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The BioPlex™ 2200 ANA Screen is intended for the qualitative screening of specific antinuclear antibodies (ANA), the quantitative detection of antibody to dsDNA, and the semi-quantitative detection of ten (10) separate antibody assays (Chromatin, Ribosomal Protein, SS-A, SS-B, Sm, SmRNP, RNP, Scl-70, Jo-1, and Centromere B) in human serum and/or EDTA or heparinized plasma.

The ANA Screen is used to screen serum or plasma (EDTA, heparin) samples and detect the presence of antinuclear antibodies as an aid in the diagnosis of systemic autoimmune diseases (Systemic Lupus Erythematosus [SLE], Mixed Connective Tissue Disease [MCTD], Undifferentiated Connective Tissue Disease [UCTD], Sjögren's Syndrome [SS], Scleroderma [Systemic Sclerosis], Dermatomyositis, Polymyositis, Rheumatoid Arthritis [RA]. CREST Syndrome, and Raynaud's Phenomenon) in conjunction with clinical findings and other laboratory tests.

The ANA Screen is intended for use with the Bio-Rad BioPlex 2200 System.

The BioPlex 2200 Medical Decision Support Software (MDSS), used in conjunction with the ANA Screen. is an optional laboratory tool that associates patient antibody results from the ANA Screen with predefined MDSS profiles that have been correlated with the following systemic autoimmune diseases: Systemic Lupus Erythematosus (SLE), Mixed Connective Tissue Disease (MCTD), Sjögren's Syndrome (SS), Scleroderma (Systemic Sclerosis) and Polymyositis.

The BioPlex 2200 Medical Decision Support Software (MDSS) is a pattern recognition algorithm that can enhance the performance of the ANA Sercen by identifying associated diagnostic patterns among its multiple assay results. The MDSS can suggest one or more possible discase associations after identifying patterns from the cleven ( 1 ) individual antibody results. The MDSS is based on the principles of the "k-nearest neighbor" 11 (kNN) statistical technique. Each "unknown" is compared to a pre-established database that contains the results for over 1,400 characterized scrappasma. Results of MDSS analysis fall into one of the following general outcomes: Negative, No Association, or Association with Disease. When the results of the MDSS analysis fall into the Association with Disease category, the MDSS software will propose a maximum of two disease classifications based upon the similarity of the current analysis to the stored results. The MDSS output can also aid in determining appropriate additional autoimmune serological testing.

Here's a summary of the acceptance criteria and the study that proves the device meets them for the BioPlex™ 2200 Medical Decision Support Software (MDSS).

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly derived from the "Percent Correct Association" data presented in the tables for both non-targeted and targeted disease classifications. The performance is reported as the percentage of correct associations between the MDSS output and the clinical diagnosis. No explicit threshold for acceptance is stated in the provided text.

2. Sample Sizes and Data Provenance

• Test Set (Clinical Study):
  • Main Cohort: 908 samples (serum, EDTA, and heparinized plasma) collected prospectively from consecutive patients in rheumatology clinics who were suspected of, or had a history consistent with, an autoimmune/connective tissue disease.
  • Matched Subset: 214 subjects from the prospective population with matched serum, EDTA (N=214), and sodium heparinized plasma (N=214) samples.
  • Additional Normals: 222 normal blood donors were added, presumed negative for autoimmune disease.
  • Total for MDSS analysis: 1,130 results (908 clinic patients + 222 normal blood donors).
  • Data Provenance: Prospectively collected from three sites in the U.S.

3. Number of Experts and Qualifications

The document states that diagnoses were made "using American College of Rheumatology (ACR) or appropriate established disease classification criteria" or "literature criteria." It also mentions "diagnosis provided by a physician." However, it does not specify the number of experts who established the ground truth for the test set, nor does it explicitly detail their specific qualifications (e.g., "radiologist with 10 years of experience").

4. Adjudication Method

The document does not explicitly describe an adjudication method for the test set. It refers to diagnoses based on established criteria and physician's input, but not a process for resolving discrepancies among multiple experts for individual cases.

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

No multi-reader multi-case (MRMC) comparative effectiveness study evaluating the effect size of human readers improving with AI vs. without AI assistance is described in the provided text. The study focuses on the standalone performance of the MDSS in relation to clinical diagnoses.

6. Standalone Performance

Yes, a standalone performance study was done. The entire "7.2 MEDICAL DECISION SUPPORT SOFTWARE (MDSS) PERFORMANCE ANALYSIS" section describes the evaluation of the MDSS algorithm's output against established clinical diagnoses. This is a standalone evaluation of the algorithm without human-in-the-loop performance.

7. Type of Ground Truth Used

The ground truth used was clinical diagnosis established by medical criteria. This includes:

• American College of Rheumatology (ACR) criteria
• American-European Consensus Group criteria
• Alaracon-Segovia or Kahn criteria
• Literature criteria (specifically for Polymyositis)
• "Diagnosis provided by a physician"

8. Sample Size for the Training Set

The software's algorithm is described as being "compared to a pre-established database that contains the results for over 1,400 characterized sera." This implies that the training set for the k-nearest neighbor (kNN) algorithm would be derived from these "over 1,400 characterized sera."

9. How Ground Truth for the Training Set Was Established

The document states the pre-established database contains "results for over 1,400 characterized sera." It does not explicitly detail how the ground truth for this training set was established, but given the nature of autoimmune disease diagnosis, it would likely involve similar established medical criteria and clinical diagnoses as the test set, applied retrospectively or through expert review. It uses the term "characterized sera," implying that these samples have known disease associations.

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