(28 days)
There are no predicate devices mentioned in the text.
Not Found
Unknown
The document describes an "automated algorithm" for rhythm classification and provides performance metrics, which are common characteristics of AI/ML systems. However, it does not explicitly mention "AI," "ML," "DNN," or provide details about the algorithm's architecture or training process, making a definitive determination impossible from this summary alone.
No.
The document explicitly states that "The ECG data displayed by the ECG app is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional." and "The ECG waveform is meant to supplement rhythm classification... and not intended to replace traditional methods of diagnosis or treatment." This indicates it's a diagnostic aid, not a therapeutic device.
Yes
The device "determines the presence of atrial fibrillation (AFib) or sinus rhythm on a classifiable waveform," which is a diagnostic function.
Yes
The device is explicitly described as a "software-only mobile medical application" in both the Intended Use and Device Description sections. While it operates in conjunction with the Apple Watch hardware for data acquisition, the regulatory submission focuses solely on the software component.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: The ECG app works by recording electrical signals from the user's body (specifically, the heart's electrical activity) via electrodes on the Apple Watch. This is a direct measurement of a physiological process within the body, not an analysis of a sample taken from the body.
- Input Modality: The input modality is listed as "Electrocardiogram (ECG)," which is a measurement of electrical activity, not a sample.
Therefore, the ECG app falls under the category of a medical device that measures physiological signals directly from the body, rather than an in vitro diagnostic device that analyzes samples taken from the body.
N/A
Intended Use / Indications for Use
The ECG app is a software-only mobile medical application intended for use with the Apple Watch to create, record, store, transfer, and display a single channel electrocardiogram (ECG) similar to a Lead I ECG. The ECG app determines the presence of atrial fibrillation (AFib) or sinus rhythm on a classifiable waveform. The ECG app is not recommended for users with other known arrhythmias.
The ECG app is intended for over-the-counter (OTC) use. The ECG data displayed by the ECG app is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal sinus rhythm and not intended to replace traditional methods of diagnosis or treatment.
The ECG app is not intended for use by people under 22 years old.
Product codes
ODA
Device Description
The device (ECG App) comprises a pair of mobile medical apps — one on Apple Watch (the Watch App) and the other on the iPhone (iPhone App) - intended to record, store, transfer, and display a single lead ECG signal similar to a lead I. The ECG Watch App is intended to analyze this single lead data and detect the presence of atrial fibrillation (referred into this document as AFib or AF) and sinus rhythm in adults. It is also intended to acquire and analyze the single lead ECG recordings for display on the iPhone. The ECG iPhone App is included in the Health App, which is intended to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.
The ECG Watch App instructs the user to take an ECG measurement by holding their finger on the digital crown of the watch also contains electrodes on the back of the device which are in continuous contact with the user's wrist. The watch acquires the electrical potential between the electrodes and digital crown. The Watch App will display a visual representation of the ECG waveform to provide information regarding signal quality during the session. The waveform displayed on the watch during the session is not intended for clinical purposes. The session will last for 30 seconds. Upon completion of the recording, the ECG Watch App analyzes the acquired ECG data and produces a waveform that is similar to a Lead I ECG for the purposes of AF and sinus rhythm evaluation, calculates average heart rate, and classifies the rhythm of the waveform (collectively called "session result").
Mentions image processing
Not Found
Mentions AI, DNN, or ML
Not Found
Input Imaging Modality
Not Found
Anatomical Site
Not Found
Indicated Patient Age Range
The ECG app is not intended for use by people under 22 years old.
Intended User / Care Setting
over-the-counter (OTC) use. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional.
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
A clinical study was performed to establish a reasonable assurance of safety and effectiveness of the ECG App.
Methods: The pivotal study was a prospective, parallel-cohort, non-randomized, multi-center, reader study using an enriched population. The study enrolled equal subjects with and without a known diagnosis of atrial fibrillation into two separate cohorts (AF Cohort and SR Cohort). Key exclusion criteria included antiarrhythmic drug use, the presence of a pacemaker or implantable cardioverter-defibrillator, and a history of abnormal life-threatening rhythms. Subjects in the SR cohort must not have any known diagnosis of AF. To be enrolled in the AF Cohort, the subject must be in atrial fibrillation at the time of enrollment.
