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510(k) Data Aggregation

    K Number
    K012478
    Manufacturer
    Date Cleared
    2001-08-20

    (18 days)

    Product Code
    Regulation Number
    862.1120
    Why did this record match?
    Device Name :

    MODIFICATION TO: I-STAT PORTABLE CLINICAL ANALYZER

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The i-STAT System is used by trained medical professionals for running clinical chemistry tests for sodium, potassium, chloride, urea nitrogen, glucose, hematocrit, calcium, blood-gases (PCO2, PO2) and blood pH, creatinine, lactic acid, and activated whole blood clotting time in a variety of test panels contained in the i-STAT test cartridges.

    Device Description

    The i-STAT System is used in conjunction with disposable i-STAT cartridges for determination of a variety of parameters in whole blood.

    AI/ML Overview

    The provided text is a 510(k) summary for the i-STAT System, primarily focusing on a software modification to address interference in the PCO2 test. It does not contain a typical study design or acceptance criteria for a new device's performance directly. Instead, it references prior substantial equivalence determinations and focuses on the modification.

    Therefore, many of the requested categories cannot be filled based on the provided document.

    Here's an analysis of what information can be extracted or inferred, and what cannot:

    1. Table of Acceptance Criteria and Reported Device Performance:

    The document does not explicitly state acceptance criteria or a detailed performance table for this specific software modification. It mentions that the software modification was made "to diminish the interference on the PCO2 test caused by the drugs thiopental sodium and propofol." This implies that the previous performance in the presence of these drugs was unsatisfactory, and the modification aims to improve it, but no specific targets (e.g., % reduction in error, specific bias limits) are provided.

    Acceptance CriteriaReported Device Performance
    Not specified in the document for this modification. The implied criterion is to diminish interference from thiopental sodium and propofol on PCO2 test results.The software has been modified to diminish the interference on the PCO2 test caused by the drugs thiopental sodium and propofol. No quantitative performance data is provided for this specific improvement.

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

    Not specified in the document.

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

    Not specified in the document.

    4. Adjudication Method for the Test Set:

    Not specified in the document.

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

    No. This device is a clinical analyzer, not an imaging device typically evaluated with MRMC studies comparing human readers.

    6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

    The i-STAT System is a standalone clinical analyzer. The software modification described likely refers to an improvement in the algorithm that processes the PCO2 measurement from the sensor, without direct human-in-the-loop performance being a separate evaluation step. However, the document does not detail a specific standalone performance study for this modification. The device itself operates in a standalone manner.

    7. The Type of Ground Truth Used:

    Not specified in the document. For a clinical analyzer, ground truth would typically be established by a reference method (e.g., a laboratory-based blood gas analyzer) for the PCO2 measurement.

    8. The Sample Size for the Training Set:

    Not applicable/Not specified. This document describes a software modification to an existing device, not the training of a new AI model in the typical sense that would involve a "training set" with ground truth in the context of machine learning. The modification likely involved algorithm adjustments based on observed interference, rather than a large-scale machine learning training pipeline.

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

    Not applicable/Not specified (as above).

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