K040467 · Clinical Data, Inc. · KNK · Mar 8, 2004 · Clinical Chemistry
Device Facts
Record ID
K040467
Device Name
VITALAB URIC ACID REAGENT
Applicant
Clinical Data, Inc.
Product Code
KNK · Clinical Chemistry
Decision Date
Mar 8, 2004
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 862.1775
Device Class
Class 1
Attributes
3rd-Party Reviewed
Indications for Use
Vitalab Uric Acid Reagent is intended for use with the Vitalab Selectra Analyzer as a system for the quantitative determination of uric acid in serum and plasma. Uric acid results may be used for the diagnosis and treatment of numerous renal and metabolic disorders, including renal failure, gout, leukemia, psoriasis, starvation or other wasting conditions, and of patients receiving cytotoxic drugs.
Device Story
Vitalab Uric Acid Reagent is a two-part enzymatic reagent kit used with the Vitalab Selectra Analyzer. It measures uric acid in serum and plasma samples via enzymatic oxidation using uricase linked to a Trinder indicator reaction (TOOS and 4-aminoantipyrine). The analyzer performs the assay, providing quantitative results to clinicians. Used in clinical laboratory settings to assist in diagnosing and monitoring renal and metabolic disorders, including gout and conditions requiring cytotoxic therapy. The device provides objective biochemical data to support clinical decision-making regarding patient metabolic status and treatment efficacy.
Clinical Evidence
Bench testing only. Precision evaluated over 10 days (n=60 per sample) with total CVs 1.2-1.6%. Linearity confirmed from 0.1 to 25.0 mg/dL (max residual 0.1 mg/dL). Method comparison (n=120) against predicate showed strong correlation (slope 0.9954, intercept -0.05 mg/dL). Interference studies assessed ascorbic acid, bilirubin, hemoglobin, and triglycerides; specific suppression effects noted for bilirubin and high triglycerides.
Technological Characteristics
Two-part liquid reagent; enzymatic colorimetric assay (uricase/Trinder indicator: TOOS and 4-aminoantipyrine). Designed for use on the Vitalab Selectra Analyzer. Linear range 0.1-25 mg/dL. Analytical method follows standard enzymatic oxidation principles.
Indications for Use
Indicated for the quantitative determination of uric acid in serum and plasma for the diagnosis and treatment of renal and metabolic disorders (e.g., renal failure, gout, leukemia, psoriasis, starvation, wasting conditions) and for patients receiving cytotoxic drugs.
Regulatory Classification
Identification
A uric acid test system is a device intended to measure uric acid in serum, plasma, and urine. Measurements obtained by this device are used in the diagnosis and treatment of numerous renal and metabolic disorders, including renal failure, gout, leukemia, psoriasis, starvation or other wasting conditions, and of patients receiving cytotoxic drugs.
Predicate Devices
Roche Uric Acid Plus Reagent Kit (k873363)
Submission Summary (Full Text)
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Clinical Data - Vitalab Uric Acid Reagent Kit
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510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION
DECISION SUMMARY
DEVICE ONLY
A. 510(k) Number:
k040467
B. Analyte:
Uric acid
C. Type of Test:
Quantitative
D. Applicant:
Clinical Data, Inc
E. Proprietary and Established Names:
Vitalab Uric Acid Reagent
F. Regulatory Information:
1. Regulation section:
21 CFR 862.1775
2. Classification:
Class I
3. Product Code:
KNK
4. Panel:
75
G. Intended Use:
1. Indication(s) for use:
Vitalab Uric Acid Reagent is intended for use with the Vitalab Selectra Analyzer as a system for the quantitative determination of uric acid in serum and plasma. Uric acid results may be used for the diagnosis and treatment of numerous renal and metabolic disorders, including renal failure, gout, leukemia, psoriasis, starvation or other wasting conditions, and of patients receiving cytotoxic drugs.
2. Special condition for use statement(s):
Prescription use
3. Special instrument Requirements:
The Vitalab Uric Acid Reagent is intended to be used with the Vitalab Selectra E Chemistry Analyzer.
