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Central Station is a network device, intended to display, record and print monitored physiological data from Nihon Kohden bedside monitors, telemetry receiver and/or transmitters.

Central Station does not perform any data processing on the data received from the Nihon Kohden compatible devices. When Central Station is connected with the Nihon Kohden bedside monitors and telemetry receivers/transmitters the Central Station can:
• Admit and discharge patients on the Nihon Kohden network.
• Display and manage compatible devices' real-time patient clinical data, vital signs, alarms and waveforms.
• Review and trend data calculated by connected Nihon Kohden devices.
• Store and transfer historical clinical data for the connected systems.
• Print patient data.

Central Station is intended for use in professional medical facilities by trained medical personnel.

Central Station is software only product that is installed on a Commercial Off the Shelf (COTS) Computer.

Central Station displays waveforms data and numerical data from a connected bedside monitor, vital sign telemeter, or multiple patient receiver unit on the screen.

Central Station is a network device, intended to provide remote patient monitoring to medical personnel. Central Station displays a list of measured values and a trend graph. Numerical data and various waveforms are color-coded for each parameter. Central Station also has the function of displaying an alarm.

Alarm indication in Central Station is displayed as a result of a judgment by the bedside monitor, vital sign telemeter, or multiple patient receiver units connected to the Central Station. Central Station itself does not have the function to perform alarm indication judgment.

The provided document is a 510(k) clearance letter for the "Central Station" device. This type of document primarily focuses on establishing substantial equivalence to a legally marketed predicate device rather than detailing specific performance acceptance criteria and study results in the same way as a full clinical trial report or a detailed design validation report would.

The document states:

• "Central Station does not perform any data processing on the data received from the Nihon Kohden compatible devices."
• "Alarm indication in Central Station is displayed as a result of a judgment by the bedside monitor, vital sign telemeter, or multiple patient receiver units connected to the Central Station. Central Station itself does not have the function to perform alarm indication judgment."
• "The results of the substantial equivalence assessment, taken together with non-clinical bench testing, software verification, and validation demonstrate that the Central station does not raise concerns regarding its safety and effectiveness compared to its predicate device and operates in accordance with claimed indications for use."

Given these statements, the "Central Station" device is essentially a display, recording, and communication hub. It does not perform diagnostic algorithms or make independent judgments that would typically necessitate the kinds of detailed performance metrics (like sensitivity, specificity, or reader agreement) that are usually established through extensive multi-reader, multi-case (MRMC) studies with expert ground truth. Its primary function is to accurately display and relay data processed by other Nihon Kohden devices.

Therefore, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" for this specific device (Central Station) would primarily revolve around:

• Software Verification and Validation (V&V): Ensuring the software correctly displays, records, and transmits data as designed, and that all features (admit/discharge, trend display, printing, network communication) function as intended without errors.
• Non-Clinical Bench Testing: Confirming interoperability with compatible devices, accuracy of data display, alarm relay, and network functionality.
• Cybersecurity Compliance: Meeting regulatory requirements for cybersecurity.

The document explicitly states that "The software documentation was prepared following the FDA's 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices' (released June 14, 2023), specifically for an Enhanced Documentation Level." and "Verification testing was conducted at the system integration level to confirm that the device software fulfills its requirements and that safety and security risk mitigations, where applicable, were effective. Additionally, system-level testing was carried out to show that the software addresses user needs. All unit, integration, and system-level tests successfully met the test protocols."

Based on the provided text, it is not possible to extract the specific quantitative performance metrics (like sensitivity, specificity, or effect sizes for human readers) that would be relevant for a device performing complex data processing or diagnostic functions. The document emphasizes substantial equivalence and basic functional verification, not advanced AI/diagnostic performance validation.

However, I can infer the spirit of typical acceptance criteria and how a device like this would be proven to meet them, guided by the information provided.

Inferred Acceptance Criteria and Device Performance for "Central Station"

Given the device's stated function (display, record, print, and relay data without processing it for diagnosis or alarm judgment), the acceptance criteria would focus on functional correctness, data integrity, interoperability, and system reliability, rather than diagnostic accuracy metrics.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Test Set Sample Size: Not explicitly stated as a "sample size" in the context of patient data for diagnostic algorithms, because the device doesn't perform diagnostic processing. The "test set" for this device would be a collection of test cases covering all functional requirements, interoperability scenarios with different compatible devices, alarm conditions (relayed), data storage limits, network loads, and user interaction flows. The document mentions "system integration level" and "system-level testing" and that "all unit, integration, and system-level tests successfully met the test protocols." This implies a comprehensive set of non-clinical, bench-level tests.
• Data Provenance: Not applicable in the sense of clinical patient data (e.g., from specific countries, retrospective/prospective studies), as the device does not process primary patient data for diagnosis. The data used for testing would be simulated, generated, or derived from compatible Nihon Kohden monitor outputs in a lab setting to verify the Central Station's display and communication functions.

