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The i-STAT BNP test is an in vitro diagnostic test for the quantitative measurement of B-Type Natriuretic Peptide (BNP) in whole blood or plasma samples using EDTA as the anticoagulant. BNP measurements can be used as an aid in the diagnosis and assessment of the severity of congestive heart failure.

The cartridge is to be used with the i-STAT 1 Analyzer bearing the (Immuno) symbol, but not with the i-STAT Portable Clinical Analyzer or the Philips Medical Systems (formerly Agilent Technologies) Blood Analysis Module (BAM). As part of the i-STAT System, the BNP test is to be used by trained health care professionals in accordance with a facility's policies and procedures.

The i-STAT BNP Controls are assayed liquid plasma used to verify the integrity of newly received i-STAT BNP cartridges.

The i-STAT BNP Calibration Verification Controls are assayed liquid plasma used to verify the calibration of i-STAT BNP cartridges throughout the reportable range.

The i-STAT BNP test is contained in a single-use test cartridge. In use, the user scans a bar code and then places approximately 17 uL of whole blood or plasma in the cartridge. After the cartridge is closed, it is inserted into the thermally controlled i-STAT 1 Analyzer, and all analytical steps are performed automatically. Patient and use information may be entered into the analyzer via a keypad during the automated analysis cycle.

As the analyzer performs several quality checks and controls the temperature of the sensors via resistive heating to the underside of the sensor chips, the substrate/wash fluid is released into a conduit within the cartridge and a metered volume of the sample over the sensor chips. The enzyme-linked antibody conjugate dissolves into the sample incubates for a controlled time. The sample is then pushed into a waste chamber and the substrate/wash solution is brought over the sensors. The alkaline phosphatase captured on the BNP sensor cleaves the substrate present in the substrate/wash fluid, giving rise to an amperometric signal that is measured.

The i-STAT BNP Controls are supplied in three levels packaged as six vials of one level per box with each vial containing 1 mL of control material. The three levels are in frozen liquid form, require no reconstitution or dilution, and are each comprised of a different level of BNP, a chemical synthetic peptide, prepared in human EDTA plasma and preserved with sodium azide. The first level of BNP is set at a typical diagnostic cutoff level while the second and third are set at higher levels spanning the range of the test. The BNP value will be provided in the value assignment sheet for each level.

The i-STAT BNP Calibration Verification Control Set is packaged as a tri-level set comprised of two vials of each of three levels per box. The three levels in the BNP Calibration Verification Control Set are exactly the same materials as those used in the Level 1, Level 2, and Level 3 control products. The only difference between this product and the i-STAT BNP Controls is the number of vials and the number of levels that are packaged together.

The provided 510(k) summary focuses on the i-STAT BNP Test and its associated controls, asserting its substantial equivalence to a predicate device, the Biosite Triage BNP test. This document describes an immunoassay, not an AI-powered device, so several of the requested categories (e.g., number of experts, adjudication method, MRMC study, training set) are not applicable. I will provide information for the relevant categories based on the provided text.

1. Table of Acceptance Criteria and Reported Device Performance

For an immunoassay like the i-STAT BNP test, "acceptance criteria" are typically demonstrated through various performance characteristics compared to a predicate device or established analytical standards. The study demonstrates the clinical performance through a method comparison study, establishing correlation with a reference method.

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

• Sample Size: 433 patient samples were used for the method comparison study. A subset of 312 samples had BNP concentrations less than 400 pg/mL.
• Data Provenance: The study was conducted "in-house" and used EDTA-anticoagulated samples. The country of origin is not specified, but the submission is to the U.S. FDA by an i-STAT Corporation based in East Windsor, NJ. The data is retrospective in the sense that existing patient samples were analyzed on both systems.

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

This is not applicable as this is an immunoassay, not a device requiring expert interpretation of images or other data. The "ground truth" for the method comparison study is the measurement obtained from the comparative method (Abbott ARCHITECT).

4. Adjudication Method for the Test Set

This is not applicable as this is an immunoassay. The comparison is between two quantitative measurement devices.

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

This is not applicable as this is an immunoassay device, not one involving human readers interpreting outputs.

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

This is a standalone diagnostic test. The performance reported is that of the i-STAT BNP test system (cartridge and analyzer) itself. There is no "human-in-the-loop" performance in the sense of a human interpreting an algorithm's output. The human operator performs standard lab technician tasks like sample loading.

7. The Type of Ground Truth Used

The ground truth for the method comparison study was the quantitative measurement of BNP obtained from the Abbott ARCHITECT system. This refers to a laboratory-based, established clinical diagnostic method.

8. The Sample Size for the Training Set

This is not applicable. For an immunoassay, there isn't a "training set" in the machine learning sense. The device is developed and validated based on chemical and biological principles. The "in-house studies" mentioned for imprecision and interference would involve experimental runs, not a training set for an algorithm.

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

This is not applicable for the reasons stated in point 8.

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