(100 days)
The Medtronic MiniMed Diabetes Data Management System -(DDMS) is a service intended to facilitate the review and analysis of information downloaded from Medtronic MiniMed insulin infusion pumps and compatible home glucose meters. Information downloaded to or entered into a PC is transmitted via the Internet to a Medtronic MiniMed database. DDMS includes numerous options for the analysis and display of this information. The displays and reports available in DDMS may be useful in identifying the impact of insulin delivery, daily activities and diet on glucose control.
DDMS is a network based software system residing on a computer server platform connected to the Internet. The system is designed to download patient data from Medtronic MiniMed insulin pumps and supported thirdparty blood glucose meters to the DDMS central database. The data contained in DDMS is accessible to users using a standard browser, i.e. Microsoft Internet Explorer, on a PC that is connected to the Internet. Communication devices and cables required to facilitate the interface with Medronic MiniMed pumps are connected to the user's PC and device data is downloaded through the PC and transmitted directly over the Internet to the DDMS server. Glucose meter interface cables are connected similarly to the user's PC for downloading to DDMS. The system provides additional capability for manual on-screen data entry of other clinical parameters for downloading to DDMS.
This premarket notification (K032164) for the Medtronic MiniMed Diabetes Data Management System (DDMS) focuses on demonstrating substantial equivalence to a predicate device, the Medtronic MiniMed Solutions Pumps and Meter software, model 7311. As such, the submission primarily emphasizes comparing the technological features and intended use rather than presenting a performance study with defined acceptance criteria for a novel device.
Therefore, many of the typical elements of a detailed performance study, such as acceptance criteria table with reported performance, sample sizes for test and training sets, expert qualifications, and ground truth establishment methods, are not explicitly provided in this 510(k) summary. The submission's goal is to show that the new system's data management functionality (specifically the internet-based data transfer and storage) does not raise new questions of safety or effectiveness compared to the predicate device, which stored data locally.
Here's a breakdown of what can be inferred or is missing based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
| Acceptance Criteria (Inferred from Substantial Equivalence Claim) | Reported Device Performance (Inferred/Stated) |
|---|---|
| Functional Equivalence: The DDMS should perform the core function of downloading, storing, and presenting patient data from insulin pumps and blood glucose meters similarly to the predicate device. | "The technological features of the new device do not differ significantly from the predicate device." |
| Data Integrity: The DDMS should accurately transmit and store patient data. (Implicit for any data management system) | While not explicitly stated with metrics, the claim of substantial equivalence implies that data integrity is maintained at a level comparable to the predicate. |
| Intended Use Compatibility: The reports generated by DDMS should be useful for diabetes patient self-management, therapy optimization, and clinical assessment. | "Reports generated by this software are designed for use as tools to assist with diabetes patient self-management, therapy optimization and clinical assessment." |
| Security/Privacy: Given the internet-based nature, an implicit requirement for data security and privacy. | Not explicitly detailed in the provided summary, but would have been addressed in the full 510(k) submission as part of general controls and software validation. |
| Usability/Accessibility: The system should allow users (patients and healthcare providers) to access reports via a standard browser. | "The data contained in DDMS is accessible to users using a standard browser, i.e. Microsoft Internet Explorer, on a PC that is connected to the Internet." |
2. Sample Size Used for the Test Set and Data Provenance:
- Not explicitly stated. Since this is a 510(k) focusing on substantial equivalence for a data management system, a formal clinical "test set" in the sense of patient outcomes or diagnostic accuracy studies is not typically required or described in the summary. The "test" would likely involve software validation and verification against functional requirements, comparing its output to the predicate.
- Data Provenance: Not specified. It's likely that the testing involved simulated data or existing patient data from devices, but the origin (e.g., country, retrospective/prospective) is not mentioned.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:
- Not applicable/Not explicitly stated. This type of information is pertinent to studies involving clinical adjudication or diagnostic accuracy, which are not the primary focus of this 510(k) summary for a data management system. The "ground truth" for a data management system would be the correct transmission, storage, and display of data, verified through software testing and validation rather than expert clinical review of output.
4. Adjudication Method for the Test Set:
- Not applicable/Not explicitly stated. See point 3.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:
- No. An MRMC study is relevant for evaluating the impact of a diagnostic or assistive device on human reader performance, typically in image interpretation or complex decision-making tasks. This 510(k) is for a data management system, not a diagnostic or AI-assisted interpretation tool. Therefore, such a study would not be expected or relevant here.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:
- Implicitly, yes, for software functionality. The "device" itself (DDMS software) would have undergone standalone testing and validation to ensure its components function as intended (e.g., data download, storage, report generation). However, this would be a software verification and validation (V&V) process, not a clinical "standalone performance" study in the sense of diagnostic accuracy metrics. The summary does not provide details on this V&V process.
7. The Type of Ground Truth Used:
- Functional/Systematic Ground Truth (Inferred): For a data management system, the "ground truth" would be established by verifying that the data transmitted, stored, and reported matches the source data from the insulin pumps and blood glucose meters exactly. This involves comparing the system's output to the known correct inputs and expected transformations, often through automated and manual software testing. It's not clinical "ground truth" like pathology for a biopsy or outcomes data.
8. The Sample Size for the Training Set:
- Not applicable/Not stated. This device is a data management system, not an AI/ML algorithm that requires a "training set." Its functionality is based on predefined logic and software architecture, not on learning from a dataset.
9. How the Ground Truth for the Training Set Was Established:
- Not applicable. As a data management system, there is no "training set" in the context of machine learning.
§ 880.5725 Infusion pump.
(a)
Identification. An infusion pump is a device used in a health care facility to pump fluids into a patient in a controlled manner. The device may use a piston pump, a roller pump, or a peristaltic pump and may be powered electrically or mechanically. The device may also operate using a constant force to propel the fluid through a narrow tube which determines the flow rate. The device may include means to detect a fault condition, such as air in, or blockage of, the infusion line and to activate an alarm.(b)
Classification. Class II (performance standards).