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The Spectra Optia® Apheresis System, a blood component separator, may be used to perform therapeutic plasma exchange.

The Spectra Optia® Apheresis System, a blood component separator. may be used to perform Red Blood Cell Exchange (RBCX) procedures for the transfusion management of Sickle Cell Disease in adults and children.

The Spectra Optia® Apheresis System, a blood component separator, may be used to reduce White Blood Cells for patients with leukocytosis at risk for leukostasis.

The Spectra Optia® Apheresis System is an automated centrifugal system that separates whole blood into its cellular and plasma components. The cleared system is comprised of three major subsystems:

1. the apheresis machine itself (centrifuge, centrifuge filler, pumps, valves, computerized safety and control systems, etc.)
2. a sterile, single-use, disposable blood tubing set
3. embedded software

The modification described in this submission introduces a new software version to the Spectra Optia Apheresis System. The current release is Version 11.3 which was cleared in K153601. The new software version is Version 12 and introduces the following new features:

• Data Management Support,
• Network Support,
• Enhancements to existing protocols including the ability to run the Red Blood Cell Exchange procedure in single-needle mode,
• General Software Maintenance Updates

The provided text describes a 510(k) premarket notification for the "Spectra Optia® Apheresis System" with an updated software version (Version 12). The submission aims to establish substantial equivalence to a predicate device (Spectra Optia® Apheresis System with Version 11.3 software).

The information provided is not sufficient to fully answer all aspects of your request, especially regarding specific acceptance criteria values and a detailed study report that would typically accompany a clinical trial or performance study. The document primarily focuses on demonstrating substantial equivalence through software and specification testing, rather than a detailed device performance study against specific, quantified acceptance criteria for clinical outcomes.

However, I can extract the available information as follows:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a table with specific, quantifiable acceptance criteria established prior to testing, nor does it present detailed numerical performance results for the device. Instead, it states high-level outcomes indicating that the device met its functional requirements and specifications.

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

• Software Testing: No specific sample size (e.g., number of test cases run) is provided, but it states "All software testing was complete."
• Specification Testing: No specific sample size (e.g., number of tests or subjects) is provided.
• Single-Needle Functionality (Simulated Patient Study): The clinical overview report mentions a "simulated-patient laboratory study evaluating varying performance measures across the multiple RBCX Procedure Types." No specific sample size (number of simulated patients or runs) is given.
• Data Provenance: Not specified. The document does not indicate country of origin or if the "simulated-patient laboratory study" was retrospective or prospective. The "clinical overview report" likely summarizes existing clinical evidence, which would be retrospective if it refers to previously published data.

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

This information is not provided in the document. The studies mentioned (software testing, specification testing, simulated-patient study) do not explicitly refer to "ground truth" established by external experts in the way that, for example, an AI diagnostic device would. For the simulated-patient study, the "ground truth" would likely be the known parameters of the simulated blood and the expected output of the device.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of the testing described (software, specification, simulated patient), an external adjudication method (like 2+1 or 3+1 for expert review) is not typically applicable or detailed in this type of 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

There is no indication of an MRMC study being performed. The device is an apheresis system, not an AI diagnostic imaging system that would typically involve human readers.

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

The provided text describes software testing, which inherently assesses the algorithm's functionality. The "simulated-patient laboratory study" for single-needle functionality assesses the device's performance in a controlled environment, which could be considered a standalone performance assessment of the system for that specific function. However, the term "standalone" in the context of AI is usually understood as the algorithm making a decision without human intervention in a diagnostic setting. This is not the context of this device.

7. The Type of Ground Truth Used

• Software and Specification Testing: The ground truth for this testing would be the predefined system requirements and software specifications. The software is expected to operate according to its design and functional specifications.
• Simulated-patient laboratory study: The ground truth would be the known properties of the simulated blood and the expected physiological outcomes or separation efficiencies established as part of the study design.

8. The Sample Size for the Training Set

This information is not applicable / not provided. The Spectra Optia® Apheresis System is a medical device with an updated software version; it is not described as an AI/ML system that utilizes a "training set" in the conventional sense for developing a predictive model. The software is developed through traditional software engineering processes.

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

This information is not applicable / not provided, as there is no mention of a "training set" for an AI/ML model.

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