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The Z-800 Infusion system is intended to provide intravenous infusion of parenteral fluids, blood and blood products to a patient under the direction or supervision of physician or other certified health care professional.

The Z-800 Infusion System consists of the infusion pumps and approved external IV administration sets. The major modifications are as following: 1. Added a counter in software for cumulative volume of fluid infused in order to monitor pump utilization. The odometer feature enables pump display a reminder for user to service the pump once a preset volume limit is reached. 2. Implemented optional set based free flow protection. Modified set based free flow protection mechanism consists of a proprietary pinch clamp on the standard IV set tubing and an optional spring loaded clamp holder module on the pump. The pinch clamp can be manually opened for priming. The clamp holder is designed such that the pinch clamp is automatically closed once it is loaded into the clamp holder. Upon pump door close, the pump door activates the pinch clamp loaded in the clamp holder to open to allow flow go through. Upon pump door open, the spring in the clamp holder forces the pinch clamp to close. When the IV set is removed from the pump, the pinch clamp remains at closed position to avoid unintended free flow. A sensor on the clamp holder detects presence of the pinch clamp. "No Clamp" status can be displayed on pump screen to inform user that the clamp is absent. 3. Implemented optional Wi-Fi communication module for serial port to enable bidirectional communication through RF signal. A self-contained serial Wi-Fi convertor module is embedded into the Z-800 infusion pump to enable wireless communication. The Z-800 infusion pump sends serial command to the Wi-Fi convertor module. The Wi- Fi convertor module translates the serial communication command from the Z-800 infusion pump to RF signals and responsible for handling communication protocol with external devices. The serial Wi-Fi convertor enables the user to initiate a query on the pump for external information by entering partial patient identifier information as search criteria. External information returned from the query may include patient information, such as name, date of birth, and IV medication order. The patient and order information are presented to the healthcare professional during infusion programming process to be confirmed for association with the infusion. The Wi-Fi convertor also enables Z-800 infusion pump to publish infusion status data, with the associated patient and order identifiers, which can be utilized by external systems such as EMR. 4. Added an ESD/EMI shielding cap for serial port to enhance pump survivability under injection of Electromagnetic noise and Electrostatic Discharge. 5. Optimized pressure sensor zero point value determination process for better accuracy. 6. Optimized peristaltic plate to maximize durability of peristaltic module. 7. Optimized peristaltic cams in peristaltic module to maximize its durability. 8. Optimized rubber feet for increased durability.

The provided text does not contain detailed information about specific acceptance criteria and a study proving the device meets them in the format requested. The document is a 510(k) summary for a Special 510(k) Device Modification for the Z-800 Infusion System. It focuses on device changes and comparison to a predicate device, rather than a clinical study with detailed performance metrics and ground truth establishment.

However, I can extract the general performance parameter for flow rate accuracy, which serves as an implicit acceptance criterion based on its comparison to the predicate device.

Here's an attempt to answer the questions based on the available information:

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

Based on the "Equivalency Matrix," the primary performance parameter stated is Flow Rate Accuracy.

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 describes engineering modifications and an equivalency comparison, not a detailed clinical study with a test set.

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 type of device (infusion pump) typically relies on engineering and laboratory testing for performance validation, not expert-based ground truth for a test set in the way a diagnostic AI might.

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

This information is not provided in the document.

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

This information is not provided in the document. The device is an infusion pump, which does not involve "human readers" or AI assistance in the context of diagnostic interpretation.

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

This information is not provided in the document in the context of a "standalone" performance study for an AI algorithm. The device itself is a standalone infusion pump, and its performance (e.g., flow rate accuracy) would have been evaluated independently in engineering tests, but these details are not given here.

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

For flow rate accuracy, the ground truth would be established through metrology and calibrated reference standards in a laboratory setting. This is implied by the nature of the device and the performance metric, but not explicitly stated as "ground truth" using those terms within the document.

