Long QT SyndromeNCT05903313
A Study to Evaluate Accuracy and Validity of the Chang Gung ECG Abnormality Detection Software
An observational study of Long QT Syndrome and Sinus Bradycardia, run by Chang Gung Memorial Hospital at 1 site in Taiwan.
Trial summary
"Chang Gung ECG Abnormality Detection Software" is a is an artificial intelligence medical signal analysis software that detect whether patients have abnormal ECG signals of 14 diseases by static 12-lead ECG. Read more about this study
- Who is it for?
- Ages 20 years and older
- Conditions the study is for
- Long QT Syndrome, Sinus Bradycardia, Sinus Tachycardia
- What is being tested?
- Chang Gung ECG Abnormality Detection Software
- How many people
- 4,306 participants
- Where
- 1 siteTaiwan
- Study dates
- 6 Oct 2023to 28 Feb 2024
- 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 20 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This is a retrospective study, and the data comes from the Chang Gung Medical Research Database(CGRD) which was an database form 6 hospitals of Chang Gung Memorial hospital. According to inclusion and exclusion criteria, de-identified static 12-lead ECG data from the database during 2006.01.01~2019.12.31 was collected, and the length of the ECG was 10 seconds.
Who may be able to join
- Equal or greater than twenty years old.
- Static 12-lead electrocardiogram of General Electric MUSE XML format file.
- The data comes from the static 12-lead electrocardiogram device of General Electric (model MAC5500).
- The electrocardiogram signal is 500 Hz.
- The Alternating current (AC) filter of the electrocardiogram signal is 60 Hz.
- The resource of original diagnosis was a cardiologist.
Who cannot join
- Cases used in the model development process.
- Lacks any electrode.
- Contain any electrode lacks a segment.
Trial locations and contacts
1 site in 1 country.
About this study
"Chang Gung ECG Abnormality Detection Software" is a is an artificial intelligence medical signal analysis software that detect whether patients have abnormal ECG signals of 14 diseases by static 12-lead ECG. The 14 diseases were
- Long QT syndrome
- Sinus bradycardia
- Sinus Tachycardia
- Premature atrial complexes
- Premature ventricular complexes
- Atrial Flutter, Right bundle branch block
- Left bundle branch block
- Left Ventricular hypertrophy
- Anterior wall Myocardial Infarction
- Septal wall Myocardial Infarction
- Lateral wall Myocardial Infarction
- Inferior wall Myocardial Infarction
- Posterior wall Myocardial Infarction
The main purpose of this study is to verify whether "Chang Gung ECG Abnormality Detection Software" can correctly identify abnormal ECG signals among patients of 14 diseases. The interpretation standard is the consensus of 3 cardiologists. The results of the software analysis will be used to evaluate the performance of the primary and secondary evaluation indicators.
Read the detailed description
Detailed procedure:
- Sample source: This is a retrospective study, and the data comes from the Chang Gung Medical Research Database(CGRD) which was an database form 6 hospitals of Chang Gung Memorial hospital. We collected de-identified static 12-lead ECG data from the database during 2006.01.01~2019.12.31, and the length of the ECG was 10 seconds.
- Sampling: In this experiment, the training dataset and the test dataset ECG were separated. Afterwards, the ECG signals are stratified according to the distribution as the test sample, and all abnormal ECG signals of 14 diseases will be independently sampled from the ECG database of the test set.
- Confirmation criteria: The ECG data will be preliminarily screened and selected by the inclusion and exclusion criteria and compiled serial numbers. Then, a cardiologist confirms that the sampling results of the ECG data do not include the exclusion criteria again.
- Physician interpretation: The ECG data will be converted into graphic files and submitted to 3 cardiologists for interpretation abnormal ECG signals of 14 related diseases. The results will be used as the standard of this study (Reference).
- Software interpretation: After confirming the test standard, input the ECG signal into Chang Gung ECG Abnormality Detection Software to analyze abnormal ECG signals of 14 diseases and interpret each ECG data.
