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The Asante Snap Insulin Pump System is indicated for continuous delivery of insulin at programmable basal and bolus rates for the management of diabetes mellitus in adult patients requiring insulin.

The Snap™ Insulin Pump System is a wearable, continuous, programmable insulin delivery system. It is a portable, battery-powered system capable of delivering insulin at fixed and variable rates. The Snap system consists of the following components and accessories:

• 1. a software-controlled, programmable, reusable Controller module;
• 2. a single-use, disposable Pump Body;
• 3. an Unomedical Asante Comfort™ or Asante Conset™ Infusion Set (K120872), or an equivalently FDA-cleared infusion set with Luer fitting using the Asante sterile, single-use disposable Infusion Set Adapter; and
• 4. accessories, including the AsanteSync™ download cradle with USB cable, and a fabric Case and plastic Clip that provide a means of attaching the Snap system to the user's belt or other clothing.
     The Controller module has a user interface to program delivery parameters for basal and bolus insulin delivery and the Pump Body provides the drive mechanism and battery power and holds the pre-filled insulin cartridges. The user obtains Eli Lilly Humalog fast-acting U100 insulin from their pharmacy and inserts the pre-filled glass cartridge into the Asante Pump Body. The Pump Body is connected to the Controller module and either (a) an Unomedical Asante Comfort or Asante Conset infusion set with proprietary connector, or (b) an Asante Infusion Set Adapter with Luer fitting that connects to commercially available Luered infusion sets to allow delivery of insulin for the management of diabetes.

The Snap Insulin Pump System is a wearable, continuous, programmable insulin delivery system. It is indicated for continuous delivery of insulin at programmable basal and bolus rates for the management of diabetes mellitus in adult patients requiring insulin.

Here's an analysis of the provided information, focusing on acceptance criteria and study details:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria with corresponding performance metrics for the device itself (e.g., insulin delivery accuracy ranges, battery life). Instead, it describes a series of tests performed to ensure the device's safety, functionality, and equivalence to a predicate device. The general acceptance criterion for all tests was "All testing met the acceptance criteria."

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

The document does not specify the sample sizes used for the various tests (e.g., how many units were subjected to electrical safety testing, water ingress testing, etc.).
The data provenance is not explicitly mentioned in terms of country of origin or whether it was retrospective or prospective. However, given that these are pre-market verification and validation activities for a medical device, the studies are inherently prospective in nature, conducted specifically to demonstrate compliance with regulatory standards.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

This type of information is not applicable to the studies described. The "tests" here are engineering and technical verification and validation studies based on international standards (e.g., IEC standards, ISO 14971) and FDA guidance, not studies involving human interpretation or expert evaluation of data to establish a "ground truth" in the diagnostic sense. The "ground truth" is defined by the physical, electrical, and software specifications and the compliance requirements of the standards themselves.

4. Adjudication Method for the Test Set

Not applicable. As noted above, these are technical verification and validation studies against defined standards and specifications, not studies requiring expert adjudication of results.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. The Snap Insulin Pump System is an insulin delivery device, not a diagnostic imaging system or an AI-powered diagnostic tool. Therefore, MRMC studies and the concept of "human readers improving with AI assistance" are not relevant to this device.

6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done

The device itself is an algorithm-driven system (software-controlled, programmable). The performance testing described (software validation, electrical safety, water ingress, EMI, risk analysis) inherently evaluates the "standalone" performance of the device's components and integrated system without human intervention during the testing phase of its technical capabilities. The device is designed for human-in-the-loop operation for insulin delivery, but the tests conducted are about its fundamental functional and safety characteristics.

7. The Type of Ground Truth Used

The "ground truth" for these tests is implicitly defined by:

• Engineering Specifications: The design requirements and specifications of the device components and system.
• International Standards: Compliance requirements of standards like IEC 60601-1, IEC 60601-1-2, IEC 60601-2-24, IEC 60529, IEC 61000-4 series, and ISO 14971.
• FDA Guidance: Requirements outlined in "Guidance of Industry and FDA Staff - Total Product Life Cycle: Infusion Pump - Premarket Notification [510(k)] Submissions Draft Guidance" and "FDA's General Principles of Software Validation; Final Guidance for Industry and FDA Staff."

The device must meet these predefined technical and regulatory benchmarks.

8. The Sample Size for the Training Set

Not applicable. This device is not an AI/Machine Learning model that requires a "training set" in the conventional sense. The "training" in manufacturing is related to the calibration and quality control of the physical and software components during production.

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

Not applicable, as there is no "training set" in the context of AI/ML for this device.

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