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The EUROIMMUN Anti-ENA Pool ELISA (IgG) is intended for the qualitative determination of IgG class antibodies against nuclear antigens (mixture of nRNP/Sm, Sm, SS-A (SS-A 60/Ro-52), SS-B, Scl-70 and ribosomal P proteins) in human serum. It is used as an aid in the diagnosis of mixed connective tissue diseases (MCTD), systemic lupus erythematosus, Sjögren's syndrome and progressive systemic sclerosis, in conjunction with other laboratory and clinical findings.

The EUROIMMUN Anti-ENA Pool Screen ELISA (IgG) consists of a microwell ELISA plate coated with a mixture of nRNP/Sm, Sm, SS-A (SS-A 60/Ro-52), SS-B, Scl-70 and ribosomal P proteins and antigens, calibrator, positive and negative control, peroxidase-labelled anti-human IgG conjugate, sample buffer, wash buffer concentrate, TMB chromogen/substrate solution and stop solution.

This document describes the performance of the EUROIMMUN Anti-ENA Pool ELISA (IgG) for the qualitative determination of IgG class antibodies against nuclear antigens.

1. Table of Acceptance Criteria (Implied) and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" with specific numerical thresholds for sensitivity and specificity that had to be met. Instead, it presents the results of clinical studies. The implied acceptance is that the device demonstrates adequate diagnostic performance for its intended use, particularly for aiding in the diagnosis of specific autoimmune diseases. The performance data is presented below:

Detailed Clinical Sensitivity by Panel:

Detailed Clinical Specificity by Panel:

2. Sample Size for Test Set and Data Provenance:

• Clinical Studies (Test Set): A total of 432 clinically characterized samples were investigated for ENA antibodies (IgG). The document does not specify the country of origin, but the context of an FDA submission suggests general compliance with US regulatory standards, and it states samples were "obtained from different sources," implying a diverse, possibly multicenter, collection. The samples are described as "clinically characterized," indicating they are from patients with confirmed diagnoses and control groups. This makes the study prospective in nature, as samples were characterized based on clinical findings and then tested.
• Method Comparison with Predicate Device: 278 clinically characterized samples were used. These samples were also from patients and control groups (49 mixed connective tissue diseases, 26 systemic lupus erythematosus, 29 Sjögren's syndrome, 22 systemic sclerosis, 20 polymyositis, 10 celiac disease, 17 Wegener's granulomatosis, 39 rheumatoid arthritis, 16 infectious disease and 50 healthy).
• Traceability (CDC ANA reference panel): The reactivity of the assay was verified using the CDC ANA reference panel. This implies a reference panel from the Centers for Disease Control and Prevention (USA).

3. Number of Experts and Qualifications:

The document mentions "clinically characterized samples" and "cooperation with different sites" for clinical studies. It also states that the predicate device comparison used "clinically characterized samples." However, it does not specify the number of experts used to establish the ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience"). Clinical characterization typically involves a team of healthcare professionals (physicians, specialists) making diagnoses based on a variety of clinical and laboratory findings.

4. Adjudication Method:

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for the test set. The ground truth relies on "clinically characterized samples," meaning the diagnoses were established through routine clinical practice, which may involve consensus among treating physicians but not necessarily a formal adjudication process for research purposes.

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

No MRMC study was performed or reported. This device is an in vitro diagnostic (IVD) test, not an imaging device that would typically involve human readers interpreting results with and without AI assistance. The comparison was against a predicate device (another ELISA test), not against human interpretation of raw data.

6. Standalone Performance:

Yes, a standalone performance study was done. The clinical sensitivity and specificity reported are for the device (algorithm) only (EUROIMMUN Anti-ENA Pool ELISA (IgG)) against the clinical ground truth.

7. Type of Ground Truth Used:

The ground truth used was expert consensus / clinical findings. The samples were described as "clinically characterized," meaning the diagnoses for the various autoimmune diseases and control conditions were established based on a combination of medical history, physical examination, laboratory tests (other than the device being tested), and specialist consultations, reflecting the "gold standard" of clinical diagnosis for these conditions.

8. Sample Size for Training Set:

The document does not explicitly mention a separate "training set" for the device's development. This is typical for ELISA-based diagnostic kits, where the assay design is based on established immunochemical principles and antigen selection rather than machine learning models that require distinct training and test sets. The studies described (reproducibility, analytical specificity, clinical validation) function more as verification and validation of a pre-defined assay.

9. How Ground Truth for Training Set Was Established:

As no explicit training set is mentioned in the context of machine learning, this question is not directly applicable. For the development of the assay, the selection of the specific antigens (nRNP/Sm, Sm, SS-A (SS-A 60/Ro-52), SS-B, Scl-70, and ribosomal P proteins) would have been based on established scientific literature and clinical relevance to the target autoimmune diseases, likely informed by studies that linked these antibodies to specific clinical conditions. The "ground truth" for antigen selection and assay design would be the medical understanding of these disease biomarkers.

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