Retrospective clinical serum samples from patients with RA and other diseases
Retrospective clinical samples were used to evaluate the clinical sensitivity and specificity of the Axis-Shield Anti-CCP assay in comparison to established clinical diagnoses.
514 subjects: 229 RA patients (43 early, 186 established), 150 asymptomatic healthy donors, and 135 non-RA patients with other diseases.; Sample Size: 514
Diastat™ Anti-CCP assay (k023285)
Clinical sensitivity and specificity for RA diagnosis
Indications for Use
The Axis-Shield Anti-CCP test is a semi-quantitative/qualitative enzyme-linked immunosorbent assay (ELISA) for the detection of the IgG class of autoantibodies specific to cyclic citrullinated peptide (CCP) in human serum (including Serum Separator Tubes) or plasma (EDTA, lithium heparin, or sodium citrate). Detection of anti-CCP antibodies is used as an aid in the diagnosis of rheumatoid arthritis (RA), and should be used in conjunction with other clinical information. Autoantibody levels represent one parameter in a multi-criterion diagnostic process, encompassing both clinical and laboratory-based assessments. For in vitro diagnostic use.
Device Story
Axis-Shield Anti-CCP is an ELISA-based in vitro diagnostic test for detecting IgG autoantibodies against cyclic citrullinated peptide in human serum or plasma. The device utilizes microtitre plates coated with synthetic CCP; patient samples are incubated, allowing specific autoantibodies to bind. Bound antibodies are detected via an enzyme-labeled anti-human IgG conjugate and TMB substrate, producing a colorimetric reaction measured by absorbance. The assay provides semi-quantitative results via interpolation from a dose-response curve or qualitative results via comparison to a reference control. Used in clinical laboratory settings by trained personnel. Results assist clinicians in the multi-criterion diagnosis of Rheumatoid Arthritis alongside other clinical and laboratory assessments.
Clinical Evidence
Clinical performance was evaluated using 514 retrospective serum samples (229 RA patients, 150 healthy, 135 non-RA disease). Using a 5.0 U/mL cutoff, the device showed 99.4% positive agreement and 98.8% negative agreement with the predicate. Clinical sensitivity for all RA was 78% (95% CI: 88.19-95.63%), with 63% for early RA and 82% for established RA. Clinical specificity was 99.3% in healthy asymptomatic donors and 97.8% in non-RA disease states (e.g., SLE, Sjögren’s, Hashimoto’s).
Indicated for use as an aid in the diagnosis of rheumatoid arthritis (RA) in patients suspected of having the disease. For prescription use only.
Regulatory Classification
Identification
A rheumatoid factor immunological test system is a device that consists of the reagents used to measure by immunochemical techniques the rheumatoid factor (antibodies to immunoglobulins) in serum, other body fluids, and tissues. Measurement of rheumatoid factor may aid in the diagnosis of rheumatoid arthritis.
Predicate Devices
Diastat™ Anti-CCP assay (k023285)
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY
A. 510(k) Number:
k110296
B. Purpose for Submission:
New device
C. Measurand:
Anti-cyclic citrullinated peptide (CCP) antibodies
D. Type of Test:
Semi-quantitative/qualitative enzyme-linked immunosorbent assay (ELISA)
E. Applicant:
Axis-Shield Diagnostics Limited
F. Proprietary and Established Names:
Axis-Shield Anti-CCP
G. Regulatory Information:
1. Regulation section:
21 CFR §866.5775 Rheumatoid factor immunological test system.
2. Classification:
Class II
3. Product code:
NHX, Antibodies, Anti-Cyclic Citrullinated Peptide (CCP)
4. Panel:
Immunology (82)
H. Intended Use:
1. Intended use(s):
The Axis-Shield Anti-CCP test is a semi-quantitative/qualitative enzyme-linked immunosorbent assay (ELISA) for the detection of the IgG class of autoantibodies specific to cyclic citrullinated peptide (CCP) in human serum (including Serum Separator Tubes) or plasma (EDTA, lithium heparin, or sodium citrate). Detection of anti-CCP antibodies is used as an aid in the diagnosis of rheumatoid arthritis (RA), and should be used in conjunction with other clinical information. Autoantibody levels represent one parameter in a multi-criterion diagnostic process, encompassing both clinical and laboratory-based assessments. For in vitro diagnostic use.
