(118 days)
The Hewlett-Packard Viridia M3/M4 (M3000A/M3046A) Patient Monitoring System, Rel.B is intended for monitoring, recording, and alarming of multiple physiological parameters of adults, pediatrics, and neonates in the hospital and medical transport environments.
The Hewlett-Packard Viridia Patient Monitor M3000A/M3046A with M3015A (Viridia M3/M4, Rel. B.). The modification is the addition of a firmware and software based change that involves the addition of the M3015A Module to the portable Viridia M3/M4 Patient Monitor System to allow sidestream CO2, and a second invasive blood pressure and temperature measurements with the unit.
The provided text is a 510(k) summary for the Hewlett-Packard Viridia Patient Monitor M3000A/M3046A with M3015A. It describes a modification to an existing device, which involves adding the M3015A Module to allow sidestream CO2, and a second invasive blood pressure and temperature measurements.
However, the summary does not contain the detailed information required to fill out your request, which typically applies to AI/ML or diagnostic devices with specific performance metrics. This document describes a hardware and firmware modification to a patient monitor, and its testing focuses on compliance with standards and equivalence to predicate devices rather than specific performance metrics like accuracy, sensitivity, or specificity for a diagnostic algorithm.
Therefore, many of the requested fields cannot be extracted from this document.
Here's a breakdown of what can and cannot be answered based on the provided text:
1. A table of acceptance criteria and the reported device performance:
- Acceptance Criteria: The text states, "Pass/Fail criteria were based on standards, where applicable, and on the specifications cleared for the predicate devices." However, it does not detail these specific criteria (e.g., specific accuracy ranges for CO2, blood pressure, or temperature).
- Reported Device Performance: The text generally states, "The test results showed substantial equivalence." It does not provide quantitative performance metrics for the added functionalities (CO2, second invasive BP, temperature).
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Based on standards and specifications of predicate devices (details not provided) | Test results showed substantial equivalence. (Specific quantitative results not provided) |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
- Sample Size for Test Set: Not specified. The document mentions "simulated systems" but gives no numbers.
- Data Provenance: Not specified. It only mentions "simulated systems."
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 applicable/Not specified. This type of device (patient monitor) does not typically involve expert review for ground truth in the same way a diagnostic imaging AI would. Testing would involve calibrated reference instruments.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not applicable/Not specified. Adjudication methods are typically relevant for human-interpretable results, which is not the primary focus of this device's validation.
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. This is not an AI/ML diagnostic device and such studies would not be relevant.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not applicable. This is a hardware/firmware modification to a patient monitor, not an AI algorithm. Its performance is inherent in its measurement capabilities.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
- Likely calibrated reference instruments and physical standards. The text mentions "simulated systems," which implies controlled inputs with known, precise values against which the device's measurements would be compared.
8. The sample size for the training set:
- Not applicable/Not specified. This device does not use machine learning in a way that would typically involve a "training set" for an algorithm. Its operation is based on established physiological measurement principles and programmed logic.
9. How the ground truth for the training set was established:
- Not applicable. No training set as described for an AI/ML model.
§ 870.1025 Arrhythmia detector and alarm (including ST-segment measurement and alarm).
(a)
Identification. The arrhythmia detector and alarm device monitors an electrocardiogram and is designed to produce a visible or audible signal or alarm when atrial or ventricular arrhythmia, such as premature contraction or ventricular fibrillation, occurs.(b)
Classification. Class II (special controls). The guidance document entitled “Class II Special Controls Guidance Document: Arrhythmia Detector and Alarm” will serve as the special control. See § 870.1 for the availability of this guidance document.