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510(k) Data Aggregation
(39 days)
THE PHILIPS INTELLIVUE PATIENT MONITORS, MODELS MP20,MP30,MP40,MP50,MP60,MP70,MP80,MP90
Indicated for use by health care professionals whenever there is a need for monitoring, recording and alarming of multiple physiological parameters of patients. Intended for monitoring, of multiple physiological parameters of adults, pediatrics and neonates in hospital environments. The MP20, MP30, MP40 and MP50 are additionally intended for use in transport situations within hospital environments.
ST Segment monitoring is restricted to adult patients only.
The transcutaneous gas measurement (tcp02 / tcpCQ2) is restricted to neonatal patients only.
The names of the devices are the Philips MP20, MP30, MP40, MP50, MP60, MP70, MP80 and MP90 IntelliVue Patient Monitors.
The provided text is a 510(k) summary for the Philips IntelliVue Patient Monitors, specifically for the introduction of Release D.04 software, which includes the calculation of Pulse Pressure Variation.
Here's an analysis of the acceptance criteria and study information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria Category | Acceptance Criteria (Implicit) | Reported Device Performance |
---|---|---|
Equivalence to Predicate | Substantial equivalence to previously cleared Philips devices (K021778, K030038, etc.) | The modified devices have the same intended use and technological characteristics as the legally marketed predicate devices. |
Performance & Functionality | Meet specifications cleared for the predicate device. | "Test results showed substantial equivalence." "The results demonstrate that the Philips IntelliVue Patient Monitor meets all reliability requirements and performance claims." |
Reliability | Meet reliability requirements. | "The results demonstrate that the Philips IntelliVue Patient Monitor meets all reliability requirements and performance claims." |
Safety | Address hazards identified in hazard analysis. | "Safety testing from hazard analysis" was performed, implying that safety criteria were met. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It generally refers to "system level tests, performance tests, and safety testing."
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not mention the use of experts to establish a ground truth for a test set, nor does it specify their number or qualifications. The evaluation appears to be based on engineering-level testing against predetermined specifications rather than clinical expert review.
4. Adjudication Method for the Test Set
The document does not describe any adjudication method. The testing seems to be based on pass/fail criteria against specifications.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
A multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned. The device is a patient monitor with software for physiological parameter calculation (Pulse Pressure Variation), not an imaging or diagnostic device typically evaluated with MRMC studies involving human readers.
6. Standalone (Algorithm Only) Performance Study
A standalone performance evaluation was done in the sense that the device's performance (specifically the Release D.04 software including Pulse Pressure Variation calculation) was tested against "specifications cleared for the predicate device." However, the term "standalone" usually implies an AI algorithm's performance independent of human input, and this document describes the overall system's performance. The "algorithm" itself (Pulse Pressure Variation calculation) was implicitly evaluated as part of the overall performance tests.
7. Type of Ground Truth Used
The ground truth for the performance evaluation appears to be based on:
- Specifications: "Pass/Fail criteria were based on the specifications cleared for the predicate device." This implies engineering and technical specifications for accuracy and functionality.
- Predicate Device Performance: The primary method of demonstrating substantial equivalence relies on showing the device performs similarly to or within the established range of the predicate device.
8. Sample Size for the Training Set
The document does not mention a training set. This is not a machine learning model in the modern sense that typically requires a separate training set. The "software" likely refers to deterministic algorithms and calculations.
9. How the Ground Truth for the Training Set Was Established
Since there is no mention of a training set, the establishment of ground truth for it is not applicable.
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