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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.

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.

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.

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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