(82 days)
DEKA Loop is intended for use with compatible integrated continuous glucose monitors (iCGM) and the DEKA alternate controller enabled (ACE) insulin infusion pump to automatically increase, and suspend delivery of basal insulin based on iCGM readings and predicted glucose values. It can also recommend, and with the user's confirmation, control the delivery of correction boluses when glucose values are predicted to exceed user configurable thresholds.
DEKA Loop is intended for the management of Type 1 diabetes mellitus in persons six years of age and greater.
DEKA Loop is intended for single patient use and requires a prescription.
DEKA Loop is an interoperable Alternate Glycemic Controller (iAGC) and works to control an ACE (Alternate Controller Enabled) insulin pump to automatically increase, decrease, and suspend delivery of basal insulin based on readings from an iCGM (integrated continuous glucose monitor) and glucose values predicted by DEKA Loop. DEKA Loop can also recommend, and with the user's confirmation, control the delivery of correction boluses when glucose values are predicted to exceed user configurable thresholds. It is controlled by an iOS app that is downloaded to a user's iPhone.
The provided document, an FDA 510(k) K234055 clearance letter for the DEKA Loop device, focuses on demonstrating substantial equivalence to a predicate device (Tidepool Loop K203689). It details the device's technological characteristics and mentions performance data primarily through in silico testing for clinical equivalence. However, the document does not directly provide specific acceptance criteria or detailed results of a study designed to prove the device meets those criteria in the format requested (e.g., a table with numerical acceptance values and reported performance). Nor does it describe patient-level ground truth establishment, expert adjudication, or MRMC studies.
The information below is extracted and inferred from the provided text, highlighting what is available and what is explicitly not mentioned or detailed in relation to your specific questions.
Here's a breakdown based on the provided text:
Device Performance Acceptance Criteria and Study Details (Based on available information)
The document primarily relies on demonstration of substantial equivalence to a predicate device (Tidepool Loop) based on technological, functional, and performance characteristics, rather than establishing de novo performance criteria. The "Performance Data" section specifically states: "Additionally, in-silico software challenge testing demonstrated clinical equivalence to the predicate device."
1. Table of Acceptance Criteria and Reported Device Performance:
The document does not provide a table with specific numerical acceptance criteria and corresponding reported device performance values. Instead, it states that "in-silico testing proves that the DEKA Loop algorithm is clinically equivalent to the Tidepool Loop Algorithm." This implies that the 'acceptance' for clinical performance was demonstrating equivalence through in silico methods.
| Characteristic | Acceptance Criteria (Implicit/Inferred) | Reported Device Performance | Notes |
|---|---|---|---|
| Clinical Performance (via Algorithm Equivalence) | Clinically equivalent to the predicate device (Tidepool Loop Algorithm) | Demonstrated clinical equivalence to Tidepool Loop Algorithm via in-silico testing. | This is the primary claim for clinical performance. Specific metrics (e.g., time-in-range, hypoglycemia events) and their acceptance thresholds are not provided for the in-silico study in this document. |
| Software Verification and Validation | Meets FDA's guidance document: "Guidance for Industry and FDA Staff - Total Product Life Cycle: Infusion Pump - Premarket Notification 510(k) Submissions Guidance" | Performed software verification and validation testing as per guidance. | General statement of compliance; no specific metrics or outcomes detailed. |
| Risk Assessment | Complies with ISO 14971 | Performed Risk Assessment including detailed hazard analysis based on ISO 14971. | General statement of compliance. |
2. Sample Size Used for the Test Set and Data Provenance:
- Sample Size (Test Set): Not explicitly stated. The document mentions "in-silico software challenge testing." This implies a simulated patient cohort, but the size of this cohort is not provided in terms of "samples."
- Data Provenance: The nature of in silico testing means it's not based on ex vivo or in vivo patient data in the traditional sense for the test set. It's a computational simulation.
- For the predicate device's clinical performance (which the subject device aims to be equivalent to), the document states: "Tidepool Loop clinical performance is supported by representative 1,250 participants in a 15 months duration real-world, observational, single arm study of DIY Loop including both pediatric and adult participants." This refers to the predicate's data, not the subject device's in silico test set.
3. Number of Experts Used to Establish Ground Truth and Qualifications:
- Not applicable / Not stated. Ground truth, in the context of an in silico study for a glycemic controller, would likely refer to the accuracy of the simulated physiological model against known physiological principles or real-world data characteristics, rather than expert annotation of medical images or diagnoses. No human experts are mentioned for establishing ground truth for the in silico test set.