Upon enrollment, the participant was coached on the appropriate posture and grip for acquiring an ECG recording using a prototype Apple Watch. After a 5-minute resting period, simultaneous 30-second ECG App and 12-lead ECG recordings were acquired. The ECG App rhythm strip was automatically classified by the algorithm as either "AF", "SR", "Unreadable", or "Unclassified." Unclassified rhythms include any rhythms with rates > 120 or 100 bpm or more than 4 ectopic beats.
Three blinded independent board-certified cardiologists reviewed all ECG recordings and assigned a classification of SR, AF, unreadable, or others. Others classification was defined to include normal sinus with premature ventricular contraction (if ≥4 beats in the strip), normal sinus with PACS, 2nd degree block, AF with a rate > 120 bpm, and supraventricular tachycardia. If the readers disagreed on the diagnosis, the final interpretation was determined by the simple majority rule.
In a subset of randomized selected subjects (Waveform Assessment Analysis Set), 3 independent certified cardiographic technicians synced and overlaid each ECG App rhythm strip with the Lead I strip of the corresponding 12-lead ECG. The first 6 consecutive distinct readable PQRST complexes were identified and used to determine if the morphology of the complexes appeared to overlay to the unaided eye. For the first two QRS complexes, the evaluators also measured and compared the R wave amplitude between the ECG App strip and the reference strip.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
SUMMARY OF NONCLINICAL/BENCH STUDIES
EVALUATION OF INPUT SIGNAL QUALITY
Electromagnetic compatibility, electrical safety, and signal acquisition information was provided. Apple Watch claims conformance to EU and FCC compliance statements including EN 301 489-1 (V2.2.20), EN 301 489-3 (V2.1.1), EN 301 489-17 (V3.2.0), EN 301 489-52 (V1.1.0), and IEC 60601-1-2 (4th Edition). Electrical safety was assessed according to IEC 62368-1 (2014). Signal acquisition and platform (hardware) performance was assessed according to IEC 60601-2-47.
PERFORMANCE TESTING - BENCH
ECG Database Testing
Testing to databases in EC57 was conducted for rhythms containing AF or normal sinus rhythm (NSR). Records from the adjudicated AHA and MIT databases were used. Each record was split into 30 second segments. Assessed ORS detection, rhythm classification, and HR. The database annotations were used as ground truth.
Summary: Of the (b) (4) AF strips in database testing, (b) (4) were classified as AF (b) (4) of readable strips).
Human Factors
A Human Factors Validation Study was performed with a total of (b) (4) participants to demonstrate the usability of the user interface. The study enrolled three user groups:
- Group 1: Users diagnosed with AF (AF, n = 17)
- Group 2: Users age 22-64 (Under 65, No AF, n = 17)
- Group 3: Users age 65+ (Over 65. No AF, n = 16)
Each group included participants with and without smartphone experience as well as participants who use iPhone and Android. Testing identified critical tasks where the user does not understand the device output or limitations.
SUMMARY OF CLINICAL INFORMATION
A clinical study was performed to establish a reasonable assurance of safety and effectiveness of the ECG App.
Methods
The pivotal study was a prospective, parallel-cohort, non-randomized, multi-center, reader study using an enriched population.
The study enrolled 602 subjects at 5 investigational sites. After exclusions, 588 eligible participants were divided into AF Cohort (N=301) and SR Cohort (N=287).
Participants took 30-second ECG App recordings simultaneously with 12-lead ECGs.
Three blinded independent board-certified cardiologists reviewed all ECG recordings.
In a subset (Waveform Assessment Analysis Set), 3 independent certified cardiographic technicians synced and overlaid ECG App rhythm strips with the Lead I strip of the corresponding 12-lead ECG, assessing morphology and R-wave amplitude.
