Audit® MicroLQ™ Spinal Fluid Control is a quality control material intended for monitoring the precision of laboratory testing procedures. When used for quality control purposes, it is recommended that each laboratory establish its own means and acceptable ranges and use the values provided only as guides. The Audit® MicroLQ™ Spinal Fluid is for In Vitro Diagnostic use only.
Device Story
Audit® MicroLQ™ Spinal Fluid is a human-based, liquid quality control material. It contains 10 analytes: Chloride, Glucose, IgA, IgG, IgM, Lactate, Lactate Dehydrogenase (LD), Microalbumin, Microprotein, and Sodium. Used in clinical laboratories to monitor the precision of testing procedures; helps verify calibration of diagnostic assays. Laboratory personnel use the material to establish internal means and acceptable ranges for test performance. Provides a guide for quality assurance in diagnostic testing; benefits patients by ensuring the accuracy and reliability of clinical laboratory results.
Clinical Evidence
No clinical data. Performance is supported by stability studies confirming a two-year shelf life (unopened at 2-8°C) and 30-day open-vial stability (at 2-8°C).
Indicated for use as an in vitro diagnostic quality control material to monitor the precision of laboratory testing procedures for specific analytes in spinal fluid. No specific patient population is targeted as this is a laboratory control product.
Regulatory Classification
Identification
A quality control material (assayed and unassayed) for clinical chemistry is a device intended for medical purposes for use in a test system to estimate test precision and to detect systematic analytical deviations that may arise from reagent or analytical instrument variation. A quality control material (assayed and unassayed) may be used for proficiency testing in interlaboratory surveys. This generic type of device includes controls (assayed and unassayed) for blood gases, electrolytes, enzymes, multianalytes (all kinds), single (specified) analytes, or urinalysis controls.
Predicate Devices
Audit™ MicroCV™ General Chemistry Linearity Set (k101216)
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY ASSAY ONLY TEMPLATE
A. 510(k) Number:
k112742
B. Purpose for Submission:
New device
C. Measurand:
Quality control materials for Chloride, Glucose, Immunoglobulin A (IgA), Immunoglobulin G (IgG), Immunoglobulin M (IgM), Lactate, Lactate Dehydrogenase (LD), Microalbumin, Microprotein, and Sodium.
D. Type of Test:
Not applicable
E. Applicant:
Aalto Scientific, Ltd.
F. Proprietary and Established Names:
Audit® MicroLQ™ Spinal Fluid Control
G. Regulatory Information:
| Product Code | Classification | Regulation Section | Panel |
| --- | --- | --- | --- |
| JJY | Class I, reserved | 21 CFR§862.1660 | 75 Clinical Chemistry |
H. Intended Use:
1. Intended use(s):
See indications for use below.
2. Indication(s) for use:
Audit® MicroLQ™ Spinal Fluid Control is a quality control material intended for monitoring the precision of laboratory testing procedures.
When used for quality control purposes, it is recommended that each laboratory
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establish its own means and acceptable ranges and use the values provided only as guides.
The Audit® MicroLQ™ Spinal Fluid is for In Vitro Diagnostic use only.
3. Special conditions for use statement(s):
The Audit® MicroLQ™ Spinal Fluid Control should not be used for calibration or standardization of the Chloride, Glucose, Immunoglobulin A (IgA), Immunoglobulin G (IgG), Immunoglobulin M (IgM), Lactate, Lactate Dehydrogenase (LD), Microalbumin, Microprotein, and Sodium.
4. Special instrument requirements:
Performance was established on the Beckman Immage 800 and Roche P-Modular analyzers.
I. Device Description:
The Audit® MicroLQ™ Spinal Fluid Control is a human based, liquid set of QC material. Each level of the set contains Chloride, Glucose, Immunoglobulin A (IgA), Immunoglobulin G (IgG), Immunoglobulin M (IgM), Lactate, Lactate Dehydrogenase (LD), Microalbumin, Microprotein, and Sodium analytes. It is used to confirm the proper calibration of Chloride, Glucose, Immunoglobulin A (IgA), Immunoglobulin G (IgG), Immunoglobulin M (IgM), Lactate, Lactate Dehydrogenase (LD), Microalbumin, Microprotein, and Sodium
All human source materials used to produce this product have been tested for HbsAg, anti-HCV, HIV-1 and HIV-2 and found to be non-reactive by FDA cleared/approved tests.
