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The DEKA ACE Pump System is intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin, ages 13 and above. The pump is able to reliably and securely communicate with compatible, digitally connected devices, including automated insulin dosing software, to receive, execute, and confirm commands from these devices. The pump is intended for single patient, home use and requires a prescription.

The bolus calculator is indicated for use for aiding the user in determining the bolus insulin dosage for management of diabetes mellitus based on consumed carbohydrates, operator-entered blood glucose, insulin to carbohydrate ratio, target glucose values, and current insulin on board.

The DEKA ACE Pump System is intended for the subcutaneous delivery of insulin, at set and variable rates, for the management of diabetes mellitus in persons requiring insulin, ages 13 and above. The Pump is able to reliably and securely communicate with compatible, digitally connected devices, including automated insulin dosing software, to receive, and confirm commands from these devices. The Pump is intended for single patient, home use and requires a prescription.

The system as described in this submission is able to be integrated with a Dexcom G6 interoperable Continuous Glycemic Controller (iCGM). This submission also details the integration process that can be used to incorporate an iAGC.

The DEKA ACE Pump System consists of the following components:

1. Pump: A durable pump that incorporates fluid delivery algorithms and interfaces to an DEKA ACE Pump cassette, Remote Interface, iCGM, and iAGC. The pump is powered by a rechargeable lithium ion battery.
2. Cassette: A single-use pumping cassette that combines microfluidic valves, a pump chamber, insulin reservoir, and Acoustic Volume Sensing (AVS) measurement chamber. The cassette interfaces to an DEKA ACE Pump and off-the-shelf infusion set.
3. Remote Interface (Controller): A wireless controller that serves as the user interface to the DEKA ACE Pump system. This includes a large color touch display for ease of use.

The provided text describes the DEKA ACE Pump System, an insulin pump, and its comparison to a predicate device (Tandem t:Slim X2 insulin pump) for FDA 510(k) clearance. The document focuses on demonstrating substantial equivalence, primarily through non-clinical performance testing rather than clinical studies involving human patients.

Here's a breakdown of the requested information based on the provided text:

1. Table of acceptance criteria and the reported device performance

The document presents basal and bolus accuracy as performance metrics, comparing the subject device (DEKA ACE Pump) to the predicate device (Tandem t:Slim X2). The acceptance criteria for these accuracies are implicitly set by matching or improving upon the predicate device's performance, and by meeting the "Special Controls" requirements for this device type.

Basal Accuracy Comparison (Example for 0.1 U/hr)

Bolus Accuracy Comparison (Example for 0.05U Bolus Accuracy - % of boluses within ranges)

Other performance criteria mentioned in the "Non-Clinical/Performance Testing" section include:

• Worst Case Accuracy
• Occlusions
• Fault Insertion
• Sound Testing
• Incidental Delivery
• Reliability
• Drug Compatibility and Particulate Testing
• System Level Functionality
• Battery Performance
• Environmental Conditions
  The acceptance criteria for these are generally that the device meets applicable standards (e.g., IEC 60601 series, ISO 10993, ISO 14971) and demonstrates performance "equivalent or better" or "meets all Special Controls requirements" compared to the predicate or established safety thresholds. Specific numerical acceptance criteria for these are not explicitly detailed in the provided text, but it states that the "subject device meets all Special Controls requirements."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The provided text only discusses non-clinical, in-vitro performance testing.

• Sample Size for Basal Accuracy: The tables show data collected over intervals of 1, 6, and 12 hours. The number of individual test runs or devices included in these measurements is not explicitly stated.
• Sample Size for Bolus Accuracy:
  • For 0.05U bolus accuracy: 800 boluses were analyzed for both predicate and subject device.
  • For 2.5U bolus accuracy (predicate) and 5U bolus accuracy (subject): 800 boluses were analyzed.
  • For 25U bolus accuracy: 256 boluses for predicate, 224 boluses for subject device.
• Data Provenance: The data is from non-clinical/performance testing (in vitro), not human patient data. Therefore, country of origin or retrospective/prospective classification in the context of human studies is not applicable. This data would have been generated in a lab setting by the manufacturer, DEKA Research & Development.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not applicable as there was no clinical study involving human data, and thus no expert consensus or ground truth established in the traditional sense of medical image analysis or diagnostic studies. The performance testing was based on direct physical measurements of insulin delivery accuracy against engineered specifications and regulatory standards.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not applicable as there was no clinical study involving human data or expert review. Adjudication methods are relevant for resolving discrepancies in expert interpretations, which did not occur here.

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

This is not applicable. The device is an insulin pump, not an AI-powered diagnostic tool requiring human interpretation. No MRMC study was conducted. The document explicitly states: "No clinical data was obtained in support of this premarket submission."

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

Yes, in a sense, the non-clinical performance testing represents the standalone performance of the device (pump mechanism and associated components) in a controlled environment, without human intervention in the delivery process itself. The data presented for basal and bolus accuracy directly reflects the algorithmically controlled delivery mechanism's performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The "ground truth" for the performance testing of an insulin pump is the physical delivery of insulin and adherence to predefined engineering specifications and regulatory standards for accuracy, flow rates, and safety features (e.g., occlusion alarms). This is established through highly precise laboratory instrumentation and metrology, not derived from expert consensus, pathology, or outcomes data.

8. The sample size for the training set

This is not explicitly stated in the document. As an insulin pump, it's a hardware device with embedded software for control. While the software might have been developed using iterative testing and tuning, the concept of a "training set" as understood in machine learning (e.g., for an AI algorithm) is generally not directly applicable or documented in this type of submission for a physical medical device's core functionality.

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

This is not applicable in the context of this 510(k) submission for an insulin pump. The device's functionality is based on known physical principles and engineered control systems, not on learning from a "training set" with established ground truth labels in the AI sense. Development would involve extensive engineering verification and validation against performance requirements and regulatory standards rather than a machine learning training paradigm.

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