(275 days)
The IgX PLEX™ Celiac Qualitative Assay is an in vitro diagnostic test for the qualitative detection of the IgA and IgG immunoglobulin classes of anti-tissue transglutaminase antibody in serum. The test is intended for use in clinical laboratories as an aid in the diagnosis of celiac disease in conjunction with other laboratory and clinical findings, and requires the SQiDworks™ Diagnostics Platform.
The IgX PLEX™ Celiac Qualitative Assay is a consumable reagent kit. It is designed to run on the SQiDworks™ Diagnostics Platform. The kit includes a Microarray Plate, Reporter mix, standards, controls, sample diluents, wash buffer concentrates and a CD-ROM.
The Assay Kit detects the presences of the IgG classes of anti-tissue transglutaminase antibody. This is performed in an integrated fashion on the SQiDworks™ Diagnostics Platform (platform) that reports both analytes simultaneously to aid in the diagnosis of Celiac Disease.
The platform automates the entire immunoassay procedure from end-to-end, including calibrator/standards and sample pipetting, sample dilution, incubation, washing, and drying. Once the assay's biochemical reactions have completed, the instrument automatically performs a multi-color fluorescent scan of each well in the microarray, analyzes the data, and generates a report containing qualitative results for both assay markers. Results for each patient sample from the IgX PLEX™ Celiac Qualitative assay are obtained simultaneously for each of the two markers using the results from one well containing one aliquot of the patient's serum. Results are reported independently.
Here's a breakdown of the acceptance criteria and study information for the IgX PLEX™ Celiac Qualitative Assay, based on the provided text:
Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Metric | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|---|
| Reproducibility | Anti-tTG-IgA Reproducibility | High (not explicitly quantified but demonstrated by results) | 92.4% to 100% |
| Anti-tTG-IgG Reproducibility | High (not explicitly quantified but demonstrated by results) | 95.8% to 100% | |
| Clinical Performance | Anti-tTG-IgA Clinical Sensitivity | High (not explicitly quantified but demonstrated by results) | 98.3% |
| Anti-tTG-IgG Clinical Sensitivity | High (not explicitly quantified but demonstrated by results) | 80.9% | |
| Anti-tTG-IgA Clinical Specificity | High (not explicitly quantified but demonstrated by results) | 94.5% | |
| Anti-tTG-IgG Clinical Specificity | High (not explicitly quantified but demonstrated by results) | 89.0% | |
| Agreement with Predicates | Anti-tTG-IgA Overall Agreement | High (not explicitly quantified but demonstrated by results) | 89.3% |
| Anti-tTG-IgG Overall Agreement | High (not explicitly quantified but demonstrated by results) | 85.1% | |
| Interference | Effect of high bilirubin levels | No significant impact | None affected |
| Effect of high hemoglobin levels | No significant impact | None affected | |
| Effect of high triglycerides levels | No significant impact | None affected | |
| Effect of high human IgG levels | No significant impact | None affected |
Study Information
-
Sample size used for the test set and the data provenance:
- The document does not specify the sample size used for the test set.
- The document does not specify the data provenance (e.g., country of origin, retrospective or prospective).
-
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. The study focuses on "clinical sensitivity" and "specificity" and "overall agreement with established predicate test systems," which suggests comparison to existing diagnostic methods rather than a panel of human experts for ground truth in the traditional sense for image-based AI.
-
Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- This information is not provided in the document.
-
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:
- No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed. This device is an in vitro diagnostic (IVD) assay that automates the immunoassay procedure and provides qualitative results, intended to aid in diagnosis in conjunction with other clinical findings, rather than an AI system assisting human readers of images.
-
If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, the performance metrics reported (reproducibility, clinical sensitivity, specificity, agreement) directly reflect the standalone performance of the IgX PLEX™ Celiac Qualitative Assay. The device is an automated system described as running "from end-to-end" and generating reports with "qualitative results." Human "in-the-loop" performance in the sense of an algorithm assisting a human reader is not applicable here as it autonomously provides the diagnostic result.
-
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- The document implies that the ground truth for "clinical sensitivity" and "specificity" and "overall agreement" was established by comparison to established predicate test systems and presumably clinical diagnoses of celiac disease. It does not explicitly state the ultimate "ground truth" method (e.g., small intestinal biopsy for celiac disease diagnosis, which is the gold standard). The phrase "aid in the diagnosis of celiac disease in conjunction with other laboratory and clinical findings" suggests that the "true" diagnosis of celiac disease, against which the assay's performance is measured, would involve a combination of clinical information, and potentially the gold standard of biopsy.
-
The sample size for the training set:
- This information is not provided. The description mentions "standards, controls, sample diluents" as part of the kit, which are generally used for calibration and quality control rather than a "training set" in the machine learning sense.
-
How the ground truth for the training set was established:
- This information is not provided. As this is an IVD assay rather than a machine learning model, the concept of a "training set" and establishing its ground truth in the typical AI/ML context doesn't directly apply in the same way.
§ 866.5660 Multiple autoantibodies immunological test system.
(a)
Identification. A multiple autoantibodies immunological test system is a device that consists of the reagents used to measure by immunochemical techniques the autoantibodies (antibodies produced against the body's own tissues) in serum and other body fluids. Measurement of multiple autoantibodies aids in the diagnosis of autoimmune disorders (disease produced when the body's own tissues are injured by autoantibodies).(b)
Classification. Class II (performance standards).