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The Atrial Fibrillation (AFib) History Feature is an over-the-counter ("OTC") software-only mobile medical application intended for users 22 years of age and over who have a diagnosis of atrial fibrillation (AFib). The feature opportunistically analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of AFib and provides the user with a retrospective estimate of AFib burden (a measure of the spent in AFib during past Apple Watch wear).

The feature also tracks and trends estimated AFib burden over time, and includes lifestyle data visualizations to enable users to understand the impact of certain aspects of their AFib. It is not intended to provide individual irregular rhythm notifications or to replace traditional methods of diagnosis, treatment, or MFib.

The feature is intended for use with the Apple Watch and the Health app on iPhone.

The Atrial Fibrillation History Feature (AFib History Feature) is comprised of a pair of mobile medical apps - one on Apple Watch and the other on the iPhone.

The AFib History Feature is intended to analyze pulse rate data collected by the Apple Watch PPG sensor on Apple Watch Series 4. Series 5. and SE to identify episodes of irregular heart rhythms consistent with AFib and provides the user with a retrospective estimate of AFib burden (a measure of the amount of time spent in AFib during past Apple Watch wear).

The AFib History Feature uses PPG pulse rhythm data from compatible Apple Watches. Apple Watch uses green LED lights paired with light-sensitive photodiodes to detect relative changes in the amount of blood flowing through a user's wrist at any given moment. When the heart beats it sends a pressure wave down the vasculature, causing a momentary increase in blood volume when it passes by the sensor. By monitoring these changes in blood flow, the sensor detects individual pulses when they reach the peripherv and thereby measure beat-to-beat intervals.

The AFib History Feature 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.

The AFib History Feature provides users visualizations of AFib burden estimate data alongside clinically relevant lifestyle data and presents estimates of AFib burden in three different ways. These visualizations empower users to observe and understand the impact of lifestyle on their AFib burden, and to better understand their condition generally.

• · Weekly Estimate an estimate of the amount of time a user was in Atrial Fibrillation over the past calendar week during watch wear, presented to the user as a percentage.
• Day of Week Estimate an estimate of the amount of time a user was in Atrial Fibrillation on each day of the week over the previous 42 days during watch wear, presented to the user as a percentage. That is, all Mondays over the past 42 days, all Tuesdays over the past 42 days.
• Time of Day Estimate an estimate of the amount of time a user was in Atrial Fibrillation on 4-hour segments of the day over the previous 42 days during watch wear, presented to the user as a percentage. That is, all 12 am - 4 am segments over the past 42 days, all 4 am - 8 am segments over the past 42 days.

The AFib History Feature is intended to serve as an extension of the predicate Irregular Rhythm Notification feature, but has been optimized for users with a diagnosis of Afib.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA document.

The document describes the Atrial Fibrillation History Feature, a software-only mobile medical application intended for users 22 years and older with a diagnosed Atrial Fibrillation (AFib). The feature analyzes pulse rate data from Apple Watch to provide a retrospective estimate of AFib burden.

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list "acceptance criteria" in a definitive table format with pass/fail thresholds. Instead, it presents performance metrics from development and clinical studies for the classification algorithm and for the AFib burden estimation.

Implicit Acceptance Criteria (Derived from Performance Data and Context):

• Rhythm Classification Algorithm Performance: High sensitivity and specificity in differentiating AFib from non-AFib rhythms.
• AFib Burden Estimation Accuracy: Close agreement between the device's weekly AFib burden estimate and the reference method.

Reported Device Performance:

2. Sample Size for the Test Set and Data Provenance

The document refers to two main test sets:

• Development Studies Test Set: Data used for evaluating the model during development and as a "last test" on a "Sequestration set" (which functions as a final test set after model locking).
  • Sample Size: Part of "over 2500 subjects" and "over 3 million pulse rate recordings". The exact number of subjects or recordings specifically in the test/sequestration sets for the classification algorithm performance is not individually quantified beyond being a split of the total development data.
  • Data Provenance: Not explicitly stated (e.g., country of origin, specific institutions). It mentions "demographically diverse populations" in recruitment. The studies are described as "development studies," implying they were specifically conducted for the purpose of algorithm development. The data appears to be prospective given it was collected from recruited subjects.
• Clinical Validation Study Test Set:
  • Sample Size: 413 enrolled subjects for the study. 280 subjects contributed data to the primary endpoint analysis for AFib burden estimation.
  • Data Provenance: Not explicitly stated (e.g., country/region). It describes "enrolled subjects wore an Apple Watch and a reference electrocardiogram (ECG) patch concurrently for up to 13 days," indicating a prospective clinical study specifically for this validation.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document refers to "reference electrocardiogram (ECG) patch concurrently" and "reference weekly burden" for establishing ground truth, both in the development studies and the clinical validation study.

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated. However, the use of "reference electrocardiogram (ECG)" strongly implies that medical professionals (e.g., cardiologists, electrophysiologists) would be involved in reading and interpreting these ECGs to establish the ground truth for AFib presence and burden.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document refers to "reference electrocardiogram (ECG)" as the ground truth. It's common practice for ECG interpretations, especially in clinical studies, to involve review by multiple experts or an adjudication committee, but the specific method (e.g., 2+1, 3+1) is not detailed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC Comparative Effectiveness Study Done? No, an MRMC study comparing human readers with and without AI assistance was not described. The study focuses on the standalone performance of the device's algorithm against a reference standard (ECG).

• Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was performed.

6. Standalone Performance (Algorithm Only)

• Was a Standalone Study Done? Yes. The performance metrics presented for both the rhythm classification algorithm and the AFib burden estimation in the development and clinical studies are indicative of the device's standalone (algorithm-only, without human-in-the-loop performance) performance. The device is a "software-only mobile medical application," and its output (AFib burden estimate) is directly compared to the reference standard.

7. Type of Ground Truth Used

• Type of Ground Truth: The primary ground truth for both the classification algorithm development/testing and the clinical validation of AFib burden estimation was established using reference electrocardiogram (ECG) data. This is considered a high-fidelity diagnostic standard for cardiac rhythm.

8. Sample Size for the Training Set

• Training Set Sample Size: The algorithm was "trained extensively using data collected in a number of development studies. In total, the studies included over 2500 subjects and collected over 3 million pulse rate recordings." The training set was a portion of this total dataset, with the data "split into four sets...: Training, Validation, Test, and Sequestration sets." The exact number of subjects or recordings specifically in the training set is not provided separately.

9. How the Ground Truth for the Training Set Was Established

• Ground Truth Establishment for Training Set: "The rhythm classification algorithm uses a convolutional neural network based architecture and was trained extensively using data collected in a number of development studies." Similar to the test sets, the ground truth for rhythms in the training data would also have been established using reference electrocardiogram (ECG) data. The document implies that these development studies also involved the collection of "pulse rate recordings on a variety of rhythms including: atrial fibrillation, normal sinus rhythm, sinus arrhythmia, and other ectopic beats (PVCs, PACs)," which would necessitate ECG interpretation to label these rhythms for training the algorithm.

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