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510(k) Data Aggregation

    K Number
    K100777
    Date Cleared
    2010-12-10

    (267 days)

    Product Code
    Regulation Number
    862.1120
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    ABL837 FLEX ANALYZER

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

    The ABL837 FLEX analyzer is intended for:
    . in vitro testing of pleural fluid samples for the pH parameter
    The pH measurement of pleural fluid can be a clinically useful tool in the management of patients with parapneumonic effusions.
    Critical values: pH >7.3 is measured in uncomplicated parapneumonic effusions. All pleural effusions with a pH of

    Device Description

    The ABL837 FLEX analyzer is a member of the ABL800 FLEX blood gas analyzer family for the whole blood measurement of blood gas, pH (whole blood and pleural fluid), electrolyte, metabolite, co-oximetry, creatinine and expired air for the parameters pO2 and pCO2.

    AI/ML Overview

    This document describes the acceptance criteria and supporting study for the ABL837 FLEX analyzer's new indication for measuring pH in pleural fluid.

    1. A table of acceptance criteria and the reported device performance:

    Study/ParameterAcceptance CriteriaReported Device Performance
    Precision (Repeatability - S₀)Not explicitly stated, but typically low variability is expected.pH 7.1: 0.012
    pH 7.3: 0.009
    pH 7.5: 0.006
    Precision (Total Precision - Sₓ)Not explicitly stated, but typically low variability is expected.pH 7.1: 0.029
    pH 7.3: 0.020
    pH 7.5: 0.028
    Method Comparison (Slope of linear fit)Difference in slope within 10% compared to predicate.1.056 (5.6% difference from slope of 1 for identity line)
    Method Comparison (Intersection with Y-axis)Not explicitly stated, but ideally close to 0.-0.393
    Method Comparison (Coherence with predicate)Identity line (X=Y, representing predicate data) lies within 95% confidence interval of linear regression in the pathological range (pH 7.0 to 7.5).Identity line lies within 95% CI; good agreement shown.
    Method Comparison (Bias at critical decision interval)Considered acceptable if within clinically reasonable limits, taking into account sample nature and imprecision of clinical decision points.0.0159 above the predicate (Roche analyzer) at pH 7.3. Considered acceptable.
    Linearity (R² value)Not explicitly stated, but typically high R² (closer to 1) for good linear fit.0.993
    Linearity (Data point distribution)Data points sufficiently distributed in the pathological range and form a straight line.Data points from 58 samples sufficiently distributed and form a straight line.

    2. Sample size used for the test set and the data provenance:

    • Precision Study:
      • Sample Size:
        • pH 7.1: 244 observations
        • pH 7.3: 256 observations
        • pH 7.5: 244 observations
    • Method Comparison and Linearity Studies:
      • Sample Size: 58 samples
    • Data Provenance: Not explicitly stated, but given the nature of the device (in vitro diagnostic analyzer) and the studies (precision, method comparison, linearity), it is highly likely that these were retrospective or prospectively collected clinical samples or spiked samples, run in a laboratory setting. The document does not specify the country of origin of the data.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

    This information is not provided in the document. The studies performed (precision, method comparison, linearity) are analytical performance studies comparing the device's measurements to itself (precision) or to a predicate device (method comparison, linearity). These types of studies typically do not involve human expert interpretation for "ground truth" in the same way clinical diagnostic studies might.

    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

    This information is not applicable/not provided as the studies are analytical performance assessments of a laboratory device measuring a quantitative parameter (pH), not human-in-the-loop diagnostic studies requiring adjudication of 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, an MRMC comparative effectiveness study was not done. This device is an automated in vitro diagnostic analyzer for measuring pH in pleural fluid, not an AI-assisted diagnostic tool that would involve human readers' interpretation.

    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

    Yes, the performance studies described (Precision, Method Comparison, Linearity) represent the standalone performance of the ABL837 FLEX analyzer. The device functions automatically to measure pH in pleural fluid.

    7. The type of ground truth used:

    • Precision Study: The ground truth is the inherent variability of the device when repeatedly measuring samples with known (or consistently stable) pH values.
    • Method Comparison and Linearity Studies: The ground truth for these studies was established by comparing the ABL837 FLEX analyzer's measurements to those of the predicate device, the Roche Omni C analyzer (K050423). The Roche device's measurements are effectively considered the reference or "ground truth" for comparison in this context.

    8. The sample size for the training set:

    This information is not applicable/not provided. The ABL837 FLEX analyzer is a device based on potentiometric technology for pH measurement, not a machine learning or AI-driven system that requires a "training set" in the conventional sense. Its operational parameters are based on established electrochemical principles and internal factory calibration/settings, not on data-driven training.

    9. How the ground truth for the training set was established:

    This information is not applicable as there is no "training set" for this device in the context of machine learning. The device's fundamental operational principles are based on known scientific methods for pH measurement, and its calibration is performed using specified calibration solutions.

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