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For in vitro diagnostic use in the quantitative determination of B-type Natriuretic Peptide (BNP) in human plasma using the ACS:180® Automated Chemiluminescence System. This assay is indicated for the measurement of plasma BNP as an aid in the diagnosis and assessment of the severity of heart failure. This test, in conjunction with other known risk factors, can also be used to predict survival in patients after myocardial infarction. This assay is not intended for use on any other system.

For in vitro diagnostic use in the quantitative determination of B-type Natriuretic Peptide (BNP) in human plasma using the ADVIA Centaur® System. This assay is indicated for the measurement of plasma BNP as an aid in the diagnosis of and assessment of the severity of heart failure. This test, in conjunction with other known risk factors, can also be used to predict survival in patients after myocardial infarction. This assay is not intended for use on any other system.

The ACS:180 and ADVIA Centaur BNP assays are fully automated two-site sandwich immunoassays using direct chemiluminescent technology, which use constant amounts of two monoclonal antibodies. The first antibody, in the Lite Reagent, is an acridinium ester labeled monoclonal mouse anti-human BNP F(ab')2 fragment specific to the ring structure of BNP. The second antibody, in the Solid Phase, is a biotinylated monoclonal mouse antihuman antibody specific to the C-terminal portion of BNP, which is coupled to streptavidin magnetic particles. Patient sample (calibrator or control materials) is incubated for 5 minutes at 37°C with the Reagent that contains the tracer antibody conjugate. Subsequently, Solid Phase reagent is added and incubated for 2.5 minutes at 37°C. An immuno-complex is formed between the BNP in the sample and the two antibody conjugates. Following incubation, the unbound antibody conjugates are washed away. The chemiluminescence of the immuno-complex signal is measured in a fuminometer. Samples with low BNP levels will have a minimum amount of bound AE label, while samples with high levels of BNP will have maximum label complex bound. Thus, a direct relationship exists between the amount of BNP present in the patient sample and the amount of relative light units (RLUs) detected by the system.

The provided text describes a 510(k) premarket notification for the ACS:180® and ADVIA Centaur® BNP Assays by Bayer Diagnostics. The document establishes substantial equivalence to a predicate device (ADVIA Centaur® B-Type Natriuretic Peptide (BNP) Assay, K031038) and discusses the assays' technological and performance characteristics.

Here's an analysis of the acceptance criteria and study information provided, structured according to your request:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" for performance metrics in a pass/fail format. However, it presents the "Performance Characteristics" of the proposed devices, which implicitly serve as the demonstrated performance that FDA found substantially equivalent to the predicate device. These performance characteristics cover various analytical aspects of the assay. Since the document states "The following table compares the performance characteristics of the current and proposed Bayer ADVIA Centaur and ACS:180 BNP assays," and the values listed under "Proposed Bayer ADVIA Centaur® and ACS:180® BNP Immunoassays" are identical to "Current Bayer ADVIA Centaur® and ACS:180® BNP Immunoassays," it implies that the new devices meet the established performance of the predicate device.

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

The document does not specify the exact sample sizes for the test sets or the data provenance (country of origin, retrospective/prospective) for each stated performance characteristic. It only presents the summary results. For clinical diagnostic assays like this, it's common for studies to involve various patient samples to assess precision, analytical sensitivity, dilution recovery, and interference. However, these details are not provided in this summary.

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

This document describes an in vitro diagnostic (IVD) assay which measures a biomarker (BNP). The "ground truth" for such assays is typically the actual concentration of the analyte, established through highly accurate and precise analytical methods, not through expert human review in the same way an imaging AI would be reviewed. Therefore, the concept of "experts" establishing ground truth for evaluating the device's analytical performance (precision, sensitivity, etc.) is not directly applicable in this context. The "ground truth" here is determined by the reference analytical methods themselves.

4. Adjudication Method for the Test Set

As explained above, for analytical performance characteristics of an IVD assay, there isn't a direct "adjudication method" in the sense of reconciling disagreements among human experts evaluating data. The performance is assessed against established analytical standards and reference methods.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for medical devices that involve human interpretation (e.g., radiologists reading images and an AI assisting them). The ACS:180 and ADVIA Centaur BNP assays are entirely automated quantitative tests, so human interpretation of the assay's direct output (BNP concentration) is not the primary focus of performance evaluation in the same way. The device's output itself is a numerical value.

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

Yes, the performance characteristics presented are for the standalone algorithm/device (the automated immunoassay system). The device provides a quantitative measurement of BNP without human intervention in the measurement process itself. The "without human-in-the-loop" aspect is inherent to automated diagnostic assays.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

The ground truth for evaluating the analytical performance of these BNP assays is based on:

• Reference Standards/Synthetic Materials: Used for calibration and establishing traceability (e.g., "synthetic human BNP (amino acid 77 to 108) in buffer-based matrix").
• Known Concentrations: Samples with precisely known BNP concentrations are used to assess accuracy, linearity, and recovery.
• Validated Analytical Methods: Comparison against established and validated laboratory methods or reference methods to verify analytical parameters like precision, sensitivity, and linearity.
• Controlled Interference Studies: Samples spiked with known interfering substances at controlled concentrations to determine the device's robustness to these interferences.

For the stated clinical indications for use (diagnosis and assessment of heart failure severity, prediction of survival after MI), the clinical utility of BNP levels is established through extensive medical literature and clinical studies that correlate BNP levels with patient outcomes, pathology (e.g., echocardiography, cardiac catheterization), and clinical diagnoses. The device's role is to accurately measure these established BNP levels. The document mentions "Decision threshold of 100 pg/mL recommended for diagnosis of heart failure. Decision threshold of 80 pg/mL recommended for prediction of survival after myocardial infarction," which are clinical ground truths, but not data used to directly evaluate the analytical performance of the device in this submission.

8. The Sample Size for the Training Set

The document does not provide details on specific "training sets" in the context of an algorithm that learns from data. This type of immunoassay is based on established biochemical principles and reagents, not a machine learning model that requires a distinct training phase with a dataset. The development and optimization of the assay would involve extensive R&D and testing with numerous samples, but these are not typically referred to as a "training set" in the sense of AI/ML.

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

As this is an immunoassay and not an AI/ML device, the concept of a "training set" with associated ground truth for learning is not directly applicable. The "ground truth" for the development and validation of such an assay would be based on:

• Precise gravimetric/volumetric measurements for reagent preparation.
• Characterization of antibodies and their binding kinetics.
• Use of highly purified and characterized synthetic BNP standards.
• Comparative analysis with established reference methods during the assay development process to ensure analytical accuracy and reliability.

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