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The EL-ANA Profiles™ is an in vitro diagnostic test for the detection and measurement of autoantibodies directed against the following autoantigens: single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), Smith, RNP/Sm, SSA (Ro) "R, SSB (La) "R (30217), Gould Carding Ribosomal Protein P, Centromere™, and Chromatin (Nucleosomes). This test system is intended as an aid in diagnosis of systemic lupus erythematosus, Sjogren's syndrome, progressive systemic sclerosis (scleroderma), drug-induced lupus and polymyositis.

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This is a 510(k) premarket notification for an in vitro diagnostic device, not an AI/ML medical device. Therefore, much of the requested information, such as sample size for test sets, number of experts, adjudication methods, MRMC studies, standalone performance, and ground truth types related to image analysis or AI model evaluation, are not applicable.

However, I can extract information relevant to the device's performance based on the provided document, even if it doesn't align perfectly with the AI/ML specific questions.

Here's the closest possible interpretation of your request based on the provided 510(k) summary for the TheraTest EL-ANA Profiles:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document (a FDA 510(k) clearance letter) does not contain a detailed table of specific acceptance criteria. FDA 510(k) clearances for in vitro diagnostics usually reference substantial equivalence to a predicate device, which implies that the new device performs at least as well as the predicate device. The performance data would typically be found in the 510(k) summary or the full submission, but is not present in this clearance letter.

Therefore, this table cannot be accurately completed from the given text. The clearance states: "We have reviewed your Section 510(k) premarket notification... and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices..." This implies that the device met the implicit acceptance criteria by demonstrating substantial equivalence.

2. Sample Size Used for the Test Set and Data Provenance

This information is not provided in the clearance letter. For an in vitro diagnostic device, the "test set" would refer to clinical samples used for validation, but the details of this are not in the provided text.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not explicitly stated in the clearance letter. For in vitro diagnostics like this, "ground truth" would typically be established through clinical diagnosis of the patients from whom the samples were collected, potentially by qualified clinicians (e.g., rheumatologists, immunologists).

4. Adjudication Method for the Test Set

This information is not provided in the clearance letter.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

MRMC studies are typically for image-based diagnostic devices where human readers interpret images. This device is an in vitro diagnostic test for autoantibodies, not an imaging device. Therefore, an MRMC study would not be applicable or performed for this type of device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

This device is an in vitro diagnostic test, not an algorithm. Its performance is inherent to the chemical and biological reactions involved in detecting the autoantibodies. Therefore, the concept of "standalone algorithm performance" is not applicable. The device is the "standalone" diagnostic tool, providing results without an intervening human interpretation step in the same way an AI model's output would need.

7. The Type of Ground Truth Used

The "ground truth" for an in vitro diagnostic test like the TheraTest EL-ANA Profiles would typically be the clinical diagnosis of the patients (e.g., diagnosis of SLE, Sjogren's syndrome, etc.) based on a combination of clinical symptoms, other laboratory tests, and expert medical opinion. The specific method for establishing this ground truth for the study supporting this 510(k) is not described in the provided document.

8. The Sample Size for the Training Set

This device is an in vitro diagnostic kit, not an AI/ML algorithm. Therefore, there is no "training set" in the context of machine learning. The device's performance is based on its analytical and clinical validation, not on a machine learning training process.

9. How the Ground Truth for the Training Set Was Established

As there is no training set for an AI/ML algorithm, this question is not applicable.

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The EL-ANA Profiles™ is intended to measure autoantibodies directed against the following autoantigens: single-stranded DNA (ssDNA), doublestranded DNA (dsDNA), Sm, RNP/Sm, SSA (Ro), SSB (La), Scl-70, Histones, Jo-1, Ribosomal Protein P, and Centromere. This system is intended as an aid in diagnosis of systemic lupus erythematosus and related rheumatic diseases.

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Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the EL-ANA Profiles™ device. Unfortunately, the provided text is a 510(k) clearance letter and an "Indications For Use" statement. It does not contain the detailed information about the acceptance criteria or the study that proves the device meets those criteria.

A 510(k) clearance letter confirms that the FDA has determined the device is substantially equivalent to a predicate device, meaning it's as safe and effective. It does not typically include the full clinical study details that would outline acceptance criteria and performance data. Those details would normally be found in the 510(k) submission itself, which is a much more extensive document.

Therefore, I cannot populate the table or answer most of your detailed questions based solely on the provided text. I can only infer what the "acceptance criteria" were from the FDA's clearance, which is substantial equivalence to a predicate device for the stated indications for use.

Here's what I can provide based on the given information:

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Information Not Provided in the Text.

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)

• Information Not Provided in the Text.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

• Information Not Provided in the Text.

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 device is an "immunological test system" for autoantibodies, not an imaging device typically analyzed by human readers in the same way. Therefore, an MRMC study as described (human readers with/without AI assistance) is not applicable to this type of device.
• Information regarding a comparative effectiveness study is not provided in the text. The 510(k) process focuses on substantial equivalence, not necessarily a comparative effectiveness study against a human standard or AI.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

• This device is an in vitro diagnostic (IVD) test. IVDs are inherently "standalone" in their analytical function; the test provides results based on the sample. The "human-in-the-loop" for this type of device is the medical professional interpreting the quantitative or qualitative results in the context of a patient's clinical presentation.
• The document does not detail the specific performance study, but IVD tests are evaluated for their analytical performance (e.g., accuracy, precision) in a standalone manner.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• For an immunological test system, the ground truth for establishing performance would typically involve:
  • Clinical diagnosis: Confirmation of diseases like systemic lupus erythematosus (SLE) or related rheumatic diseases based on established diagnostic criteria (e.g., ACR criteria) and patient follow-up, which can involve expert consensus or established clinical practice.
  • Reference methods/assays: Comparison against well-characterized and established assays for the same autoantibodies, often considered "gold standards" or established methods in the field.
• The specific type of ground truth used in the EL-ANA Profiles™ study is not provided in this text.

8. The sample size for the training set

• Information Not Provided in the Text. This document is about FDA clearance, not the development process. Furthermore, for a laboratory test like this, the concept of a "training set" in the machine learning sense is not generally applicable unless a computational algorithm is specifically being "trained" to interpret complex patterns, which is not indicated here for a standard immunoassay. Instead, assay parameters are optimized during development.

9. How the ground truth for the training set was established

• Information Not Provided in the Text. (See the reasoning for point 8).

In summary: The provided FDA 510(k) clearance letter confirms substantial equivalence but does not offer the granular detail of the performance study sought by your questions. This detailed information would be found in the original 510(k) submission, which is a much more extensive document.

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