Clinical trial NCT05872516: Atrial Fibrillation Detecting Software Gung Atrial Fibrillation Detecting Software
Official title: A Study to Evaluate Accuracy and Validity of the Chang Gung Atrial Fibrillation Detecting Software
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Atrial Fibrillation
Keywords: artificial intelligence
Lead sponsor: Chang Gung Memorial Hospital
Sponsor class: OTHER
First posted: 2023-05-24
Enrollment: 788
Sex: ALL
Minimum age (years): 20
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Sensitivity
timeFrame: baseline
Secondary outcomes:
measure: Specificity
timeFrame: baseline
measure: Accuracy
timeFrame: baseline
measure: Area Under the receiver operating characteristic Curve
timeFrame: baseline
measure: Positive predictive value
timeFrame: baseline
measure: Negative predictive value
timeFrame: baseline
measure: False positive rate
timeFrame: baseline
measure: False negative rate
timeFrame: baseline
Other outcomes:
Locations:
city: Taoyuan City
country: Taiwan
facility: Chang Gung memorial hospital
Study officials:
affiliation: Associate Professor and Director Division of Rheumatology
name: Chang-Fu Kuo, MD/Ph.D
role: STUDY_CHAIR
References:
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.
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.
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.
citation: 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.