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510(k) Data Aggregation

    K Number
    K213540
    Manufacturer
    Date Cleared
    2022-05-20

    (193 days)

    Product Code
    Regulation Number
    870.4210
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K981613, K162774

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    Quantum SuperPAC Tubing set is a set of tubing intended for use during cardiopulmonary bypass for a duration up to 6 hours.
    Quantum SuperPAC Cardioplegia Set is a tubing set used for the infusion of cardioplegia solutions and physiological fluids during cardiac surgery procedures on the heart and great vessels for up to six hours.

    Device Description

    Quantum SuperPAC Tubing set devices are designed to connect different devices that are not provided in the Quantum SuperPAC tubing set such as oxygenators, pumps, reservoirs, filters, and other cardiopulmonary bypass components into circuits used in surgical procedures requiring extracorporeal support, for a maximum duration of 6 hours.
    Quantum SuperPAC Cardioplegia set devices are designed to connect different devices that are not provided in the Quantum SuperPAC tubing set for the infusion of cardioplegia solutions and physiological fluids during cardiac surgery procedures on the heart and great vessels for up to six hours.
    Quantum SuperPAC devices (all variants) are non-toxic, non-pyrogenic, and sterilized by ethylene oxide. Devices are intended for single use only and are not to be resterilized by the user.
    All the device surfaces in contact with blood are treated with a phosphorylcholine-based coating.
    Quantum SuperPAC devices (all variants) are mainly constituted of polyvinyl chloride (PVC) DOP free tubing and additional components composing the set; different variants are available, varying for tubing dimension and set configuration in order to address customer and surgical procedure specifications.

    AI/ML Overview

    This document is a 510(k) summary for the Quantum SuperPAC Tubing Set and Quantum SuperPAC Cardioplegia Set. It details the device's intended use, comparison to predicate devices, and performance testing. However, it does not contain information regarding studies that establish acceptance criteria for device performance in the manner of an AI/ML algorithm's effectiveness in diagnostic tasks, nor does it provide details on sample sizes, ground truth establishment, or expert adjudication typical for such studies.

    Instead, this document focuses on demonstrating substantial equivalence to existing medical devices through non-clinical performance and biocompatibility testing for a medical device (tubing sets) rather than a diagnostic AI system.

    Therefore, many of the requested categories are not applicable to the information provided in this 510(k) summary, as it pertains to a physical medical device.

    Here's the breakdown based on the provided text:

    1. A table of acceptance criteria and the reported device performance

    The document does not explicitly present a table of acceptance criteria alongside reported device performance in the context of an AI/ML diagnostic system. Instead, it states that "All testing passed by meeting the established requirements set for the devices." The performance tests conducted are listed as:

    • Operating Parameters
    • Mechanical Integrity
    • Device pressure drop
    • Spallation and Tubing Life
    • Connection strength

    These tests were mainly performed according to ISO 15676. Additional tests included:

    • Evaluation of product shelf life (according to EP/UPS requirements)
    • Validation of EtO Sterilization process (according to ISO 11135:2014)
    • Packaging Validation tests (according to ISO 11607-1:2019, ASTM F1886/F1886M-16, EN 868-5 and ASTM F1929-15)
    • Biocompatibility (according to ISO 10993-1:2018 and FDA Guidance)

    The document asserts that the devices met these established requirements, implying that the acceptance criteria were defined by these standards and the device's performance aligned with them.

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

    This information is not applicable as the document describes non-clinical performance testing for a physical medical device, not a diagnostic AI/ML system requiring a test set of data.

    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 the document describes non-clinical performance testing for a physical medical device, not a diagnostic AI/ML system requiring expert-established ground truth.

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

    This information is not applicable as the document describes non-clinical performance testing for a physical medical device.

    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 information is not applicable as the document describes non-clinical performance testing for a physical medical device, not a diagnostic AI/ML system.

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

    This information is not applicable as the document describes a physical medical device, not an algorithm.

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

    This information is not applicable for the type of device described. The "ground truth" for this device would be its adherence to performance specifications and safety standards as determined by the listed non-clinical tests (e.g., mechanical integrity tests, biocompatibility tests).

    8. The sample size for the training set

    This information is not applicable as the document describes a physical medical device, not a machine learning model.

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

    This information is not applicable as the document describes a physical medical device, not a machine learning model.

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