NSTEMI - Non-ST Segment Elevation MINCT04682756
A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model
An observational study of NSTEMI - Non-ST Segment Elevation MI and Unstable Angina, run by First Affiliated Hospital of Xinjiang Medical University at 1 site in China.
Trial summary
Early diagnosis of NSTEMI and UA patients is mainly through the construction of machine learning model. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- NSTEMI - Non-ST Segment Elevation MI, Unstable Angina
- What is being tested?
- The model of machine learning
- How many people
- 2,500 participants
- Where
- 1 siteChina
- Study dates
- 20 Dec 2020to 1 Jun 2022
- How long will I take part?
- 3 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- Adult, Older adult
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.
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
The first affiliated Hospital of Xinjiang Medical University
Ürümqi, Xinjiang, 830000
About this study
Early diagnosis of NSTEMI and UA patients is mainly through the construction of machine learning model.
Read the 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.
- Being tested
- The model of machine learning
- Keywords
- NSTEMI, UA, Machine Learning
- Medical subject headings
- Non-ST Elevated Myocardial Infarction, Angina, Unstable
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The model of machine learningDiagnostic test
Early diagnosis of NTEMI patients by machine learning model
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: The model of machine learning | |
| 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. Receives: Diagnostic Test: The model of machine learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Within 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Dec 2020
- Study start20 Dec 2020
- Primary completion20 Dec 2021 (estimated)
- Study completion1 Jun 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (15)Papers the sponsor has linked to this study.
- Other trials for NSTEMI - Non-ST Segment Elevation MIStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Other
- Enrolment
- 2,500 participants (actual)
- Target duration
- 3 Months
Sponsor and investigators
- Lead sponsor
- First Affiliated Hospital of Xinjiang Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Shihezi University
- Responsible party
- Principal investigator, Xiang Ma, First Affiliated Hospital of Xinjiang Medical University
- Investigators
- Aikeliyaer Ainiwaer, M.D, Principal investigator, First Affiliated Hospital of Xinjiang Medical UniversityQuan Qi, Ph.D, Study director, College of Information and Technology, Shihezi UniversityYi Ying Du, M.D, Principal investigator, First Affiliated Hospital of Xinjiang Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04682756
- Sponsor's protocol ID
- XMa
- Status last verified
- Dec 2020
- First submitted
- 19 Dec 2020
- Last updated
- 31 Dec 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Machine learning model to identify patients with UA and NSTMI
Publications
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.
PubMed 31555327Ambale-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.
PubMed 28794054Weng 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.
PubMed 28376093Patel 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. 2021 Apr 1;22(4):392-400. doi: 10.1097/PCC.0000000000002626.
PubMed 33332868Groepenhoff 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.
PubMed 33323438Kwon 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.
PubMed 31671144Chowdhury 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):2780. doi: 10.3390/s19122780.
PubMed 31226858Wu 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.
PubMed 31046985Bernatz S, Ackermann J, Mandel P, Kaltenbach B, Zhdanovich Y, Harter PN, Doring 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, Kollermann 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.
PubMed 32676784Md 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.
PubMed 33155096Allen 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.
PubMed 33094035Eberhard 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.
PubMed 32968637Ma 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. 2021 Apr;22(4):535-546. doi: 10.3348/kjr.2019.0969. Epub 2020 Nov 30.
PubMed 33289360Lee 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.
PubMed 32811651Zheng 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. No abstract available.
PubMed 29887469
Study at a glance
From the registry record, last updated by the sponsor on 31 Dec 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multicenter Study on Early Diagnosis of NSTE-ACS Patients Based on Machine Learning Model
- ClinicalTrials.gov ID
- NCT04682756
- Acronym / sponsor ID
- XMa
- Keywords
- NSTEMI, UA, Machine Learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,500 participants (actual)
- Lead sponsor
- First Affiliated Hospital of Xinjiang Medical UniversityUniversity, hospital or other
- Collaborators
- Shihezi University
- Study start
- 20 Dec 2020 (actual)
- Primary completion
- 20 Dec 2021 (estimated)
- Study completion
- 1 Jun 2022 (estimated)
- First posted
- 24 Dec 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Other
- Target follow-up
- 3 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| CNN model | – | Diagnostic Test: The model of machine learningElectronic 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 | – | Diagnostic Test: The model of machine learningThrough 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. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- 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).
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| The model of machine learningEarly diagnosis of NTEMI patients by machine learning model | Diagnostic test | CNN model, XG boost |
Primary outcome
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Dec 2020.
- Background: PubMed 31555327Cuocolo 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.
- Background: PubMed 28794054Ambale-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.
- Background: PubMed 28376093Weng 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.
- Background: PubMed 33332868Patel 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. 2021 Apr 1;22(4):392-400. doi: 10.1097/PCC.0000000000002626.
- Background: PubMed 33323438Groepenhoff 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.
- Background: PubMed 31671144Kwon 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.
- Background: PubMed 31226858Chowdhury 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):2780. doi: 10.3390/s19122780.
- Background: PubMed 31046985Wu 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.
- Background: PubMed 32676784Bernatz S, Ackermann J, Mandel P, Kaltenbach B, Zhdanovich Y, Harter PN, Doring 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, Kollermann 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.
- Background: PubMed 33155096Md 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.
- Background: PubMed 33094035Allen 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.
- Background: PubMed 32968637Eberhard 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.
- Individual participant dataSharing planned: Undecided. Machine learning model to identify patients with UA and NSTMI
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Aikeliyaer Ainiwaer, M.D principal investigator
First Affiliated Hospital of Xinjiang Medical University
Quan Qi, Ph.D study director
College of Information and Technology, Shihezi University
Yi Ying Du, M.D principal investigator
First Affiliated Hospital of Xinjiang Medical University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of NSTEMI - Non-ST Segment Elevation MI and Unstable Angina, run by First Affiliated Hospital of Xinjiang Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 31 Dec 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accurate diagnosis of NSTEMI from patients with acute chest pain (time frame: Within 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04682756, which also lists 15 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Dec 2020.