2. Indication(s) for use:
Same as Intended Use
3. Special conditions for use statement(s):
For prescription use only
4. Special instrument requirements:
96-well plate/strip reader capable of measuring optical density (OD) at 450 nm
I. Device Description:
Each device contains the following components:
- A microtitre plate with 8 x 12-well break-apart strips coated with purified synthetic CCP containing modified arginine residues (CCP2 peptides);
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- Ready-to-use calibrators (human plasma with or without IgG antibodies against CCP);
- Positive and negative assay controls (human plasma with or without IgG antibodies against CCP);
- Ready-to-use reference control;
- Goat anti-human IgG horseradish peroxidase conjugate;
- TMB substrate;
- Sample diluent (5x);
- Wash buffer (10x);
- Ready-to-use stop solution (0.25 mol/L sulphuric acid solution).
# J. Substantial Equivalence Information:
1. Predicate device name(s) and 510(k) number(s):
Diastat™ Anti-CCP assay (k023285)
2. Comparison with predicate:
| Similarities | | |
| --- | --- | --- |
| Item | Device | Predicate |
| | Axis-Shield Anti-CCP (FCCP600) | Diastat™ Anti-CCP (FCCP200) |
| Intended Use/Indications for Use | A semi-quantitative/qualitative enzyme-linked immunosorbent assay (ELISA) for the detection of the IgG class of autoantibodies specific to cyclic citrullinated peptide (CCP) in human serum or plasma. For in vitro diagnostic use | Same |
| Indications for Use | To aid in the diagnosis of rheumatoid arthritis | Same |
| Technology | ELISA, 96-well plate; 8 x 12 well microtitre strips | Same |
| Capture antigen | Synthetic cyclic citrullinated peptide (CCP), second generation. | Same |
| Cut-off | 5.0 U/mL | Same |
| Quantitation | Results determined from a standard calibration curve (0, 2, 8, 30, 100, 300 U/mL) generated on each microtitre plate | Same |
| Specimen Type | Human serum (SST) or plasma (EDTA, lithium heparin or sodium citrate) | Same |
| Expected values in asymptomatic population | 0.05 – 3.8 U/mL | Same |
| Control | Negative and positive kit controls | Same |
| Calibration | Qualitative protocol: The amount of Conjugate bound by the sample is compared with that bound by the | Same |
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| Similarities | | |
| --- | --- | --- |
| Item | Device | Predicate |
| | Reference Control. Semi-quantitative protocol: The concentration of anti-CCP autoantibody can be estimated by interpolation from a dose-response curve based on calibrators | |
| Storage conditions | Store kit components at 2-8°C. Do not freeze kits. | Same |
| Interference | Hemoglobin (up to 400 mg/dL), bilirubin (up to 0.2 mg/mL), intralipid (up to 15 mg/mL), rheumatoid factor (up to 200 IU/mL) do not interfere with anti-CCP antibody results | Same |
| Differences | | |
| --- | --- | --- |
| Item | Device | Predicate |
| | Axis-Shield Anti-CCP (FCCP600) | Diastat™ Anti-CCP (FCCP200) |
| Conjugate Antibody | Horseradish peroxidase-labeled goat polyclonal antibody to human IgG | Alkaline phosphatase-labeled murine monoclonal antibody to human IgG |
| Substrate | 3,3',5,5'-Tetramethylbenzidine (TMB). | Mg2+, phenolphthalein monophosphate (PMP) |
| Calibration | 0, 2, 8, 30, 100, 300 U/mL | 0, 2, 8, 30, 100 U/mL |
| Assay End-Point | Color, read at 450 nm | Color, read at 540-565 nm |
| Calibrator Range | 0-300 U/mL | 0-100 U/mL |
| Assay Range | 1.04-300 U/mL | 0.05-100 U/mL |
| Analytical Sensitivity | Limit of detection = 1.04 U/mL. | Lower limit of detection (Mean + 2 SD of zero calibrator) = 0.05 U/mL. |
