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510(k) Data Aggregation

    K Number
    K100306
    Manufacturer
    Date Cleared
    2010-05-04

    (90 days)

    Product Code
    Regulation Number
    870.5800
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K992079, K942664

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

    The Kendall SCD Compression System is designed to increase venous blood flow in the at risk patient, including bariatric and morbidly obese patients, in order to help prevent deep vein thrombosis and pulmonary embolism.

    Device Description

    The proposed devices, Sequential Compression Comfort Sleeves, are the compressible limb sleeve component of a pneumatic compression system for applying pressure to a patient's leg for the prevention of deep vein thrombosis (DVT) and pulmonary embolism (PE).

    Sequential Compression Comfort Sleeves are compatible with the SCD Express Controller (K040511), SCD Response Controller (K992079) and SCD Sequel Controller (K942664), collectively referred to as SCD Controllers. SCD Controllers provide air to the sleeves through tubing connected to each bladder of the sleeve delivering an intermittent, sequentially gradient pneumatic pressure to the limb. The controller system monitors the pressure in the bladders and releases the pressure by venting the air from the sleeve bladders through a valve system within the controllers.

    The proposed single-patient-use, disposable sleeves are available in knee, thigh and thigh Tear-Away styles. The Tear-Away function allows the clinician to convert the patient from a thigh length sleeve to a knee length sleeve without changing to a new sleeve. TThe sleeve connects to a controller via a 3-lumen tubing set. The tubing set connects to the sleeve via a low-profile connector that can be disengaged to allow for patient ambulation without sleeve removal.

    AI/ML Overview

    The provided text describes the Kendall SCD Sequential Compression Comfort Sleeves (K100306), which are compressible limb sleeves designed to prevent deep vein thrombosis (DVT) and pulmonary embolism (PE). The submission focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria for AI performance.

    Therefore, the requested information elements related to AI performance, ground truth, expert consensus, and sample sizes for training/test sets are not applicable or cannot be extracted from the provided text. The document describes a traditional medical device submission, not an AI/ML device submission.

    Here's an analysis based on the available information:

    1. Table of Acceptance Criteria and Reported Device Performance

    The submission states, "There are no FDA performance standards established and required for the proposed device." Instead, it refers to general standards and product verification/validation testing. The acceptance criteria for the sleeves themselves would revolve around their functional and material properties, rather than diagnostic accuracy metrics typically seen with AI.

    Acceptance Criteria (Inferred from "Product Testing" and "Product Comparison")Reported Device Performance
    Biocompatibility: Materials are safe for patient contact.Compliance with ISO 10993-1.
    Packaging Integrity/Durability: Withstands shipping and handling.Compliance with ISTA 2A (Preshipment Testing Procedures).
    Functional Equivalence: Delivers intermittent, sequentially gradient pneumatic pressure effectively.Demonstrated through product verification and validation testing, including hemodynamic testing on healthy human subjects, showing identical function and mode of operation to predicate devices.
    Material Construction: Comfort, durability, and ability to form bladders.Constructed of four layers (two PVC layers, polyester mesh, wickable polyester fabric) forming a "pillowcase" around bladders, with tubing welded inside, described as making the sleeves "more comfortable" than predicate devices.
    Compatibility: Works with specified controllers.Designed to be used with SCD Express Controller (K040511), SCD Response Controller (K992079), and SCD Sequel Controller (K942664).
    Intended Use Equivalence: Prevents DVT and PE in at-risk patients.Identical intended use to predicate devices and supported by extensive clinical literature on IPC devices.
    Safety and Effectiveness: No major risks, acceptable risk/benefit ratio.Extensive published clinical literature on IPC devices confirms safety and performance, with no major risks identified.

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

    • Test Set Sample Size: The document mentions "hemodynamic testing on healthy human subjects." However, the specific number of subjects is not provided.
    • Data Provenance: The hemodynamic testing was on "healthy human subjects." The country of origin is not specified. The submission primarily relies on a review of "published and unpublished clinical literature" regarding IPC devices, which would have diverse international provenance and be retrospective in nature for the published literature.

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

    This is not applicable for this type of device submission. The "ground truth" for a pneumatic compression sleeve is its physical function and physiological effect (blood flow increase), not a diagnostic label requiring expert consensus. The "experts" involved would be engineers for functional testing and clinicians/researchers for physiological assessment, but their number and specific qualifications for establishing a "ground truth" in the AI sense are not detailed.

