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

Unstable Angina Clinical Trial

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04682756
• Other study ID #: XMa
• Status: Active, not recruiting
• Start date: December 20, 2020
• Est. completion date: June 1, 2022

Study information

• Verified date: December 2020
• Source: First Affiliated Hospital of Xinjiang Medical University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 2500
• Est. completion date: June 1, 2022
• Est. primary completion date: December 20, 2021
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

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.

Related Conditions & MeSH terms

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

Intervention

Diagnostic Test:
• The model of machine learning
Early diagnosis of NTEMI patients by machine learning model

Locations

Country	Name	City	State
China	The first affiliated Hospital of Xinjiang Medical University	Ürümqi	Xinjiang

Sponsors (2)

Lead Sponsor	Collaborator
First Affiliated Hospital of Xinjiang Medical University	Shihezi University

Country where clinical trial is conducted

China, 

References & Publications (15)

Allen B, Molokie R, Royston TJ. Early Detection of Acute Chest Syndrome Through Electronic Recording and Analysis of Auscultatory Percussion. IEEE J Transl Eng Health Med. 2020 Sep 30;8:4900108. doi: 10.1109/JTEHM.2020.3027802. eCollection 2020. — View Citation

Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, Gomes AS, Folsom AR, Shea S, Guallar E, Bluemke DA, Lima JAC. Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis. Circ Res. 2017 Oct 13;121(9):1092-1101. doi: 10.1161/CIRCRESAHA.117.311312. Epub 2017 Aug 9. — View Citation

Bernatz S, Ackermann J, Mandel P, Kaltenbach B, Zhdanovich Y, Harter PN, Döring C, Hammerstingl R, Bodelle B, Smith K, Bucher A, Albrecht M, Rosbach N, Basten L, Yel I, Wenzel M, Bankov K, Koch I, Chun FK, Köllermann J, Wild PJ, Vogl TJ. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features. Eur Radiol. 2020 Dec;30(12):6757-6769. doi: 10.1007/s00330-020-07064-5. Epub 2020 Jul 16. — View Citation

Chowdhury MEH, Alzoubi K, Khandakar A, Khallifa R, Abouhasera R, Koubaa S, Ahmed R, Hasan MA. Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents. Sensors (Basel). 2019 Jun 20;19(12). pii: E2780. doi: 10.3390/s19122780. — View Citation

Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol. 2019 Aug;16(8):601-607. doi: 10.11909/j.issn.1671-5411.2019.08.002. Review. — View Citation

Eberhard M, Nadarevic T, Cousin A, von Spiczak J, Hinzpeter R, Euler A, Morsbach F, Manka R, Keller DI, Alkadhi H. Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience. Cardiovasc Diagn Ther. 2020 Aug;10(4):820-830. doi: 10.21037/cdt-20-381. — View Citation

Groepenhoff F, Eikendal ALM, Bots SH, van Ommen AM, Overmars LM, Kapteijn D, Pasterkamp G, Reiber JHC, Hautemann D, Menken R, Wittekoek ME, Hofstra L, Onland-Moret NC, Haitjema S, Hoefer I, Leiner T, den Ruijter HM. Cardiovascular imaging of women and men visiting the outpatient clinic with chest pain or discomfort: design and rationale of the ARGUS Study. BMJ Open. 2020 Dec 15;10(12):e040712. doi: 10.1136/bmjopen-2020-040712. — View Citation

Kwon JM, Jeon KH, Kim HM, Kim MJ, Lim S, Kim KH, Song PS, Park J, Choi RK, Oh BH. Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction. PLoS One. 2019 Oct 31;14(10):e0224502. doi: 10.1371/journal.pone.0224502. eCollection 2019. — View Citation

Lee HC, Park JS, Choe JC, Ahn JH, Lee HW, Oh JH, Choi JH, Cha KS, Hong TJ, Jeong MH; Korea Acute Myocardial Infarction Registry (KAMIR) and Korea Working Group on Myocardial Infarction (KorMI) Investigators. Prediction of 1-Year Mortality from Acute Myocardial Infarction Using Machine Learning. Am J Cardiol. 2020 Oct 15;133:23-31. doi: 10.1016/j.amjcard.2020.07.048. Epub 2020 Jul 26. — View Citation

Ma Q, Ma Y, Yu T, Sun Z, Hou Y. Radiomics of Non-Contrast-Enhanced T1 Mapping: Diagnostic and Predictive Performance for Myocardial Injury in Acute ST-Segment-Elevation Myocardial Infarction. Korean J Radiol. 2020 Nov 30. doi: 10.3348/kjr.2019.0969. [Epub ahead of print] — View Citation

Md Idris N, Chiam YK, Varathan KD, Wan Ahmad WA, Chee KH, Liew YM. Feature selection and risk prediction for patients with coronary artery disease using data mining. Med Biol Eng Comput. 2020 Dec;58(12):3123-3140. doi: 10.1007/s11517-020-02268-9. Epub 2020 Nov 6. — View Citation

Patel BB, Sperotto F, Molina M, Kimura S, Delgado MI, Santillana M, Kheir JN. Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model. Pediatr Crit Care Med. 2020 Dec 16;Publish Ahead of Print. doi: 10.1097/PCC.0000000000002626. [Epub ahead of print] — View Citation

Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017 Apr 4;12(4):e0174944. doi: 10.1371/journal.pone.0174944. eCollection 2017. — View Citation

Wu CC, Hsu WD, Islam MM, Poly TN, Yang HC, Nguyen PA, Wang YC, Li YJ. An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain. Comput Methods Programs Biomed. 2019 May;173:109-117. doi: 10.1016/j.cmpb.2019.01.013. Epub 2019 Jan 31. — View Citation

Zheng Y, Li T. Letter to the Editor concerning the article "Machine learning for prediction of 30-day mortality after ST elevation myocardial infarction". Int J Cardiol. 2018 Sep 1;266:41. doi: 10.1016/j.ijcard.2017.11.061. — View Citation

* Note: There are 15 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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