The Varelisa ® ReCombi ANA Profile EIA kit is designed for the qualitative determination of eight antinuclear antibodies in human serum or plasma to aid in the diagnosis of SLE (systemic lupus erythematosus), scleroderma (progressive systemic sclerosis and CREST syndrome), MCTD (mixed connective tissue disease), SS (Sjogren’s syndrome) and polymyositis/dermatomyositis. The Varelisa ReCombi ANA Profile individually detects antibodies against dsDNA, U1RNP (RNP 70, A, C), SmD, SS-A/Ro (52 kDa, 60 kDa), SS-B/La, Scl-70, CENP-B and Jo-1. For in vitro diagnostic use only.
Device Story
Varelisa ReCombi ANA Profile is an indirect noncompetitive enzyme immunoassay (EIA) for qualitative detection of eight specific antinuclear antibodies (dsDNA, U1RNP, SmD, SS-A/Ro, SS-B/La, Scl-70, CENP-B, Jo-1) in human serum or plasma. Microplate wells are coated with recombinant nuclear antigens or synthetic peptides (SmD). Patient samples are incubated; specific antibodies bind to antigens. Enzyme-labeled secondary antibody (rabbit anti-human IgG-HRP) binds to antigen-antibody complexes. Substrate addition produces color proportional to antibody concentration. Results are measured spectrophotometrically at 450nm and interpreted against a cut-off calibrator. Used in clinical laboratories by trained personnel. Output aids clinicians in diagnosing autoimmune conditions. Modification replaces calf-thymus-derived Sm antigen with synthetic Sm[D] peptide.
Clinical Evidence
No clinical diagnostic sensitivity/specificity studies provided. Analytical performance includes precision (intra-assay CV 1.8-7.9%, inter-assay CV 1.0-7.2%) and analytical sensitivity (0.0-0.1 Ratio). Method comparison against predicate (k042629) using 200 samples showed 100% positive agreement and 97.7% negative agreement. Interference study confirmed heparin interference with Sm antigen.
Technological Characteristics
Indirect noncompetitive ELISA; 96-well microtiter plate format. Antigens: recombinant nuclear antigens, synthetic peptides (SmD), plasmid DNA. Conjugate: Rabbit anti-human IgG Horseradish Peroxidase. Substrate: chromogenic. Energy source: spectrophotometric reader (450nm). Qualitative interpretation based on ratio to cut-off control.
Indications for Use
Indicated for qualitative detection of eight antinuclear antibodies (dsDNA, U1 RNP, SmD, SS-A/Ro, SS-B/La, Scl-70, CENP-B, Jo-1) in human serum or plasma to aid diagnosis of SLE, scleroderma, CREST syndrome, MCTD, Sjögren's syndrome, and polymyositis/dermatomyositis. For prescription use only.
Regulatory Classification
Identification
An antinuclear antibody immunological test system is a device that consists of the reagents used to measure by immunochemical techniques the autoimmune antibodies in serum, other body fluids, and tissues that react with cellular nuclear constituents (molecules present in the nucleus of a cell, such as ribonucleic acid, deoxyribonucleic acid, or nuclear proteins). The measurements aid in the diagnosis of systemic lupus erythematosus (a multisystem autoimmune disease in which antibodies attack the victim's own tissues), hepatitis (a liver disease), rheumatoid arthritis, Sjögren's syndrome (arthritis with inflammation of the eye, eyelid, and salivary glands), and systemic sclerosis (chronic hardening and shrinking of many body tissues).
