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510(k) Data Aggregation

    K Number
    K103564
    Device Name
    VENOFER PUMP
    Date Cleared
    2011-02-10

    (66 days)

    Product Code
    Regulation Number
    876.5860
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The intended use of the Venofer Pump 1.1 is identical to the unmodified device.

    The Venofer® Pump Is an optional accessory for use on Fresenius 2008T Hemodialysis Machines and is intended to administer Venofer®, an iron sucrose, to treat iron deficiency anemia in patients with chronic kidney disease undergoing hemodialysis, where Venofer®, in conjunction with hemodialysis, is prescribed by a physician.

    Fresenius 2008T is indicated for acute and chronic dialysis therapy.

    Device Description

    The Venofer Pump Module is an optional module for use on Fresenius 2008T Hemodialysis Machines. The module is designed to administer Venofer, an iron sucrose supplement, during dialysis treatments. The Venofer Pump Module is to be used in accordance with the approved Venofer Indications for Use and the physician's prescription.

    The Venofer Pump Module fits into the module compartment of Fresenius 2008T Machines and consists of a control panel, vial holder, fluld detector, and a peristaltic pump.

    AI/ML Overview

    This document is a 510(k) Summary for the Venofer Pump Version 1.1, a device intended to administer Venofer (iron sucrose) during hemodialysis. This filing is a "Special 510(k)" for a modification to an existing device (Venofer Pump K093964).

    Crucially, this document focuses on demonstrating substantial equivalence through non-clinical verification testing (performance, safety, and software) and does NOT describe a study involving human patients or a process for establishing ground truth from expert consensus for diagnostic accuracy.

    Therefore, most of the requested information regarding clinical studies, sample sizes, expert involvement, and ground truth establishment will not be applicable or available in this specific document.

    Here's an analysis based on the provided text:


    1. Table of acceptance criteria and the reported device performance

    Acceptance Criteria CategoryReported Device Performance (Summary from this document)
    Venofer Pump Performance TestingFunctional testing demonstrated that the device performs as designed and expected. This included specific verification tests with defined objectives, test articles, test methods and procedures, and pre-defined acceptance or pass/fail criteria. A system-level hazard analysis confirmed the device does not perform in an unexpected and/or unsafe manner. (Specific quantitative performance metrics are not detailed in this summary.)
    Venofer Pump Safety TestingProduct safety testing demonstrated that the device performs per FDA Consensus Standards. This included: - Electromagnetic Compatibility (EMC): Tested to IEC 60601-1-2: 2007.- Biocompatibility: Tested to AAMI / ANSI / ISO 10993-1:2009 for all new patient-fluid contacting materials. (Specific quantitative results or pass/fail criteria for these tests are not detailed in this summary.)
    Venofer Pump Software TestingSoftware testing demonstrated the device software meets the design input requirements. This included: - Structure chart/flow chart of software architecture.- Summary of software development procedures, including changes.- Software requirements specification with traceability to hazard analysis.- Verification and validation test plans, including pass/fail criteria and traceability to requirements.- System-level test results.- Current software version number and date of latest revision. (Specific quantitative test results or detailed pass/fail criteria are not detailed in this summary.)

    Study Proving Device Meets Acceptance Criteria:

    The study referenced is the "510(k) Verification Testing Non-Clinical Summary" which includes "Performance, Safety, and Software Testing." The document emphasizes that "The conclusions drawn from this testing demonstrates that the Venofer Pump is as safe, as effective, and performs at least as safely and effectively as the legally marketed devices identified as predicate devices to which it was compared."


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

    • Sample Size for Test Set: This refers to the number of devices or components tested, not patient data. The document does not specify the exact number of units or test iterations for the performance, safety, or software verification tests. It generally refers to "the device" implying tests were conducted on one or more representative units.
    • Data Provenance: Not applicable. This is not a study involving patient data. The testing was non-clinical (laboratory/engineering) and would have been conducted by the manufacturer, Renal Solutions, Inc. (USA).
    • Retrospective or Prospective: Not applicable. This applies to clinical data collection, not non-clinical device verification.

    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)

    • Not Applicable. This device is a pump for administering medication, not an AI/diagnostic device that requires expert-established ground truth from medical imaging or patient data. The "ground truth" for this device's performance is its adherence to engineering specifications, safety standards, and software requirements, which are verified through the non-clinical testing described.

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

    • Not Applicable. Adjudication methods like 2+1 or 3+1 refer to how discrepancies in expert opinions are resolved to establish a definitive ground truth in diagnostic studies. This is a non-clinical device verification, so such adjudication is not performed. Acceptance was based on pre-defined pass/fail criteria for engineering and safety tests.

    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. An MRMC study is relevant for diagnostic devices that assist human readers (e.g., radiologists interpreting images). This device is a pump, not an AI-assisted diagnostic tool.

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

    • Not Applicable in the traditional sense of an AI algorithm. This is a hardware device with embedded software. The "standalone" performance was effectively what the document refers to as "Venofer Pump Performance Testing" and "Venofer Pump Software Testing" – verifying the device and its embedded software performed according to specifications and safety requirements without human intervention during the test cycles, but it's not an AI algorithm performing a diagnostic task.

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

    • Not Applicable/Defined Differently. For this type of device, "ground truth" is defined by:
      • Engineering Specifications: The pre-defined functional requirements and performance parameters of the pump (e.g., flow rate accuracy, pressure limits).
      • Regulatory Standards: Compliance with recognized FDA Consensus Standards (e.g., IEC 60601-1-2 for EMC, ISO 10993-1 for Biocompatibility).
      • Software Requirements: The predefined design input requirements for the device's embedded software.
      • Hazard Analysis: Confirmation that the device does not perform in an unexpected and/or unsafe manner.

    8. The sample size for the training set

    • Not Applicable. This device does not use machine learning or AI that requires a "training set" of data. Its functionality is based on deterministic programming and electro-mechanical design, not learned patterns from data.

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

    • Not Applicable. As there is no training set for an AI algorithm, there is no ground truth to establish for it.
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