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The Soteria Bed Barrier is a cover for compatible bed decks and mattresses to provide a protective physical barrier between the equipment and the patient. The device is intended to prevent soiling of bed decks and mattresses, helping to reduce contamination during use.

The Soteria Bed Barriers are classified as level 3 per AAMI Standard PB70 Liquid barrier performance and classification of protective apparel and drapes intended for use in healthcare facilities.

The Soteria Bed Barrier is a mattress cover that fits securely over a healthcare mattress and bed deck and attaches to the bed frame. The device is available in multiple configurations to fit specific mattresses and beds, attaching with hooks or clips on each side and the head and foot section of the mattress fit inside a pocket allowing fitted sheets to work over the mattress. Color coding of the label used by the laundry and edge ribbon correspond to compatible beds and mattresses, each barrier model has a different ribbon color. The Soteria Bed Barrier helps to reduce contamination of the underlying surfaces by preventing the penetration of fluids. The Soteria Bed Barrier is a nonsterile reusable device that is laundered in accordance with the validated procedure to remove microbes and spores between each patient use. The Soteria Bed Barrier is removed from the mattress/bed deck for laundering upon soiling or between patient uses. After laundering and inspection, the product is then folded and wrapped with appropriate labels to be used again.

The provided document is a 510(k) Premarket Notification for the Soteria Bed Barrier, a Class I medical device. The document details the device's characteristics, indications for use, and a comparison to a predicate device. The core of this submission is to demonstrate "substantial equivalence" to a legally marketed predicate, not to prove improved human performance or algorithm-only performance. Therefore, many of the typical acceptance criteria for AI/ML-based diagnostic devices, such as MRMC studies, expert consensus ground truth, or training set details, are not applicable here.

This document focuses on the physical and material performance of a barrier product, not on a diagnostic or assistive AI system.

Here's an analysis based on the provided text, addressing the points where information is available and noting where it's not applicable given the device type:

Device: Soteria Bed Barrier
Device Type: Mattress Cover for Medical Purposes (Class I)

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: The document does not explicitly state the numerical sample size (e.g., number of fabric samples or cleaning cycles) for each test. Instead, it refers to "all test specimens" or "each test replicate," implying that multiple samples were used as per standard testing protocols for these types of materials.
• Data Provenance: Not applicable in the context of patient data. The "test set" here refers to physical samples of the Soteria Bed Barrier material. The testing appears to be laboratory-based performance testing conducted by the manufacturer. No country of origin for "data" (in the sense of patient data) is relevant here. The studies were prospective in the sense that the manufacturer conducted these specific tests on their device samples to demonstrate compliance with standards.

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

• Not Applicable. This is a physical device, not a diagnostic or AI algorithm requiring expert interpretation of medical images or data. The "ground truth" is established by adherence to recognized engineering and material testing standards (e.g., AATCC, ASTM, 16 CFR).

4. Adjudication Method for the Test Set

• Not Applicable. As this is material and performance testing against objective, standardized criteria, no human adjudication (e.g., 2+1, 3+1) is relevant. The results are quantitative measurements against predefined thresholds.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• Was it done? No. This type of study (MRMC) is relevant for diagnostic devices, particularly those involving human readers of medical images, often with AI assistance. The Soteria Bed Barrier is a physical barrier, so an MRMC study is not applicable.
• Effect Size: Not applicable.

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

• Was it done? No. This concept applies to AI algorithms. The device is a physical product. Its "performance" is inherently "standalone" in that it performs its function (barrier protection, cleanability) without human intervention in the function itself, but this is distinct from an AI algorithm's standalone performance.

7. The Type of Ground Truth Used

• The "ground truth" for this device's performance is established by objective, quantitative measurements against recognized industry standards and regulatory requirements. For example:
  • Water resistance (AATCC 42, AATCC 127) measured in grams of weight gain or cm of water pressure.
  • Strength (ASTM D5034, D5587, D751) measured in pounds-force (lbf).
  • Flammability (16 CFR Part 1610) measured in burn time.
  • Cleaning validation measured in µg/cm² of residual protein/hemoglobin.
  • Laundering validation measured in % reduction of specific microbial strains.
• This is not "expert consensus," "pathology," or "outcomes data" in the medical sense, but rather compliance with engineering and material science metrics.

8. The Sample Size for the Training Set

• Not Applicable. This is not an AI/Machine Learning device that requires a "training set." The device itself is manufactured; it does not "learn."

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

• Not Applicable. (See point 8).