Study Endpoints
Primary Endpoint
Sensitivity and specificity of the ECG App algorithm in detecting AF compared with physician-adjudicated 12-lead ECG. Performance goals were 90% sensitivity and 92% specificity.
Secondary Endpoint
The ECG app produces a waveform that provides clinically equivalent information to the gold standard (Lead I ECG). This was assessed by:
- Qualitative assessment: Proportion of paired ECG strips appearing to overlay to the unaided eye > 0.80
- Quantitative assessment: Proportion of paired R-wave amplitude measurements within 2 mm of each other > 0.80
Results
The study enrolled a total of 602 subjects. 588 eligible participants (AF Cohort: N=301, SR Cohort: N=287). Median age 71 years (22-92).
ECG App Automated AF Detection
Among the recordings where the algorithm output a diagnosis (Classifiable Analysis Set), AF was correctly diagnosed with 98.3% sensitivity (97.5% LCB: 95.8%) and 99.6% specificity (97.5% LCB: 97.7%). The study met the primary endpoint.
Probability that a subject with AF would receive an AF diagnosis from the ECG App, considering unreadable and unclassified results, was 85.2%.
Pr (ECG App = SR Reference = SR): 90.5% (95% CI: 86.3%, 93.8%)
Pr (ECG App = AF Reference = AF): 85.2% (95% CI: 80.5%, 89.2%)
ECG App ECG Recording
Waveform Assessment: 125 (99.2%) subjects had an ECG App waveform considered clinically equivalent to the gold standard (97.5% LCB: 95.7%), meeting the secondary endpoint performance goal of 80% (p
§ 870.2345 Electrocardiograph software for over-the-counter use.
(a)
Identification. An electrocardiograph software device for over-the-counter use creates, analyzes, and displays electrocardiograph data and can provide information for identifying cardiac arrhythmias. This device is not intended to provide a diagnosis.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Clinical performance testing under anticipated conditions of use must demonstrate the following:
(i) The ability to obtain an electrocardiograph of sufficient quality for display and analysis; and
(ii) The performance characteristics of the detection algorithm as reported by sensitivity and either specificity or positive predictive value.
(2) Software verification, validation, and hazard analysis must be performed. Documentation must include a characterization of the technical specifications of the software, including the detection algorithm and its inputs and outputs.
(3) Non-clinical performance testing must validate detection algorithm performance using a previously adjudicated data set.
(4) Human factors and usability testing must demonstrate the following:
(i) The user can correctly use the device based solely on reading the device labeling; and
(ii) The user can correctly interpret the device output and understand when to seek medical care.
(5) Labeling must include:
(i) Hardware platform and operating system requirements;
(ii) Situations in which the device may not operate at an expected performance level;
(iii) A summary of the clinical performance testing conducted with the device;
(iv) A description of what the device measures and outputs to the user; and
(v) Guidance on interpretation of any results.
0
DE NOVO CLASSIFICATION REQUEST FOR ECG APP
REGULATORY INFORMATION
FDA identifies this generic type of device as:
Electrocardiograph software for over-the-counter use. An electrocardiograph software device for over-the-counter use creates, analyzes, and displays electrocardiograph data, and can provide information for identifying cardiac arrhythmias. This device is not intended to provide a diagnosis.
NEW REGULATION NUMBER: 21 CFR 870.2345
CLASSIFICATION: Class II
PRODUCT CODE: ODA
BACKGROUND
DEVICE NAME: ECG App
SUBMISSION NUMBER: DEN180044
DATE OF DE NOVO: August 14, 2018
CONTACT: Apple Inc. One Apple Park Way Cupertino, CA 95014
INDICATIONS FOR USE
The ECG app is a software-only mobile medical application intended for use with the Apple Watch to create, record, store, transfer, and display a single channel electrocardiogram (ECG) similar to a Lead I ECG. The ECG app determines the presence of atrial fibrillation (AFib) or sinus rhythm on a classifiable waveform. The ECG app is not recommended for users with other known arrhythmias.
The ECG app is intended for over-the-counter (OTC) use. The ECG data displayed by the ECG app is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal sinus rhythm and not intended to replace traditional methods of diagnosis or treatment.