- Statistical analysis:
After the software interpretation is completed, it will be compared with the results of the physician's interpretation and analyze the primary and secondary evaluation indicators.
- Conditions
- Long QT Syndrome, Sinus Bradycardia, Sinus Tachycardia, Premature Atrial Complexes, Premature Ventricular Complexes, Atrial Flutter
- Being tested
- Chang Gung ECG Abnormality Detection Software
- Medical subject headings
- Long QT Syndrome, Tachycardia, Sinus, Atrial Premature Complexes, Ventricular Premature Complexes, Atrial Flutter, Bundle-Branch Block, Hypertrophy, Left Ventricular, Myocardial Infarction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Chang Gung ECG Abnormality Detection SoftwareDrug
Also called CGMH-EAD-001
This device is expected to be used for the static 12-lead ECG to detect whether there are abnormal ECG signals related to diseases and outputs the results.
| Study group | Role | What they receive |
|---|---|---|
| Software diagnosis | Software diagnosis with gold standard of 3 cardiologists' interpretation. Receives: Drug: Chang Gung ECG Abnormality Detection Software |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity and Specificity
The rate of test results that correctly indicate the presence and absence.
Measured: baseline
Secondary outcomes (1)
Area Under the receiver operating characteristic Curve
A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jun 2023
- Study start6 Oct 2023
- Primary completion31 Jan 2024 (estimated)
- Study completion28 Feb 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Long QT SyndromeStudies 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
- Retrospective
- Enrolment
- 4,306 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chang Gung Memorial Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Chang-Fu Kuo, MD/Ph.D, Study chair, Associate Professor and Director Division of Rheumatology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05903313
- Sponsor's protocol ID
- 202300710A5
- Status last verified
- May 2023
- First submitted
- 6 Jun 2023
- Last updated
- 11 Jan 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Malik J, Devecioglu OC, Kiranyaz S, Ince T, Gabbouj M. Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks. IEEE Trans Biomed Eng. 2022 May;69(5):1788-1801. doi: 10.1109/TBME.2021.3135622. Epub 2022 Apr 21.
PubMed 34910628Acharya U.R., Fujita H., Lih O.S., Adam M., Tan J.H., Chua C.K. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neu-ral network Knowl.-Based Syst., 132 (sep.15) (2017), pp. 62-71
Bos JM, Attia ZI, Albert DE, Noseworthy PA, Friedman PA, Ackerman MJ. Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram. JAMA Cardiol. 2021 May 1;6(5):532-538. doi: 10.1001/jamacardio.2020.7422.
PubMed 33566059Ribeiro AH, Ribeiro MH, Paixao GMM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MPS, Andersson CR, Macfarlane PW, Meira W Jr, Schon TB, Ribeiro ALP. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020 Apr 9;11(1):1760. doi: 10.1038/s41467-020-15432-4.
PubMed 32273514U. Rajendra Acharya, Hamido Fujita, Oh Shu Lih, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Automated detection of arrhythmias using different intervals of tachycardia ECG seg-ments with convolutional neural network, Information Sciences, Volume 405, 2017, Pages 81-90, ISSN 0020-0255
Jeong DU, Lim KM. Convolutional neural network for classification of eight types of arrhythmia using 2D time-frequency feature map from standard 12-lead electrocardiogram. Sci Rep. 2021 Oct 14;11(1):20396. doi: 10.1038/s41598-021-99975-6.
PubMed 34650175Kwon JM, Jeon KH, Kim HM, Kim MJ, Lim SM, Kim KH, Song PS, Park J, Choi RK, Oh BH. Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography. Europace. 2020 Mar 1;22(3):412-419. doi: 10.1093/europace/euz324.
