(273 days)
The ZEUS System (Zio Watch), as part of the Zio Watch Service, is intended to process and analyze electrocardiogram (ECG) and photoplethysmogram (PPG) based data to detect and report on the presence of Atrial Fibrillation (AF) over the monitoring period. The report provides ECG information for the intended user to diagnose AF and contextual information for AF, both to be interpreted based on clinical judgment and experience. It is indicated for use on adult patients 22 years or older who are susceptible to developing or who have been diagnosed with AF. It is not intended for use on critical care patients.
The ZEUS System (Zio Watch), the subject device of this 510(k) submission, is a software as a medical device (SaMD) system consisting of a collection of modules designed to process and analyze data from the Zio Watch into a curated report of preliminary findings intended for use by clinicians to aid in AF diagnosis.
The subject ZEUS System utilizes an artificial intelligence (Al) based ECG Analysis Software (ECGDL) to generate the initial ECG-based cardiac information provided to the clinician in Transmission Reports. In addition, continuously recorded PPG-based data is processed by a separate artificial intelligence (AI) based analysis software, the AF Context Engine (ACE), that detects the presence of AF. Specifically, the subject ZEUS System (Zio Watch) utilizes machine learning techniques for both the ECGDL and AF Context Engine algorithms.
These results are also presented along with the ECG data in the Zio Watch Transmission Reports. The reported cardiac information includes AF detection (including PPG-based AF summary) and heart rate measurements. The ECG-based preliminary findings in the Zio Watch Transmission Reports are quality reviewed by Certified Cardiographic Technicians (CCTs) prior to publishing. After CCT review, the report containing the preliminary findings and associated ECG are provided to clinicians via a secure website.
Here's a breakdown of the acceptance criteria and the study used to prove the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
The document doesn't explicitly state numerical acceptance criteria in a table format for a specific performance study. Instead, it focuses on demonstrating substantial equivalence to predicate devices and adherence to recognized standards. However, the core performance is related to AF detection. The acceptance criteria can be inferred as the device successfully performing its intended function of detecting and reporting AF from ECG and PPG data, with sufficient accuracy for clinical interpretation.
| Acceptance Criteria (Inferred) | Reported Device Performance (Summary from Validation) |
|---|---|
| Functional Performance: Process and analyze ECG and PPG-based data to detect and report on the presence of Atrial Fibrillation (AF) over the monitoring period, providing ECG information for diagnosis and contextual information for AF. | The ACE algorithm processed PPG-based data from the Zio Watch and provided AF presence/absence determinations. The ECGDL algorithm analyzed ECG recordings to provide beats, runs, rhythms, and heart rate detection. These functionalities were validated against CCT-reviewed reference rhythm labels. |
| Safety and Efficacy: The device does not raise new questions of safety or effectiveness compared to predicate devices. Meets design requirements and intended use. | "Safety and performance...evaluated and verified...in conformance with FDA-recognized consensus standards and FDA guidance documents...nonclinical verification and performance test results established that the device meets its design requirements and intended use, that the design differences with the cleared device do not raise new questions of safety and efficacy." |
| Risk Management: Potential hazards evaluated and controlled during development. | "During development, potential hazards were evaluated and controlled by risk management activities, including risk analysis, risk mitigation, verification and benefit-risk analysis." |
| Adherence to Standards: Conformance with relevant FDA-recognized consensus standards (e.g., ISO 14971, IEC 62304, IEC 60601-2-47, AAMI EC57) and guidance documents (e.g., Cybersecurity, Software in Medical Devices, Interoperable Devices, 510(k) Program). | The document explicitly lists several FDA-recognized consensus standards and FDA guidance documents that the device's design verification and validation testing conformed to (Table 2 in the original document). |
2. Sample Size Used for the Test Set and Data Provenance:
-
Verily Prospective Study (ACE validation set):
- Sample Size: Not explicitly stated as a number of patients or recordings. It describes "multi-day PPG recordings" from the Zio Watch along with "ECG-based, CCT-reviewed reference rhythm labels" obtained from simultaneously worn Zio XT Patches. Patient demographics are provided (see below).
