Atrial Fibrillation Detecting Software Gung Atrial Fibrillation Detecting Software

Atrial Fibrillation Clinical Trial

Official title:

A Study to Evaluate Accuracy and Validity of the Chang Gung Atrial Fibrillation Detecting Software

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05872516
• Other study ID #: 202200717A3
• Status: Completed
• Phase: N/A
• Start date: July 11, 2022
• Est. completion date: April 10, 2023

Study information

• Verified date: April 2023
• Source: Chang Gung Memorial Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Chang Gung Atrial Fibrillation Detection Software is an artificial intelligence electrocardiogram signal analysis software that detects whether a patient has atrial fibrillation by static 12-lead ECG signals. This study is a non-inferiority test based on the control group. The main purpose is to verify whether Chang Gung atrial fibrillation detection software can correctly identify atrial fibrillation in patients with atrial fibrillation, and can be used to provide a reference for doctors to detect atrial fibrillation.

Description:

This study is a retrospective study, and the data is from the six hospitals of Chang Gung Medical Research Database (CGRD). We collected de-identified static 12-lead electrocardiogram (ECG) data from the database during the period of January 1, 2006, to December 31, 2019.

We created a training set and a testing set of ECG data from the CGRD. Then, we stratified and sampled ECG signals from the testing set according to the actual proportion to obtain the experimental sample.

The computer first preliminarily screened and selected ECG data that met the inclusion and exclusion criteria, and then numbered them sequentially. A cardiologist confirmed that the sampled ECG data did not include exclusion criteria.

The ECG data were converted into images and interpreted for the presence or absence of atrial fibrillation by three cardiologists. Their results were used as the gold standard (reference) for this study.

After determining the experimental standards, the ECG signals were inputted into the Chang Gung Atrial Fibrillation Detection software for analysis and interpretation of each ECG data.

After the software interpretation was completed, the results were compared with the interpretations of the physicians, and the primary and secondary evaluation indicators were analyzed accordingly.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 788
• Est. completion date: April 10, 2023
• Est. primary completion date: February 8, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 20 Years to 100 Years

Eligibility

Inclusion Criteria:

• 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.

Exclusion Criteria:

• Cases used in the model development process.

• Lacks any electrode.

• Contain any electrode lacks a segment.

• Misplaced leads

Related Conditions & MeSH terms

• Atrial Fibrillation

Intervention

Device:
• Chang Gung Atrial Fibrillation Detecting Software
This software is expected to be used in clinical testing to interpret the static 12-lead ECG of adults who are over 20 years old and suspected of having atrial fibrillation, detect whether there is a signal of atrial fibrillation, and output the results for clinicians Near-instant auxiliary diagnostic use.

Locations

Country	Name	City	State
Taiwan	Chang Gung memorial hospital	Taoyuan City

Sponsors (1)

Lead Sponsor	Collaborator
Chang Gung Memorial Hospital

Country where clinical trial is conducted

Taiwan, 

References & Publications (4)

Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomstrom-Lundqvist C, Boriani G, Castella M, Dan GA, Dilaveris PE, Fauchier L, Filippatos G, Kalman JM, La Meir M, Lane DA, Lebeau JP, Lettino M, Lip GYH, Pinto FJ, Thomas GN, Valgimigli M, Van Gelder IC, Van Putte BP, Watkins CL; ESC Scientific Document Group. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur Heart J. 2021 Feb 1;42(5):373-498. doi: 10.1093/eurheartj/ehaa612. No abstract available. Erratum In: Eur Heart J. 2021 Feb 1;42(5):507. Eur Heart J. 2021 Feb 1;42(5):546-547. Eur Heart J. 2021 Oct 21;42(40):4194. — View Citation

Mant J, Fitzmaurice DA, Hobbs FD, Jowett S, Murray ET, Holder R, Davies M, Lip GY. Accuracy of diagnosing atrial fibrillation on electrocardiogram by primary care practitioners and interpretative diagnostic software: analysis of data from screening for atrial fibrillation in the elderly (SAFE) trial. BMJ. 2007 Aug 25;335(7616):380. doi: 10.1136/bmj.39227.551713.AE. Epub 2007 Jun 29. — View Citation

US Preventive Services Task Force; Curry SJ, Krist AH, Owens DK, Barry MJ, Caughey AB, Davidson KW, Doubeni CA, Epling JW Jr, Kemper AR, Kubik M, Landefeld CS, Mangione CM, Silverstein M, Simon MA, Tseng CW, Wong JB. Screening for Atrial Fibrillation With Electrocardiography: US Preventive Services Task Force Recommendation Statement. JAMA. 2018 Aug 7;320(5):478-484. doi: 10.1001/jama.2018.10321. — View Citation

Wong KC, Klimis H, Lowres N, von Huben A, Marschner S, Chow CK. Diagnostic accuracy of handheld electrocardiogram devices in detecting atrial fibrillation in adults in community versus hospital settings: a systematic review and meta-analysis. Heart. 2020 Aug;106(16):1211-1217. doi: 10.1136/heartjnl-2020-316611. Epub 2020 May 11. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Sensitivity	The rate of test results that correctly indicate the presence.	baseline
Secondary	Specificity	The rate of test results that correctly indicate the absence.	baseline
Secondary	Accuracy	The rate of all test results that correctly indicate.	baseline
Secondary	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.	baseline
Secondary	Positive predictive value	The proportions of positive results in statistics and diagnostic tests that are true positive results	baseline
Secondary	Negative predictive value	The proportions of negative results in statistics and diagnostic tests that are true negative results	baseline
Secondary	False positive rate	The rate of test result which wrongly indicates that a particular condition or attribute is present	baseline
Secondary	False negative rate	The rate of test result which wrongly indicates that a particular condition or attribute is absent	baseline