Predicate Devices
Varelisa® ReCombi ANA Profile (k993109)
Varelisa® Sm Antibodies (k042629)
Submission Summary (Full Text)
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510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION
DECISION SUMMARY
A. 510(k) Number:
k050625
B. Purpose for Submission:
Modification of the manufacturer’s existing cleared device (Varelisa® ReCombi ANA Profile) by replacing the Sm antigen with the Sm[D] antigen (k042629)
C. Measurand:
Anti-Sm[D] antibodies
D. Type of Test:
Qualitative Enzyme Immunoassay (EIA)
E. Applicant:
Sweden Diagnostics (Germany) GmbH
F. Proprietary and Established Names:
Varelisa® ReCombi ANA Profile
G. Regulatory Information:
1. Regulation section:
21 CFR 866.5100 Antinuclear Antibodies Immunological
2. Classification:
II
3. Product code:
LJM, Antinuclear antibody, Antigen and Control
4. Panel:
Immunology 82
H. Intended Use:
1. Intended use(s):
The Varelisa ® ReCombi ANA Profile EIA kit is designed for the qualitative determination of eight antinuclear antibodies in human serum or plasma to aid in the diagnosis of SLE (systemic lupus erythematosus), scleroderma (progressive systemic sclerosis and CREST syndrome), MCTD (mixed connective tissue disease), SS (Sjogren’s syndrome) and polymyositis/dermatomyositis. The Varelisa ReCombi ANA Profile individually detects antibodies against dsDNA, U1RNP (RNP 70, A, C), SmD, SS-A/Ro (52 kDa, 60 kDa), SS-B/La, Scl-70, CENP-B and Jo-1. For in vitro diagnostic use only.
2. Indication(s) for use:
Same as intended use.
3. Special conditions for use statement(s):
For prescription use only.
4. Special instrument requirements:
Microplate reader capable of measuring OD at 450nm.
I. Device Description:
For detection of Sm[D], the modified assay contains microplate wells coated with synthetic peptides (Sm[D]) in place of Sm. All other reagents are the same as the previously cleared device.
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J. Substantial Equivalence Information:
1. Predicate device name(s):
Varelisa® ReCombi ANA Profile (No. 12996) and Varelisa® Sm Antibodies
2. Predicate 510(k) number(s):
k993109 and k042629
3. Comparison with predicate:
| Similarities | | |
| --- | --- | --- |
| Item | New Device | Predicate Device |
| Indications for use | To aid in the diagnosis of SLE (systemic lupus erythematosus), scleroderma (progressive systemic sclerosis and CREST syndrome), MCTD (mixed connective tissue disease), SS (Sjogrens syndrome), and polymyositis/dermatomyositis. | Same |
| Technology | ELISA | Same |
| Assay Format | Qualitative | Same |
| Sample dilution | 1:101 dilution | Same |
| Enzyme-Conjugate | Rabbit anti-human IgG Horseradish Peroxidase | Same |
| Substrate, wash buffer and stop solution | Same | Same |
| Incubation times | 30, 30 and 10 minutes | Same |
| Platform | 96 well microtitre plates | Same |
| Result interpretation (ratio compared to cut-off control) | Negative: <1.0
Equivocal: 1.0 – 1.4
Positive: >1.4 | Same |
| Matrix | Serum and plasma (EDTA, citrate) | Same |
| Antigens | dsDNA, U1RNP (RNP 70, A, C), SS-A/Ro (52 kDa, 60 kDa), SS-B/La, Scl-70, CENP-B and Jo-1 | Same |
| Differences | | |
| Item | Device | Predicate |
| Antigen | Synthetic human Sm[D] peptide. | Sm antigen purified from calf thymus. |
| Sample diluent | 20mL 5X Concentrate | 100 mL ready to use |
K. Standard/Guidance Document Referenced (if applicable):
None referenced.
L. Test Principle:
The Varelisa ReCombi ANA Profile is an indirect noncompetitive enzyme
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immunoassay for the individual qualitative determination of dsDNA, U1RNP (RNP 70, A, C), Sm[D], SS-A/Ro (52 kDa, 60 kDa), SS-B/La, Scl-70, CENP-B and Jo-1 antibodies in serum and plasma. The wells of a microplate are coated with human recombinant nuclear antigens, synthetic peptides (SmD) or plasmid DNA. Antibodies specific for the nuclear antigens are present in a patient sample bind to these nuclear antigens. In a second step the enzyme labeled second antibody (conjugate) binds to the antigen-antibody complex which leads to the formation of an enzyme labeled conjugate-antibody-antigen 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 is proportional to the initial concentration of the respective antibodies in the patient sample. The results are read spectrophotometrically and are interpreted by comparison to a cut-off calibrator.
