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510(k) Data Aggregation
(161 days)
The ChloraShield™ Antimicrobial Dressing is intended for use as a wound dressing to absorb exudate and to cover and protect catheter sites. Common applications include IV catheters, other intravascular catheters and percutaneous devices.
The ChloraShield™ Antimicrobial Dressing is intended to fill the need for a transparent primary catheter dressing that can be applied easily while surrounding the wound site. The dressing is an oval shaped dressing intended to cover and protect a catheter site and absorb exudates. The dressing is transparent and conformable allowing for easy visual and manual inspection of IV sites. The dressing is impregnated with Chlorhexidine Gluconate (CHG), a persistent, broad spectrum antimicrobial and antifungal activity against a wide range of gram positive and gram negative organisms. The antimicrobial agent protects the dressing from bacterial penetration and colonization. The patch complements other infection control measures by providing continued IV site protection while also allowing for good site visualization and inspection. ChloraShield has not been clinically tested for its ability to reduce local infections, catheter-related blood stream infections (CRBSI) or skin colonization of microorganisms commonly related to CRBSI. The product is sold in a sterile condition, opened in the operating field and then applied to the catheter site. Like the predicate devices, the ChloraShield ™ Dressing is easy to apply and easy to remove. The product offering includes: The product number is: # 410100 standard package - one dressing or patch per sterile package.
The provided text describes a 510(k) premarket notification for the ChloraShield™ Antimicrobial Dressing. This document focuses on demonstrating substantial equivalence to predicate devices, primarily through non-clinical and a limited clinical study assessing tolerability, rather than a comparative effectiveness study in the context of AI. Therefore, much of the requested information about AI-specific studies, such as multi-reader multi-case studies, effect sizes of AI assistance, standalone algorithm performance, and sample sizes for training sets (which are relevant for AI/ML models), is not applicable or present in this regulatory submission for a physical medical device.
However, I can extract information related to the acceptance criteria for the tolerability study, its methodology, and the reported performance.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance |
|---|---|---|
| Device Tolerability | Tolerability (frequency and severity of skin reactions) at skin sites across a wear period of seven days. | The tolerability of the proposed device was found to be equivalent to the predicate devices. The safety assessment aimed to evaluate if an exaggerated use model (one initial high dose exposure (7 times intended clinical use) on Day 1, followed by daily descending exposure of one patch each day over 7 days) would cause irritation or adverse events in normal healthy volunteers. The study results indicate that the device met this criterion by demonstrating equivalence to predicate devices under these conditions, without reporting significant irritation or adverse events. |
| Safety and Effectiveness | Device is safe and effective, and its performance is substantially equivalent to the two predicate devices. | The applicant concluded that the ChloraShield™ Antimicrobial Dressing is safe and effective, and its performance is substantially equivalent to the two predicate devices. This conclusion is based on both non-clinical tests (biocompatibility, sterility, packaging, and antimicrobial testing using CLSI standards) and the tolerability clinical study. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for the Test Set (Clinical Study): 45 subjects
- Data Provenance: The document does not explicitly state the country of origin. The study was conducted as a "clinical study" involving "normal healthy volunteers." It can be inferred as a prospective study as it describes the methodology for patient enrollment and observation.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The context of "experts" and "ground truth" typically applies to diagnostic devices where human interpretation is involved. For this device (an antimicrobial dressing), the primary outcome measured in the clinical study was "tolerability (frequency and severity of skin reactions)."
- Number of Experts: Not specified as "experts" in the clinical study sense for establishing a diagnostic ground truth. The assessment of skin reactions would have been performed by trained clinical personnel (e.g., nurses, doctors) observing the subjects.
- Qualifications of Experts: Not specified.
4. Adjudication Method for the Test Set
Not applicable in the context of skin reaction assessment in this type of tolerability study. Adjudication methods (like 2+1 or 3+1) are typically used for resolving discrepancies in expert interpretations of images or diagnostic findings, which is not the nature of this study.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done: No.
This is a physical medical device, not an AI/ML algorithm. Therefore, an MRMC study and analysis of human reader improvement with AI assistance are not relevant.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done: No.
This product is a physical medical device. The concept of an "algorithm only" performance is not applicable.
7. The Type of Ground Truth Used (for the clinical study)
The "ground truth" for the clinical study was the observed and documented frequency and severity of skin reactions on the study participants under the specified exaggerated use model. This is an objective clinical observation, not a diagnostic pathology or outcome data in the traditional sense, but rather a direct measurement of the device's tolerability.
8. The Sample Size for the Training Set
No training set is mentioned as this device is a physical product and not an AI/ML model. The "training set" concept is relevant for machine learning algorithms.
9. How the Ground Truth for the Training Set was Established
Not applicable, as there is no training set for an AI/ML model.
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