Patient samples were used in method comparison studies to evaluate the performance of the new device against a predicate device (ABX Pentra Calcium AS CP) and to assess matrix effects between serum and plasma.
ELITech Clinical Systems CALCIUM ARSENAZO is intended for the quantitative in vitro diagnostic determination of total calcium in human serum, plasma and urine using ELITech Clinical Systems Selectra Pro Series Analyzers. It is not intended for use in Point of Care settings. Calcium measurements are used in the diagnosis and treatment of parathyroid disease, a variety of bone diseases, chronic renal disease and tetany (intermittent muscular contractions or spasms).
Device Story
In vitro diagnostic reagent kit for quantitative total calcium determination in human serum, plasma, and urine; utilizes Arsenazo III colorimetric method. Input: patient serum, plasma, or urine samples. Process: Arsenazo III forms a blue complex with calcium in a neutral medium; color intensity measured via ELITech Clinical Systems Selectra Pro Series Analyzers. Output: quantitative calcium concentration (mg/dL). Used in clinical laboratories by professional staff; not for point-of-care. Healthcare providers use results to diagnose/treat parathyroid, bone, and renal diseases, and tetany. Benefits: provides accurate, automated calcium monitoring to support clinical decision-making.
Clinical Evidence
No clinical data. Performance was established via bench testing, including precision (total %CV 0.8-1.7%), linearity (R² > 0.99), and method comparison against the predicate (serum R²=0.986, urine R²=0.995). Interference testing followed CLSI EP07-A2 protocols.
Technological Characteristics
Mono-reagent system: 100 mmol/L MES buffer (pH 6.50), 200 μmol/L Arsenazo III. Colorimetric dye-binding principle. Quantitative measurement via Selectra ProM Analyzer. Traceable to NIST SRM 956c. Software performs automated calculations and 1:5 sample dilution.
Indications for Use
Indicated for the quantitative in vitro diagnostic determination of total calcium in human serum, plasma, and urine to aid in the diagnosis and treatment of parathyroid disease, bone diseases, chronic renal disease, and tetany. Not for Point of Care use.
Regulatory Classification
Identification
A calcium test system is a device intended to measure the total calcium level in serum. Calcium measurements are used in the diagnosis and treatment of parathyroid disease, a variety of bone diseases, chronic renal disease and tetany (intermittent muscular contractions or spasms).
Predicate Devices
Horiba ABX PENTRA Calcium (k123171)
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY ASSAY ONLY TEMPLATE
A. 510(k) Number:
k151113
B. Purpose for Submission:
New Device
C. Measurand:
Calcium
D. Type of Test:
Quantitative, colorimetric chemistry test
E. Applicant:
ELITech Clinical Systems
F. Proprietary and Established Names:
ELITech Clinical Systems CALCIUM ARSENAZO
G. Regulatory Information:
| Product Code | Classification | Regulation Section | Panel |
| --- | --- | --- | --- |
| CJY | Class II | 21 CFR § 862.1145 Calcium Test System | Clinical Chemistry (75) |
H. Intended Use:
1. Intended use(s):
See indications for use below
2. Indication(s) for use:
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ELITech Clinical Systems CALCIUM ARSENAZO is intended for the quantitative in vitro diagnostic determination of total calcium in human serum, plasma and urine using ELITech Clinical Systems Selectra Pro Series Analyzers. It is not intended for use in Point of Care settings.
Calcium measurements are used in the diagnosis and treatment of parathyroid disease, a variety of bone diseases, chronic renal disease and tetany (intermittent muscular contractions or spasms).
3. Special conditions for use statement(s):
For in vitro diagnostic use only
For prescription use only
4. Special instrument requirements:
Selectra ProM Analyzer
I. Device Description:
The ELITech Clinical Systems Calcium Arsenazo kit reagents are one reagent systems. The kit consists of a mono-reagent R whose composition is: 100 mmol/L MES buffer (pH 6.50), 200 μmol/L Arsenazo III.
