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The POGO Automatic Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips. The POGO Automatic Blood Glucose Monitoring System is intended to be used by a single person and should not be shared.

The POGO Automatic Blood Glucose Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The POGO Automatic Blood Glucose Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use. The POGO Automatic Blood Glucose Monitoring System is indicated for use in adults and adolescents (13 and up).

POGO Automatic Test Cartridges are for use with the POGO Automatic Meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips.

The POGO BGMS is a quantitative assay for the detection of glucose in capillary whole blood sampled from the fingertip. The system includes multiple glucose-oxidase-based dry-reagent test strips housed in a cartridge and a photometer to read the glucose-dependent color change from the in-use strip. The meter uses calibration information from a barcode on the disposable cartridge to convert the reflectance information into a plasma-equivalent glucose value. A Bluetooth Low Energy (BLE) module in the meter allows the user to authorize the POGO meter to send data wirelessly, securely, and automatically after each test to specified electronic computing devices.

The POGO BGMS automates finger lancing, blood sample collection and placement onto the test strip, and calculation of the blood glucose result, and so requires significantly fewer steps than existing BGMSs to obtain a glucose result. The POGO BGMS accomplishes this via a 10-test cartridge where each foil-sealed test includes a hollow lancet, spring, and test strip. The user does not need a separate lancing device since the lancing mechanism is built into individual test cells of the cartridge. The blood sample is drawn into the test through capillary action and the meter, by monitoring the time and degree of blood spreading on the reagent pad, can detect when the test is under-filled and allow the user a limited amount of time to add blood to the test trip to obtain a glucose result rather than an error. Additionally, used tests are retained within the cartridge for added user convenience.

The provided document is a 510(k) summary for the POGO Automatic Blood Glucose Monitoring System (POGO BGMS). It describes the device, its intended use, and compares it to a predicate device (an earlier version of the same system).

However, this document does not contain the detailed information necessary to fully answer your request regarding acceptance criteria and the study that proves the device meets them. Specifically, it lacks:

• A table of acceptance criteria and reported device performance for glucose measurement accuracy. While it lists general characteristics, it does not provide specific performance metrics like accuracy studies (e.g., ISO 15197 compliance data).
• Sample sizes used for testing.
• Ground truth establishment and expert qualifications.
• Information about MRMC studies, standalone algorithm performance, or training set details.

The document primarily focuses on demonstrating substantial equivalence to a predicate device based on modifications that are largely non-functional (BLE capability, display changes, speaker, feet). It asserts that the fundamental technological characteristics, blood sampling, blood glucose measurement, and algorithm remain unchanged from the predicate. Therefore, it relies on the predicate's proven performance for establishing substantial equivalence in its core function (glucose measurement).

Based on the provided text, I can infer the following:

The acceptance criteria for the POGO Automatic Blood Glucose Monitoring System's glucose measurement performance is that it performs as safely and effectively as the predicate device (K162203). The document explicitly states: "The subject modifications for the POGO BGMS do not affect any of the above attributes that were included in the substantial equivalence section of the predicate device and relied upon for the substantial equivalence finding for the predicate device, K162203." It also mentions "Same fundamental technological characteristics. The following remain unchanged: blood ● sampling and blood glucose measurement, use of the same cartridge (which contains the reagent system used to measure blood glucose), technology and procedure to acquire a blood sample and monitor the color development on the reagent pad and the algorithm to calculate a glucose value from the reaction at the reagent pad."

Therefore, the "study that proves the device meets the acceptance criteria" for glucose measurement is implicitly the original study data for the predicate device (K162203), as the core measurement technology has not changed. The current submission focuses on demonstrating that the modifications (BLE, display, etc.) do not degrade this established performance.

Here's what can be extracted and what is missing based on your request:

Acceptance Criteria for the POGO Automatic Blood Glucose Monitoring System

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance:

• Test Set Sample Size: Not specified for any of the performance tests. The document only states that "Multiple nonclinical tests were identified via risk assessment activities as requirements to demonstrate comparable safety and effectiveness."
• Data Provenance: Not specified (e.g., country of origin). The document implies the tests were conducted by the manufacturer, Intuitv Medical, Inc. The data is retrospective in the sense that it relies on the predicate's established performance and new testing for the modifications.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable / Not specified. This type of information is usually relevant for AI/ML diagnostic devices where human expert consensus establishes ground truth for image interpretation or diagnosis. For a blood glucose monitoring system, the ground truth for glucose measurements would typically be established by a laboratory reference method (e.g., YSI analyzer), rather than expert consensus.

4. Adjudication method for the test set:

• Not applicable / Not specified. See point 3.

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:

• No. This is not relevant for a blood glucose monitoring system, which provides a direct quantitative measurement and does not involve human interpretation of complex medical images or data requiring AI assistance.

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

• Yes (in principle, for the core function). The device itself performs the glucose measurement and calculation ("algorithm to calculate a glucose value from the reaction at the reagent pad"). The performance refers to the device's accuracy against a known standard. The 510(k) submission states that this core algorithm and measurement technology are unchanged from the predicate device. Therefore, the standalone performance was established for the predicate device.

7. The type of ground truth used:

• Laboratory Reference Method. For blood glucose monitoring systems, the ground truth for glucose concentration is typically established by a highly accurate laboratory analyzer (e.g., YSI glucose analyzer) that is well-calibrated and traceable to international standards. This document does not explicitly state the reference method, but it is standard practice for such devices.

8. The sample size for the training set:

• Not applicable / Not specified. Blood glucose monitoring systems typically use a chemical reaction and photometric measurement with a pre-defined calibration curve. They are not generally "trained" in the typical machine learning sense with a "training set" of patient data that needs a ground truth established in the same way as an AI algorithm for image recognition. The "algorithm" here refers to the fixed mathematical conversion from optical signal to glucose concentration, derived from chemical principles and calibrated during manufacturing. While calibration data is used, it's not described as a "training set" in this context.

9. How the ground truth for the training set was established:

• Not applicable. As above, the device uses a chemical-photometric principle. Calibration curves are established using samples with known glucose concentrations, typically measured by a laboratory reference instrument (the "ground truth").

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