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Bed linens made with DermaTherapy® fabrics are intended for use by patients 18 years of age and older in hospital settings who are susceptible to pressure ulcers. The DermaTherapy Bed Linens help to reduce the likelihood of patients developing pressure ulcers by reducing moisture, friction and shear on the patient's skin.

DermaTherapy uses a silk-like fabric to minimize moisture, friction, and shear between the skin and the bedsupport surface. Continuous-filament yarns woven into the silk-like synthetic DermaTherapy fabric provide a smooth surface, free of broken or discontinuous fibers. This enhanced smoothness helps minimize the potential for irritation and abrasion of sensitive skin.

DermaTherapy Bed Linens consists of pillow cases, top flat sheets, and underpads. The subject devices are made from DermaTherapy fabrics which are plain-weave constructions of 100% continuous-filament yarns. The polyester yarns have a non-round fiber cross-section to create micro-channels to facilitate moisture wicking and rapid drying. The yarns used in DermaTherapy fabrics are commercially available products, typically used in apparel.

The only technological difference in the fabric construction between the subject device and the predicate device is the addition of a secondary static-dissipative polyester yarn at 0.8% of the total fabric weight. The static-dissipative yarn is incorporated to reduce the potential for static electricity that may occur during institutional laundering processes. The company has made no other modifications to the fabric or the manufacturing process of turning the fabric into the final product.

This document describes data for a medical device called "DermaTherapy Bed Linens," which are intended to reduce the likelihood of pressure ulcers in hospital patients. Here's a breakdown of the requested information:

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

The document does not explicitly state "acceptance criteria" with numerical targets for each performance characteristic. Instead, it lists the "Performance Characteristics" and the "Test Method / Standard" used to evaluate the device. The conclusion states that "bench testing demonstrated that the device met performance specifications" and "demonstrated substantially equivalent device performance." It also includes clinical study results showing a reduction in pressure ulcer incidence.

Given the information, the table below will combine the performance characteristics (which can be inferred as criteria for evaluation) and the clinical performance results.

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Institutional Bedding (bed sheets and pillow cases) made with DermaTherapy™ fabrics is intended for use by patients in a hospital, healthcare or home setting who are susceptible to or may have mild atopic dermatitis.

The device that is the subject of this 510(k) premarket notification is generally described as Institutional Bedding made with DermaTherapy™ fabrics. Institutional bedding in this case refers to bed linens, generally comprised of a flat top sheet, a fitted or flat bottom sheet, and pillow cases that encase pillows on beds used in hospitals and/or in other healthcare or home settings.

DermaTherapy fabrics are plain-weave constructions of 100% contineous-filament yarns. The preferred embodiment of the technology involves 100% nylon yarns in one direction of the fabric, with 100% polyester yarns in the other direction. The polyester varns have a non-round fiber cross-section to create micro-channels to facilitate moisture wicking and rapid drying. The yarns used in DermaTherapy fabrics are commercially available products, typically used in women's lingerie or intimate apparel. Once woven, DermaTherapy fabrics are treated with a durable antimicrobial treatment to control bacteria and fungi growth on the fabrics. The antimicrobial treatment applied to DermaTherapy fabrics is a commercially available technology commonly used in the textile industry.

The provided text describes a 510(k) premarket notification for "Institutional Bedding made with DermaTherapy™ fabrics". It specifies the intended use, device description, and a brief summary of performance data. However, it does not provide specific acceptance criteria in a table format, nor does it detail a study proving the device meets quantifiable acceptance criteria in the manner typically expected for more complex medical devices like AI algorithms.

The document states: "A clinical study was performed and included 37 subjects with mild atopic dermatitis. Results showed statistically significant improvements in the severity of atopic dermatitis, eczema, the level of itching, and perceived quality of life after eight weeks of using DermaTherapy bedding." This is the core of the performance data.

Given the information, I can synthesize what's available and point out the missing elements to answer your request.

Missing Information:
The document does not provide the following details that would be crucial for a comprehensive answer to your request, especially concerning AI device studies:

• A clear table of specific, quantifiable acceptance criteria (e.g., sensitivity, specificity, AUC thresholds).
• Any details about the "statistical significance" beyond the statement itself (e.g., p-values, confidence intervals, effect sizes).
• Sample sizes for test or training sets in the context of an AI device, as this is a textile product.
• Data provenance, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance, as these are concepts relevant to AI/diagnostic device evaluation, not typically for medical bedding.
• The type of ground truth and how it was established with the level of detail requested.

