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The ReDS System is intended for use by qualified healthcare practitioners, under the direction of a physician, in hospitals, hospital-type facilities and home environments, for the non-invasive monitoring and management of patients with fluid management problems in a variety of medically accepted clinical applications. The ReDS System is indicated for patients:

• With fluid management problems
• Taking diuretic medication
• Living with heart failure
• Recovering from a coronary artery disease-related event

The ReDS System ("ReDS" or "ReDS System") is a non-invasive thoracic base wave impedance monitor that provides measurement of patient lung fluid content. ReDS™ technology measures wave impedance of biological tissues. Low power electromagnetic (EM) signals are emitted into the body, and intercepted by sensors on the body. The wave impedance is a function of the distributed tissue conductivity and capacitance properties, and is representative of tissue fluid content.

The system consists of a Sensor Unit connected to a bedside console, as well as a cloud application. The Sensor Unit encases two sensors and an attachment mechanism. The Bedside Console is an enclosure housing the electronic modules, an embedded computer and a touch-screen display. The device software provides the management of the device as well as analysis of the measured signals, graphic display of readings, reporting, and communications functionality to enable remote patient monitoring.

The provided document is a 510(k) summary for the ReDS System V2.7, seeking substantial equivalence to a predicate device, the ReDS Wearable System V2.6. As such, the study described focuses on demonstrating that the new device (V2.7) performs equivalently to the existing cleared device (V2.6), rather than establishing de novo performance criteria against a specific medical condition's ground truth.

Therefore, the acceptance criteria and study design are framed around equivalence to a predicate device, not necessarily absolute clinical accuracy or performance based on an independent ground truth outside of this comparison.

Here's an analysis of the document to address your request:

Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by demonstrating "substantial equivalence" to the predicate device (ReDS V2.6). The performance reported is in terms of this equivalence, rather than specific numerical thresholds for diagnostic accuracy against a disease state.

Study Details:

The document describes a comparison study to demonstrate substantial equivalence, rather than a de novo clinical trial establishing absolute accuracy against a gold standard for a specific disease or outcome.

1. Sample size used for the test set and the data provenance:

   • Test Set (Clinical Data): The document states "Clinical data was provided to further assess the ReDS System V2.7 equivalency to ReDS System V2.6." However, it does not specify the sample size for this clinical test set.
   • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective for the clinical testing.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Given that this is an equivalence study between two versions of the same device, and not a study validating the device's accuracy against a specific physiological ground truth (e.g., direct measurement of lung fluid volume), the concept of "experts establishing ground truth for the test set" in the traditional sense (e.g., radiologists annotating images) is not directly applicable here.
   • The "ground truth" for this study is essentially the performance of the predicate device (ReDS V2.6). The clinical study aimed to show that V2.7's performance was equivalent to V2.6's.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not applicable in the context of this equivalence study. There is no mention of independent reviewers or an adjudication process for clinical "ground truth" labels, as the study's goal was to compare the two device versions.

4. 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, an MRMC comparative effectiveness study was not conducted or described. The ReDS System measures lung fluid content; it is not described as an AI-assisted diagnostic imaging tool where human readers "improve with AI assistance." The device is a direct measurement system.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • The ReDS System itself is a standalone device that measures lung fluid content algorithmically. It's described as a "non-invasive thoracic base wave impedance monitor that provides measurement of patient lung fluid content." The "clinical data" section (Page 5) effectively serves as a standalone performance assessment in comparison to the predicate device. The performance of the algorithm within V2.7 was assessed as part of the overall device performance against V2.6.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For the clinical study's "ground truth," the performance of the predicate device (ReDS V2.6) was implicitly used as the reference point for comparison. The study aimed to show that the new device (V2.7) did not significantly differ in its measurements or clinical utility from the established V2.6.
   • The document does not detail a separate, independent "gold standard" for fluid assessment (e.g., invasive measurements, specific clinical outcomes data) against which both devices were tested. The core claim is equivalence to the predicate.

7. The sample size for the training set:

   • Not applicable / Not explicitly stated. This document describes the validation of a medical device hardware and software system, not the training of a machine learning model. While an internal algorithm exists (which was updated with a "stricter signal quality assessment mechanism" in V2.7), the summary does not suggest a discrete "training set" in the common machine learning sense. The development of the algorithm would have involved internal data sets, but these are not reported as a "training set" in the context of this 510(k) summary.

8. How the ground truth for the training set was established:

   • Not applicable. As above, the summary does not detail a machine learning training process or corresponding ground truth establishment.

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