PubMed 31800031Makimoto H, Hockmann M, Lin T, Glockner D, Gerguri S, Clasen L, Schmidt J, Assadi-Schmidt A, Bejinariu A, Muller P, Angendohr S, Babady M, Brinkmeyer C, Makimoto A, Kelm M. Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction. Sci Rep. 2020 May 21;10(1):8445. doi: 10.1038/s41598-020-65105-x.
PubMed 32439873
Study at a glance
From the registry record, last updated by the sponsor on 11 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study to Evaluate Accuracy and Validity of the Chang Gung ECG Abnormality Detection Software
- ClinicalTrials.gov ID
- NCT05903313
- Acronym / sponsor ID
- 202300710A5
- Conditions
- Long QT Syndrome, Sinus Bradycardia, Sinus Tachycardia, Premature Atrial Complexes, Premature Ventricular Complexes, Atrial Flutter
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 4,306 participants (estimated)
- Lead sponsor
- Chang Gung Memorial HospitalUniversity, hospital or other
- Study start
- 6 Oct 2023 (actual)
- Primary completion
- 31 Jan 2024 (estimated)
- Study completion
- 28 Feb 2024 (estimated)
- First posted
- 15 Jun 2023 (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.
"Chang Gung ECG Abnormality Detection Software" is a is an artificial intelligence medical signal analysis software that detect whether patients have abnormal ECG signals of 14 diseases by static 12-lead ECG. The 14 diseases were
- Long QT syndrome
- Sinus bradycardia
- Sinus Tachycardia
- Premature atrial complexes
- Premature ventricular complexes
- Atrial Flutter, Right bundle branch block
- Left bundle branch block
- Left Ventricular hypertrophy
- Anterior wall Myocardial Infarction
- Septal wall Myocardial Infarction
- Lateral wall Myocardial Infarction
- Inferior wall Myocardial Infarction
- Posterior wall Myocardial Infarction
The main purpose of this study is to verify whether "Chang Gung ECG Abnormality Detection Software" can correctly identify abnormal ECG signals among patients of 14 diseases. The interpretation standard is the consensus of 3 cardiologists. The results of the software analysis will be used to evaluate the performance of the primary and secondary evaluation indicators.
Detailed description
Detailed procedure:
- Sample source: This is a retrospective study, and the data comes from the Chang Gung Medical Research Database(CGRD) which was an database form 6 hospitals of Chang Gung Memorial hospital. We collected de-identified static 12-lead ECG data from the database during 2006.01.01~2019.12.31, and the length of the ECG was 10 seconds.
- Sampling: In this experiment, the training dataset and the test dataset ECG were separated. Afterwards, the ECG signals are stratified according to the distribution as the test sample, and all abnormal ECG signals of 14 diseases will be independently sampled from the ECG database of the test set.
- Confirmation criteria: The ECG data will be preliminarily screened and selected by the inclusion and exclusion criteria and compiled serial numbers. Then, a cardiologist confirms that the sampling results of the ECG data do not include the exclusion criteria again.
- Physician interpretation: The ECG data will be converted into graphic files and submitted to 3 cardiologists for interpretation abnormal ECG signals of 14 related diseases. The results will be used as the standard of this study (Reference).
- Software interpretation: After confirming the test standard, input the ECG signal into Chang Gung ECG Abnormality Detection Software to analyze abnormal ECG signals of 14 diseases and interpret each ECG data.
- Statistical analysis:
After the software interpretation is completed, it will be compared with the results of the physician's interpretation and analyze the primary and secondary evaluation indicators.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Software diagnosis | – | Drug: Chang Gung ECG Abnormality Detection SoftwareSoftware diagnosis with gold standard of 3 cardiologists' interpretation. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This is a retrospective study, and the data comes from the Chang Gung Medical Research Database(CGRD) which was an database form 6 hospitals of Chang Gung Memorial hospital. According to inclusion and exclusion criteria, de-identified static 12-lead ECG data from the database during 2006.01.01~2019.12.31 was collected, and the length of the ECG was 10 seconds.
Key inclusion criteria (6 listed)
- Equal or greater than twenty years old.