| Imprecision | Within-run CV: 2.1% to 8.8% Between-run CV: 4.1 to 8.9% from 3.7 to 205.2 U/mL | Intra-assay CV: 7.6% to 10.5% Inter-assay CV: 7.6% to 13.6% from 5.4 to 34.1 U/mL |
| Interference | Total Protein up to 120 mg/mL does not interfere with anti-CCP antibody results | Not reported or assessed |
# K. Standard/Guidance Document Referenced (if applicable):
CLSI EP05-A2 Evaluation of Precision Performance of Quantitative Measurement Methods; Approved Guideline-Second Edition
CLSI EP06-A Evaluation of the Linearity of Quantitative Measurement
CLSI EP07-A2 Interference Testing in Clinical Chemistry; Approved Guideline - Second Edition
CLSI EP09-A2 Method Comparison and Bias Estimation Using Patient Samples;
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# Approved Guideline
CLSI EP17-A Protocols for Determination of Limits of Detection and Limits of Quantitation
# L. Test Principle:
The Axis-Shield Anti-CCP assay is an ELISA based on the detection of IgG autoantibodies in human serum or plasma towards a synthetic CCP containing modified arginine residues (CCP2 peptides). The test provides an additional tool in the diagnosis of patients with RA. The wells of the microtitre strips are coated with purified CCP2 peptides. Patient sample is added and specific autoantibodies in diluted serum or plasma bind to the antigen-coated surface. The wells are then washed to remove unbound components. In the second incubation, the conjugate, an enzyme-labeled polyclonal antibody to human IgG, binds to any surface-bound autoantibodies. After further washing, specific autoantibodies are detected by incubation with the Substrate. Addition of Stop Solution terminates the reaction, resulting in a colored end-product. The amount of Conjugate bound is measured in absorbance units. In the qualitative protocol, the amount of Conjugate bound by the sample is compared with that bound by the Reference Control. In the semi-quantitative protocol, the concentration of anti-CCP autoantibody can be estimated by interpolation from a dose-response curve based on Calibrators.
# M. Performance Characteristics (if/when applicable):
# 1. Analytical performance:
a. Precision/Reproducibility: Two kit combinations were used throughout the study including reagents, positive and negative controls, quality control (QC) controls and reference control. Testing was carried out by at least two operators. The sample and controls were assayed in replicates of two, twice daily, for 20 days $(n = 80$ for each control/sample) and the sampling order was randomized on the individual testing days throughout the study.
Quantitative assay: nine human plasma or serum based samples were used for the study (20 days x 2 runs per day x2 replicates=80 determinations per sample; 40 per kit lot combination). The sample concentrations are detailed in the table below:
| Sample | Target Concentration (U/mL) |
| --- | --- |
| Assay positive control | 25.0 |
| Assay reference control | 5.0 |
| Serum sample 1 | 5.0 |
| QC 1 (plasma) | 4.1 |
| QC 2 (plasma) | 7.8 |
| QC 3 (plasma) | 16.0 |
| QC 4 (plasma) | 56.0 |
| QC 5 (serum) | 97.0 |
| QC 6 (serum) | 146.0 |
Five additional samples (A1-A5) were tested to verify imprecision of the study. These samples were prepared from processed human plasma to achieve target CCP concentrations of 5.2, 3.9, 185.0, 191.4, 205.2 U/mL. Assays were
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run in duplicates, twice daily for 5 days, using 2 operators and 2 kit combinations, resulting in a total of $n = 40$ observations for each sample.