## M. Performance Characteristics (if/when applicable):
### 1. Analytical performance:
Data presented are for all eight analytes.
#### a. Precision/Reproducibility:
i. Study design: Three samples (equivocal, low positive, high positive) per analyte were analyzed in 5 runs, with 5 replicates per run. Calibrator and control were analyzed in triplicates. Within one day one operator carried out the analyses.
ii. Results/Acceptance criteria: Target values for the study were set at variance intra assay = <12% and inter assay = <8%. Intra-assay variation ranged from 1.8% to 7.9% and inter-assay variation ranged from 1.0% to 7.2% and all were within the target values.
| Analyte | Sample | Mean (Ratio) | Variability (CV %) | |
| --- | --- | --- | --- | --- |
| | | | Intra-assay | Inter-assay |
| dsDNA | equivocal | 1.2 | 2.4 | 3.9 |
| | low positive | 2.3 | 3.4 | 4.3 |
| | high positive | 3.0 | 2.4 | 5.9 |
| U1RNP | equivocal | 1.4 | 3.3 | 2.5 |
| | low positive | 2.0 | 3.6 | 3.8 |
| | high positive | 3.4 | 2.1 | 7.2 |
| Sm[D] | equivocal | 1.1 | 2.3 | 2.3 |
| | low positive | 1.9 | 3.7 | 5.4 |
| | high positive | 3.3 | 1.8 | 3.2 |
| SS-A/Ro | equivocal | 1.1 | 3.9 | 1.9 |
| | low positive | 2.7 | 3.0 | 1.3 |
| | high positive | 4.5 | 3.4 | 3.3 |
| SS-B/La | equivocal | 1.3 | 2.7 | 2.5 |
| | low positive | 1.9 | 2.2 | 5.6 |
| | high positive | 3.1 | 1.9 | 2.9 |
| Scl-70 | equivocal | 1.2 | 3.9 | 4.5 |
| | low positive | 2.5 | 4.1 | 5.6 |
| | high positive | 3.9 | 3.1 | 4.5 |
| CENP | equivocal | 1.1 | 2.8 | 1.0 |
| | low positive | 1.8 | 3.2 | 3.4 |
| | high positive | 3.3 | 3.2 | 4.0 |
| Jo-1 | equivocal | 1.3 | 2.2 | 2.8 |
| | low positive | 2.1 | 7.9 | 3.1 |
| | high positive | 2.7 | 3.8 | 5.0 |
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b. Linearity/assay reportable range:
Not applicable.
c. Traceability, Stability, Expected values (controls, calibrators, or methods): Not applicable.
d. Detection limit:
The sample diluent (ready to use) was measured 30 times per analyte (10 modules of 3 different solid phase batches). Calibrator and Control were run in singlicate. The value for the analytical sensitivity (detection limit) was calculated as the mean of the optical densities (OD) of the sample diluent plus three times the standard deviations (SD) (expressed in Ratios). Specifications were: the mean plus 3 SD of the OD of the Sample Diluent should be lower than or equal to 0.3 for each analyte. The analytical sensitivity for the eight analytes ranged from 0.0 to 0.1. The new device met the specifications.
