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Control-IQ+ technology is intended for use with compatible integrated continuous glucose monitors (iCGM) and alternate controller enabled (ACE) pumps to automatically increase, decrease, and suspend delivery of basal insulin based on iCGM readings and predicted glucose values. It can also deliver correction boluses when the glucose value is predicted to exceed a predefined threshold.

Control-IQ+ technology is intended for the management of Type 1 diabetes mellitus in persons 2 years of age and greater and of Type 2 diabetes mellitus in persons 18 years of age and greater.

Control-IQ+ technology is intended for single patient use and requires a prescription.

Control-IQ+ technology (Control-IQ+, the device) is a software-only device intended for the management of type 1 and type 2 diabetes mellitus. The device controls insulin delivery from a compatible alternate controller enabled insulin pump (ACE pump) based on inputs provided by a compatible integrated continuous glucose monitor (iCGM) and inputs provided by the user (e.g., carbohydrate intake, exercise, and sleep schedule). Control-IQ+ technology is meant to be installed on a compatible ACE pump.

Control-IQ+ technology has three different modes: Normal, Sleep, and Exercise. The glucose targets are not individually customizable in these modes but can change based on the mode selected. During normal mode, Control-IQ+ technology aims to control glucose within a target range of 112.5 - 160 mg/dL. During sleep mode, this range is changed to 112.5-120 mg/dL. and it is changed to 140-160 mg/dL during exercise mode.

Control-IQ+ technology includes an integrated feature whereby iCGM values are automatically populated into the glucose field of the integrated bolus calculator when Control-IQ + technology is active (i.e., the device is operating in closed-loop mode). This feature is disabled when Control-IQ is turned off.

Control-IQ+ technology requires users to input their weight and their total daily insulin requirement, which should be established with the help of a health care provider before using the device.

Here's an analysis of the provided text to extract information about acceptance criteria and the study proving the device meets them, presented in the requested format.

It's important to note that the provided text is an FDA 510(k) clearance letter and a 510(k) summary. These documents primarily focus on demonstrating substantial equivalence to a predicate device, rather than providing a detailed technical breakdown of novel acceptance criteria for algorithm performance in the same way one might expect for a new AI/ML device where performance metrics (e.g., sensitivity, specificity, accuracy) are the primary basis of clearance.

For Control-IQ+ technology, the key change is the expansion of the intended use to include Type 2 diabetes patients, supported by a pivotal clinical study. The algorithm itself is stated to be "unchanged," implying that its core performance characteristics (e.g., glucose control targets, basal rate adjustments) are those already established for Type 1 diabetes with the predicate device. Therefore, the "acceptance criteria" here largely pertain to the clinical safety and effectiveness in the new population, rather than the internal algorithmic performance metrics.

Acceptance Criteria and Device Performance Study for Control-IQ+ Technology

Given that the device is a refined version of an already cleared product ("Control-IQ technology" K232382) and the core algorithm is "unchanged," the acceptance criteria for this 510(k) submission (K243823) are primarily focused on demonstrating the safety and effectiveness of expanding its indications to include Type 2 diabetes patients. The primary evidence for this is a clinical study.

1. Table of Acceptance Criteria and Reported Device Performance

As this is a 510(k) focusing on an expanded user population for an existing algorithm rather than a de novo submission for a novel AI algorithm with specific performance cutoffs, the acceptance criteria are framed in terms of clinical outcomes rather than typical AI performance metrics like sensitivity/specificity.

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

• Test Set (Clinical Study):
  • Sample Size: 319 subjects
  • Data Provenance: Not explicitly stated (e.g., country of origin), but implies a multi-site prospective clinical trial. The study was a "randomized controlled trial."
  • Retrospective/Prospective: Prospective

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

This information is not provided in the document. For a clinical trial of an automated glycemic controller, the "ground truth" for evaluating effectiveness is typically objective clinical measurements (e.g., HbA1C tests, CGM data, adverse events) rather than expert consensus on diagnostic images. Clinical outcomes are inherently the "ground truth." Expert physicians would design the trial, manage patients, and interpret results, but they aren't establishing a "ground truth" per se in the way radiologists might do for disease detection.

4. Adjudication Method for the Test Set

This information is not provided. For a randomized controlled trial measuring objective metrics like HbA1C, formal adjudication in the sense of a diagnostic agreement (e.g., 2+1 radiology consensus) is generally not applicable, as the outcomes are quantitative and measured directly.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, What was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• MRMC Study: No, an MRMC study was not done. This type of study is relevant for diagnostic imaging AI, where human readers (e.g., radiologists) interpret images with or without AI assistance.
• Effect Size of Human Improvement with AI: Not applicable, as this device is an automated glycemic controller, not an AI assisting human interpretation of medical images. The clinical study compares the device's performance directly against a control group (CGM with continued insulin injections), rather than evaluating human reader performance with and without AI assistance.

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

• The "Clinical Testing" section describes a pivotal clinical study where Control-IQ+ technology's performance (an automated insulin delivery system) was evaluated in patients. While it's implied that the system operates autonomously based on CGM inputs and user settings, the study measures the system's performance in a real-world setting with human users interacting with the device, not a purely standalone algorithmic evaluation in a simulated environment. The study is effectively evaluating the device's performance as intended for clinical use.

7. The Type of Ground Truth Used

• The primary ground truth for the clinical study was outcomes data, specifically the change in HbA1C (Glycated Hemoglobin), which is a key clinical endpoint for diabetes management. Other clinical measures from the iCGM (e.g., glucose values, time in range) and safety outcomes would also serve as ground truth for evaluating the device's performance.

8. The Sample Size for the Training Set

• This information is not provided in the document. The document states that "The design of the Control-IQ+ algorithm is unchanged" from its predicate. This implies that the training of the core algorithm was done prior to the predicate device's clearance and is not being re-evaluated for this submission. The 510(k) is primarily about demonstrating safety and effectiveness in the expanded user population, not re-training or re-validating the core algorithm's initial development.

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

• This information is not provided as the algorithm itself is stated to be unchanged from the predicate device. Details about the original algorithm's development, including its training data and how its ground truth was established, would have been part of the predicate device's (K232382) submission.

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