3. Number of Experts and Qualifications for Ground Truth

• Not Applicable in the traditional sense for diagnostic AI. The ground truth for this device's performance would be the expected output based on its functional specifications and the known inputs from the connected Nihon Kohden devices. For example, if a connected monitor transmits an HR of 70 bpm, the ground truth is that the Central Station must display 70 bpm. These "ground truths" are established by engineering design specifications, not human expert consensus on clinical findings.
• Experts Involved: Software engineers, quality assurance engineers, subject matter experts on the physiological monitoring systems, and potentially clinical users for usability and workflow testing. Their qualifications would be in device design, software development, testing, and clinical application.

4. Adjudication Method for the Test Set

• Not Applicable in the context of clinical interpretation adjudication (e.g., 2+1 radiologist consensus). Adjudication in this context would be internal to the software development and testing process:
  • Test Pass/Fail Criteria: Predetermined pass/fail criteria for each test case.
  • Bug/Defect Resolution: Issues found during testing are logged as bugs, investigated by engineers, and resolved, followed by retesting.
  • Verification Sign-off: Test leads or design engineers review test results and formally sign off on the successful completion of verification.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No, not performed for this device. An MRMC study is relevant for AI systems that assist human readers in making diagnostic decisions (e.g., radiologists reading X-rays with AI assistance). The Central Station device does not perform any such diagnostic assistance; it merely displays data from other (likely already cleared) devices. Its function is analogous to a monitor or a remote display unit, not a diagnostic AI.

6. Standalone (Algorithm Only) Performance

• Not Applicable. This device does not have a "standalone algorithm" that performs diagnostic or data processing functions independently. Its function is entirely dependent on receiving data from other compatible Nihon Kohden devices.

7. Type of Ground Truth Used

• Functional Specifications and Truth from Connected Devices: The ground truth for this device is based on its functional design specifications (e.g., "display received data," "store data for X hours," "print Y parameters") and the verified output from the connected Nihon Kohden bedside monitors and telemetry systems. It's about data integrity and display accuracy, not clinical outcomes or pathology.

8. Sample Size for the Training Set

• Not Applicable. This device is described as "software only product" that "does not perform any data processing on the data received," and "Central Station itself does not have the function to perform alarm indication judgment." This strongly implies it is a rule-based or deterministic system, not a machine learning/AI system that requires a "training set" in the common sense (i.e., for learning patterns from data for prediction or classification). Therefore, there is no training set.

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

• Not Applicable. As there is no training set for an AI algorithm, there is no corresponding ground truth establishment process for a training set.

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The HomMed Central Station intended use is to retrospectively receive, display, and store certain monitored physiological parameters received from adult and pediatric (over 12 years of age) patients within healthcare and home environments. Central Station, Version 3.3H includes a HL7 interface to transfer data from HomMed Central Station to vendor systems and storage media. The physiologic patient parameters available for retrospective display and evaluation include NIBP, pulse rate, SpO2, temperature, and weight. Additionally monitoring device data from optional commercial stand-alone products including glucometry, spirometry, prothrombin time, ECG and digital camera images can also be received, displayed and stored. The Central Station displays patient data and system alerts for review and interpretation by physicians and/or physician supervised nurses. Central Station is a retrospective data monitor system and is not intended for emergency use or real-time monitoring.

The HomMed Central Station is a software system that operates on a commercially available PC system with the minimum performance specifications consistent with typical PC hardware and equipment specifications. The HomMed Central Station is a software system that accepts data from the HomMed Sentry Patient Monitors.

The provided text describes the HomMed Central Station, Version 3.3H, a device intended to retrospectively receive, display, and store physiological parameters. However, the document does not contain information regarding specific acceptance criteria, a detailed study proving performance, sample sizes for test/training sets, expert qualifications for ground truth, adjudication methods, MRMC studies, or how ground truth was established beyond a general statement of "software validation results."

The "Performance Data" section states that "The software validation results demonstrated that the HomMed Central Station 3.3H System was in compliance with the guidelines and standards referenced in the FDA reviewer's guides and that it performed within its specifications and functional requirements." This is a high-level summary and does not provide the specific details requested in the prompt.

Therefore, I can only provide limited information based on the text.

Here's a breakdown of the requested information based on the provided document:

1. Table of acceptance criteria and the reported device performance

The document does not specify quantitative acceptance criteria. The performance is stated generally:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample size for the test set: Not specified in the provided document.
• Data provenance: Not specified in the provided document. The device itself retrospectively receives data, but the provenance of data used for validation is not mentioned.

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)

• Not specified in the provided document.

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

• Not specified in the provided 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

• No MRMC comparative effectiveness study is mentioned or implied. The device is a data display and storage system, not an AI-powered diagnostic tool for human reader improvement.

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

• The document implies a standalone software validation was performed ("The HomMed Central Station is a software system... The software validation results demonstrated..."), but specific details on the methodology (e.g., comparison to a reference standard without human involvement) are not provided. The phrase "algorithm only" might not be fully applicable as it's a data management system, not a diagnostic algorithm.

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

• Not specified. The statement "performed within its specifications and functional requirements" suggests validation against predetermined software requirements and potentially functional testing, but the nature of the "ground truth" for the physiological parameters themselves is not detailed.

8. The sample size for the training set

• Not specified in the provided document.

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

• Not specified in the provided document.

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