H. Device Description:
The Vitalab Uric Acid Reagent and the Vitalab Selectra Analyzer are used as a system for the quantitative analysis of uric acid in serum and plasma. The Vitalab Uric Acid Reagent is intended to
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be calibrated with the Vitalab Serum Calibrator and is supplied as a two liquid-stable component. The sample may be added to the first reagent component allowing for sample blank reading before the addition of the start reagent. Uric acid concentrations are calculated from the change in absorbance at 546 nm after the completion of the reaction.
I. Substantial Equivalence Information:
1. Predicate device name(s): Roche Uric Acid Plus Reagent Kit, product 1661850
2. Predicate K number(s): k873363
3. Comparison with predicate
| Similarities | | |
| --- | --- | --- |
| Item | Vitalab Uric Acid | Roche Uric Acid Plus |
| Intended Use | Similar | Similar |
| Type of test | Quantitative | Quantitative |
| Principle | Enzymatic oxidation by uricase with TOOS/peroxidase indicator system | Similar |
| Measurement | Enzymatic endpoint at approximately 550 nm (546 nm) | Similar |
| Differences | | |
| Item | Vitalab Uric Acid | Roche Uric Acid Plus |
| Sample type | Serum, plasma | Serum, plasma and urine |
| Analytical range | 0.1 to 25 mg/dL | 0.2 to 25 mg/dL |
J. Standard/Guidance Document Referenced (if applicable): NCCLS EP3-T, NCCLS EP7-P
K. Test Principle:
The Vitalab Uric Acid Reagent determines uric acid through enzymatic oxidation by uricase linked to a Trinder indicator reaction utilizing N-ethyl-N-(hydroxy-3-sulfopropyl)-toluidine (TOOS) and 4-aminoantipyrine. The resulting increase in absorbance at 546 nm is proportional to the uric acid concentration of the sample.
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# L. Performance Characteristics (if/when applicable):
## 1. Analytical performance:
### a. Precision/Reproducibility:
Precision is demonstrated by the replicate assay of commercially available control serum. Each sample is assayed in triplicate twice per day over 10 days using the Vitalab Uric Acid Reagent on a Selectra E Analyzer. Precision statistics, calculated analogous to the method described in NCCLS Guideline EP3-T, are shown below.
Precision of Uric Acid Recoveries in mg/dL
| Sample | n | Within Run | | Total | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | mean | 1SD | %CV | 1SD | %CV |
| Serum 1 | 60 | 2.5 | 0.02 | 0.7% | 0.04 | 1.6% |
| Serum 2 | 60 | 6.8 | 0.04 | 0.7% | 0.09 | 1.4% |
| Serum 3 | 60 | 11.1 | 0.07 | 0.6% | 0.13 | 1.2% |
### b. Linearity/assay reportable range:
The linear range of the assay is from 0.1 to 25.0 mg/dL. Ten standards ranging from 0.0 to 30.0 mg/dL are prepared by dissolving uric acid in an ammonium hydroxide matrix to span the linear range of the application. These standards are assayed on a Vitalab Selectra in ascending order over four independently calibrated analytical runs. Standard recoveries are compared to standard concentrations by least squares linear regression through the origin. A residual statistic is calculated for each standard as the difference between the mean recovery and its predicted value from the regression statistics.
The maximum residual is 0.1 mg/dL uric acid indicating linearity throughout the linear range.
### c. Traceability (controls, calibrators, or method):
Calibrator set points are traceable to NIST SRM 913.
### d. Detection limit:
Normal saline is assayed thirty times in a single analytical run. The detection limit is calculated as the mean plus two standard deviations of the results. The observed mean and standard deviation are both 0.0 mg/dL. The detection limit of the assay is 0.1 mg/dL uric acid, which is the round-off error of the assay.
### e. Analytical specificity:
Potential interference from ascorbic acid, icterus (bilirubin), hemolysis (hemoglobin) and lipemia (triglycerides) is determined in four separate studies. In each study, a serum pool with approximately normal uric acid levels is prepared from individual patient specimens and is divided into two aliquots. One aliquot is spiked with the potential interfering substance. The other aliquot is diluted with normal saline, if necessary, to mimic the dilution the spiked pool. These aliquots are then blended to prepare test pools with the interferant concentrations listed below. The red blood cell (RBC) hemolysate, which is used to spike the high pool for the hemolysis test, is prepared from at least five patient specimens according to the Osmotic Shock Procedure described in NCCLS Document EP7-P, Volume 6 No.13.
