(73 days)
The Irregular Rhythm Notification Feature is a software-only mobile medical application that is intended to be used with the Apple Watch. The feature analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification to the user. The feature is intended for over-the-counter (OTC) use. It is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present; rather is intended to opportunistically surface a notification of possible AFib when sufficient data are available for analysis. These data are only captured when the user is still. Along with the user's risk factors the feature can be used to supplement the decision for AFib screening. The feature is not intended to replace traditional methods of diagnosis or treatment.
The feature has not been tested for and is not in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with AFib.
Irregular Rhythm Notification Feature 2.0 (IRNF 2.0) is comprised of a pair of mobile medical apps - One on Apple Watch and the other on the iPhone.
IRNF 2.0 is intended to analyze pulse rate data collected by the Apple Watch PPG sensor on Apple Watch Series 3. Series 4. Series 5. and SE to identify exisodes of irreqular heart rhythms consistent with AFib and provide a notification to the user. It is a background screening tool and there is no way for a user to initiate analysis of pulse rate data. IRNF 2.0 iPhone App is part of the Health App. which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.
IRNF 2.0 Watch App refers to the rhythm classification algorithm, confirmation cvcle algorithm, and the AFib notification generation. If an irreqular heart rhythm consistent with Afib is identified and confirmed through the confirmation cycle, IRNF 2.0 Watch app will notify the user and transfer the AFib notification to the iPhone App through HealthKit sync. In addition to indicating the finding of signs of AFib, the notification will encourage the user to seek medical care.
IRNF 2.0 iPhone App contains the onboarding and educational materials that a user must review prior to use. IRNF 2.0 iPhone App is designed to work in combination with IRNF 2.0 Watch App and will display a history of all prior AFib notifications. The user is also able to view a list of times of the irreqular rhythms contributing to the notification.
The provided text describes the Irregular Rhythm Notification Feature (IRNF) 2.0 app, a software-only mobile medical application for detecting irregular heart rhythms suggestive of Atrial Fibrillation (AFib) using Apple Watch pulse rate data. Below is a detailed breakdown of the acceptance criteria and study proving the device meets them, based on the provided document:
Acceptance Criteria and Reported Device Performance
The document states that the clinical performance of the IRNF 2.0 app was assessed, with specific metrics reported:
| Metric | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Person-level Sensitivity (AFib) | Not explicitly stated but inferred as non-inferior to predicate | 88.6% |
| Person-level Specificity (AFib) | Not explicitly stated but inferred as non-inferior to predicate | 99.3% |
Note: The document states "IRNF 2.0 person-level sensitivity (88.6%) and specificity (99.3%) were both demonstrated to be non-inferior to those of the predicate device." While a specific numerical acceptance criterion for sensitivity and specificity isn't explicitly listed, the demonstration of non-inferiority to the predicate device (which had a reported 78.9% sensitivity for concordant AFib and 98.2% for AFib and other clinically relevant arrhythmias) serves as the implicit acceptance benchmark.
Study Details
Here's the information about the study that proves the device meets the acceptance criteria:
2. Sample Size Used for the Test Set and Data Provenance:
- Test Set Sample Size: The clinical validation study (referred to as the "clinical study" under 5.7 Clinical Performance) involved 573 participants.
- For the primary endpoint analysis, 432 participants contributed data to determine sensitivity, with 140 of these presenting with AFib.
- 292 participants contributed data to the analysis of device specificity.
- Data Provenance: The document does not explicitly state the country of origin of the data. It is implied to be a prospective study, as it involved enrolled subjects wearing an Apple Watch and a reference ECG patch concurrently.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:
- The document does not specify the number or qualifications of experts used to establish the ground truth for the test set. It states that the reference was an "electrocardiogram (ECG) patch concurrently." This implies a medical standard for AFib diagnosis, but the human interpretation component by experts is not detailed.
4. Adjudication Method for the Test Set:
- The document does not describe a specific adjudication method (e.g., 2+1, 3+1) for the test set. The ground truth appears to be established directly from the reference ECG patch data.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:
- No, an MRMC comparative effectiveness study involving human readers assisting with or without AI was not described for this device. The study primarily focuses on the standalone performance of the IRNF 2.0 app against a reference standard.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone performance study was done. The reported sensitivity (88.6%) and specificity (99.3%) refer to the device's performance in identifying irregular heart rhythms suggestive of AFib based on pulse rate data, without human interpretation in the loop for the primary performance metrics. The feature provides a notification to the user, encouraging them to seek medical care, but its performance metrics are of the algorithm only.
7. The Type of Ground Truth Used:
- The ground truth for the clinical study was established using a reference electrocardiogram (ECG) patch, worn concurrently by participants. AFib was "identified on the reference ECG patch."
8. The Sample Size for the Training Set:
- The document states that the new rhythm classification algorithm "was trained extensively using data collected in a number of development studies." These studies "included over 2500 subjects." While the exact size of the training set itself is not broken out numerically, it's part of this larger "over 2500 subjects" pool. The data was split into Training, Validation, Testing, and Sequestration sets.
9. How the Ground Truth for the Training Set Was Established:
- The ground truth for the training set was established from "data collected in a number of development studies" that included "over 3 million pulse rate recordings on a variety of rhythms including: atrial fibrillation, normal sinus rhythm, sinus arrhythmia, and other ectopic beats (PVCs, PACs)."
- It's inferred that these "rhythms" were definitively classified to serve as ground truth for training the convolutional neural network. While the exact method of establishing ground truth for the training data (e.g., direct ECG correlation, expert annotation of ECGs) is not explicitly detailed, the mention of specific rhythm types suggests a medically validated classification.
§ 870.2790 Photoplethysmograph analysis software for over-the-counter use.
(a)
Identification. A photoplethysmograph analysis software device for over-the-counter use analyzes photoplethysmograph data and provides information for identifying irregular heart rhythms. 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 must demonstrate the performance characteristics of the detection algorithm under anticipated conditions of use.
(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 demonstrate the ability of the device to detect adequate photoplethysmograph signal quality.
(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.