- Provenance: Prospective study.
- Country of Origin: Regional Demographics (USA): Midwest: 8.0%, Mountain: 32.1%, West: 32.1%, Northeast: 14.3%, South: 13.4%.
-
ZWAF Database (ECGDL validation set):
- Sample Size: Not explicitly stated as a number of recordings. It consists of "ECG data obtained from Zio Watch as used in the Study Watch AF Detection At Home study ('Verily Prospective Study')". Patient demographics are provided (see below).
- Provenance: This dataset is derived from the "Study Watch AF Detection At Home study" (Verily Prospective Study), which is noted as retrospective in reference to its use for the ZWAF database for ECGDL validation.
- Country of Origin: Regional Demographics (USA) from the Verily Prospective Study (Midwest: 8.0%, Mountain: 32.1%, West: 32.1%, Northeast: 14.3%, South: 13.4%).
Demographics for both validation sets:
- Patients: At least 22 years or older who are at risk of having an AF event, as determined by having a diagnosis of paroxysmal AF.
- Age: Median = 67 [25%, 75%] = [59, 73]
- Gender: 45.5% Female
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
- Number of Experts: Not explicitly stated as a specific number. The text refers to "Certified Cardiographic Technicians (CCTs)". It implies multiple CCTs were involved in reviewing the reference ECG data to establish ground truth.
- Qualifications of Experts: "Certified Cardiographic Technicians (CCTs)". No further details on their years of experience are provided in this document.
4. Adjudication Method for the Test Set:
- The document states that the ground truth for the validation sets was based on "ECG-based, CCT-reviewed reference rhythm labels". This suggests that the CCTs' review served as the gold standard.
- No specific multi-expert adjudication method (like 2+1 or 3+1) is mentioned for resolving discrepancies among CCTs, if any. The phrasing "CCT-reviewed" implies their consensus or, at minimum, their expert determination.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- No MRMC comparative effectiveness study was mentioned or performed. The document explicitly states: "No clinical testing was performed in support of this premarket notification." The focus was on the performance of the algorithm itself against expertly reviewed ground truth.
6. Standalone (Algorithm Only) Performance:
- Yes, standalone performance was done for both algorithms. The validation studies described for the "AF Context Engine (ACE)" and "ECG Deep Learned (ECGDL)" algorithms assessed their performance in detecting AF and analyzing ECG data, respectively, against CCT-reviewed ground truth, without a human-in-the-loop component during the performance evaluation for substantial equivalence. The "ZEUS System (Zio Watch)" itself is described as a "software as a medical device (SaMD) system consisting of a collection of modules designed to process and analyze data".
7. Type of Ground Truth Used:
- Expert Consensus (CCT-reviewed ECG data): The primary ground truth for both the ACE and ECGDL algorithm validation was derived from "ECG-based, CCT-reviewed reference rhythm labels". For ACE, these were from simultaneously worn Zio XT Patches. For ECGDL, it was ECG data obtained from the Zio Watch itself, which was also subject to CCT review to establish ground truth.
8. Sample Size for the Training Set:
- Not explicitly stated as a number of recordings or patients. The text mentions "thousands of recordings" for both ECGDL and ACE algorithms.
9. How the Ground Truth for the Training Set Was Established:
- The training data for both the ECGDL and ACE algorithms came from "continuous cardiac recordings from compatible cardiac monitors" that "have already undergone Certified Cardiographic Technician (CCT) review." This indicates that the ground truth for the training data was also established by CCTs.
§ 870.1425 Programmable diagnostic computer.
(a)
Identification. A programmable diagnostic computer is a device that can be programmed to compute various physiologic or blood flow parameters based on the output from one or more electrodes, transducers, or measuring devices; this device includes any associated commercially supplied programs.(b)
Classification. Class II (performance standards).