| Analyte | Mean OD (n=30) | Standard deviation (SD) | Analytical Sensitivity | |
| --- | --- | --- | --- | --- |
| | | | OD | Ratio |
| dsDNA | 0.005 | 0.005 | 0.021 | 0.1 |
| U1RNP | 0.016 | 0.007 | 0.038 | 0.0 |
| Sm[D] | 0.010 | 0.006 | 0.027 | 0.0 |
| SS-A/Ro | 0.010 | 0.007 | 0.030 | 0.1 |
| SS-B/La | 0.006 | 0.004 | 0.018 | 0.0 |
| Scl-70 | 0.005 | 0.005 | 0.021 | 0.0 |
| CENP | 0.007 | 0.004 | 0.020 | 0.0 |
| Jo-1 | 0.008 | 0.007 | 0.029 | 0.1 |
e. Analytical specificity:
Interference Study: Interference study data were referred to the two cleared devices: Varelisa ReCombi ANA Profile (k993109) for the 7 antigens and Varelisa Sm Antibodies (k042629) for the $\mathrm{Sm}[\mathrm{D}]$ antigen. Data showed heparin interfered with the measurement of Sm antibodies and lipemic, hemolyzed or microbially contaminated samples could give poor results.
Crossreactivity to other Autoantibodies: Ten CDC International ANA Human Reference Sera were analyzed in singlicate together with the Calibrator and Control. The results are depicted in the table below and are comparable to the predicate device. The new device detected the expected targets except for sera CDC5 and CDC 10. CDC5 was found to react with in addition to Sm, The false positive reaction with U1RNP was due to the presence of high titers of antibodies directed against the RNP 70, A, and C in CDC 5. Western blot analysis confirmed that CDC5 did not react with U1RNP. CDC 10 reacted with SS-A/Ro in addition to Jo-1. The false positive result was due to the presence of antibodies to the SS-A $52\mathrm{kDa}$ protein. The co-occurrence of antibodies to SS-A 52 in sera of patients with idiopathic inflammatory myopathy was described in Rutjes et al., 1997.
Panel Table: Results for the International ANA Human Reference Panel from the Center of Disease Control (CDC)
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| Sample | Target | New device (U/ml) | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | ds | U1 | Sm | Ro | La | Scl | Cen | Jo |
| CDC | dsDNA & week Sm | 7.1 | 0.5 | 1.0 | 0.2 | 0.0 | 0.1 | 0.2 | 0.1 |
| CDC | SS-B/La | 0.2 | 0.2 | 0.2 | 2.6 | 3.3 | 0.1 | 0.1 | 0.1 |
| CDC 3 | speckled pattern, U1RNP, SS-A/Ro, | 0.2 | 3.4 | 0.2 | 3.0 | 2.8 | 0.1 | 0.1 | 0.1 |
| CDC 4 | U1-RNP | 0.2 | 3.3 | 0.2 | 0.1 | 0.0 | 0.1 | 0.1 | 0.1 |
| CDC 5 | Sm | 0.5 | 2.8 | 4.5 | 0.2 | 0.0 | 0.2 | 0.2 | 0.1 |
| CDC 6 | nucleolar pattern | 0.2 | 0.3 | 0.4 | 0.3 | 0.2 | 0.3 | 0.2 | 0.1 |
| CDC 7 | SS-A/Ro | 0.6 | 0.1 | 0.2 | 3.5 | 0.1 | 0.1 | 0.1 | 0.1 |
| CDC 8 | CenP | 0.2 | 0.1 | 0.1 | 0.1 | 0.0 | 0.1 | 4.7 | 0.1 |
| CDC 9 | Scl-70 | 0.4 | 0.2 | 0.2 | 0.1 | 0.0 | 3.8 | 0.2 | 0.1 |
| CDC | Jo-1 | 0.1 | 0.1 | 0.1 | 1.8 | 0.0 | 0.1 | 0.1 | 4.2 |
Positive results are in bold letters, disagreements with the target specificity are shaded in gray.
1 reported as weak Sm positive by Tan E.M. et al. (1999)
* SS-B/La usually does not occur without SS-A/Ro
f. Assay cut-off:
The equivocal range and the cut-off of the new device were determined by analyzing 100 serum samples from apparently healthy Caucasian blood donors (50 males and 50 females). The serum samples were analyzed in singlicate together with Calibrator and Control. The specification for the study was that the $95^{\text{th}}$ percentile should lie below the lower limit of the equivocal range for the parameter. The equivocal range of the new device was 1.0 to 1.4. The results are depicted in the tables and histogram below. The $95^{\text{th}}$ percentile of 100 normal healthy controls ranged from 0.1 to 0.8 which met the specifications.