The ECG app is not intended for use by people under 22 years old.
1
LIMITATIONS
The device has only been evaluated for the detection of AFib or normal sinus rhythm and is not intended to detect any other type of arrhythmia. It cannot detect heart attacks. If you ever experience chest pain, pressure, tightness, or what you think is a heart attack, call emergency services.
Apple Watch may be unable to collect data when Apple Watch is in close vicinity to strong electromagnetic fields (e.g. electromagnetic anti-theft systems, metal detectors).
DO NOT wear your Apple Watch during a medical procedure (e.g., magnetic resonance imaging, diathermy, lithotripsy, cautery and external defibrillation procedures).
DO NOT change your medication without talking to your doctor.
Not intended for use by individuals under age 22.
Not intended for use by individuals previously diagnosed with AFib.
Notifications made by this feature are potential findings, not a complete diagnosis of cardiac conditions. All notifications should be reviewed by a medical professional for clinical decision-making.
Apple does not guarantee that you are not experiencing an arrhythmia or other health conditions even in the absence of an irregular rhythm notification. You should notify your physician if you experience any changes to your health.
The clinical study did not quantitatively assess the quality of the ECG waveform produced by the ECG App. The ECG produced by the ECG App is not intended for clinical use or as the basis for diagnosis or treatment. The ECG waveform is only intended for informational use.
PLEASE REFER TO THE LABELING FOR A COMPLETE LIST OF WARNINGS, PRECAUTIONS AND CONTRAINDICATIONS.
DEVICE DESCRIPTION
The device (ECG App) comprises a pair of mobile medical apps — one on Apple Watch (the Watch App) and the other on the iPhone (iPhone App) - intended to record, store, transfer, and display a single lead ECG signal similar to a lead I. The ECG Watch App is intended to analyze this single lead data and detect the presence of atrial fibrillation (referred into this document as AFib or AF) and sinus rhythm in adults. It is also intended to acquire and analyze the single lead ECG recordings for display on the iPhone. The ECG iPhone App is included in the Health App, which is intended to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.
2
Image /page/2/Figure/0 description: The image shows an Apple Watch and an iPhone displaying ECG data, connected by a "HealthKit Sync" arrow. The watch face displays a heart rate of 74 BPM and a timer of 26 seconds, with a message to avoid moving arms. The iPhone screen shows an ECG detail with a sinus rhythm, an average of 82 BPM, and a message indicating no signs of atrial fibrillation, along with options for data export and recording deletion.
Figure 1: Apple Watch App and iPhone App components of the ECG App device
The ECG Watch App instructs the user to take an ECG measurement by holding their finger on the digital crown of the watch also contains electrodes on the back of the device which are in continuous contact with the user's wrist. The watch acquires the electrical potential between the electrodes and digital crown. The Watch App will display a visual representation of the ECG waveform to provide information regarding signal quality during the session. The waveform displayed on the watch during the session is not intended for clinical purposes. The session will last for 30 seconds. Upon completion of the recording, the ECG Watch App analyzes the acquired ECG data and produces a waveform that is similar to a Lead I ECG for the purposes of AF and sinus rhythm evaluation, calculates average heart rate, and classifies the rhythm of the waveform (collectively called "session result").
3
Image /page/3/Picture/0 description: The image shows a close-up of an Apple Watch displaying an electrocardiogram (ECG) reading. The watch face shows a heart rate graph and the text "26 sec". A finger is touching the side of the watch, likely to initiate or monitor the ECG function. The watch has a light green band.
Figure 2: Taking a Measurement with the Digital Crown and the Watch App
The ECG rhythm will be classified into one of the following categories:
-
- Sinus rhythm
-
- Atrial Fibrillation
-
- Inconclusive
There are two categories of Inconclusive rhythms: one for high heart rate, low heart rate or other arrhythmias; and one that is the result of poor signal quality and therefore unreadable by the algorithm.