4. Adjudication Method for the Test Set:
- Not applicable / Not stated. Given the in silico nature and lack of human expert involvement in "ground truth" establishment as typically understood in AI imaging, no adjudication method is described.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:
- No. An MRMC study is relevant for human-in-the-loop performance studies, particularly in medical imaging where radiologists or clinicians interpret cases. The DEKA Loop is an automated glycemic controller. The document does not describe any study where human readers (e.g., clinicians) used the DEKA Loop (or a simulated version) to assess its comparative effectiveness against a standard of care or the predicate with outcomes like improved blood glucose control. The stated clinical performance evaluation was in silico device-to-predicate algorithm equivalence.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, implicitly. The "in-silico software challenge testing" is an algorithm-only (standalone) performance evaluation. It assessed the DEKA Loop algorithm's performance against the predicate's algorithm in a simulated environment, without direct human intervention in the loop of glucose regulation within the test.
7. The Type of Ground Truth Used:
- For the in-silico testing, the ground truth would be based on the simulated physiological model's behavior, which is designed to accurately represent human glucose metabolism and insulin action under various conditions. This is a form of simulated data / model-based ground truth rather than expert consensus, pathology, or direct patient outcomes data from a clinical trial for the subject device itself. The goal was to prove "clinical equivalence to the predicate device," meaning the in silico performance mirrored what the predicate device achieved in its real-world clinical study.
8. The Sample Size for the Training Set:
- Not stated. As this is a 510(k) submission for an existing algorithm (the "Loop" algorithm, which DEKA Loop is shown to be equivalent to), and not a novel AI/ML algorithm requiring de novo training, details about its original training set (if any, as an "algorithm" might be more deterministic control logic than a learned AI model in some cases) are not provided in this regulatory document. The focus is on the validation that the DEKA Loop implementation of the algorithm is equivalent to the predicate's.
9. How the Ground Truth for the Training Set was Established:
- Not stated. Refer to point 8. If the algorithm involved machine learning, its original training (if any) would have required a separate dataset and ground truth establishment method, which is not detailed in this 510(k) summary. Given the description ("predicts glucose levels... based on prior iCGM readings, insulin delivery history, and user input... uses that prediction to adjust insulin delivery"), it sounds more like a model-based predictive control algorithm rather than a deep learning model trained on a large dataset with ground truth labels.
§ 862.1356 Interoperable automated glycemic controller.
(a)
Identification. An interoperable automated glycemic controller is a device intended to automatically calculate drug doses based on inputs such as glucose and other relevant physiological parameters, and to command the delivery of such drug doses from a connected infusion pump. Interoperable automated glycemic controllers are designed to reliably and securely communicate with digitally connected devices to allow drug delivery commands to be sent, received, executed, and confirmed. Interoperable automated glycemic controllers are intended to be used in conjunction with digitally connected devices for the purpose of maintaining glycemic control.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) An appropriate, as determined by FDA, clinical implementation strategy, including data demonstrating appropriate, as determined by FDA, clinical performance of the device for its intended use, including all of its indications for use.
(A) The clinical data must be representative of the performance of the device in the intended use population and in clinically relevant use scenarios and sufficient to demonstrate appropriate, as determined by FDA, clinical performance of the device for its intended use, including all of its indications for use.
(B) For devices indicated for use with multiple therapeutic agents for the same therapeutic effect (
e.g., more than one type of insulin), data demonstrating performance with each product or, alternatively, an appropriate, as determined by FDA, clinical justification for why such data are not needed.(C) When determined to be necessary by FDA, the strategy must include postmarket data collection to confirm safe real-world use and monitor for rare adverse events.
(ii) Results obtained through a human factors study that demonstrates that an intended user can safely use the device for its intended use.
(iii) A detailed and appropriate, as determined by FDA, strategy to ensure secure and reliable means of data transmission with other intended connected devices.
(iv) Specifications that are appropriate, as determined by FDA, for connected devices that shall be eligible to provide input to (
e.g., specification of glucose sensor performance) or accept commands from (e.g., specifications for drug infusion pump performance) the controller, and a detailed strategy for ensuring that connected devices meet these specifications.(v) Specifications for devices responsible for hosting the controller, and a detailed and appropriate, as determined by FDA, strategy for ensuring that the specifications are met by the hosting devices.
