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510(k) Data Aggregation
(56 days)
VARELISA CARDIOLIPIN IGA ANTIBODIES, MODELS 15748 & 15796
The Varelisa Cardiolipin IgA Antibodies EIA kit is designed for the semiquantitative and qualitative determination of IgA antibodies against cardiolipin in serum or plasma to aid in the diagnosis of antiphospholipid syndrome (APS) and to evaluate the thrombotic risk in patients with systemic lupus erythematosus (SLE).
The Varelisa Cardiolipin IgA Antibodies is an indirect noncompetitive enzyme immunoassay for the semiquantitative and qualitative determination of IgA antibodies against cardiolipin in human serum or plasma. The wells of a microplate are coated with bovine cardiolipin antigen. Antibodies specific for cardiolipin present in the patient sample bind to the antigen. In a second step an enzyme labeled second antibody (Conjugate) binds to the antigenantibody complex which leads to the formation of an enzyme labeled antigen-antibody sandwich complex. The enzyme labeled antigen-antibody complex converts the added substrate to form a colored solution. The rate of color formation from the chromogen is a function of the amount of Conjugate complexed with the bound antibody and thus is proportional to the initial concentration of the respective antibodies in the patient sample.
The provided text describes a 510(k) submission for a modified medical device, the Varelisa® Cardiolipin IgA Antibodies assay. The submission primarily focuses on demonstrating substantial equivalence to a predicate device rather than detailing specific acceptance criteria and a standalone study proving its performance against those criteria.
However, based on the information provided, we can infer some aspects related to the study conducted for comparison.
Here's an analysis of the provided information:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state numerical acceptance criteria (e.g., specific sensitivity, specificity, accuracy thresholds). The primary "acceptance criteria" for a 510(k) submission, as implied, is demonstrating substantial equivalence to a predicate device.
The reported device performance is described qualitatively in the "Laboratory equivalence" section:
Acceptance Criteria (Inferred from Substantial Equivalence) | Reported Device Performance |
---|---|
Comparability with predicate device (Varelisa® Cardiolipin (IgA) Antibodies) | Data from a comparison study analyzing positive, equivocal, and negative sera, externally defined Calibrators, and samples from apparently healthy subjects show comparability. |
Assay performs as expected from medical literature | "The data show that the assay performs as expected from the medical literature." |
Substantial equivalence to predicate device | "all available data support that the new/modified device... is substantially equivalent to the predicate/original device..." |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size for Test Set: Not explicitly stated. The document mentions "positive, equivocal and negative sera," "externally defined Calibrators," and "samples from apparently healthy subjects (normal population)," but no specific numbers.
- Data Provenance: Not explicitly stated. It's likely the study was conducted in Germany, given the manufacturer's location (Pharmacia Deutschland GmbH) and the company contact person's address. The type of study (retrospective or prospective) is also not specified.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable. The ground truth for this type of in vitro diagnostic device (immunological test) is typically established by the results of the predicate device, known positive/negative samples, or established clinical definitions of the condition, rather than expert interpretation of images or clinical cases. There's no mention of experts establishing ground truth in the context of radiologists or similar roles.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. This concept is relevant for studies where human interpretation or consensus is required to establish ground truth or classify cases. For an immunoassay, the results are quantitative or semi-quantitative and typically don't require adjudication between human readers.
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
Not applicable. This device is an in vitro diagnostic (IVD) immunoassay, not an AI-assisted diagnostic tool that aids human readers. Therefore, an MRMC study and analysis of human reader improvement with AI assistance are not relevant.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, implicitly. The "comparison study" and "laboratory equivalence" assessment described refer to the performance of the device itself (the assay) as a standalone diagnostic tool. The purpose is to show that the modified assay yields comparable results to the predicate assay.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The ground truth appears to be established by:
- Reference to the predicate device (Varelisa® Cardiolipin (IgA) Antibodies) through comparison studies with positive, equivocal, and negative sera.
- Externally defined Calibrators, which are reference materials with known concentrations.
- Samples from apparently healthy subjects, which serve as negative controls.
- The overall understanding of the medical literature regarding anti-cardiolipin antibodies in the context of APS and SLE.
8. The sample size for the training set
Not applicable in the conventional sense of machine learning. This is an immunoassay, not a machine learning algorithm that requires a "training set." The assay itself is a chemical and biological measurement system.
9. How the ground truth for the training set was established
Not applicable, as it's not a machine learning model with a training set. The assay's "calibration" would involve using known standards and calibrators, as mentioned, but these are part of the assay's operational parameters rather than a "training set" for an algorithm.
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