(141 days)
The ApexPro Telemetry System is intended for use under the direct supervision of a licensed healthcare practitioner. The system is designed to acquire and monitor physiological data for ambulating patients within a defined coverage area. The system processes this physiological data to detect various ECG arrhythmia events and select physiological parameter limit violations.
The ApexPro Telemetry System is intended to be installed in the hospital or clinical environment in order to provide clinicians with patient physiological data, while allowing for patient mobility. These systems are typically deployed in sub acute care areas in hospitals or clinical sites where patient mobility can enhance the effectiveness of the medical procedures administered.
The physiological parameters monitored include ECG, non-invasive blood pressure, noninvasive temperature and SpO2. The ApexPro Telemetry System is intended to provide ECG data via Ethernet to the computer platform for processing. The ApexPro Telemetry System is also intended to provide physiologic data over the Unity Network to clinical information systems for display.
The ApexPro Telemetry System provides clinicians with patient data while allowing for patient mobility. The system consists of the following main components:
• The patient worn data acquisition transmitters
• Receiver Infrastructures
• Computer platforms hosting the ApexPro Software
• Computer platforms hosting the central station application
• Accessories to the patient-worn data acquisition transmitters
Serviceability tools
The provided text describes a 510(k) submission for the ApexPro Telemetry System, which is a design modification. However, it does not contain a table of specific acceptance criteria or detailed results of a study demonstrating the device meets those criteria, as typically found in a comprehensive clinical or performance validation report.
Instead, it lists the quality assurance measures applied during development and states a general conclusion about its safety and effectiveness compared to a predicate device.
Here's an analysis based on the available information, addressing what is and is not present:
1. Table of Acceptance Criteria and Reported Device Performance
(Information Not Provided in Detail)
The document does not include a quantifiable table of acceptance criteria or specific performance metrics with reported values. It broadly states:
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified quantitatively in this document. General criteria include compliance with voluntary standards, and demonstrating the device is "as safe, as effective, and performs as well as the predicate device." | Not specified quantitatively in this document. The conclusion states that "The results of these measurements demonstrated that the ApexPro Telemetry System is as safe, as effective, and performs as well as the predicate device." |
The "Test Summary" lists types of tests conducted:
- Risk Analysis
- Requirements Review
- Design Reviews
- Testing on Unit Level (Module verification)
- Integration testing (System Verification)
- Final Acceptance testing (Validation)
- Performance testing
- Safety and environmental testing
These are categories of testing, not specific acceptance criteria or performance results.
2. Sample Size Used for the Test Set and Data Provenance
(Information Not Provided)
The document does not specify the sample size for any test set or the provenance of data (e.g., country of origin, retrospective or prospective).
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts
(Information Not Provided)
There is no mention of experts being used to establish ground truth for a test set, nor their number or qualifications. The submission focuses on internal testing and comparison to a predicate device.
4. Adjudication Method for the Test Set
(Information Not Provided)
No adjudication method is described, as the document does not detail a test set requiring expert adjudication.
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
(Not Applicable / Information Not Provided)
This device (ApexPro Telemetry System) is a physiological patient monitor, not an AI-assisted diagnostic imaging or interpretation tool for human readers. Therefore, an MRMC study comparing human reader performance with and without AI assistance is not relevant to this device and is not mentioned.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
(Partially Applicable/Implied)
The submission implies that performance testing was done on the system itself (algorithm/device only) to ensure it performs "as well as the predicate device." This would be a form of standalone testing, as it doesn't describe human interaction being part of the primary performance evaluation shown here. The "Performance testing" mentioned in the Test Summary would have evaluated the device's inherent capabilities, including its ability to "detect various ECG arrhythmia events and select physiological parameter limit violations" according to its intended use.
7. The Type of Ground Truth Used
(Implied Standard Device Performance)
For a telemetry system, the "ground truth" for its performance would typically come from:
- Known, validated physiological signals: Using signal generators or previously recorded, carefully annotated cardiac or physiological waveforms to test the device's detection and measurement accuracy (e.g., against established medical standards for arrhythmia detection).
- Comparison to a predicate device: The document explicitly states the system "employs the same functional technology as the predicate devices" and that it was demonstrated to be "as safe, as effective, and performs as well as the predicate device." This indicates the predicate device's established performance serves as a benchmark for ground truth.
8. The Sample Size for the Training Set
(Not Applicable / Information Not Provided)
This submission describes a telemetry system, not an AI/machine learning algorithm that typically requires a "training set" in the conventional sense. The "development" process would involve engineering and software validation, not machine learning model training.
9. How the Ground Truth for the Training Set was Established
(Not Applicable / Information Not Provided)
As this is not an AI/ML device that uses a "training set," this question is not applicable.
§ 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.