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510(k) Data Aggregation

    K Number
    K032235
    Date Cleared
    2004-01-28

    (191 days)

    Product Code
    Regulation Number
    862.1117
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Triage® BNP Test is intended for use with the Triage® Meter for the rapid in vitro quantitative measurement of B-Type Natriuretic (BNP) in human capillary whole blood, venous whole blood or plasma specimens using EDTA as the anticoagulant. The test is used as an aid in the diagnosis and assessment of scverity of congestive heart failure (also referred to as heart failure). The test also is used for the risk stratification of patients with acute coronary syndromes.

    Device Description

    The Triage® BNP Test is intended for use with the Triage® Meter for the rapid in vitro quantitative measurement of B-Type Natriuretic Peptide (BNP) in human capillary whole blood, venous whole blood or plasma specimens using EDTA as the anticoagulant. The test is used as an aid in the diagnosis and assessment of severity of congestive heart failure (also referred to as heart failure). The test also is used for the risk stratification of patients with acute coronary syndromes.

    AI/ML Overview

    Here's an analysis of the provided text regarding the Triage® B-Type Natriuretic Peptide (BNP) Test, structured according to your requested information:

    1. Table of Acceptance Criteria and the Reported Device Performance

    The provided text focuses on the equivalence of capillary whole blood samples to venous whole blood samples for the Triage® BNP Test, rather than specific diagnostic accuracy acceptance criteria (e.g., sensitivity, specificity, AUC). Therefore, the acceptance criteria are related to the comparability of these sample types.

    Acceptance Criteria (Implicit)Reported Device Performance
    Statistical Equivalence: No significant difference (p < 0.05) between results obtained from capillary whole blood and venous whole blood samples using the Wilcoxon Signed Ranks test.Results obtained from capillary whole blood were not significantly different from results obtained using venous whole blood.
    Correlation: Capillary whole blood results should correlate well with venous whole blood results.Data were analyzed by linear regression (correlation mentioned, but specific coefficient not provided).
    Robustness to Sample Collection: Equivalence should not be influenced by multiple finger sticks for capillary blood collection.The correlation was not influenced by the number of fingersticks needed to collect enough sample to run the test.
    Imprecision: Imprecision using whole blood should be comparable to imprecision using EDTA-anticoagulated plasma.Imprecision studies indicated that the test imprecision using whole blood is comparable to the imprecision using EDTA-anticoagulated plasma.

    2. Sample Size Used for the Test Set and the Data Provenance

    • Sample Size for the Test Set: 67 paired samples (capillary whole blood and venous whole blood from the same individual).
    • Data Provenance: Not explicitly stated (e.g., country of origin). The study was a "comparison... from the same individual," implying a prospective collection of these paired samples. It's an in-house study by Biosite Incorporated.

    3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

    This type of study does not involve expert consensus for "ground truth" in the diagnostic sense. The "ground truth" here is the established method of testing (venous whole blood/plasma) to which the new sample type (capillary whole blood) is being compared. The determination of "ground truth" for the BNP measurement itself would have been established through a different, prior validation. No experts were used to establish ground truth for this specific comparison study. The comparison is against an existing, validated method.

    4. Adjudication Method for the Test Set

    Not applicable. This study compares two quantitative measurements from different sample types from the same individual. The results were "blinded," meaning the analysts did not know the sample source (capillary vs. venous) when performing the test and recording results, but there was no "adjudication" in the sense of resolving conflicting interpretations.

    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. This is a study comparing different biological sample types for a quantitative biomarker test, not a reader study involving human interpretation of imaging or other complex data, nor does it involve AI.

    6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

    Yes, in a way. The performance assessment is for the device (Triage® BNP Test) measuring BNP levels from different sample types. While human operators are involved in drawing blood and running the test, the performance itself is an analytical comparison of the device's output with different input matrices, rather than human interpretation. It's a standalone analytical performance evaluation.

    7. The Type of Ground Truth Used

    The "ground truth" for this specific study is the BNP measurement obtained from venous whole blood specimens, as this is the established or reference method against which the capillary whole blood specimens are being compared. The overall "ground truth" for BNP as a biomarker for heart failure would be clinical diagnosis and outcomes data, but that is not the focus of this particular study.

    8. The Sample Size for the Training Set

    Not explicitly stated or applicable for this type of analytical validation study. This study is an analytical comparison of sample types, not a machine learning model that requires a training set.

    9. How the Ground Truth for the Training Set Was Established

    Not applicable, as there is no machine learning "training set" in this context.

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