    4. Adjudication Method for the Test Set

    This is not applicable. Adjudication methods like 2+1 or 3+1 are used for establishing ground truth in diagnostic studies, typically involving multiple human readers for image or data interpretation. This submission concerns 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

    No, an MRMC comparative effectiveness study was not done. This is not an AI-assisted diagnostic device.

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

    No, a standalone algorithm performance study was not done. This is not an AI-powered device.

    7. The Type of Ground Truth Used

    For the device itself, the "ground truth" is its functional performance (e.g., pressure delivery, material integrity) as measured by engineering tests and its physiological effect (increased venous blood flow) as measured in hemodynamic studies. For the claim of DVT/PE prevention, the "ground truth" is established by the extensive body of clinical literature on IPC devices, demonstrating their effectiveness in preventing these outcomes.

    8. The Sample Size for the Training Set

    Not applicable. This is not an AI device that requires a training set for machine learning models.

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

    Not applicable. See point 8.

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    K Number
    K012979
    Date Cleared
    2002-05-08

    (245 days)

    Product Code
    Regulation Number
    870.5800
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K942664

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

    The sequential compression sleeve is designed to increase venous blood flow in the non-ambulatory patient in order to help prevent deep vein thrombosis and pulmonary embolism.

    Device Description

    The compressible sleeve is a garment used for compressing patient's lower limbs by inflation with air to aid the blood flow back towards the heart to prevent deep vein thrombosis (DVT) in patients at risk. The inflatable sleeve consists of inflatable air bladders and tubing, and are connected to the OEM equipment using specific connector(s). The MDS reprocessed sleeve pair is cleaned, inspected and sterilized in sealed individual pouches and returned to the originating hospital.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for a reprocessed compressible limb sleeve. The submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than presenting a study with specific acceptance criteria and detailed performance data often seen for novel devices or software.

    Here's an analysis based on the provided information, addressing your points where possible:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria CategorySpecific CriteriaReported Device PerformanceComments
    Functional and Safety CharacteristicsNo significant difference in functional and safety characteristics between new and reprocessed devices.Data indicates no significant difference in functional and safety characteristics between new and reprocessed devices.This is the overarching acceptance criteria for demonstrating substantial equivalence. Specific metrics for "functional and safety characteristics" are not detailed in this summary.
    Technological CharacteristicsSubstantially equivalent in materials, design, and intended use to the sleeves as originally distributed.The reprocessed sleeves are substantially equivalent (in materials, design, and intended use) to the sleeves as originally distributed.This is a declaration of equivalence rather than a specific performance metric.

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

    • Test Set Sample Size: Not explicitly stated. The document mentions "Comparative bench testing," which implies a sample was used, but the size of this sample is not provided.
    • Data Provenance: The study was "conducted at Medical Device Services, Inc." It appears to be a bench test conducted by the manufacturer, rather than a clinical study with patient data. Therefore, concepts like "country of origin of the data" or "retrospective/prospective" are not directly applicable in the way they would be for patient-centric studies.

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

    • Not Applicable. This submission describes bench testing for a physical medical device (a reprocessed limb sleeve), not a diagnostic algorithm that requires expert interpretation for ground truth. The "ground truth" here would relate to the functional and safety specifications of the device itself, likely assessed through engineering tests and measurements.

    4. Adjudication Method for the Test Set

    • Not Applicable. As this is bench testing and not an expert-based ground truth establishment, adjudication methods like 2+1 or 3+1 are not relevant.

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

    • No. This is not an AI-powered diagnostic device, and therefore, an MRMC study is not applicable. The device is a reprocessed compressible limb sleeve.

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

    • No. This is not an algorithm, so standalone performance is not relevant.

    7. The Type of Ground Truth Used

    • The "ground truth" for this comparative bench testing would be the functional and safety specifications of the original new device (Kendall Model 6325 SCD Sequential Compression System). The reprocessed devices were tested against how the original, new devices are expected to perform. This likely involved quantitative measurements of parameters like inflation pressure, cycle times, air leakage, material integrity, and biocompatibility (though the latter isn't explicitly detailed in the summary).

    8. The Sample Size for the Training Set

    • Not Applicable. This is not an AI/ML algorithm that requires a training set.

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

    • Not Applicable. As there is no training set for an AI/ML algorithm.
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