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510(k) Data Aggregation
(29 days)
The Spacelabs Smart Disclosure System, Model 92810 is indicated for use in clinical situations where there is a need for review of physiological waveform information and alarm events up to 72 hours after the fact. Smart Disclosure is also indicated in those situations where a retrospective analysis of monitoring patients' ECG waveform data, that can be annotated and edited, is desired.
The intended use of the Spacelabs Smart Disclosure is to interface with the Spacelabs monitoring network, providing the user with a means of recalling waveform information and performing retrospective analysis. The most recent 72 hours of monitored patient ECG waveform data can be analyzed, with each analysis limited to 24 hours or less.
The Spacelabs Healthcare (Spacelabs) Smart Disclosure System (Smart Disclosure), Model 92810, is a software only medical device that resides on a server or a client workstation. It is an update to the predicate device, the Spacelabs Full Disclosure, Model 91810, cleared by FDA in 510(k) submission K063490. Smart Disclosure is presented as an icon that, when selected, opens Smart Disclosure on a client workstation.
Smart Disclosure allows the recall and retrospective review of up to 72 hours of electrocardiogram (ECG) and other physiological waveforms and alarm events that are stored in the Spacelabs Network Database. The clinician can review vital signs information which enhances the ability to evaluate a patient's history. Smart Disclosure is able to reproduce the waveform data and events on the client workstation graphic display as well as in printed reports. The clinician can review infrequent events and ensures that onsets and terminations of the events are captured, and make standard-sized tracings using a network-connected laser printer. Additionally, the clinician can perform a shape-based retrospective analysis of the ECG waveform data using the Smart Disclosure shape-based retrospective algorithm.
The information provided in the 510(k) summary for the Spacelabs Smart Disclosure System, Model 92810 (K110779) indicates that its premarket notification is based on demonstrating substantial equivalence to a predicate device (Spacelabs Medical Inc. / Full Disclosure System, Model 91810, K063490).
Here's an analysis of the provided text in relation to your questions:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly state specific, quantifiable acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) in a table format for a clinical performance study. Instead, it relies on a general statement of compliance with internal requirements and established software development and validation standards.
| Acceptance Criteria (Stated Generally) | Reported Device Performance |
|---|---|
| Compliance with predetermined specifications | "Test results indicated that the Smart Disclosure complies with its predetermined specification and with the applicable Standards." |
| Performance in accordance with internal requirements | "The Smart Disclosure was tested for performance in accordance with internal requirements." |
| Compliance with FDA Software Guidance documents | Implied by adherence to various FDA guidance documents for software in medical devices. |
| Safety and effectiveness | "The results of these activities demonstrate that the Smart Disclosure is safe and effective when used in accordance with its intended use and labeling." |
2. Sample Size Used for the Test Set and Data Provenance
This information is not provided in the given 510(k) summary. The summary focuses on software validation and verification rather than clinical performance testing with a specific test set of patient data.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
This information is not provided. As there's no mention of a clinical performance study involving a test set with ground truth established by experts, this detail is absent.
4. Adjudication Method
This information is not provided. Without a clinical performance study and expert-established ground truth, an adjudication method is not applicable.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
A MRMC comparative effectiveness study is not mentioned in the summary. The submission is a software update and emphasizes technological equivalence and software validation, not a comparison of human reader performance with and without AI assistance.
6. Standalone Performance Study
A standalone performance study (algorithm only, without human-in-the-loop performance) is not explicitly described in terms of specific performance metrics against a defined ground truth. The "retrospective analysis of the ECG waveform data using the Smart Disclosure shape-based retrospective algorithm" is mentioned as a feature, but its standalone performance against quantifiable metrics and ground truth is not detailed. The "performance testing" referenced is primarily software-centric, focused on internal requirements and specifications.
7. Type of Ground Truth Used
The document does not specify a type of ground truth (e.g., expert consensus, pathology, outcomes data) as it primarily describes software validation and verification. If the "shape-based retrospective algorithm" was evaluated, the ground truth for that specific algorithm's performance is not detailed.
8. Sample Size for the Training Set
This information is not provided. The summary does not discuss machine learning model training or a specific training set. The device is described as a "software only medical device" that performs recall, review, and retrospective analysis of existing data, rather than a system that learns from a training set in the typical machine learning sense.
9. How the Ground Truth for the Training Set Was Established
This information is not provided, as there is no mention of a training set or machine learning components in the typical sense for which ground truth would be established.
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