J. Substantial Equivalence Information:
1. Predicate device name(s):
Horiba ABX PENTRA Calcium
2. Predicate 510(k) number(s):
k123171
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3. Comparison with predicate:
| Similarities and Differences | | |
| --- | --- | --- |
| Item | Candidate Device
Calcium Arsenazo | Predicate Device
ABX PENTRA Calcium |
| Intended Use | Same | The reagent is for the quantitative determination of calcium concentration. |
| Measurand | Same | Calcium |
| Assay Method | Same | Colormetric |
| Measuring Range | Serum, Plasma : 5.00 – 15.00 mg/dL
Urine: 1.50 – 18.00 mg/dL | Serum, Plasma : 4.00 – 18.05 mg/dL
Urine: 0.64 – 18.05 mg/dL |
| Matrix | Same | Serum, plasma and urine |
| Instrument platform | Selectra Pro M | ABX Pentra 400 |
K. Standard/Guidance Document Referenced (if applicable):
- CLSI Guideline, EP5-A2 Evaluation of Precision Performance of Clinical Chemistry Devices – Second Edition
- CLSI Guideline, EP9-A2 Method Comparison and Bias Estimation Using Patient Samples – Second Edition
- CLSI Guideline, EP6-A Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical Approach; Approved Guideline
- CLSI Guideline, EP7-A2 Interference Testing in Clinical Chemistry; Approved Guideline – Second Edition
- CLSI Guideline, EP17-A Protocols for Determination of Limits of Detection and Limits of Quantitation; Approved Guideline
- FR EN 13640:2002 Stability Testing of In Vitro Diagnostic Reagents
- CLSI Guideline, EP9-A2 Method Comparison and Bias estimation Using Patient Samples; Approved Guideline
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L. Test Principle:
The assay utilizes a dye binding procedure in which calcium forms a blue-purple complex with Arsenazo under acidic condition. Calcium reacts with Arsenazo in an acidic solution to form a blue-purple colored complex. The intensity of color produced is directly proportional to the calcium concentration in the sample.
M. Performance Characteristics (if/when applicable):
1. Analytical performance:
a. Precision/Reproducibility:
Within run and total precision studies were performed according to CLSI EP5. Studies were performed by conducting two runs per day, two measures per run, for 3 levels of serum and urine samples, on 2 Selectra ProM instruments for 20 operating days. Results are presented in the tables below.
| | N | Sample Mean (mg/dL) | Within Run %CV | Total %CV |
| --- | --- | --- | --- | --- |
| Serum (mg/dL) | 80 | 8.28 | 1.1 | 1.7 |
| | 80 | 10.32 | 0.5 | 1.4 |
| | 80 | 12.96 | 0.5 | 1.0 |
| | N | Sample Mean (mg/dL) | Within Run %CV | Total %CV |
| --- | --- | --- | --- | --- |
| Urine (mg/dL) | 80 | 4.53 | 1.3 | 1.8 |
| | 80 | 10.89 | 0.5 | 1.2 |
| | 80 | 17.51 | 0.3 | 0.8 |
b. Linearity/assay reportable range:
Serum:
Linearity was evaluated according to CLSI EP6-A by comparing observed versus expected values for 11 equally-spaced serum samples. Samples were prepared from high (15.35 mg/dL) and low (4.94 mg/dL) analyte concentration serum pools. Each sample was evaluated in triplicate on the Selectra ProM instrument.
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Urine:
Linearity was evaluated by mixing a high (18.60 mg/dL) and low (1.45 mg/dL) urine concentrations to obtain 11 levels. Each sample was evaluated in triplicate on the Selectra ProM instrument.
Linear regression results from these studies are found in the table below:
| | Slope | Intercept | R² | Standard Error | Concentration Ranges tested |
| --- | --- | --- | --- | --- | --- |
| Serum | 1.0173 | -0.2048 | 0.9976 | 0.19 | 4.94 to 15.35 mg/dL |
| Urine | 1.0003 | -0.1174 | 0.9983 | 0.25 | 1.45 to 18.60 mg/dL |
The linearity supports the sponsor’s claimed reportable range of 5.00 – 15.00 mg/dL for serum and 1.50 – 18.00 mg/dL for urine.
Automatic dilution of 1: 5 by the instrument is available for sample that has concentration greater than the measuring range.
c. Traceability, Stability, Expected values (controls, calibrators, or methods):
ELITech Clinical Systems ELICAL 2 calibrator was cleared under k132399.