Here's the information derived from the provided text, structured to best answer your request, with a clear indication of what is not present:

Acceptance Criteria and Study Details for "Institutional Bedding made with DermaTherapy™ fabrics"

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not specify quantitative thresholds for "statistically significant improvements" or "satisfactory results". The acceptance criteria are inferred from the reported outcomes.

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

• Test Set Sample Size: 37 subjects.
• Data Provenance: The document does not explicitly state the country of origin but implies a single clinical study conducted for the 510(k) submission. It is a prospective clinical study.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• This is not applicable in the context of this device (medical bedding) and the type of study performed. The outcomes (severity of atopic dermatitis, eczema, itching, quality of life) would likely have been assessed by clinicians or through self-reported questionnaires, not by "experts establishing ground truth" in the diagnostic sense. The document does not specify who conducted the assessments.

4. Adjudication Method for the Test Set

• This is not applicable as the study involved clinical outcomes on human subjects rather than expert review of diagnostic cases.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• No, an MRMC comparative effectiveness study was not done. This type of study is typically performed for diagnostic devices, particularly those involving image interpretation by human readers.

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

• No, this is not a software/AI device, so the concept of standalone algorithm performance is not applicable. The device itself is "Institutional Bedding made with DermaTherapy™ fabrics".

7. The Type of Ground Truth Used

• The "ground truth" in this context would be the clinical assessment of the subjects' conditions (severity of atopic dermatitis, eczema, level of itching, and perceived quality of life) by qualified healthcare professionals or through validated patient-reported outcome measures. The document does not specify the exact assessment tools or methodologies.

8. The Sample Size for the Training Set

• This concept is not applicable as this is not an AI or machine learning device. There is no "training set." The study involved 37 subjects for evaluating the direct effect of the bedding.

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

• Not applicable, as there is no training set for an AI algorithm.

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Z-Net reduces for up to 15 months house dust mites and their allergens in bedding, a leading cause of allergenic rhinitis, allergenic asthma, and allergenic eczema. Reductions are based on comparisons with untreated bed covers.

The Z-Net allergy control device consists of a mattress cover and pillow cover created from polyester impregnated with permethrin. The treated fabric reduces the levels of house dust mites (HDM) and their allergens in the mattress and pillow which, in turn, provides relief to individuals suffering from atopic allergies.

Here's an analysis of the provided text to fulfill your request:

Acceptance Criteria and Study Proving Device Meets Criteria:

Based on the provided 510(k) summary, the device is the Z-Net mattress and pillow covers, which are polyester impregnated with permethrin, intended to reduce house dust mites (HDM) and their allergens. The primary acceptance criterion is the long-term reduction of HDMs and their allergens in mattresses.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not explicitly stated as a number of subjects or items. The study refers to "test and placebo groups" and "dust samples" collected over time. It does not provide the specific count of mattresses, pillows, or homes involved.
• Data Provenance: The primary study was conducted at the London School of Hygiene and Tropical Medicine (LSHTM), indicating international data (United Kingdom). The study was prospective, described as a "two-year, double-blind study."

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

• The document does not mention the number of experts used to establish the ground truth for the test set.
• It refers to the LSHTM laboratory conducting the analysis of dust samples, suggesting laboratory technicians or researchers with expertise in dust mite and allergen quantification. Specific qualifications (e.g., allergist, microbiologist, with X years of experience) are not provided.

4. Adjudication Method for the Test Set:

• The document describes the primary study as a "two-year, double-blind study." This indicates that neither the participants (those using the covers) nor the researchers/analysts were aware of whether they were in the permethrin-treated group or the placebo group. This blinding serves as a form of "adjudication" against bias in data collection and initial analysis, but it's not an adjudication method in the sense of multiple experts independently reviewing findings. No other multi-expert adjudication method (like 2+1 or 3+1) is mentioned.

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 device is a physical product (mattress/pillow cover) and not an AI-based diagnostic tool. Therefore, the concept of "human readers improving with AI vs without AI assistance" is not applicable here.

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

• No, a standalone algorithm-only performance study was not done. As mentioned above, this is a physical device, not an algorithm.

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

• The ground truth was established through direct measurement of biological markers:
  • Quantification of "House Dust Mites (HDM)"
  • Quantification of "HDM allergens"
  • These measurements were derived from "dust samples" collected from mattresses.

8. The sample size for the training set:

• Not applicable. This device is a physical product that works through a chemical impregnation, not a machine learning or AI model. Therefore, there is no "training set" in the context of algorithm development.

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

• Not applicable. As there is no training set for an AI/algorithm, no ground truth was established for it.

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