- Static 12-lead electrocardiogram of General Electric MUSE XML format file.
- The data comes from the static 12-lead electrocardiogram device of General Electric (model MAC5500).
- The electrocardiogram signal is 500 Hz.
- The Alternating current (AC) filter of the electrocardiogram signal is 60 Hz.
Key exclusion criteria (3 listed)
- Cases used in the model development process.
- Lacks any electrode.
- Contain any electrode lacks a segment.
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 |
|---|---|---|
| Chang Gung ECG Abnormality Detection SoftwareAlso: CGMH-EAD-001This device is expected to be used for the static 12-lead ECG to detect whether there are abnormal ECG signals related to diseases and outputs the results. | Drug | Software diagnosis |
Primary outcome
Sensitivity and Specificity
Time frame: baseline
The rate of test results that correctly indicate the presence and absence.
1 secondary outcome
Area Under the receiver operating characteristic Curve
Time frame: Baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jan 2024.
- Background: PubMed 34910628Malik J, Devecioglu OC, Kiranyaz S, Ince T, Gabbouj M. Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks. IEEE Trans Biomed Eng. 2022 May;69(5):1788-1801. doi: 10.1109/TBME.2021.3135622. Epub 2022 Apr 21.
- BackgroundAcharya U.R., Fujita H., Lih O.S., Adam M., Tan J.H., Chua C.K. Automated detection of coronary artery disease using different durations of ECG segments with convolutional neu-ral network Knowl.-Based Syst., 132 (sep.15) (2017), pp. 62-71
- Background: PubMed 33566059Bos JM, Attia ZI, Albert DE, Noseworthy PA, Friedman PA, Ackerman MJ. Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram. JAMA Cardiol. 2021 May 1;6(5):532-538. doi: 10.1001/jamacardio.2020.7422.
- Background: PubMed 32273514Ribeiro AH, Ribeiro MH, Paixao GMM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MPS, Andersson CR, Macfarlane PW, Meira W Jr, Schon TB, Ribeiro ALP. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020 Apr 9;11(1):1760. doi: 10.1038/s41467-020-15432-4.
- BackgroundU. Rajendra Acharya, Hamido Fujita, Oh Shu Lih, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Automated detection of arrhythmias using different intervals of tachycardia ECG seg-ments with convolutional neural network, Information Sciences, Volume 405, 2017, Pages 81-90, ISSN 0020-0255
- Background: PubMed 34650175Jeong DU, Lim KM. Convolutional neural network for classification of eight types of arrhythmia using 2D time-frequency feature map from standard 12-lead electrocardiogram. Sci Rep. 2021 Oct 14;11(1):20396. doi: 10.1038/s41598-021-99975-6.
- Background: PubMed 31800031Kwon JM, Jeon KH, Kim HM, Kim MJ, Lim SM, Kim KH, Song PS, Park J, Choi RK, Oh BH. Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography. Europace. 2020 Mar 1;22(3):412-419. doi: 10.1093/europace/euz324.
- Background: PubMed 32439873Makimoto H, Hockmann M, Lin T, Glockner D, Gerguri S, Clasen L, Schmidt J, Assadi-Schmidt A, Bejinariu A, Muller P, Angendohr S, Babady M, Brinkmeyer C, Makimoto A, Kelm M. Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction. Sci Rep. 2020 May 21;10(1):8445. doi: 10.1038/s41598-020-65105-x.
- Individual participant dataSharing planned: No
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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.
Chang-Fu Kuo, MD/Ph.D study chair
Associate Professor and Director Division of Rheumatology
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 Long QT Syndrome and Sinus Bradycardia, run by Chang Gung Memorial Hospital at 1 site in Taiwan.
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 6 inclusion and 3 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 11 Jan 2024. 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 Taiwan. 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?
Sensitivity and Specificity (time frame: baseline). 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 NCT05903313, which also lists 8 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 11 Jan 2024.