4
| # | UI Output | Definition | Algorithm Output |
|---|---|---|---|
| 1 | Title: Sinus Rhythm | ||
| Description: This ECG does | |||
| not show signs of Atrial | |||
| Fibrillation. | Regular rhythm with a HR | ||
| between 50-100 bpm and | |||
| less than 4 ectopic beats | regular_rhythm | ||
| 2 | Title: Atrial Fibrillation | ||
| Description: This ECG | |||
| shows signs of AFib. | |||
| If this is an unexpected | |||
| result, you should talk to | |||
| your doctor. | AF with a HR between | ||
| 50-100 bpm |
AF with a HR between
101-120 bpm | Afib
Afib_HighHR |
| 3 | Title: Inconclusive
Description: Your ECG is
inconclusive and will be
saved.
If you repeatedly get this
result or you're not feeling
well, you should talk to your
doctor. | Regular rhythm with a HR
greater than 100 bpm
HR over 120
HR under 50
"Other" Rhythms: Rhythms
other than AF or regular
rhythm) | Unclassified_SinusTach
Unclassified_HighHR
Unclassified_LowHR
Unclassified_other |
| 4 | Title: Inconclusive
Description: Your ECG is
inconclusive due to a poor
reading but will be saved. | Poor Recording (e.g., noise,
artifact, or poor signal
quality) | Unreadable |
Figure 3: ECG App analysis outputs
Once the ECG Watch App analyzes the ECG data, the Watch App displays the rhythm classification, average heart rate, and a description of the rhythm classification to the user on their Apple Watch. The session result is saved in Watch HealthKit and is then retrieved and stored in HealthKit on the paired iPhone.
Once the user sees the result of a given session on the Apple Watch App, the user will have the opportunity to pick from the following list of symptoms, which will be saved as part of the session result in Watch HealthKit:
- Rapid, pounding, or fluttering heartbeat ●
- Skipped heartbeat ●
- . Fatigue
- Shortness of breath ●
- . Chest tightness or pain
- Fainting ●
- Dizziness ●
- Other ●
- None ●
5
SUMMARY OF NONCLINICAL/BENCH STUDIES
EVALUATION OF INPUT SIGNAL QUALITY
To support the ability of the ECG App to obtain an ECG of sufficient quality for display and analysis, electromagnetic compatibility, electrical safety, and signal acquisition information was provided, in addition to clinical testing. Specifically, the Apple Watch claims conformance to EU and FCC compliance statements. The FCC listing includes all information needed for 47 CFR compliance. The Apple Watch conforms to EU standards EN 301 489-1 (V2.2.20), EN 301 489-3 (V2.1.1), EN 301 489-17 (V3.2.0), and EN 301 489-52 (V1.1.0). These standards were used as a comparator for IEC 60601-1-2, which is an FDA recognized consensus standard for medical device EMC. The following comparison data was submitted for the normative EMC standards referenced by EN 301 489-1 V2.2.20 and IEC 60601-1-2 (4th Edition):
- Radiated/Conducted Emissions
- Voltage Fluctuations and Flicker ●
- Harmonic Emissions ●
- Electrostatic Discharge ●
- Radiated Immunity and proximity fields
- . Conducted Immunity
- Electrical Fast Transient/Burst ●
- Surge Immunity .
- Voltage Dips/Interruptions
- Power Frequency Magnetic Fields ●
- . Common Emitters
Electrical safety was assessed according to IEC 62368-1 (2014), "Audio/video, information and communication technology equipment - Part 1: Safety requirements." Signal acquisition and platform (hardware) performance was assessed according to IEC 60601-2-47, "Particular requirements for the basic safety and essential performance of ambulatory electrocardiographic systems." Platform performance testing included:
- Input differential range
- Input common-mode range
- ADC sampling rate ●
- ADC effective resolution ●
- Bandwidth ●
- Common-mode rejection
- Gain accuracy
- Linearity and dynamic range ●
- Input impedance ●
- . System noise
- Frequency response ●
- Amplitude response
- Gain setting and stability ●
- Ambient temperature, humidity, and atmospheric pressure ●
6
MAGNETIC RESONANCE (MR) COMPATIBILITY
The device is not intended for use in an MR environment.