Precision data for each of the studies is summarized in the tables below:
| 20-Day Imprecision Data N=80 | | Between-Reagent | | Between-Day | | Between-Run | | Within-Run | | Total | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Sample I.D. | Mean (U/mL) | SD | % CV | SD | % CV | SD | % CV | SD | % CV | SD | % CV |
| Positive control | 20.4 | 0.1 | 0.7 | 0.0 | 0.0 | 1.2 | 6.0 | 0.8 | 3.9 | 1.7 | 7.2 |
| QC 1 | 3.8 | 0.1 | 3.5 | 0.1 | 2.6 | 0.3 | 7.2 | 0.3 | 7.4 | 0.4 | 11.2 |
| QC 2 | 8.3 | 0.2 | 2.1 | 0.0 | 0.0 | 0.7 | 8.5 | 0.3 | 3.9 | 0.8 | 9.6 |
| QC 3 | 15.6 | 0.5 | 2.9 | 0.0 | 0.0 | 0.9 | 5.9 | 0.4 | 2.3 | 1.1 | 7.0 |
| QC 4 | 54.5 | 1.2 | 2.3 | 0.9 | 1.7 | 3.3 | 6.0 | 2.3 | 4.2 | 4.3 | 7.9 |
| QC 5 | 95.7 | 1.5 | 1.6 | 3.7 | 3.8 | 6.5 | 6.8 | 2.9 | 3.0 | 8.2 | 8.5 |
| QC 6 | 138.6 | 5.2 | 3.7 | 4.0 | 2.9 | 7.6 | 5.5 | 5.2 | 3.7 | 11.3 | 8.1 |
| Ref Control | 5.1 | 0.0 | 0.0 | 0.2 | 4.1 | 0.2 | 4.3 | 0.3 | 5.9 | 0.4 | 8.4 |
| Sample 1 | 4.8 | 0.0 | 0.0 | 0.3 | 5.1 | 0.4 | 7.8 | 0.2 | 3.7 | 0.5 | 10.1 |
| 5-Day Imprecision Data N=40 | | Between-Reagent | | Between-Day | | Between-Run | | Within-Run | | Total | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Sample I.D. | Mean (U/mL) | SD | % CV | SD | % CV | SD | % CV | SD | % CV | SD | % CV |
| Sample 1A | 5.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3 | 5.8 | 0.2 | 4.0 | 0.4 | 7.0 |
| Sample 2A | 3.9 | 0.0 | 0.3 | 0.0 | 0.0 | 0.3 | 7.5 | 0.1 | 3.5 | 0.3 | 8.2 |
| Sample 3A | 185.0 | 0.0 | 0.0 | 4.0 | 2.1 | 10.4 | 5.6 | 6.2 | 3.4 | 12.7 | 6.9 |
| Sample 4A | 191.4 | 3.5 | 1.8 | 0.7 | 0.4 | 8.8 | 4.6 | 8.8 | 4.6 | 12.9 | 6.8 |
| Sample 5A | 205.2 | 3.3 | 1.6 | 0.0 | 0.0 | 11.4 | 5.5 | 6.8 | 3.3 | 13.6 | 6.6 |
Qualitative precision: Three samples were evaluated for the repeatability/reproducibility of the assay. The data is presented as positive, negative or borderline outcome as a percent of the 40 samples run in that study. The results are summarized in the table below:
| Sample | Lot | N | Mean (U/mL) | Overall mean (U/mL) | % Negative | % Borderline | % Positive |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Serum sample 1 | 001 | 40 | 4.8 | 4.8 | 36.3 | 41.3 | 22.5 |
| | 003 | 40 | 4.8 | | 42.5 | 32.5 | 25.0 |
| QC 1 (plasma) | 001 | 40 | 3.7 | 3.8 | 97.5 | 1.3 | 1.3 |
| | 003 | 40 | 3.9 | | 97.5 | 2.5 | 0.0 |
| QC 2 (plasma) | 001 | 40 | 8.2 | 8.4 | 0.0 | 0.0 | 100.0 |
| | 003 | 40 | 8.5 | | 0.0 | 0.0 | 100.0 |
b. Linearity/assay reportable range:
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Linear range was determined using a total of 13 samples pools with high anti-CCP levels (4 plasma sample pools, 2 serum sample pools, 4 spiked serum sample pools and 2 spiked plasma sample pools) diluted using an anti-CCP negative sample, according to the dilution scheme recommended in CLSI document EP6-A. Samples were tested in triplicates. Polynomial regression was used to assess linearity. The observed concentrations (Y) were plotted against the expected concentrations (X) at each dilution, for each of the samples. The % recoveries were 76.6 to 114.1%. The results are summarized in the table below:
| Sample I.D | Dilution Range (GPL-U/mL) | Slope (95% CI) | Y-intercept (95% CI) | R² | %CV Range |
| --- | --- | --- | --- | --- | --- |
| 1 | 0.0 – 247.9 | 0.9826 (0.9412, 1.0239) | +1.3762 (-4.8899, 7.6422) | 0.991 | 0.76 – 4.10 |
| 2 | 0.4 – 97.9 | 0.9573 (0.9114, 1.0032) | -0.1462 (-2.8103, 2.5178) | 0.996 | 0.57 – 27.11 |
| 3 | 0.7 – 15.1 | 1.0069 (0.9670, 1.0467) | -0.0546 (-0.4179, 0.3088) | 0.9973 | 0.78 – 20.37 |
| 4 | 0.6 – 48.1 | 1.0279 (0.9744, 1.0814) | -0.7815 (-2.3107, 0.7477) | 0.9953 | 0.37 – 10.63 |
| 5 | 0.2 – 123.2 | 1.0234 (0.9705, 1.0763) | -1.6047 (-5.4605, 2.2512) | 0.9953 | 0.42 – 22.33 |
| 8 | 0.0 – 181.1 | 0.997 (0.9598, 1.0341) | -2.304 (-6.2855, 1.6775) | 0.9976 | 0.52 – 5.40 |
| 9 | 0.0 – 283.6 | 1.0208 (0.9566, 1.0850) | -5.6586 (-16.4355, 5.1183) | 0.9931 | 0.73 – 6.31 |
| 10 | 0.0 – 377.2 | 0.9599 (0.8765, 1.0433) | -9.9781 (-28.5916, 8.6355) | 0.9869 | 1.35 – 6.67 |
| 11 | 0.0 – 254.5 | 1.0026 (0.9533, 1.0519) | -4.2706 (-11.6998, 3.1580) | 0.9958 | 0.52 – 6.79 |
| 12 | 0.0 – 268.5 | 1.0196 (0.9771, 1.0622) | -9.0985 (-15.8556, 2.3414) | 0.9969 | 1.58 – 8.14 |
| 13 | 0.0 – 306.8 | 1.0107 (0.9616, 1.0599) | -9.7967 (-18.722, 0.8715) | 0.9959 | 1.37 – 9.10 |
Axis-Shield anti-CCP assay claimed reportable range is 1.04 U/mL (LoD) - 300.0 U/mL.
c. Traceability, Stability, Expected values (controls, calibrators, or methods): Traceability: There is no recognized standard reference material for anti-CCP.
Calibrators and Controls (positive and negative) are prepared in-house and arbitrary units are assigned during the development process. The Calibrators and Controls for the Axis-Shield anti-CCP submission device are the same final formulation as those previously cleared for use with Diastat™ anti-CCP device (k023285) with the exception of the additional 300U/mL calibrator. Calibrator values are summarized in the table below:
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| Calibrator | Anti-CCP U/mL |
| --- | --- |
| 1 | 0 |
| 2 | 2 |
| 3 | 8 |
| 4 | 30 |
| 5 | 100 |
| 6 | 300 |
Control values are summarized in the table below:
| Control | Target concentration | Concentration Range |
| --- | --- | --- |
| Negative | < 2.0 U/mL | < 2.0 U/mL |
| Positive | 25.0 U/mL | 13.0 – 37.0 U/mL |
| Reference | 5.0 U/mL | 2.7 – 7.3 U/mL |
Stability: The claimed shelf life is 52 weeks at $2 - 8^{\circ}\mathrm{C}$ . The claimed open vial stability is 3 months from first opening. Real-time stability studies are ongoing; current data supports 13 weeks but testing will continue until at least 108 weeks.
# d. Detection limit:
Limit of Blank (LoB): An anti-CCP negative serum sample was diluted 1/100 in sample diluent and run for a total of $n = 60$ . LoB = Result at position [0.95 x $N_B + 0.5$ ], where $N_B$ is the number of blank measurements ( $n = 60$ ). The LoB was the average of the ranked results at positions 57 and 58. LOB was determined to be $0.348U/mL$ .
