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The IRNF 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 the feature 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 intended for use in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with AFib.

IRNF 2.0 is comprised of a pair of mobile medical apps - One on Apple Watch and the other on the iPhone.

IRNE 2.0 is intended to analyze pulse rate data collected by the Apple Watch PPG sensor on Apple Watch Series 3-8, Series SE, and Apple Watch Ultra to identify episodes of irregular heart rhythms consistent with AFib and provides 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 tachogram classification algorithm, confirmation cycle algorithm, and the AF notification generation. If an irreqular heart rhythm consistent with AFib is identified, IRNF 2.0 Watch App will transfer the AFib notification to IRNF 2.0 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 on-boarding and educational materials that a user must review prior to enabling AFib notifications. 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 when each of the irregular tachograms contributing to the notification was generated.

The provided text describes the Irregular Rhythm Notification Feature (IRNF) 2.0. However, the document provided is a 510(k) summary and clearance letter for a Predetermined Change Control Plan (PCCP) for IRNF 2.0, rather than a standalone study proving the device meets acceptance criteria for initial clearance.

The document indicates that the subject device (IRNF 2.0) is identical to its predicate device (also IRNF 2.0, K212516), with the only difference being the implementation of a PCCP. This PCCP outlines anticipated modifications to the software and the methods for implementing those changes. Therefore, the acceptance criteria and study data for the initial clearance of IRNF 2.0 (K212516) would be the most relevant information, which is not entirely detailed in this document.

However, the PCCP does specify test methods and acceptance criteria that will be used to demonstrate substantial equivalence for future modifications made under the plan. I will extract information primarily related to these future modification criteria and the study that would be performed to meet them.

Here's a breakdown based on the provided text, focusing on the PCCP and what it implies for future studies:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria described here are for future modifications to the algorithm under the PCCP, showing substantial equivalence to the performance of the existing IRNF 2.0. The document does not provide the absolute performance of IRNF 2.0 itself in this section, but rather the performance target for modified algorithms relative to IRNF 2.0.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document states that for future modifications under the PCCP, "each will meet minimum demographic requirements for age, sex, race, and skin tone derived from the demographics of the United States." It does not specify an exact numerical sample size for the test set.
• Data Provenance: The document implies that validation test datasets will be "representative of the intended use population" and mentions "demographics of the United States." This suggests the data will primarily be from the United States. It does not explicitly state whether the data will be retrospective or prospective for these future validation activities.

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 of experts or their qualifications for establishing ground truth, either for the initial clearance of IRNF 2.0 or for the future modifications under the PCCP.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the test set, either for the initial clearance of IRNF 2.0 or for the future modifications under the PCCP.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The IRNF is described as a "software-only mobile medical application" providing notifications to the user, not a tool for human readers to interpret.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, the document implies that standalone performance studies were done (or will be done for future modifications). The device is described as "software-only" and "analyzes pulse rate data... and provides a notification to the user." The acceptance criteria for future modifications explicitly refer to the algorithm's sensitivity, specificity, and positive predictive value, which are metrics of standalone algorithm performance.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used (e.g., expert consensus, pathology, outcomes data). In the context of "irregular heart rhythms suggestive of atrial fibrillation (AFib)," the ground truth would typically be established by a gold standard method such as a 12-lead ECG interpreted by a cardiologist, or a continuous ECG monitor.

8. The Sample Size for the Training Set

The document states that for future modifications to the tachogram classification algorithm, the plan is to "retrain algorithm with additional datasets." It does not specify the sample size for the training set, either for the original IRNF 2.0 or for the "additional datasets" mentioned for future retraining.

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

The document does not specify how the ground truth for the training set was established, either for the original IRNF 2.0 or for future retraining datasets.

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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 the feature 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 intended for use in people under 22 years of age. It is also not intended for use in individuals previously diagnosed with AFib.

The Irregular Rhythm Notification Feature comprises a pair of mobile medical apps, one on Apple Watch and the other on the iPhone. The Irregular Rhythm Notification Feature analyzes pulse rate data collected by the Apple Watch photoplethysmograph (PPG) sensor to identify episodes of irregular heart rhythms consistent with atrial fibrillation (referred to in this document as AF or AFib) and provides 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. The Irregular Rhythm Notification Feature 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. Users must opt-in and go through onboarding prior to use of the Irregular Rhythm Notification Feature.

