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The Spacelabs Healthcare Xprezzon Bedside Monitor passively displays data generated by Spacelabs parameter modules, Flexport interfaces, and other Spacelabs SDLC based products as waveform and numeric displays, trends and alarms. Key monitored parameters available on the model 91393, when employing the Spacelabs Command Module, consist of ECG, respiration, invasive and noninvasive blood pressure, Sp02, temperature and cardiac output. Additional parameters and interfaces to other systems are also available depending on the parameter modules employed.

The Spacelabs Healthcare Xprezzon Bedside Monitor is intended to alert the user to alarm conditions that are reported by Spacelabs Healthcare parameter modules and/or other devices via Flexport interfaces. The patient monitors are also capable of displaying alarm conditions on other monitors that are on the network through the Alarm Watch feature.

The Spacelabs Healthcare Xprezzon Bedside Monitor may also function as a generic display or computer terminal. As a generic display or terminal, the patient monitor allows network based applications to open windows and display information the Xprezzon and other networked monitors.

The Spacelabs Healthcare Xprezzon Bedside Monitor is designed to communicate with a variety of external devices such as displays, network devices, serial devices, user input devices, audio systems, and local/remote recorders.

The Xprezzon Bedside Monitors is intended.for use under the direct supervision of a licensed healthcare practitioner, or by personnel trained in proper use of the equipment in a hospital environment.

The Spacelabs Healthcare Xprezzon Bedside Monitor, Model 91393, is a component of the Spacelabs Healthcare Patient Monitoring System. The Xprezzon Bedside Monitor accepts inputs from the family of Spacelabs Parameter Modules. The monitor accept and displays parameter information, waveform and numeric data, and alarm conditions including arrhythmia information received from the same family of modules.

The Xprezzon Bedside monitor, model 91393, is configured at installation to operate independent of or connected to the Spacelabs Patient monitoring Network. As an independent bedside monitor the device operates from AC and. presents waveform, numeric data, and alarm conditions, including arrhythmia information, received from parameter modules. When physically networked these monitors are able to share their information with a central station or with other monitors on the network according to conditions establish by the user/system administrator. They are also able to connect, via the healthcare institution's network, through Dynamic Network Access (DNA) to other applications available on the network.

Here's an analysis of the provided 510(k) summary regarding the Spacelabs Healthcare Xprezzon Bedside Monitor, focusing on acceptance criteria and supporting studies.

Important Note: The provided 510(k) summary focuses heavily on demonstrating substantial equivalence to a predicate device and does not detail specific, quantitative clinical performance metrics or studies in the way a typical AI/software as a medical device (SaMD) submission would. The "acceptance criteria" discussed below are inferred from the document's claims of functional equivalence and compliance with standards, rather than explicitly stated performance targets for a novel algorithm.

Acceptance Criteria and Reported Device Performance

This 510(k) summary does not present a table of quantitative acceptance criteria with corresponding performance metrics like sensitivity, specificity, or accuracy. Instead, it asserts that the device meets acceptance criteria through validation that supports compliance with standards and verifies accurate data presentation, intended clinical workflows, and user needs, based on functional equivalence to a predicate device.

Given the nature of the device (a bedside monitor displaying data from other modules), the "acceptance criteria" are implicitly tied to the proper functioning of the display and interface, and its ability to accurately present information and alarms generated by external parameter modules.

Study Details

The provided document describes the validation of the device, but it is not a clinical study in the typical sense of evaluating the performance of a diagnostic or predictive algorithm against ground truth with specific metrics (e.g., sensitivity, specificity). Instead, it is a device validation focused on engineering and software verification.

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

   • The document does not specify a "test set" in terms of patient data or clinical cases. The validation appears to be primarily laboratory-based and functional testing of the monitor's ability to display data.
   • Given the nature of the device (a display monitor), data provenance from specific countries or retrospective/prospective collection is not applicable in the context of this summary.

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

   • This information is not provided and is likely not relevant as "ground truth" for clinical performance metrics (like those for an arrhythmia detection algorithm) is not part of this specific 510(k) for the bedside monitor itself. The monitor displays ground truth as provided by separate parameter modules.
   • The document mentions "user's clinical needs," implying input from healthcare professionals, but specific details on their number or qualifications for establishing ground truth are absent.

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

   • This is not applicable/not provided. The validation described is not a clinical adjudication study for diagnostic accuracy.

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:

   • No MRMC study was done. This device is a bedside monitor displaying data from other devices, not an AI-powered diagnostic or interpretive tool that assists human readers.

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

   • The "algorithm" here refers to the software controlling the display and networking of the monitor. A "standalone" performance evaluation in the context of a diagnostic algorithm is not applicable. The device's performance is inherently tied to its function as an interface and display. Its "standalone" performance would be its ability to correctly process and display inputs, which was part of the "rigorous testing" mentioned.

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

   • The document does not specify a type of ground truth in the context of clinical outcomes or pathology. For a bedside monitor, "ground truth" for validation would likely involve:
     • Reference signals/simulations: Using calibrated signal generators to simulate physiological data and ensure the monitor accurately displays it.
     • Predicate device comparison: Ensuring the Xprezzon displays data consistent with the predicate device.
     • Functional requirements testing: Verifying that different modules integrate correctly and that alarm conditions are triggered and displayed as expected according to predefined functional specifications.

7. The sample size for the training set:

   • This device is not an AI/machine learning model that requires a "training set" of data. Therefore, this information is not applicable/not provided. The software development process mentioned follows standard software engineering practices, not AI model training.

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

   • As there is no "training set" for an AI model, this question is not applicable.

Summary of the Study:

The "study" described is a developmental and verification/validation engineering process rather than a clinical trial. It involved:

• Rigorous testing to support compliance of software to standards.
• Validation that the software was developed following a robust software development process.
• Test programs to verify:
  • Accurate presentation of parameter data from external modules.
  • Support for intended clinical workflows.
  • Meeting user's clinical needs.

The entire submission hinges on demonstrating substantial equivalence to the predicate device (Spacelabs Medical Ultraview SL Patient Monitors, Model 91387), despite minor hardware and software updates, concluding that the new device is "as safe and effective." No novel claims of diagnostic or predictive accuracy are being made by the monitor itself; it acts as a display and interface for information generated by other cleared devices.

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