; 🇨🇳 Ürümqi, Xinjiang, China