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The Access® CEA assay is a paramagnetic particle, chemiluminescent immunoassay for the quantitative determination of Carcinoembryonic antigen (CEA) in human serum using the ACCESS Immunoassay System. CEA measured by the Access CEA Immunoassay, is used as an aid in the management of cancer patients.

The ACCESS CEA Immunoassay Reagents and the ACCESS Immunoassay Analyzer comprise the ACCESS Immunoassay System for the quantitative determination of CEA in human serum.

The provided text describes a 510(k) submission for the ACCESS® CEA Assay, a device intended for the quantitative determination of carcinoembryonic antigen (CEA) in human serum to aid in the management of cancer patients. The submission focuses on demonstrating substantial equivalence to a predicate device, the Abbott IMx CEA Microparticle Enzyme Immunoassay.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" for the ACCESS CEA Immunoassay in numerical thresholds. Instead, it presents summaries of studies demonstrating its performance in comparison to the predicate device and in various analytical tests. The underlying acceptance logic for substantial equivalence would be that the performance of the new device is comparable to or better than the predicate.

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

• Correlation: 146 samples, ranging from 0 to 1000 ng/ml.
• Expected Range: 234 apparently healthy males and females.
• Monitoring Data: "Longitudinal serum samples from previously diagnosed cancer patients." The exact number of patients or samples is not specified.
• Recovery, Precision, Specificity, Hook Effect, Analytical Sensitivity: Specific sample sizes for these analytical studies are not detailed beyond "human serum samples" or "spiked into serum samples."
• Data Provenance: Not explicitly stated (e.g., country of origin). The studies appear to be quantitative laboratory analyses of human serum samples. The context of a US 510(k) submission generally implies that at least some data would be relevant to the US population, but this is not guaranteed for all sample types. The data is retrospective in the sense that the samples were analyzed after collection, but the studies themselves were conducted prospectively to evaluate the device.

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

• Ground Truth for Correlation, Recovery, Precision, Hook Effect, Analytical Sensitivity: The "ground truth" for these analytical studies is based on the quantitative measurements derived from the reference methods or the expected values of spiked/diluted samples. No human experts are explicitly mentioned for establishing ground truth in these specific analytical performance tests for the test set.
• Ground Truth for Monitoring Data: The "monitoring data" relies on "concurrent clinical assessments." This implies that clinicians or medical professionals were involved in evaluating the patient's condition over time. The number and qualifications of these experts are not explicitly stated, but they would typically include oncologists or other physicians managing cancer patients.
• Ground Truth for Expected Range: This involves classifying individuals as "apparently healthy." This classification is typically done by medical professionals, but details about the number and qualifications are not provided.

4. Adjudication Method for the Test Set

• The document does not describe a formal "adjudication method" in the context of multiple human readers or reviewers for the quantitative analytical studies (correlation, precision, etc.). These are objective quantitative measurements.
• For the "monitoring data" and "clinical assessments," while multiple clinicians might be involved in patient management, a specific adjudication process (like 2+1 or 3+1 consensus) for establishing a definitive ground truth across different clinicians is not described in the summary.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. This type of study typically involves human readers interpreting cases (e.g., medical images) with and without AI assistance to measure reader performance improvement. The ACCESS CEA Immunoassay is a diagnostic assay measuring a biomarker, not an imaging interpretation device. The "monitoring data" involved comparing the assay results with clinical assessments, which is a form of clinical utility assessment, but not an MRMC study.

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

Yes, the studies described are primarily standalone performance assessments of the ACCESS CEA Immunoassay. The device itself is an automated immunoassay system (ACCESS Immunoassay Analyzer and ACCESS CEA Immunoassay Reagents). The results it produces (CEA values) are quantitative outputs. The comparisons to the predicate device and the analytical performance tests (precision, recovery, specificity, etc.) evaluate the device's performance inherently in a standalone manner, without human intervention in the generation of the CEA value. While clinicians use these results, the performance evaluation in this 510(k) focuses on the analytical accuracy and reliability of the device's output.

7. The Type of Ground Truth Used

• Correlation: The ground truth is the measurement obtained from the predicate device, the Abbott IMx CEA Immunoassay.
• Expected Range: The ground truth is established by clinical assessment identifying "apparently healthy" individuals, and the measured CEA values in that population.
• Monitoring Data: The ground truth is "concurrent clinical assessments" of cancer patients, implying a physician's overall evaluation of the patient's condition and disease progression.
• Recovery: The ground truth is the known concentration of CEA in diluted or spiked samples.
• Precision, Specificity, Hook Effect, Analytical Sensitivity: The ground truth largely relies on the inherent properties of the samples (e.g., known concentrations, presence/absence of interferents) and accepted laboratory standards for analytical performance.

8. The Sample Size for the Training Set

The document does not explicitly mention a "training set" in the context of developing a machine learning algorithm. This device is an immunoassay, which functions based on chemical and immunological reactions, not a machine learning model that requires a discrete training phase. Therefore, the concept of a training set as understood in AI/ML is not applicable here. The development and validation of the assay would have involved various internal optimization and verification studies, but these would not typically be called a "training set" in this context.

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

As explained above, the concept of a "training set" for an immunoassay device is not applicable in the same way it is for an AI/ML device. The ground truth for developing and optimizing such an assay would involve using characterized samples with known CEA concentrations, spiked samples, and samples from well-defined patient populations, as well as adherence to established analytical chemistry and immunology principles.

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