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510(k) Data Aggregation

    K Number
    K183099
    Date Cleared
    2019-02-28

    (113 days)

    Product Code
    Regulation Number
    870.1425
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The QUANTIEN™ Measurement System is indicated to provide hemodynamic information for use in the diagnosis and treatment of coronary or peripheral artery disease.

    The QUANTIEN measurement system is intended for use in the 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.

    Device Description

    The QUANTIENTTM Measurement System is a diagnostic computer designed to record, compute, display and store data from PressureWireTM guidewire and other external transducers. The information is displayed as graphs as well as numerical values on the screen. Relevant display data includes: systolic, diastolic and mean blood pressure, heart rate, Fractional Flow Reserve (FFR), Resting Full-Cycle Ratio (RFR) index, Pd/Pa and data from ECG.

    Information on the display screen may also be transferred to the cathlab monitoring system or an offsite video monitor. Recorded procedures can be viewed on a PC, with application specific viewing software installed such as RadiView, for post procedural review and analysis.

    Quantien allows for importing of a patient work list from the hospital DICOM system, exporting of recorded measurement data to DICOM or to an external server location or portable (USB) memory device.

    AI/ML Overview

    Here's a summary of the acceptance criteria and study details for the QUANTIEN™ Measurement System with Software Version 1.12.1. as presented in the provided document:

    Acceptance Criteria and Device Performance

    Acceptance Criteria CategoryAcceptance Criteria (Threshold)Reported Device Performance (RFR-FFR Hybrid Method)
    Diagnostic AccuracyNot explicitly stated as a numerical threshold, but implicitly defined by the comparative equivalence to iFR-FFR.93.6% [91.1%, 95.6%]
    Percent Positive AgreementNot explicitly stated as a numerical threshold.91.3% [86.9%, 94.5%]
    Percent Negative AgreementNot explicitly stated as a numerical threshold.95.8% [92.6%, 97.9%]
    Positive Predictive Value (PPV)Not explicitly stated as a numerical threshold.95.2% [91.6%, 97.6%]
    Negative Predictive Value (NPV)Not explicitly stated as a numerical threshold.92.3% [88.4%, 95.1%]
    Diagnostic Accuracy Outside the Gray ZoneNot explicitly stated as a numerical threshold.88.5% [84.1%, 92.0%]

    Note: The acceptance criteria are implicitly defined by demonstrating equivalence to the iFR-FFR hybrid approach, which itself serves as a benchmark for diagnostic utility in assessing coronary artery disease. The study aims to show that the RFR-FFR hybrid method performs comparably to the established iFR-FFR method.

    Study Details

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

      • Sample Size: Not explicitly stated as a numerical value for the overall study. However, the results are presented with 95% confidence intervals, which usually implies a sufficient sample size was used for statistical significance.
      • Data Provenance: Prospective study. The country of origin of the data is not specified in the provided document.
    2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

      • The document does not specify the number of experts or their qualifications used to establish the ground truth.
      • Instead, the "ground truth" for the comparison is RFR-FFR and iFR-FFR hybrid methods, where FFR is typically considered a reference standard in fractional flow reserve studies. The document does not describe an independent "expert consensus" or manual ground truth labeling for the images/data used. The comparison is between two algorithmic approaches.
    3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

      • The document does not describe a specific adjudication method like 2+1 or 3+1. The study is a comparison of two physiological assessment methods (RFR-FFR vs. iFR-FFR), not a reader-based adjudication process for image interpretation.
    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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done to evaluate human reader improvement with AI assistance. This study focuses on the comparison of two physiological indices (RFR and iFR) against FFR as the reference, not on human reader performance.
    5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

      • Yes, the performance metrics (Diagnostic Accuracy, Agreement, PPV, NPV) presented for "RFR-FFR" and "iFR-FFR" represent the standalone performance of these hybrid methods. They involve algorithmic computation and interpretation based on defined thresholds (e.g., RFR < 0.86 for positive, FFR ≤ 0.8 for positive). There isn't a human-in-the-loop element described in these performance metrics.
    6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

      • The "ground truth" or reference standard used in this comparative study is Fractional Flow Reserve (FFR), which is a widely accepted physiological measure for assessing the hemodynamic significance of coronary artery stenoses. The study compares the RFR-FFR and iFR-FFR hybrid approaches against this standard.
    7. The sample size for the training set:

      • The document states, "No new clinical testing was completed, nor relied upon, in support of this Traditional 510(k)." This implies that the RFR algorithm and its established thresholds were likely developed and validated prior to this submission, possibly through other studies. Therefore, the training set size is not provided in this document as it pertains to development data that was not newly generated for this submission.
    8. How the ground truth for the training set was established:

      • Given the statement in point 7, the document does not describe how the ground truth for any training set was established for the RFR algorithm. The presented clinical testing is a validation study of the already established RFR method, not a development study.
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