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| Interfering Substance | Levels tested |
| --- | --- |
| Ascorbic acid | 0.6, 1.2, 1.8, 2.4, 3.0 mg/dL |
| Ditaurobilirubin | 8, 16, 24, 32, 40 mg/dL (as bilirubin) |
| RBC hemolysate | 40, 80, 120, 160, 200 mg/dL (as hemoglobin) |
| Intralipid, 20% | 400, 800, 1,200, 1,600 and 2,000 mg/dL (as triglycerides) |
Each set of original and spiked pools are assayed in an alternating order 9 and 6 times respectively in a single analytical run. Differences in recoveries between the original and spiked pools are reported with t-statistics. Statistically significant differences greater than 0.25 mg/dL are reported on the package insert.
Ascorbic acid has no effect on recoveries. Bilirubin at 8 and 24 mg/dL suppresses recoveries approximately 0.4 and 1.3 mg/dL respectively. Red blood cell hemolysate added to a hemoglobin concentration of 160 mg/dL decreases recoveries by 0.3 mg/dL. The addition of Intralipid to 800 mg/dL triglycerides does not affect uric acid results. However, at 1,200 and 2,000 mg/dL triglycerides, uric acid results are suppressed by 0.7 and 3.4 mg/dL respectively.
f. Assay cut-off:
Not applicable
2. Comparison studies:
a. Method comparison with predicate device:
Random specimens from individual anonymous adult patients are collected from local clinical labs. These unaltered samples are supplemented with additional specimens with elevated uric acid levels to yield a total of 60 serum and 60 heparinized plasma specimens. These specimens are randomly assorted into groups of 15 serum and 15 plasma specimens each. One group of serum and plasma specimens are assayed in each of four runs using the Vitalab Selectra Uric Acid Application and the Roche Uric Acid Plus Reagent on the Hitachi 704 after calibrating each reagent with its required calibrator.
The serum results, plasma results and the combined results for both specimen types are each compared by Deming regression assuming equal variances between methods. Regression statistics are given below.
Serum Correlation
| | Value | 95% Confidence Interval |
| --- | --- | --- |
| Intercept | -0.08 mg/dL | -0.18 to 0.018 mg/dL |
| Slope | 1.0064 | 0.990 to 1.022 |
| s_{y,x} | 0.07 mg/dL | |
| n | 60 | |
| range | 3.6 to 10.2 mg/dL | |
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Plasma Correlation
| | Value | 95% Confidence Interval |
| --- | --- | --- |
| Intercept | -0.01 mg/dL | -0.10 to 0.07 mg/dL |
| Slope | 0.9840 | 0.969 to 0.999 |
| s_{y,x} | 0.09 mg/dL | |
| n | 60 | |
| range | 2.1 to 10.6 mg/dL | |
Combined Correlation
| | Value | 95% Confidence Interval |
| --- | --- | --- |
| Intercept | -0.05 mg/dL | -0.11 to 0.02 mg/dL |
| Slope | 0.9954 | 0.984 to 1.007 |
| s_{y,x} | 0.08 mg/dL | |
| n | 120 | |
| range | 2.1 to 10.6 mg/dL | |
Where x = Competitive Reagent Results
y = Selectra Results
b. Matrix comparison:
Serum and plasma specimens are individually compared to the predicate method by Deming regression. The substantial overlap in the 95% confidence intervals of the regression statistics indicates equivalency between the two matrices.
3. Clinical studies:
a. Clinical sensitivity:
Not applicable
b. Clinical specificity:
Not applicable
c. Other clinical supportive data (when a and b are not applicable):
Not applicable
4. Clinical cut-off:
Not applicable
5. Expected values/Reference range:
Reference ranges are established in the literature and quoted from Tietz Textbook of Clinical Chemistry, Third Edition, Burtis and Ashwood, editors, W. B. Saunders Company (1999). The expected values are 3.5 to 7.2 mg/dL for males and 2.6 to 6.0 mg/dL for females.
M. Conclusion:
Based upon a Third Party Review of the information provided in this 510(k), this device is substantially equivalent to devices regulated by 862.1775, acid, uric, uricase (colorimetric); Class I.
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.