(30 days)
FFR and DFR™ are intended for use in catheterization and related cardiovascular specialty laboratories to compute, and display various physiological parameters based on the output from one or more electrodes, transducers, or measuring devices.
FFR and DFR are indicated to provide hemodynamic information for use in the diagnosis and treatment of patients that undergo measurement of physiological parameters.
The AVVIGO Guidance System is a medical device system that consists of a touchscreen tablet with battery, a digital pen, a power supply and cable which can be mounted to a mobile pole via the pole docking station or set on tabletop via the desktop docking station. The tablet is a nonsterile, non-implantable tablet computer controlled by a graphic user interface (GUI) displayed on a touchscreen. The tablet is powered by either AC line power or a lithium polymer battery.
The system software displays patient's blood pressure measurements that are received from the coronary pressure guidewire and transducer that is connected to a FFR Link. The FFR Link digitizes and wirelessly streams the data which is displayed on the tablet.
The provided text describes the AVVIGO™ Guidance System, a medical device intended for use in catheterization laboratories to compute and display physiological parameters like FFR (Fractional Flow Reserve) and DFR (Diastolic Hyperemia-free Ratio). The FDA 510(k) summary focuses on demonstrating substantial equivalence to a predicate device, the iLab™ Polaris Multi-Modality Guidance System, rather than presenting a standalone study with detailed acceptance criteria and performance metrics for the AVVIGO™ Guidance System's diagnostic accuracy.
Therefore, the requested information specifically regarding acceptance criteria for a diagnostic performance study, reported device performance against those criteria, sample sizes, ground truth establishment methods for test and training sets, expert qualifications, and MRMC study details cannot be fully extracted from this document, as it emphasizes non-clinical performance and substantial equivalence.
However, based on the non-clinical performance mentioned, here's what can be inferred and what information is missing:
1. Table of acceptance criteria and the reported device performance:
The document does not explicitly state acceptance criteria in terms of diagnostic accuracy (e.g., sensitivity, specificity, AUC) for FFR/DFR calculation. Instead, it refers to conformity with standards and successful completion of verification and validation testing for software, hardware, packaging, and electrical safety.
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Software Conformance | Passed IEC 62304 and FDA Guidance for Industry (May 11, 2005) requirements for software in medical devices. |
| Hardware Conformance | Passed FDA Guidance for Industry (August 14, 2013) for Radio Frequency Wireless Technology, ANSI AAMI ES 60601-1, and IEC 60601-1-2 (Edition 3). |
| Electrical Safety | In compliance with ANSI/AAMI ES60601-1:2005+A2 (R2012) A1 and other applicable electrical standards. |
| Functional Modality (FFR/DFR display) | The system shall calculate and display FFR and DFR values as specified, based on Pd and Pa trend values, non-zero Pa, and recording initiation. It displays Pa/Pd waveforms and DFR windows during recording. |
2. Sample size used for the test set and the data provenance:
This information is not provided in the document. The document states "Non-clinical Performance" and explicitly says "Not applicable. A determination of Substantial Equivalence for this modification is not based on clinical data. Substantial Equivalence is based on non-clinical performance data." This indicates that no clinical test set for FFR/DFR diagnostic performance was used.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable, as no clinical test set with ground truth for diagnostic performance was described.
4. Adjudication method for the test set:
Not applicable, as no clinical test set with ground truth for diagnostic performance was described.
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:
No MRMC study was mentioned or performed, as the substantial equivalence determination was based on non-clinical performance. The device is a computational system for physiological parameters, not an AI to assist human readers in interpreting images or complex data in an MRMC setting.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The device itself is a standalone algorithm/system for computing and displaying FFR/DFR. The non-clinical performance testing validated its functionality against technical requirements and standards. However, the document doesn't provide specific standalone performance metrics like accuracy against a gold standard for FFR/DFR values, but rather confirms its compliance with relevant engineering and software standards for operation and display.
7. The type of ground truth used:
For the non-clinical performance, the "ground truth" would be the expected output or behavior according to the design specifications and applicable medical device standards (e.g., correct calculation of FFR from simulated pressure inputs, accurate display of waveforms). No clinical ground truth (like pathology, expert consensus on disease presence based on FFR/DFR, or outcomes data) was used for this 510(k) submission.
8. The sample size for the training set:
Not applicable. The document describes a "Guidance System" that calculates and displays physiological parameters, not an AI/machine learning model that would typically undergo a training phase with a dedicated dataset. The software development likely involved unit testing, integration testing, and system validation, but not in the context of a "training set" for an AI model.
9. How the ground truth for the training set was established:
Not applicable, as there was no described training set for an AI/machine learning model. The software's functionality is based on established physiological equations and data processing methods, not learned patterns from a training dataset.
§ 870.1425 Programmable diagnostic computer.
(a)
Identification. A programmable diagnostic computer is a device that can be programmed to compute various physiologic or blood flow parameters based on the output from one or more electrodes, transducers, or measuring devices; this device includes any associated commercially supplied programs.(b)
Classification. Class II (performance standards).