Statistical evaluation for $n = 100$ Samples per analyte (*[Ratio])
| Analyzer | dsDNA | U1RNP | Sm | SS-A/Ro | SS-B/La | Scl-70 | CENP | Jo-1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| n | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
| Median* | 0.3 | 0.3 | 0.2 | 0.1 | 0.0 | 0.1 | 0.1 | 0.1 |
| Mean* | 0.3 | 0.3 | 0.2 | 0.2 | 0.0 | 0.1 | 0.2 | 0.1 |
| SD* | 0.2 | 0.2 | 0.2 | 0.6 | 0.1 | 0.1 | 0.1 | 0.0 |
| Mean + 2 SD* | 0.8 | 0.6 | 0.6 | 1.4 | 0.1 | 0.4 | 0.3 | 0.2 |
| 95% Percentile* | 0.8 | 0.6 | 0.4 | 0.3 | 0.1 | 0.3 | 0.3 | 0.2 |
| 98% Percentile* | 1.1 | 0.8 | 0.6 | 0.5 | 0.2 | 0.5 | 0.3 | 0.2 |
Statistical evaluation for samples separated depending on age and gender
| Gender | male | | | | | female | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Age | ≤30 | 31 - 40 | 41 - 50 | 51 - 60 | ≥60 | ≤30 | 31 - 40 | 41 - 50 | 51 - 60 | ≥60 |
| n | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
| Parameter | dsDNA | | | | | | | | | |
| Median | 0.3 | 0.3 | 0.3 | 0.2 | 0.3 | 0.3 | 0.3 | 0.3 | 0.2 | 0.3 |
| Mean | 0.3 | 0.3 | 0.4 | 0.2 | 0.3 | 0.4 | 0.5 | 0.3 | 0.3 | 0.4 |
| SD | 0.2 | 0.1 | 0.2 | 0.1 | 0.1 | 0.3 | 0.5 | 0.1 | 0.2 | 0.3 |
| Mean + 2 SD | 0.8 | 0.4 | 0.7 | 0.4 | 0.5 | 1.0 | 1.5 | 0.5 | 0.8 | 1.0 |
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| Gender | male | | | | | female | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Age | ≤30 | 31-40 | 41-50 | 51-60 | ≥60 | ≤30 | 31-40 | 41-50 | 51-60 | ≥60 |
| n | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
| 95% | 0.7 | 0.4 | 0.7 | 0.4 | 0.4 | 0.9 | 1.3 | 0.5 | 0.7 | 0.9 |
| Percentile | | | | | | | | | | |
| 98% | 0.8 | 0.4 | 0.8 | 0.4 | 0.4 | 1.0 | 1.6 | 0.5 | 0.7 | 1.0 |
| Percentile | | | | | | | | | | |
| Parameter | U1RNP | | | | | | | | | |
| Median | 0.2 | 0.2 | 0.3 | 0.3 | 0.3 | 0.3 | 0.2 | 0.3 | 0.3 | 0.3 |
| Mean | 0.2 | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 | 0.4 | 0.4 |
| SD | 0.1 | 0.1 | 0.2 | 0.1 | 0.1 | 0.1 | 0.2 | 0.1 | 0.3 | 0.2 |
| Mean + 2 SD | 0.4 | 0.5 | 0.7 | 0.4 | 0.6 | 0.6 | 0.6 | 0.5 | 1.0 | 0.7 |
| 95% | | | | | | | | | | |
| Percentile | 0.4 | 0.5 | 0.6 | 0.4 | 0.5 | 0.5 | 0.6 | 0.5 | 0.9 | 0.6 |
| 98% | | | | | | | | | | |
| Percentile | 0.4 | 0.5 | 0.8 | 0.4 | 0.6 | 0.5 | 0.6 | 0.5 | 1.0 | 0.6 |
| Parameter | Sm[D] | | | | | | | | | |
| Median | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 |
| Mean | 0.2 | 0.3 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.4 | 0.2 | 0.2 |
| SD | 0.1 | 0.1 | 0.0 | 0.1 | 0.0 | 0.1 | 0.1 | 0.4 | 0.1 | 0.1 |
| Mean + 2 SD | 0.3 | 0.5 | 0.3 | 0.5 | 0.3 | 0.3 | 0.3 | 1.2 | 0.4 | 0.5 |
| 95% | | | | | | | | | | |