Limit of Detection (LoD): four serum samples (mean concentrations of 1.35, 0.70, 0.68 and $0.52\mathrm{U / mL}$ ) were assayed by two operators in replicates of eight, using two lots of kit components for a total of 15 replicates per sample and $n = 60$ . $\mathrm{LoD} = \mathrm{LoB} + (1.643\times$ pooled SD). The claimed LoD is 1.04 U/mL.
# e. Analytical specificity:
Endogenous Interference: Endogenous interferents tested included hemoglobin, bilirubin, triglyceride (intralipid solution), rheumatoid factor and total protein (bovine serum albumin, BSA). Six samples with differing anti-CCP concentrations (range from $2.7\mathrm{U / mL}$ to $261~\mathrm{U / mL}$ ) were spiked with at least two different concentrations of the interfering substances. The results are summarized in the table below:
| Potential Interfering compound | No interference found up to the following concentrations |
| --- | --- |
| Hemoglobin | 4 mg/mL |
| Bilirubin | 0.2 mg/mL |
| Triglyceride (Intralipid Solution) | 15 mg/mL |
| Rheumatoid factor | 200 IU/mL |
| Total Protein (BSA) | 120 mg/mL |
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Hook Effect/Over the Range Results: Two plasma samples with elevated anti-CCP concentrations (2809 U/mL and 2736 U/mL) were used to establish whether a possible hook effect is observed in the Axis-Shield Anti-CCP (FCCP600) assay. Samples were diluted to create a range from 3000 U/mL to < 100 U/mL. Calibrators, kit controls and the samples dilutions were tested in duplicate and the data generated was plotted for visual assessment of assay high dose hook. The percentage recoveries of the dilution-corrected samples on the test device are within ± 20% of the actual concentrations determined on the predicate device. No high dose hook effect was observed.
f. Assay cut-off:
The Axis-Shield Anti-CCP (FCCP600) uses the same cut-off value as the predicate device: k023285 (FCCP200)
Semi-Quantitative Protocol:
Samples with results ≤ 5 U/mL are defined as negative.
Samples >5 U/mL are defined as positive.
Qualitative Protocol:
| Absorbance ratio | Result interpretation |
| --- | --- |
| < 0.95 | Negative |
| ≥ 0.95 to ≤ 1.0 | Borderline – recommend repeat testing |
| > 1.0 | Positive |
2. Comparison studies:
a. Method comparison with predicate device:
Specimens were collected from 514 subjects including 229 RA patients, 150 asymptomatic, apparently healthy and 135 non-RA patients with other diseases. Diagnosis of RA was made according to the 1987 American College of Rheumatology (ACR) revised criteria. Specimens spanned the Axis-Shield Anti-CCP (FCCP600) measurement range.
Using a cutoff of 5.0 U/mL, concordance analysis was performed on all sample (n=514). The results are summarized below:
| | Diastat™ Anti-CCP (FCCP200) | | | |
| --- | --- | --- | --- | --- |
| | | Positive | Negative | Total |
| Axis-Shield Anti-CCP (FCC600) | Positive | 179 | 4 | 183 |
| | Negative | 1 | 330 | 331 |
| | Total | 180 | 334 | 514 |
% Positive agreement = 99.4% (179/180)
% Negative agreement = 98.8% (330/334)
% Total agreement = 99.0% (509/514)
In addition, 65 of the 514 samples that were within the measurement range of both the new and predicate device (1.04 U/mL to 100 U/mL) and 8 additional
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samples around the assay cut-off were assayed in duplicate with both Diastat™ Anti-CCP FCCP200 and Axis-Shield Anti-CCP FCCP600 assays. Results of the regression analysis of the 73 samples are summarized in the table below:
| n | Slope | Slope 95% CI | Intercept | Intercept 95% CI | r |
| --- | --- | --- | --- | --- | --- |
| 73 | 0.920 | 0.83 to 0.99 | 1.06 | 0.57 to 1.73 | 0.94 |
b. Matrix comparison:
Nineteen matched serum and plasma samples were collected in the following anticoagulant tubes: serum clot tube, serum separator tube (SST), Potassium EDTA plasma tube, Lithium Heparin plasma tube, Sodium Citrate plasma tube. Specimens spanned the Axis-Shield Anti-CCP (FCCP600) measurement range. In order to obtain samples with varying CCP concentrations covering the measurable range of the assay (1.04 U/mL – 300 U/mL), 15 of the 19 serum/plasma samples were spiked with a high anti-CCP positive sample and 4 samples were tested unmodified. In addition to these samples, 16 spiked-in serum/plasma matched specimens with anti-CCP concentrations at approximately 2.5 U/mL and 5.5 U/mL for the different tube type and anticoagulant were evaluated. Samples with results <LoD were excluded from the study analysis.
