(21 days)
Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients.
The Philips SureSigns Series Patient Monitors, SureSigns VM4, VM6, and VM8 Patient Monitors are multi-parameter patient monitors. The modifications include a new OEM CO2 module, addition of an apnea alarm, a new main board, modified front end board, alternate internal component speaker, optional bar code reader, optional RS232 serial port adaptor, several new accessories, and software enhancements. Impedance respiration is added to SureSigns VM4.
1. Acceptance Criteria and Reported Device Performance
The provided text only states that "Pass/Fail criteria were based on the specifications cleared for the predicate device, the specifications of the subject device and test results showed substantial equivalence." It does not explicitly list specific acceptance criteria or quantitative performance metrics. It generally asserts that the device "meet all reliability requirements and performance claims."
| Acceptance Criteria | Reported Device Performance |
|---|---|
| (Not explicitly stated in the provided text, but implied to be based on predicate device specifications and subject device specifications for various physiological parameters like NBP, SpO2, CO2, impedance respiration, etc.) | "meets all reliability requirements and performance claims" and "supports a determination of substantial equivalence." |
2. Sample Size and Data Provenance
The document does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective or prospective nature of the data). It broadly mentions "system level tests, performance tests, and safety testing from hazard analysis."
3. Number and Qualifications of Experts for Ground Truth
This information is not provided in the document. The type of device (patient monitor) and the nature of modifications (hardware and software enhancements to existing parameters, addition of alarms) suggest that "ground truth" might be established through comparisons with reference measurement devices or established clinical standards, rather than expert consensus on image interpretation, for example.
4. Adjudication Method
The document does not mention any adjudication method.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No multi-reader multi-case (MRMC) comparative effectiveness study is mentioned. The device is a patient monitor, not an AI-assisted diagnostic tool that would typically involve human readers interpreting output. The evaluation focuses on technical performance and equivalence to a predicate device.
6. Standalone Performance (Algorithm Only)
The device itself is a standalone patient monitor, and its performance was evaluated. However, the term "standalone" in the context of AI often implies an algorithm's performance without any human-in-the-loop. In this case, the device monitors physiological parameters and provides alarms directly, so its performance is inherently standalone in generating these outputs. The text indicates that "testing activities establish the performance, functionality, and reliability characteristics of the subject devices."
7. Type of Ground Truth Used
The document does not explicitly state the type of ground truth used. However, for physiological monitoring devices, the ground truth would typically be established by:
- Reference standard devices: Comparing the device's readings against highly accurate and calibrated reference instruments for each physiological parameter (e.g., a clinical-grade blood pressure cuff for NBP, a calibrated pulse oximeter for SpO2, a gas analyzer for CO2).
- Physical simulators: For certain parameters, using simulators that can generate precise physiological waveforms or values.
- Clinical standards/established norms: Ensuring basic functionality and alarm thresholds align with recognized clinical practices.
8. Sample Size for the Training Set
The document does not specify any training set sample size. This type of device (patient monitor with hardware and software modifications to existing parameters) is unlikely to have a "training set" in the sense of machine learning models requiring large datasets for training. Its development would involve engineering design, component testing, and system-level validation against specifications, not typically a data-driven training process in the modern AI sense.
9. How Ground Truth for the Training Set Was Established
As no training set is mentioned (see point 8), there is no information on how its ground truth would have been established.
§ 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.