Traceability for calcium is to NIST SRM 956c materials.
d. Detection limit:
Limit of Blank (LoB), Limit of detection (LoD), and limit of quantification (LoQ) were determined according to CLSI EP17-A with the Selectra ProM. LoB was determined using a blank sample tested over multi-runs over multi-days. For LoD and LoQ, testing using serum samples and urine samples was conducted over multiple days, with one run across two instruments, and with two reagent lots. The LoB, LoD, and LoQ results are as follows:
| | Serum (mg/dL) | Urine (mg/dL) |
| --- | --- | --- |
| LoB | 0.04 | 0.09 |
| LoD | 0.04 | 0.15 |
| LoQ | 5.00 | 1.50 |
The sponsor claimed the following measuring ranges: 5.00 – 15.00 mg/dL for serum and 1.50 – 18.00 mg/dL for urine
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# e. Analytical specificity:
Interferences due to unconjugated bilirubin, conjugated bilirubin, triglycerides, ascorbic acid, acetylsalicylic acid and acetaminophen were investigated following the recommended sample levels in CLSI EP07-A2 protocol. Two serum sample pools at two different calcium concentrations (8.0 and $12.0\mathrm{mg / dL}$ ) and two urine pools at two different calcium concentrations (4.0 and $16.0\mathrm{mg / dL}$ ) were used for the study. Sponsor defines non-significant interference as bias of $< 10\%$ between the spiked and unspiked samples. The results of the highest concentration tested without significant interference are summarized in the table below.
Serum:
| Interferent | Test range |
| --- | --- |
| Unconjugated bilirubin | up to 30.0 mg/dL |
| Conjugated bilirubin | up to 29.5 mg/dL |
| Hemoglobin | up to 500 mg/dL |
| Triglycerides | up to 1726 mg/dL |
| Magnesium | up to 12.0 mg/dL |
| Ascorbic acid | up to 20 mg/dL |
| Acetylsalicylic Acid | up to 200 mg/dL |
| Acetaminophen | up to 30 mg/dL |
Urine:
| Interferent | Test range |
| --- | --- |
| Conjugated bilirubin | up to 29.5 mg/dL |
| Hemoglobin | up to 500 mg/dL |
| Ascorbic acid | up to 10 mg/dL |
| Urea | up to 5000 mg/dL |
| Uric Acid | up to 100 mg/dL |
| Magnesium | up to 10 mg/dL |
| pH | 2.5 to 6.0 |
Sponsor includes the following statement and references in the labeling:
"Other compounds may interfere. Users should refer to the following literature references"
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1. Berth, M. & Delanghe, J. Protein precipitation as a possible important pitfall in the clinical chemistry analysis of blood samples containing monoclonal immunoglobulins: 2 case reports and a review of literature, Acta Clin Belg., (2004), 59, 263.
2. Young, D. S., Effects of preanalytical variables on clinical laboratory tests, 2nd Ed., AACC Press, (1997).
3. Young, D. S., Effects of drugs on clinical laboratory tests, 4th Ed., AACC Press, (1995).
f. Assay cut-off:
Not applicable
2. Comparison studies:
a. Method comparison with predicate device:
**Serum Method Comparison:**
Two sets of method comparison studies, serum and urine, were performed according to the CLSI EP9-A2 guideline. The serum study was completed with 106 human serum samples (5 spiked, 5 diluted and 96 unaltered samples) spanning the linear range of the assay. The urine study was completed with 52 native samples spanning the linear range of the assay. Each serum and urine sample was analyzed in singlet using the Selectra ProM analyzer (test method) and in duplicate on the predicate ABX Pentra Calcium AS CP reagent on an ABX Pentra analyzer. Comparison of individual test values versus mean predicate values yielded the following results:
| Matrix | Slope | Intercept | R² | Conc. Range Tested |
| --- | --- | --- | --- | --- |
| Serum | 0.949 | 0.41 | 0.986 | 5.07 – 14.79 mg/dL |
| Urine | 0.936 | 0.20 | 0.995 | 1.50 – 17.14 mg/dL |
b. Matrix comparison
73 paired serum and plasma patient specimens (in lithium heparin samples, ranging from 5.19 to 14.38 mg/dL), were tested on ELITech Clinical Systems Selectra ProM Analyzer according to CLSI protocol EP09-A2. 10% of the samples were either diluted (5) or spiked (2) to achieve the full range for comparison.
Regression analysis: y = 0.976x + 0.26, R² = 0.993
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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 provided in the labeling from literature as follows:
Serum/ Plasma⁽¹⁾:
8.6 - 10.3 mg/dL
Urine⁽²⁾ (for a urinary volume of 1.5 L per day):
100 – 300 mg/24h*
1. Wu, A.H.B., Tietz Clinical Guide to Laboratory Test, 4th Ed., (W.B. Saunders Company), (2006), 66.
2. Endres, D.B., Rude, R.K., Disorders of Bone, Tietz Fundamentals of Clinical Chemistry, 6th Ed., Burtis, C.A., Ashwood, E.R., Bruns, D.E. (Saunders), (2008), 711.
Each laboratory should establish and maintain its own reference values. The values given are used as guidelines only.
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.
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.