(vi) Documentation demonstrating that appropriate, as determined by FDA, measures are in place (
e.g., validated device design features) to ensure that safe therapy is maintained when communication with digitally connected devices is interrupted, lost, or re-established after an interruption. Validation testing results must demonstrate that critical events that occur during a loss of communications (e.g., commands, device malfunctions, occlusions, etc.) are handled and logged appropriately during and after the interruption to maintain patient safety.(vii) A detailed plan and procedure for assigning postmarket responsibilities including adverse event reporting, complaint handling, and investigations with the manufacturers of devices that are digitally connected to the controller.
(2) Design verification and validation documentation must include appropriate design inputs and design outputs that are essential for the proper functioning of the device that have been documented and include the following:
(i) Risk control measures to address device system hazards;
(ii) Design decisions related to how the risk control measures impact essential performance; and
(iii) A traceability analysis demonstrating that all hazards are adequately controlled and that all controls have been validated in the final device design.
(3) The device shall include appropriate, as determined by FDA, and validated interface specifications for digitally connected devices. These interface specifications shall, at a minimum, provide for the following:
(i) Secure authentication (pairing) to connected devices;
(ii) Secure, accurate, and reliable means of data transmission between the controller and connected devices;
(iii) Sharing of necessary state information between the controller and any connected devices (
e.g., battery level, reservoir level, sensor use life, pump status, error conditions);(iv) Ensuring that the controller continues to operate safely when data is received in a manner outside the bounds of the parameters specified;
(v) A detailed process and procedures for sharing the controller's interface specification with connected devices and for validating the correct implementation of that protocol; and
(vi) A mechanism for updating the controller software, including any software that is required for operation of the controller in a manner that ensures its safety and performance.
(4) The device design must ensure that a record of critical events is stored and accessible for an adequate period to allow for auditing of communications between digitally connected devices, and to facilitate the sharing of pertinent information with the responsible parties for those connected devices. Critical events to be stored by the controller must, at a minimum, include:
(i) Commands issued by the controller, and associated confirmations the controller receives from digitally connected devices;
(ii) Malfunctions of the controller and malfunctions reported to the controller by digitally connected devices (
e.g., infusion pump occlusion, glucose sensor shut down);(iii) Alarms and alerts and associated acknowledgements from the controller as well as those reported to the controller by digitally connected devices; and
(iv) Connectivity events (
e.g., establishment or loss of communications).(5) The device must only receive glucose input from devices cleared under § 862.1355 (integrated continuous glucose monitoring system), unless FDA determines an alternate type of glucose input device is designed appropriately to allow the controller to meet the special controls contained within this section.
(6) The device must only command drug delivery from devices cleared under § 880.5730 of this chapter (alternate controller enabled infusion pump), unless FDA determines an alternate type of drug infusion pump device is designed appropriately to allow the controller to meet the special controls contained within this section.
(7) An appropriate, as determined by FDA, training plan must be established for users and healthcare providers to assure the safety and performance of the device when used. This may include, but not be limited to, training on device contraindications, situations in which the device should not be used, notable differences in device functionality or features compared to similar alternative therapies, and information to help prescribers identify suitable candidate patients, as applicable.
(8) The labeling required under § 809.10(b) of this chapter must include:
(i) A contraindication for use in pediatric populations except to the extent clinical performance data or other available information demonstrates that it can be safely used in pediatric populations in whole or in part.
(ii) A prominent statement identifying any populations for which use of this device has been determined to be unsafe.
(iii) A prominent statement identifying by name the therapeutic agents that are compatible with the controller, including their identity and concentration, as appropriate.
(iv) The identity of those digitally connected devices with which the controller can be used, including descriptions of the specific system configurations that can be used, per the detailed strategy submitted under paragraph (b)(1)(iii) of this section.
(v) A comprehensive description of representative clinical performance in the hands of the intended user, including information specific to use in the pediatric use population, as appropriate.
(vi) A comprehensive description of safety of the device, including, for example, the incidence of severe hypoglycemia, diabetic ketoacidosis, and other relevant adverse events observed in a study conducted to satisfy paragraph (b)(1)(i) of this section.
(vii) For wireless connection enabled devices, a description of the wireless quality of service required for proper use of the device.
(viii) For any controller with hardware components intended for multiple patient reuse, instructions for safely reprocessing the hardware components between uses.