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Accessory to a piston syringe for needleless access to sterile drug vials without the use of needles.

The ICU Medical Universal Vial Access Device is a single use - universally adaptable and needleless drug container access disposable. The proprietary technology of the Clave enables the device to have a needleless port for withdrawal of medications or other IV fluids as directed by the physician.

This document (K080989) is a 510(k) premarket notification for the ICU Medical Universal Vial Access Device. It is a submission to demonstrate substantial equivalence to legally marketed predicate devices, not a study presenting specific performance data or a statistical analysis with detailed acceptance criteria.

Therefore, the requested information regarding acceptance criteria, reported performance, sample sizes, expert ground truth, adjudication methods, MRMC studies, standalone performance, and training set details cannot be fully extracted from the provided text. The document focuses on regulatory compliance and substantial equivalence, not a detailed technical performance study report.

Here's a breakdown of what can be inferred from the provided text, and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Not explicitly stated as quantifiable metrics with pass/fail thresholds in the provided text. The document refers to conforming to "requirements of published international standards as well as those FDA recognized standards and/or published guidelines." This is a general statement of compliance, not a specific set of acceptance criteria with numerical targets.
• Reported Device Performance: While the document states the device "conform[s] to the requirements" and has an "established and validated history of meeting the 10⁻⁶ level" for Sterility Assurance Level (SAL), it does not provide specific test results comparing the new device's performance against numerical acceptance criteria.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not mentioned. No information is provided about specific test sets, their size, or the number of units tested.
• Data Provenance: Not mentioned. There is no information about country of origin, or whether any data used for internal verification was retrospective or prospective.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Not applicable. The document does not describe a study involving expert-established ground truth for a test set. This is a regulatory submission for substantial equivalence based on engineering and design considerations, not an AI/diagnostic device performance study.
• Qualifications of Experts: Not applicable.

4. Adjudication method for the test set:

• Not applicable. There is no mention of a test set requiring adjudication in the context of this 510(k) submission.

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:

• MRMC Study: No. This is not an AI-assisted diagnostic device, but a medical accessory (vial access device). Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant or described.
• Effect Size: Not applicable.

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

• Standalone Performance: Not applicable. This is a physical medical device, not an algorithm, so "standalone (algorithm only)" performance is not a relevant concept for this submission.

7. The type of ground truth used:

• Not applicable in the context of a diagnostic performance study. The 510(k) submission relies on demonstrating substantial equivalence to predicate devices, compliance with established standards, and internal verification processes to ensure the device's functional and safety characteristics. The "ground truth" here would be established engineering and biological safety standards, along with the proven performance of the predicate devices.

8. The sample size for the training set:

• Not applicable. This is not an AI/machine learning device, so there is no concept of a "training set."

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

• Not applicable, as there is no training set.

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Churchill Medical Systems Vial Access Spike with Needleless Connector is used in I.V. therapy to pierce glass and plastic stopper top vials for the transfer of medical fluids. This device climinates the use of metal hypodermic needles to reduce the chance of inadvertent needle sticks.

The Churchill Medical Systems vial access device is a sterile, single use, one piece shrouded plastic cannula designed to penetrate septums covering plastic or glass medication vials. The device is intended to be marketed with and without needleless connector port permanently attached.

The provided text describes a 510(k) summary for a medical device called the "Universal Vial Access Spike with Needleless Connector" by Churchill Medical Systems, Inc. However, the document does not contain the detailed information necessary to answer all sections of your request regarding acceptance criteria and performance studies.

Based on the information available:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states that the device is manufactured and tested in accordance with certain physical, chemical, and biological specifications. It references standards but does not provide specific quantitative acceptance criteria or detailed results.

2. Sample size used for the test set and the data provenance:

• Sample size: Not specified.
• Data provenance: Not specified, but generally, such studies for 510(k) submissions are conducted internally by the manufacturer or their contracted labs. Given the device type, it's likely physical and chemical lab testing, not patient data. Retrospective or Prospective: N/A (not applicable as it's not a clinical study with patient data).

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 in the document. The nature of the device (a vial access spike) suggests that "ground truth" would be established through laboratory testing against predefined specifications rather than expert consensus on medical images or clinical outcomes.

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

• This information is not provided. Given it's a device for fluid transfer and needle stick prevention, not diagnostic imaging, adjudication methods like 2+1 or 3+1 are not typically relevant.

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. This type of study (MRMC for AI assistance) is not applicable to this device. The device is a physical medical instrument, not an AI-powered diagnostic tool.

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

• No. This is not an AI algorithm. Its performance is evaluated based on its physical and material properties and its ability to prevent needle sticks, which is a standalone function of the device itself.

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

• The "ground truth" for this device would be the specific, quantifiable parameters defined by the referenced industry standards (ISO 10993, USPXX111, ISO 11607-1, ISO 11135, USP Pyrogenicity test requirements) and internal physical testing requirements. For example, burst pressure for the spike, material compatibility, sterility assurance level, and ability to prevent needle sticks in simulated use.

8. The sample size for the training set:

• N/A. This device does not involve a "training set" in the context of machine learning or AI.

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

• N/A. This device does not involve a "training set" or "ground truth" for training in the machine learning sense. The "ground truth" for its physical performance characteristics is established by adherence to recognized standards and validated internal test methods.

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