For all samples, Passing Bablok regression plots were generated by plotting the mean concentration observed from the control tube type (serum) versus the mean concentration for each test collection tube. The corresponding regression slopes and intercepts as well as correlation coefficients are summarized in the table below:
| Matrix Comparison | N | Slope (95% CI) | Intercept (95% CI) | Correlation (95% CI) |
| --- | --- | --- | --- | --- |
| Serum vs SST | 32 | 0.957 (0.94, 1.01) | 0.176 (-0.10, 0.28) | 0.996 (0.991, 0.998) |
| Serum vs EDTA | 33 | 0.928 (0.92, 1.10) | 1.23 (-0.15, 0.19) | 0.996 (0.991, 0.998) |
| Serum vs Lithium Heparin | 32 | 0.946 (0.92, 0.96) | 0.20 (0.13, 0.32) | 0.999 (0.998, 1.000) |
| Serum vs Sodium Citrate | 31 | 0.955 (0.94, 1.00) | 0.052 (-0.11, 0.14) | 0.998 (0.996, 0.999) |
3. Clinical studies:
a. Clinical Sensitivity:
A total of 229 frozen retrospective sera with clinical characterization were assayed. Diagnosis of RA was made according to the American College of Rheumatology’s (ACR) criteria. The results are summarized below:
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10
| Samples | Confirmed RA (n) | FCC 600 Positive | FCC600 Clinical Sensitivity | 95% CI |
| --- | --- | --- | --- | --- |
| Early RA | 43 | 27 | 63% | 50.55 to 78.44 |
| Established RA | 186 | 152 | 82% | 86.34 to 94.91 |
| All RA | 229 | 179 | 78% | 88.19 to 95.63 |
b. Clinical specificity:
Samples from non-RA diseased (n=135) and healthy asymptomatic (n=150) donors were tested. The results are summarized below:
Healthy asymptomatic 149/150 = 99.3% 95% CI = 85.4 to 94.4%
Non RA disease states 132/135 = 97.8% 95% CI = 83.8 to 93.7%
Non RA total 281/285 = 98.6% 95% CI = 91.8 to 96.9%
The non-RA specimens are categorized in the following table:
| Non-RA Disease Specimens | Total n | Positive n | Clinical specificity |
| --- | --- | --- | --- |
| Total | 135 | 3 | 97.8% |
| Inflammatory Polyarthritis | 41 | 1 | 97.6% |
| EBV IgG Positive | 18 | 1 | 94.4% |
| Hashimoto’s Thyroiditis | 17 | 0 | 100% |
| Sjögren’s Syndrome | 16 | 1 | 93.8% |
| Systemic Lupus Erythematosus | 16 | 0 | 100% |
| Vasculitis | 5 | 0 | 100% |
| Scleroderma | 5 | 0 | 100% |
| Osteoarthritis | 4 | 0 | 100% |
| Crohn’s Disease | 3 | 0 | 100% |
| Raynaud’s Phenomenon | 3 | 0 | 100% |
| Ulcerative Colitis | 2 | 0 | 100% |
| Psoriatic Arthritis | 2 | 0 | 100% |
| Reactive Arthritis | 1 | 0 | 100% |
| Ankylosing Spondylitis and Polymyositis | 2 | 0 | 100% |
c. Other clinical supportive data (when a. and b. are not applicable): Not applicable
4. Clinical cut-off:
Same as assay cut-off.
5. Expected values/Reference range:
Expected values in the normal population should be negative. Each laboratory should establish a reference range appropriate to their patient populations and clinical practice.
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
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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.