| Percentile | 0.3 | 0.5 | 0.3 | 0.4 | 0.3 | 0.3 | 0.3 | 1.0 | 0.3 | 0.4 |
| 98% | | | | | | | | | | |
| Percentile | 0.4 | 0.6 | 0.3 | 0.5 | 0.3 | 0.3 | 0.3 | 1.3 | 0.4 | 0.5 |
| Parameter | SS-A/Ro | | | | | | | | | |
| Median | 0.1 | 0.1 | 0.1 | 0.1 | 0.2 | 0.2 | 0.1 | 0.2 | 0.1 | 0.1 |
| Mean | 0.2 | 0.1 | 0.1 | 0.1 | 0.2 | 0.6 | 0.2 | 0.6 | 0.2 | 0.1 |
| SD | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 1.3 | 0.1 | 1.4 | 0.1 | 0.1 |
| Mean + 2 SD | 0.3 | 0.2 | 0.2 | 0.2 | 0.3 | 3.2 | 0.4 | 3.3 | 0.4 | 0.2 |
| 95% | | | | | | | | | | |
| Percentile | 0.3 | 0.2 | 0.2 | 0.2 | 0.2 | 2.5 | 0.3 | 2.6 | 0.4 | 0.2 |
| 98% | | | | | | | | | | |
| Percentile | 0.4 | 0.2 | 0.2 | 0.2 | 0.2 | 3.5 | 0.4 | 3.7 | 0.4 | 0.2 |
| Parameter | SS-A/La | | | | | | | | | |
| Median | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Mean | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.1 | 0.0 | 0.0 |
| SD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.1 | 0.0 | 0.0 |
| Mean + 2 SD | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.2 | 0.1 | 0.3 | 0.0 | 0.1 |
| 95% | | | | | | | | | | |
| Percentile | 0.1 | 0.0 | 0.0 | 0.1 | 0.1 | 0.2 | 0.1 | 0.3 | 0.0 | 0.1 |
| 98% | | | | | | | | | | |
| Percentile | 0.1 | 0.0 | 0.0 | 0.1 | 0.1 | 0.2 | 0.1 | 0.4 | 0.0 | 0.1 |
| Parameter | Scl-70 | | | | | | | | | |
| Median | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 |
| Mean | 0.2 | 0.1 | 0.1 | 0.1 | 0.2 | 0.1 | 0.1 | 0.2 | 0.1 | 0.1 |
| SD | 0.1 | 0.0 | 0.0 | 0.0 | 0.2 | 0.1 | 0.1 | 0.2 | 0.0 | 0.1 |
| Mean + 2 SD | 0.3 | 0.2 | 0.2 | 0.2 | 0.6 | 0.3 | 0.3 | 0.6 | 0.2 | 0.4 |
| 95% | | | | | | | | | | |
| Percentile | 0.3 | 0.2 | 0.2 | 0.2 | 0.5 | 0.3 | 0.3 | 0.6 | 0.2 | 0.3 |
| 98% | | | | | | | | | | |
| Percentile | 0.3 | 0.2 | 0.2 | 0.2 | 0.6 | 0.3 | 0.3 | 0.6 | 0.2 | 0.4 |
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| Gender | male | | | | | female | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Age | ≤30 | 31 - 40 | 41 - 50 | 51 - 60 | ≥60 | ≤30 | 31 - 40 | 41 - 50 | 51 - 60 | ≥60 |
| n | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 |
| Median | 0.2 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 |
| Mean | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.1 | 0.1 |
| SD | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 |
| Mean + 2 SD | 0.4 | 0.3 | 0.2 | 0.2 | 0.3 | 0.3 | 0.3 | 0.2 | 0.2 | 0.2 |
| 95% | 0.3 | 0.3 | 0.2 | 0.2 | 0.2 | 0.3 | 0.3 | 0.2 | 0.2 | 0.2 |
| Percentile | 0.4 | 0.3 | 0.2 | 0.2 | 0.2 | 0.3 | 0.3 | 0.2 | 0.2 | 0.2 |
| 98% | | | | | | | | | | |
| Percentile | | | | | | | | | | |
| Parameter | Jo-1 | | | | | | | | | |
| Median | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 |