8. The sample size for the training set

This information is not provided in the document. The document describes modifications to an existing medical device, not the development and training of a new AI algorithm where a "training set" would be relevant. The "optimization" mentioned (e.g., pressure sensor, peristaltic plate/cams) would typically involve engineering design iterations and testing, not machine learning training.

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

This information is not provided in the document, as there is no mention of a "training set" in the context of AI or machine learning.

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The Zyno Medical Administration Set is a device used to administer fluids from a container to a patient's vascular system through a needle or catheter inserted into a vein.

The Zyno Medical's I.V. Administration Set is a single use, latex-free, non-DEHP device sterilized with Ethylene Oxide gas. It is utilized to administer fluids from a container to a patient's vascular system through a catheter or needle inserted into a vein. Depending on configuration, the device may include the following components: tubing, protective cap(s), drip chamber (s) with spike, tubing, back check valve(s), needleless Y-site(s), slide clamp(s), roller clamp(s), pinch clamp(s), 0.22 micron filter or 1.2 micron filter, male luer lock (with cap), and pressure activated valve(s). Seven (7) representative administration sets and one (1) representative extension set are described in the application package to illustrate the device confiqurations.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Zyno Medical Administration Set (K120685).

Important Note: The provided text is a 510(k) summary for a medical device that is substantially equivalent to existing predicate devices. This type of submission relies heavily on demonstrating that the new device is as safe and effective as a legally marketed device, rather than proving novel performance against specific, quantifiable acceptance criteria in a clinical study. Therefore, many of the typical "AI/software device" study questions (like MRMC, training sets, expert ground truth) are not applicable to this type of submission.

Acceptance Criteria and Reported Device Performance

Given the nature of this 510(k) summary, the "acceptance criteria" are primarily related to demonstrating substantial equivalence to predicate devices and meeting relevant safety standards. There aren't specific numerical performance metrics (like sensitivity/specificity for a diagnostic AI) listed in the summary.

Study Details (Applicable to this type of device/submission)

This 510(k) is for a basic medical device (intravascular administration set), not an AI/software device. Therefore, the study conducted is primarily non-clinical bench testing for substantial equivalence. Clinical studies in the AI/software sense were not necessary or performed.

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

   • Sample Size: The document states, "When appropriate, predicate devices were tested using the exact same method and sample size, for direct comparison of results." However, the specific number of units tested for the Zyno Medical Administration Set itself is not explicitly stated in the provided summary. This information would typically be found in the full 510(k) submission, not the summary.
   • Data Provenance: This refers to bench testing. The testing was conducted by or on behalf of Zyno Medical, LLC in Natick, MA, USA. It is prospective bench testing based on design specifications to confirm performance.

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

   • Not Applicable. For a physical device like an I.V. administration set, "ground truth" isn't established by human experts in the same way it would be for an AI diagnostic. Ground truth is derived from engineering specifications, industry standards, and measurable physical properties (e.g., flow rates, leak integrity, material strength).

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

   • Not Applicable. As there are no human interpretive "readings" involved, there's no adjudication method in play for the test set. Performance is objectively measured against specifications.

4. 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:

   • Not Applicable. This is a physical medical device, not an AI or software-assisted diagnostic tool. No MRMC study was conducted.

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

   • Not Applicable. This is not an algorithm or AI device. The device's performance is standalone in the sense that its physical properties and function are tested independently of a human "loop" for its intended use (fluid administration).

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

   • The "ground truth" for this device's testing is based on engineering specifications, recognized industry standards, and performance characteristics of legally marketed predicate devices. For example, ground truth for sterility is a sterile state, ground truth for material properties is adherence to chemical/physical specifications, and ground truth for functional performance (e.g., integrity, flow) is meeting pre-defined acceptable ranges.

7. The sample size for the training set:

   • Not Applicable. This is not an AI/machine learning device; therefore, there is no "training set."

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

   • Not Applicable. See point 7.

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