SOFTWARE
A failure or latent flaw in the ECG App could indirectly result in user injury; therefore, the software of this device is considered to have a "Moderate" level of concern. The submission contained all the elements of software documentation corresponding to the "Moderate" level of concern, as outlined in the FDA guidance document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." Documentation describing the software/firmware, software specifications, architecture design, software development environment, traceability, revision level history, unresolved anomalies, cybersecurity, and interoperability provide the foundation that the software will operate in a manner as described in the specifications. Hazard analysis was performed to characterize software risks including device malfunction and measurement related errors. The submission included verification and validation (V&V) testing to ensure that mitigation measures were successful.
PERFORMANCE TESTING - BENCH
ECG Database Testing
Testing to databases in EC57 was conducted for rhythms containing AF or normal sinus rhythm (NSR):
- (b) (4) records from the adjudicated AHA and MIT databases were used .
- Each record was split into 30 second segments for a total of (b) (4) .
- Assessed ORS detection, rhythm classification, and HR .
The database annotations were used as ground truth. If a strip included any portion or period of AF derived from the annotations it was labeled as AF. Everything else was labeled not AF and used for assessing the false positive rate. The only exclusions from the TP/FP statistics were the (b) (4) " by the algorithm (21 AF, non-AF strips. (b) (4) were labeled as AF by the Of the (b) (4) (b) (4) algorithm (false positives) and(b) (4) were true negatives (either sinus rhythm or inconclusive) (b) (4) of the available records were used for AF assessment.
| Table 1: Database Testing Results | ||||
|---|---|---|---|---|
| Database Annotation | ||||
| AF | NSR | Total | ||
| Algorithm | ||||
| Determination | AF | (b) | (4) | |
| NSR | ||||
| Unread/Unclass | ||||
| Total |
Table 1: Database Testing Re -14
7
were (b) (4) AF strips in database testing, of which: were "unreadable" were classified as NSR were "unclassified" (1 for being low HR) (b) (4) were classified as AF (b) (4) of readable strips)
Human Factors
participants to A Human Factors Validation Study was performed with a total of demonstrate the usability of the user interface. The study enrolled®®®user groups:
- Group 1 Users diagnosed with AF (AF, n = 17) .
- Group 2 Users age 22-64 (Under 65, No AF, n = 17) ●
- Group 3 Users age 65+ (Over 65. No AF, n = 16) .
Each group included participants with and without smartphone experience as well as participants who use iPhone and Android.
Testing identified critical tasks as those tasks where the user does not understand the output from the device, or limitations of the device, and fails to seek medical care if there is a need based on the results from the app. Each task was assessed for completion and success criteria were clearly defined. Testing also collected subjective feedback in a written open response questionnaire and post-test interview.
SUMMARY OF CLINICAL INFORMATION
A clinical study was performed to establish a reasonable assurance of safety and effectiveness of the ECG App.
Methods
The pivotal study was a prospective, parallel-cohort, non-randomized, multi-center, reader study using an enriched population. The study enrolled equal subjects with and without a known diagnosis of atrial fibrillation into two separate cohorts (AF Cohort and SR Cohort). Key exclusion criteria included antiarrhythmic drug use, the presence of a pacemaker or implantable cardioverter-defibrillator, and a history of abnormal life-threatening rhythms. Subjects in the SR cohort must not have any known diagnosis of AF. To be enrolled in the AF Cohort, the subject must be in atrial fibrillation at the time of enrollment.
Upon enrollment, the participant was coached on the appropriate posture and grip for acquiring an ECG recording using a prototype Apple Watch. After a 5-minute resting period, simultaneous 30-second ECG App and 12-lead ECG recordings were acquired. The ECG App rhythm strip was automatically classified by the algorithm as either "AF", "SR", "Unreadable", or
8
"Unclassified." Unclassified rhythms include any rhythms with rates > 120 or 100 bpm or more than 4 ectopic beats.
