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The ElastiMed's SACS device is a portable and lightweight device intended to provide compression in both sustained and intermittent settings by stimulating blood flow in the legs. The SACS device is intended for:

• -Aid in the prevention of DVT.
• -Enhance blood circulation.
• -Diminish post-operative pain and swelling.
• -Reduce wound healing time.
• -Aid in the treatment and healing of stasis dermatitis, venous stasis ulcers, arterial and diabetic leg ulcers, chronic venous insufficiency, chronic lymphedema, and reduction of edema in the lower limbs.
• As a prophylaxis for DVT by persons expecting to be stationary for long periods of time. -Reduction of edema associated with soft tissue injuries, such as burns, postoperative or postimmobilization edema or ligament sprains.

The device can be used in the home or clinical setting.

The ElastiMed SACS is a lightweight, portable, rechargeable battery powered device, aiding in the stimulation of blood flow in the lower limb through sequential compression.

The SACS device imbeds three Electro Active Polymer (EAP) straps, that sequentially stretch and contract when stimulated by an electric charge. The timely sequential upward contractions of the straps compress the limb, allowing for the repetitive squeezing and relieving actions that mimic normal muscle contractions. The device is supplied with a rechargeable battery which is incased in the fabric casing, and a microprocessor which is imbedded within the control box, located at the front of the unit. The one touch operation control box also imbeds a buzzer, LED light indicator, micro speaker, and a micro-USB port (for charging). The device can be used in the home or clinical setting.

The provided text is a 510(k) summary for the ElastiMed SACS device. It describes the device, its intended use, and claims substantial equivalence to predicate devices, but it does not contain a detailed study proving the device meets specific acceptance criteria with reported device performance data, sample sizes, ground truth establishment, or expert details. The "Performance Data" section only offers a high-level summary of nonclinical validation without revealing specific results or acceptance criteria.

Therefore, many of the requested items cannot be extracted from the provided text.

Here is what can be inferred and what is missing:

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

The document does not provide a table of acceptance criteria with corresponding device performance metrics. It generally states that "nonclinical validation... verified performance attributes, pressure delivery, geometrical attributes and system durability have shown that the SACS device has performance characteristics consistent with the predefined requirements". However, it does not detail what those "predefined requirements" (acceptance criteria) are or what the specific "performance attributes" found were.

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 provided in the document. The document refers to "nonclinical validation" but does not specify sample sizes or data provenance.

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 provided. Since the described validation is "nonclinical," it is unlikely to involve human experts establishing ground truth in the way described for medical imaging.

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

This information is not provided.

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

A Multi-Reader, Multi-Case (MRMC) comparative effectiveness study was not conducted or reported. This type of study is typically for evaluating the impact of AI on human diagnostic performance, which is not applicable to a compression device like SACS based on the provided information, which focuses on physical performance characteristics.

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

The document mentions "nonclinical validation including electrical safety, EMC, software validation, environmental / shipping, life cycle (durability and shelf-life), and performance testing." This implies standalone testing of the device's physical and software performance. However, there are no specific performance metrics or acceptance criteria provided for these tests.

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

For the "nonclinical validation" mentioned, the "ground truth" would likely be based on engineering specifications, measurement standards, and physical test methods (e.g., pressure transducers for pressure delivery, calipers for geometrical attributes, endurance cyclers for durability). The document does not explicitly state the types of ground truth used but implies it relates to measurable physical and electrical properties.

8. The sample size for the training set

This information is not provided. The SACS device, as described, is a mechanical compression device with microprocessor control, not an AI/Machine Learning algorithm that typically requires a "training set" of data in the way a diagnostic software does. The "software validation" mentioned would likely pertain to functional verification and validation of the embedded software's logic and control, not to the training of a learning model.

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

Not applicable, as the device doesn't appear to use a "training set" in the context of machine learning for which ground truth would be established. The software validation would rely on established requirements and testing against those.

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