Search Results

The ADVIA 1650 Chemistry System is an automated, clinical chemistry analyzer that can run tests on human serum, plasma, or urine in random access, batch, and STAT modes at a throughput rate of 1200 photometric tests per hour and 450 electrolyte tests per hour. The photometric analyzer performs clinical chemistry and immuno-turbidimetric methods. The electrylyte portion of the analyzer measures the sodium, and chloride concentrations in serum, plasma or urine samples based on a potentiometric procedure that uses ion-selective electrodes. The ADVIA 1650 is intended for use in conjunction with certain reagents to measure a variety of analytes contained in human fluids.

The ADVIA 1650 Chemistry System is an automated, clinical chemistry analyzer that can run tests on human serum, plasma, or urine in random access, batch, and STAT modes at a throughput rate of 1200 photometric tests per hour and 450 electrolyte tests per hour. The photometric analyzer performs clinical chemistry and immuno-turbidimetric methods. The electrylyte portion of the analyzer measures the sodium, and chloride concentrations in serum, plasma or urine samples based on a potentiometric procedure that uses ion-selective electrodes.

The Bayer ADVIA® 1650 Chemistry System is an automated, clinical chemistry analyzer designed to measure various analytes in human serum, plasma, or urine. The 510(k) submission (K990346) provides performance data for 21 clinical methods, comparing the ADVIA 1650 to predicate devices. This response focuses on the Albumin method as a representative example, as the provided text doesn't explicitly state acceptance criteria in a unified table but rather presents performance characteristics individually for each assay.

1. Table of Acceptance Criteria and Reported Device Performance (Albumin Method)

Since explicit acceptance criteria are not provided, we infer performance metrics based on the comparison with predicate devices and typical clinical laboratory standards. The performance metrics presented below are those reported for the ADVIA 1650 and its predicate device (Technicon DAX) for Albumin, as detailed in the submission.

2. Sample Sizes and Data Provenance (for Albumin Method)

• Test Set Sample Size:

  • Imprecision:
    • Serum: Not explicitly stated, but typically involves multiple replicates run over several days (e.g., 20 days, 2 runs/day, 2 replicates/run = 80 data points per level). The low CV percentages suggest a sufficient number of runs.
  • Correlation:
    • Serum (MSK site): 156 samples
    • Plasma(y), Serum(x) (comparison against ADVIA 1650 itself): 58 samples (likely for method comparison between different specimen types on the same device)

• Data Provenance: The document generally indicates studies were performed at different sites, for example, "Serum: MSK" (likely Memorial Sloan Kettering or a similar institution) and "Plasma(y), Serum(x) ADVIA 1650". Specific country of origin is not detailed, but the submission is to the FDA (USA), implying studies were conducted according to US regulatory standards. The data appears to be prospective as it's generated specifically for device validation through imprecision and correlation studies.

3. Number of Experts and Qualifications for Ground Truth:

• For in vitro diagnostic devices measuring analytes like Albumin, the "ground truth" is typically established by reference methods or validated predicate devices. The study compares the ADVIA 1650's results to those obtained from established predicate devices (e.g., Technicon DAX).
• Therefore, the concept of "number of experts" establishing ground truth in the context of image interpretation or clinical diagnosis by human readers does not apply directly here. The predicate devices are considered the "expert" or "gold standard" for measurement.
• The qualifications of the laboratory personnel performing these tests are implied to be standard clinical laboratory professionals, but specific details on their years of experience are not provided.

4. Adjudication Method for the Test Set:

• This concept is not directly applicable to quantitative analytical devices like the ADVIA 1650 when comparing against a predicate or reference method. Adjudication is generally used in studies where human interpretation or consensus is required to establish ground truth (e.g., in imaging studies).
• For quantitative measurements, statistical methods like regression analysis (as shown by R and Syx values) are used to assess agreement between the new device and the comparison system.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• No, an MRMC comparative effectiveness study was not done. This type of study assesses how human readers perform with and without AI assistance, which is irrelevant for a standalone chemical analyzer like the ADVIA 1650.

6. Standalone Performance:

• Yes, standalone performance was done. The imprecision data (Total CV %) for the ADVIA 1650 itself, without comparison to a human-in-the-loop, directly demonstrates the algorithm's performance in terms of repeatability and reproducibility. The "Analytical Range" and "Interfering Substances" also represent standalone performance evaluations.

7. Type of Ground Truth Used:

• The ground truth used for the test set is based on the measurements obtained from established predicate devices (e.g., Technicon DAX) which are considered validated and clinically acceptable methods for measuring Albumin. This falls under the category of comparison to a reference method or predicate device.

8. Sample Size for the Training Set:

• For this type of in vitro diagnostic device, the concept of a "training set" in the context of machine learning (where algorithms learn from large datasets) does not apply. The ADVIA 1650 is a pre-programmed chemistry analyzer; its methods are developed and validated through chemical principles and instrument design, not through iterative machine learning on a training set of patient data.
• The data presented (imprecision, correlation, interference) are part of the validation and verification studies to ensure the device performs as intended, not a machine learning "training" process.

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

• As explained above, there is no "training set" in the machine learning sense for this device. The methods employed by the ADVIA 1650 are based on established analytical chemistry principles and reagent formulations. The "ground truth" for developing and calibrating these methods would be based on reference materials, calibrators with known concentrations, and established chemical reaction kinetics, all validated through rigorous laboratory practices rather than through a ground truth established by experts or pathology for a training dataset.

Ask a specific question about this device