Three blinded independent board-certified cardiologists reviewed all ECG recordings and assigned a classification of SR, AF, unreadable, or others. Others classification was defined to include normal sinus with premature ventricular contraction (if ≥4 beats in the strip), normal sinus with PACS, 2nd degree block, AF with a rate > 120 bpm, and supraventricular tachycardia. If the readers disagreed on the diagnosis, the final interpretation was determined by the simple majority rule.
In a subset of randomized selected subjects (Waveform Assessment Analysis Set), 3 independent certified cardiographic technicians synced and overlaid each ECG App rhythm strip with the Lead I strip of the corresponding 12-lead ECG. The first 6 consecutive distinct readable PQRST complexes were identified and used to determine if the morphology of the complexes appeared to overlay to the unaided eye. For the first two QRS complexes, the evaluators also measured and compared the R wave amplitude between the ECG App strip and the reference strip.
Study Endpoints
Primary Endpoint
Sensitivity and specificity of the ECG App algorithm in detecting AF compared with physician-adjudicated 12-lead ECG. The sensitivity and specificity performance goals were set at 90% and 92% respectively. Per the protocol, only readable and classifiable (Classifiable Analysis Set) paired recordings are included in the diagnostic performance assessment.
Secondary Endpoint
The ECG app produces a waveform that provides clinically equivalent information to the gold standard (Lead I ECG). The following criteria assess the endpoint
-
- Qualitative assessment The proportion of paired ECG strips appear to overlay to the unaided eye > 0.80
-
- Quantitative assessment The proportion of paired R-wave amplitude measurements within 2 mm of each other > 0.80
Results
Subject characteristics
The study enrolled a total of 602 subjects at 5 investigational sites. Subject disposition is provided in Figure below. The study analysis excluded 14 subjects in the SR cohort due to a history of paroxysmal AF.
9
Image /page/9/Figure/0 description: This image is a flowchart showing the enrollment and eligibility of participants in a study. The study enrolled 602 participants, but 14 did not meet the eligibility criteria. This left 588 eligible participants, who were divided into two cohorts: AF Cohort (N=301) and SR Cohort (N=287). All participants in both cohorts completed the study, with zero withdrawals in either cohort.
Figure 4: Flow Chart of Subject Disposition
The median age was 71 years, ranging from 22 to 92 years. Comparing to the SR cohort, subjects in the AF cohort were older (mean age 73.8 vs. 59.5) and less likely to be female (30.6% vs. 55.4%). Most AF subjects had a history of permanent AF (58.1%) or persistent AF (34.9%). A vast majority of SR subjects (87.5%) had no prior history of heart rhythm abnormalities, other rhythm abnormalities include Atrial Flutter (AFL) (n=1, 0.3%), Atrial Tachycardia (n=2, 0.7%), and first-degree AV block (n=15, 5.2%). Aside from AF, the most common concomitant conditions reported by enrolled subjects were hypertension (55.3%), hyperlipidemia (43.4%), and drug hypersensitivity (32.9%).
ECG App Automated AF Detection
The ECG App strip and reference 12 lead ECG Classifications were shown in the table below:
| Reference 12 Lead ECG Final Result | |||||
|---|---|---|---|---|---|
| ECG App Algorithm | SR | AF | Other | Unreadable | Total |
| Sinus Rhythm | 238 | 4 | 4 | 1 | 247 |
| Atrial Fibrillation | 1 | 236 | 2 | 2 | 241 |
| Unclassified | 6 | 7 | 6 | 0 | 19 |
| Unreadable | 18 | 30 | 1 | 0 | 49 |
| Device Result Not Reported | 32 | 13 | 1 | 0 | 46 |
| Total | 295 | 290 | 14 | 3 | 602 |
Table 2: ECG App and Reference Strip Classifications
10
Of the 602 enrolled subjects who completed the study, 46 did not have an ECG App result. The reasons for ECG App result not reported are listed in the table below.
| Exclusion Criterion | Number of Subjects |
|---|---|
| Paroxysmal AF protocol deviation | 14* |
| Data Interval |