(127 days)
The Spacelabs Healthcare Telemetry Receiver, Model 96280, is intended to provide the Spacelabs Healthcare monitoring system with adult, pediatric and neonatal patient data of patients connected to Spacelabs Healthcare telemetry transmitters. Data includes physiological waveforms and calculations, cardiac arrhythmia and ST data, and patient demographic information to monitor adequacy of treatment or to exclude causes of symptoms.
The Spacelabs Healthcare (Spacelabs) Telemetry Receiver, Model 96280, (ETR) is a new version of a currently marketed Spacelabs product. The Spacelabs ETR offers receipt and analysis of patient data for those patients connected to a Spacelabs Healthcare telemetry transmitter. The Spacelabs ETR provides for data communication using the TCP/IP network protocol employed in the Xhibit Central Station, Model 96102, (Xhibit) (K122146) network of hardwired and/or ETR monitored patients. Xhibit is the primary alarming device for the ETR telemetry receiver system.
This FDA 510(k) clearance document for the Spacelabs Healthcare Telemetry Receiver, Model 96280, indicates a traditional medical device (hardware and embedded software) rather than an AI/ML-driven device. As such, the information typically associated with AI/ML device studies (such as ground truth establishment involving experts, training/test set sizes for AI, MRMC studies, or standalone algorithm performance) is not available or applicable in the provided text.
The acceptance criteria and performance summary primarily revolve around compliance with established industry standards for medical electrical equipment, software development, electrical safety, electromagnetic compatibility, and performance testing for physiological monitoring.
Here's a breakdown of the available information based on your request:
Acceptance Criteria and Reported Device Performance
The document does not explicitly state quantitative "acceptance criteria" in a table format for specific performance metrics of the device as one might expect for an AI/ML diagnostic device (e.g., sensitivity, specificity, AUC). Instead, the "acceptance criteria" are implied by adherence to various established medical device standards and internal requirements. The "reported device performance" is essentially that the device was tested and found to comply with these standards and its predetermined specifications.
| Acceptance Criteria (Implied by Standards Compliance) | Reported Device Performance (Compliance) |
|---|---|
| Software developed to robust process (e.g., IEC 62304, FDA Guidance for software, cybersecurity) | Complies with predetermined specifications and applicable standards/guidance |
| Electrical safety (IEC 60601-1: 2005) | Complies with applicable standards |
| Electromagnetic compatibility (IEC 60601-1-2: 2007) | Complies with applicable standards |
| Performance testing (e.g., ANSI/AAMI EC-57: 2012 for cardiac rhythm/ST-segment, IEC 60601-1-8 for alarm systems, IEC 60601-2-27 for ECG monitoring, IEC 80601-2-61 for pulse oximeter, IEC 62366 for usability engineering) | Complies with predetermined specifications and applicable standards |
Study Details (Based on available information)
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Sample size used for the test set and the data provenance:
- The document does not specify a "test set" in the context of an AI/ML algorithm evaluation. Performance testing was conducted in a bench setting based on established standards. The type or size of patient data or physiological signals used for these bench tests is not detailed (e.g., how many ECG recordings, how many simulated arrhythmia events).
- Data provenance is not mentioned as this is a device clearance based on engineering and safety standards, not a clinical trial with patient data.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. This is not an AI/ML diagnostic device requiring expert-adjudicated ground truth. The "ground truth" for compliance with engineering standards is the adherence to specifications as measured by laboratory testing.
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Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not applicable. There's no human adjudication process described for establishing ground truth as it would be for an AI/ML system.
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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. This device is a telemetry receiver, not an AI/ML system designed to assist human readers in interpretation. There is no mention of an MRMC study.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- The device itself functions as a "standalone" physiological monitoring system, receiving and analyzing patient data. The performance tests ("Performance Testing – Bench") assess the device's inherent capabilities against technical standards. However, this is not a "standalone algorithm" in the context of an AI/ML submission where an algorithm's diagnostic performance is evaluated in isolation.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc):
- The "ground truth" for this type of device is defined by the technical specifications and requirements outlined in the referenced standards (e.g., an arrhythmia detection algorithm must correctly identify a pre-defined set of arrhythmias from test data as per the standard). This is an engineering truth rather than a clinical ground truth established by medical experts for diagnostic accuracy.
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The sample size for the training set:
- Not applicable. This is not an AI/ML device and therefore does not have a "training set" in that context.
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How the ground truth for the training set was established:
- Not applicable. See point 7.
In summary, the provided document details the FDA clearance of a traditional telemetry receiver based on its compliance with established medical device standards for electrical safety, electromagnetic compatibility, software robustness, and performance (e.g., ECG analysis, alarm systems, pulse oximetry). It does not involve AI/ML technology, and thus the specific types of studies and criteria relevant to AI/ML devices (like training/test sets, expert adjudication, MRMC studies) are not present in this submission.
§ 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.