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510(k) Data Aggregation
(272 days)
ADVIA Centaur EBV-VCA IgM
The ADVIA Centaur EBV-VCA IgM (EBVM) assay is for in vitro diagnostic use in the qualitative detection of lgM antibodies to the viral capsid antigen (VCA) of the Epstein-Barr virus (EBV) in human pediatric (2-21 years old) and adult serum and plasma (EDTA and lithium heparin) using the ADVIA Centaur XP system. When used in conjunction with other EBV markers, this assay is intended for use as an aid in the diagnosis of Epstein-Barr virus infection, such as infectious mononucleosis.
The ADVIA Centaur EBV-VCA IgM assay is a fully automated 2-step sandwich immunoassay using acridinium ester chemiluminescent technology. The specimen is incubated with the Ancillary Well Reagent and the Solid Phase, which contains an EBV-VCA IgM specific antigen. Antigen-antibody complexes will form if anti EBV-VCA IgM antibody is present in the specimen. The Lite Reagent contains monoclonal anti-human IgM labeled with acridinium ester and is used to detect EBV-VCA IgM in the specimen.
Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:
Device: ADVIA Centaur EBV-VCA IgM
1. Table of Acceptance Criteria and Reported Device Performance:
The document implicitly defines acceptance criteria through the reported performance metrics. For a diagnostic device intended to aid in the diagnosis of infection, common acceptance criteria would include achieving certain levels of Positive Percent Agreement (PPA) and Negative Percent Agreement (NPA) compared to a reference method, especially in relevant clinical populations (e.g., primary acute infection). Precision and reproducibility are also key acceptance criteria for laboratory assays.
Acceptance Criteria Category | Specific Metric (Implicit) | Acceptance Range (Implicit based on predicate or general expectations for diagnostic assays) | Reported Device Performance |
---|---|---|---|
Clinical Performance (Overall Population) | NPA (Negative Percent Agreement) | High agreement with reference assay for negative samples (e.g., >95%) | 96.7% (95% CI: 95.6% - 97.5%) |
PPA (Positive Percent Agreement) | Good agreement with reference assay for positive samples (e.g., >60-70%) | 68.9% (95% CI: 58.7% - 77.5%) | |
Clinical Performance (Primary Acute Infection) | PPA (Positive Percent Agreement) | High agreement in acute cases (e.g., >90%) | 90.6% (95% CI: 79.7% - 95.9%) |
Clinical Performance (Pediatric Population) | NPA (Negative Percent Agreement) | High agreement for negative pediatric samples | 95.3% (95% CI: 92.8% - 96.9%) |
PPA (Positive Percent Agreement) | Good agreement for positive pediatric samples | 84.2% (95% CI: 72.6% - 91.5%) | |
Clinical Performance (Pediatric Primary Acute Infection) | PPA (Positive Percent Agreement) | High agreement in acute pediatric cases | 91.3% (95% CI: 79.7% - 96.6%) |
Analytical Precision (Total Precision) | CV (Coefficient of Variation) | Typically 0.80 Index. | Ranged from 3.4% to 4.7% for serum, and 3.7% to 4.9% for plasma samples. Control 2 showed 9.3% CV. |
Specimen Equivalency (Correlation Coefficient) | Correlation coefficient (r) | Close to 1.00 for equivalent matrices | 1.00 for EDTA plasma vs. serum, and 1.00 for lithium heparin plasma vs. serum. |
Interference | Bias | Reactive samples: ±10% bias. Nonreactive samples: ±0.10 Index. | Substances tested were found not to interfere within specified limits. |
Cross-reactivity | Low reactivity in presence of other infections/conditions | Minimal false positives | Out of 371 samples with other conditions, 348 were nonreactive (93.8%), and 23 were reactive (6.2%). The comparative assay found 353 nonreactive (95.1%) and 16 reactive (4.3%). This suggests generally low cross-reactivity, with some instances where both the new device and the comparative assay show reactivity. |
Stability (Onboard Reagent) | Duration (days) | A specified period for practical use | 28 days |
Stability (Onboard Calibrators) | Duration (hours) | A specified short period | 8 hours |
Stability (Opened Vial Calibrators) | Duration (days) | A specified period for practical use | 60 days (at 2-8°C) |
2. Sample Size and Data Provenance:
- Clinical Study Test Set:
- Total Study Population: 1428 leftover samples (Population 1) + 202 samples with known EBV VCA IgM positive result (Population 2). Total = 1630 samples.
- Population 1: Collected over a contiguous time period from individuals for whom an EBV test was ordered.
- Population 2: Samples with a known EBV VCA IgM positive result.
- Pediatric Population: 479 samples (from Population 1) + 155 samples (from Population 2). Total = 634 samples.
- Data Provenance: The document states "leftover samples were collected over a contiguous time period" and "multisite clinical study," suggesting diverse geographical sources and a real-world setting. It does not explicitly state country of origin but implies a clinical laboratory setting. The samples are retrospective (leftover samples).
- Total Study Population: 1428 leftover samples (Population 1) + 202 samples with known EBV VCA IgM positive result (Population 2). Total = 1630 samples.
3. Number of Experts and Qualifications for Ground Truth:
- This information is not explicitly provided in the document. The comparison is made against an "FDA-cleared EBV VCA IgM reference assay."
- For equivocal reference assay results, the ground truth was "resolved by 2 other comparative assays." This implies an algorithmic or rule-based resolution rather than human expert consensus for these specific cases.
4. Adjudication Method for the Test Set:
- The document states: "Equivocal reference assay results were resolved by 2 other comparative assays." This indicates a form of algorithmic or rule-based adjudication rather than a human expert consensus method (like 2+1 or 3+1). If the two additional comparative assays agreed, that would constitute a form of resolution. If they disagreed, the method for final resolution is not detailed.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
- No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This device is an in vitro diagnostic (IVD) assay, not an imaging device or a device requiring human interpretation as part of its primary function where reader performance would be a direct outcome. The performance is assessed by comparing the assay's output to a reference method, not by how human readers improve with or without AI assistance.
6. Standalone (Algorithm Only) Performance:
- Yes, a standalone performance study was done. The entire clinical and analytical performance evaluation describes the ADVIA Centaur EBV-VCA IgM assay's performance independently (as an algorithm/assay) against a reference standard or for its inherent analytical characteristics (precision, reproducibility). There is no "human-in-the-loop" component described for the assay's function itself; it is an automated immunoassay.
7. Type of Ground Truth Used:
- The primary ground truth was established by an "FDA-cleared EBV VCA IgM reference assay."
- For cases where the reference assay was equivocal, "2 other comparative assays" were used for resolution.
- For defining "primary acute infection," the presence of "either EBV IgM or heterophile antibodies, and the absence of EBNA IgG" was used – this relies on a combination of other serological markers/tests.
8. Sample Size for the Training Set:
- The document does not provide information on the training set for the ADVIA Centaur EBV-VCA IgM assay. As a chemical immunoassay, its "training" involves the development and optimization of its chemical components, reagents, and detection parameters, using internal development studies that are typically not detailed as "training sets" in the same way as machine learning models. The reported studies are for validation/testing.
9. How the Ground Truth for the Training Set Was Established:
- As the document does not mention a "training set" in the context of machine learning, this question is not applicable/not addressed by the provided text. The development process for such an immunoassay typically involves extensive R&D, analytical characterization, and optimization using well-characterized samples, but this is distinct from establishing ground truth for a machine learning training dataset.
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