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510(k) Data Aggregation
(149 days)
IMMAGE IMMUNOCHEMISTRY SYSTEM CERULOPLASMIN (CER) REAGENT
The IMMAGE Immunochemistry System Ceruloplasmin (CER) Reagent, when used in coniunction with Beckman IMMAGE™ Immunochemistry Systems and Beckman Calibrator 2, is intended for the quantitative determination of human ceruloplasmin by rate nephelometry.
The IMMAGE Immunochemistry System CER Reagent in conjunction with Beckman Calibrator 2, is intended for use in the quantitative determination of ceruloplasmin concentrations in human serum samples on Beckman's IMMAGE Immunochemistry System.
Here's an analysis of the provided text regarding the acceptance criteria and study for the IMMAGE™ Immunochemistry System Ceruloplasmin (CER) Reagent:
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Metric | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Method Comparison | Strong linear correlation (r approaching 1) and minimal bias (slope approaching 1, intercept approaching 0) to a predicate device. | Slope: 0.996, Intercept: -2.43, r: 0.995 (n=104 serum samples) |
| Stability | Reagent maintains performance over time (not explicitly stated, but demonstrated through stability testing). | "Stability Study Results" mentioned, but specific data and criteria are not provided in the excerpt. |
| Imprecision (Within Run) | Low coefficient of variation (%CV) for repeated measurements. | Serum Level 1: 3.1% CV (Mean 13.6 mg/dL) |
| Serum Level 2: 2.4% CV (Mean 49.3 mg/dL) | ||
| Serum Level 3: 3.1% CV (Mean 88.0 mg/dL) | ||
| Low Serum Level 1: 5.8% CV (Mean 1.4 mg/dL) | ||
| Low Serum Level 2: 3.9% CV (Mean 4.3 mg/dL) | ||
| Imprecision (Total) | Low coefficient of variation (%CV) for repeated measurements over multiple runs/days. | Serum Level 1: 3.8% CV (Mean 13.6 mg/dL) |
| Serum Level 2: 3.5% CV (Mean 49.3 mg/dL) | ||
| Serum Level 3: 4.3% CV (Mean 88.0 mg/dL) | ||
| Low Serum Level 1: 6.6% CV (Mean 1.4 mg/dL) | ||
| Low Serum Level 2: 3.9% CV (Mean 4.3 mg/dL) |
Note: The acceptance criteria for stability and imprecision are implied by the nature of these tests in diagnostic device validation. The document states that the data "supports a finding of substantial equivalence," which suggests that these performance metrics met the internal or regulatory thresholds for equivalence.
2. Sample size used for the test set and the data provenance
- Method Comparison Test Set Sample Size: 104 serum samples.
- Imprecision Test Set Sample Size: For each of the five serum levels tested, 80 data points were collected for "Within Run" and "Total" precision, and 30 data points for the "Low Serum Levels". This refers to the number of individual measurements or replicates.
- Data Provenance: The document does not explicitly state the country of origin. It is a submission by Beckman Instruments, Inc. in Brea, California, USA, so it's most likely US-based data, but this is not confirmed. The studies are prospective in nature, as they involve testing the device's performance characteristics.
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. For an immunochemistry system that quantifies ceruloplasmin, the ground truth would typically be established by a reference method or a predicate device. The document uses a predicate device (Beckman Array Systems CER Reagent) as the reference for the method comparison, rather than expert consensus on individual results.
4. Adjudication method for the test set
- This information is not applicable and therefore not provided in the document. Adjudication is typically used when there are subjective interpretations (e.g., medical imaging) requiring multiple experts to reach a consensus. For a quantitative immunoassay, the "ground truth" is derived from a reference measurement or predicate assay, not through expert adjudication of individual results.
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
- This information is not applicable and therefore not provided in the document. MRMC studies are specific to diagnostic devices where human readers (e.g., radiologists) interpret results, often with and without AI assistance. This device is a fully automated immunochemistry system for quantitative measurement, not an AI-assisted diagnostic tool for human interpretation.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- Yes, the studies presented (Method Comparison and Imprecision) represent the standalone performance of the IMMAGE™ Immunochemistry System Ceruloplasmin (CER) Reagent. The system is designed for automated quantitative determination, meaning it operates without human intervention in the result generation process once the sample is loaded and the assay initiated. The reported performance metrics are for the device itself.
7. The type of ground truth used
- The ground truth for the method comparison study was established by the predicate device, the Beckman Array Systems CER Reagent. This means the IMMAGE System's results were compared against the established results from an already approved and commercially distributed device to demonstrate substantial equivalence. For imprecision, the "ground truth" is internal consistency and reproducibility of the device itself.
8. The sample size for the training set
- This information is not provided in the document. This device is an immunochemistry assay, not a machine-learning or AI-based system that typically requires a distinct "training set" for model development. The development process would involve formulation, optimization, and internal testing, but not in the same sense as an AI algorithm's training phase.
9. How the ground truth for the training set was established
- This information is not provided and is not applicable in the context of this type of diagnostic device. As explained above, there isn't a "training set" in the machine learning sense for this immunochemistry system. The development and validation process would rely on established analytical chemistry principles and performance characteristics, rather than data-driven model training.
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