The Irregular Rhythm Notification Feature is not intended to diagnose atrial fibrillation, and is not intended to be used to guide clinical treatment or care.

Here's a summary of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance for Irregular Rhythm Notification Feature

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly define a "table of acceptance criteria" with specific numerical targets in the same way an academic paper might. However, by synthesizing the "Clinical Study" section and the "Risks to Health" with their "Mitigation Measures," we can infer the key performance metrics and their achieved results. The pre-specified performance goal was explicitly stated for the primary endpoint.

2. Sample Size for the Test Set and Data Provenance

• Clinical Study Test Set:

  • Full Analysis Set (FAS): 269 subjects
  • Analyzable ECG monitor and tachogram data: 226 subjects (after exclusions)
  • Irregular tachograms recorded: 2634 (out of 10432 total tachograms during monitoring)
  • Subjects receiving at least one alert: 57 (25.2% of the 226 analyzable subjects)

• Data Provenance: The data was collected from a large, prospective, single-arm study conducted to investigate PPG data from Apple Watch for AF-related irregular heart rhythm identification. The sub-study enrolled participants who had already received one prior Irregular Rhythm notification. The country of origin is not explicitly stated but implies a broad user base given Apple's global reach, although the study itself was managed by Apple Inc. in Cupertino, CA.

• Bench Testing Test Set:

  • External Aggressor Condition Testing:
    • Riding in a car (vibration): 44 subjects, 1434 measurements
    • Targeted Hand + finger motions: 20 subjects, 246 measurements
    • Low perfusion: 102 subjects, 2461 measurements
    • Hand Tremors: 143 subjects, 936 measurements
  • Skin Tone Performance: 1124 subjects, 1.3 million measurements

• Human Factors and Usability Study Test Set: 37 participants (16 "Active Interest", 21 "Passive Interest")

3. Number of Experts and Qualifications for Ground Truth - Clinical Study

• Number of Experts: Unspecified exact number of independent cardiologists. It states "independent cardiologists" (plural).
• Qualifications: "Independent cardiologists" (no further details on experience or specialization, but the title implies appropriate medical expertise).

4. Adjudication Method for the Test Set - Clinical Study

The document states: "For each one-minute irregular rhythm episode (tachogram) identified by the software, the corresponding patch ECG recording was extracted and classified by independent cardiologists as either 'Sinus rhythm', 'AF', 'Unreadable', or 'Other Irregular Rhythm.'" This implies a single expert classification or a pre-defined process without explicitly stating a consensus method like 2+1 or 3+1.

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

• No, an MRMC comparative effectiveness study was not done or reported comparing human readers with and without AI assistance. The clinical study focused on the standalone performance of the device's notification feature against an ambulatory ECG.

6. Standalone (Algorithm Only) Performance

• Yes, the primary clinical study was a standalone (algorithm only) performance evaluation. The device independently identified irregular rhythm episodes, which were then compared to the ground truth established by ambulatory ECG and cardiologist review. There was no human-in-the-loop component for the detection performance evaluation itself.

7. Type of Ground Truth Used

• Clinical Study:
  • For the primary and secondary endpoints related to AF detection, the ground truth was established by 7-day ambulatory patch ECG recordings, which were then "classified by independent cardiologists as either 'Sinus rhythm', 'AF', 'Unreadable', or 'Other Irregular Rhythm.'" This combines outcomes data (ECG) with expert consensus/interpretation.
• Bench Testing:
  • Functional performance: Compared to "commercial, FDA-cleared clinical ECG."
• Human Factors Study:
  • User comprehension: Observational data and subjective evaluations of user responses to questions.

8. Sample Size for the Training Set

The document does not explicitly state the sample size for the training set used to develop the Irregular Rhythm Notification Feature algorithm. It only describes the evaluation of the algorithm on the described test sets.

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

The document does not explicitly describe how the ground truth for the training set was established. It focuses only on the validation study. It can be inferred that similar methods (ECG data interpreted by cardiologists) would likely have been used during development, but this is not detailed in the provided text.

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