| Mean | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 |
| SD | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 |
| Mean + 2 SD | 0.3 | 0.1 | 0.2 | 0.2 | 0.1 | 0.3 | 0.2 | 0.2 | 0.1 | 0.2 |
| 95% | 0.3 | 0.1 | 0.1 | 0.2 | 0.1 | 0.2 | 0.2 | 0.1 | 0.1 | 0.2 |
| Percentile | | | | | | | | | | |
| 99% | 0.4 | 0.1 | 0.1 | 0.2 | 0.1 | 0.3 | 0.2 | 0.2 | 0.1 | 0.2 |
| Percentile | | | | | | | | | | |

Histogram
# 2. Comparison studies:
Data presented are for SmD only.
a. Method comparison with predicate device:
i. Study design: Refer to the method comparison data in Varelisa ReCombi ANA Profile (k993109) and Varelisa Sm Antibodies
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(k042629). Since the major difference between the new and predicate device is Sm, a comparative study was performed between the SmD of the new device and the Sm (B, B', D) of the predicate device. The new test was also compared to a semiquantitative test (k042629). One hundred and eighty samples positive for at least one autoantibody and 20 samples from blood donors were analyzed in single determinations. Calibrators and Controls were analyzed in duplicates. Results of both comparisons are depicted in the tables below. Equivocal results were regarded as negative.
## Correlation of New and Predicate device
| Sm | Predicate device | | | |
| --- | --- | --- | --- | --- |
| | Positive | Equivocal | Negative | Σ |
| Positive | 16 | 3 | 8 | 27 |
| Equivocal | 1 | 2 | 5 | 8 |
| Negative | 5 | 1 | 159 | 165 |
| Σ | 22 | 6 | 172 | 200 |
Positive Agreement 72.7% (16/22)
Negative Agreement 93.8% (167/178)
Total Agreement 91.5% (183/200)
## Correlation of New Device and Semiquantitative test (K042629)
| Sm | Semiquantitative test | | | |
| --- | --- | --- | --- | --- |
| | Positive | Equivocal | Negative | Σ |
| Positive | 23 | 3 | 1 | 27 |
| Equivocal | 0 | 2 | 6 | 8 |
| Negative | 0 | 1 | 164 | 165 |
| Σ | 23 | 6 | 171 | 200 |
Positive Agreement 100% (23/23)
Negative Agreement 97.7% (173/177)
Total Agreement 98.0% (196/200)
b. Matrix comparison:
The Sm[D] study included the use of serum, heparin plasma, citrate plasma and EDTA plasma. The conclusion of the study is that the use of heparin interfered with the Sm antigen.
3. Clinical studies:
a. Clinical Sensitivity: Not given.
b. Clinical specificity: Not given.
c. Other clinical supportive data (when a. and b. are not applicable): Not Applicable.
4. Clinical cut-off: Refer to Assay cut-off.
5. Expected values/Reference range: Refer to Assay cut-off.
{8}
N. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10.
O. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.