J. Substantial Equivalence Information:
1. Predicate device name(s):
Audit™ MicroCV™ General Chemistry Linearity Set
2. Predicate K number(s):
k101216
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3. Comparison with predicate:
| Similarities | | |
| --- | --- | --- |
| Item | New Device | Predicate |
| Characteristics | Audit® MicroLQ™ Spinal Fluid Control (k112742) | Audit™ MicroCV™ General Chemistry Linearity Set (k101216) |
| Intended Use | The Audit® MicroLQ™ Spinal Fluid Control is a quality control material intended for monitoring the precision of laboratory testing procedures. | Same |
| Analyzers | P-Modular and Beckman Immage 800 | Beckman Immage 800 |
| Format | Liquid | Same |
| Preservatives | Sodium azide | Same |
| Stability | 2-8°C until expiration date | Same |
| Matrix | Human serum | Same |
| Differences | | |
| --- | --- | --- |
| Item | New Device | Predicate |
| Characteristics | Audit® MicroLQ™ Spinal Fluid Control (k112742) | Audit™ MicroCV™ General Chemistry Linearity Set (k101216) |
| Type of Analytes | Chloride, Glucose, Immunoglobulin A (IgA), Immunoglobulin G (IgG), Immunoglobulin M (IgM), Lactate, Lactate Dehydrogenase (LD), Microalbumin, Microprotein, and Sodium. | Alpha-1-Antitrypsin, Complement C3, Complement C4, Immunoglobulin G, Immunoglobulin A, Immunoglobulin M and Transferrin. |
| Number of Analytes | 10 | 7 |
| Levels per set | 2 | 5 |
| Contents | 6 x 3 ml | 5 x 5 ml |
| Open vial stability | 30 days at 2 to 8°C | 24 hours at 2 to 8°C |
K. Standard/Guidance Document Referenced (if applicable):
None were referenced
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L. Test Principle:
Not applicable.
M. Performance Characteristics (if/when applicable):
1. Analytical performance:
a. Precision/Reproducibility:
Not applicable
b. Linearity/assay reportable range:
Not applicable
c. Traceability, Stability, Expected values (controls, calibrators, or methods):
Traceability
All components of the The Audit® MicroLQ™ Spinal Fluid Control are obtained from a commercial vendor and inspected in-house.
Stability
Shelf life and open vial stability:
Real-time testing at 2-8°C was conducted and is still on-going. The stability study protocol and acceptance criteria have been reviewed and found to be acceptable. The current accelerated stability test results and ongoing real time stability studies support two year shelf life stability and 30 days open vial stability when stored at 2-8°C.
Value Assignment
The control ranges as determined using Beckman Immage 800 analyzer for Immunoglobulin A, Immunoglobulin G, and Immunoglobulin M and Roche Modular-P analyzer for Chloride, Glucose, Lactate, Lactate Dehydrogenase, Microalbumin, Microprotein, and Sodium are provided in the assigned value sheet for each lot release. Values were assigned from replicate measurements as ±15% CV around the mean. Representative values are shown in the table below.
In the labeling the sponsor recommends that each end user laboratory establish its own means and acceptable ranges and use the assigned value sheet as guidance only.
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| Analyte | Reagent | Analyzer | Units | Level 1 Mean | Level 2 Mean |
| --- | --- | --- | --- | --- | --- |
| Chloride | Roche | P-Modular | mEq/L | 124.8 | 76.8 |
| Glucose | Roche | P-Modular | mg/dL | 64 | 34 |
| Lactate | Roche | P-Modular | mmol/L | 2.4 | 36.0 |
| LD | Roche | P-Modular | IU/L | 22 | 50 |
| Microalbumin, | Pointe | P-Modular | mg/dL | 17.1 | 26.4 |
| Microprotein | Pointe | P-Modular | mg/dL | 30.9 | 80.6 |
| Sodium | Roche | P-Modular | mEq/L | 156.3 | 84.7 |
| Immunoglobulin A | Beckman | Immage 800 | mg/dL | 1.20 | 2.85 |
| Immunoglobulin G | Beckman | Immage 800 | mg/dL | 4.77 | 42.74 |
| Immunoglobulin M | Beckman | Immage 800 | mg/dL | 0.800 | 1.689 |
d. Detection limit:
Not applicable
e. Analytical specificity:
Not applicable
f. Assay cut-off:
Not applicable
2. Comparison studies:
a. Method comparison with predicate device:
Not applicable
b. Matrix comparison:
Not applicable
3. Clinical studies:
a. Clinical Sensitivity:
Not applicable
b. Clinical specificity:
Not applicable
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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:
The expected values are provided in the labeling for each specific lot.
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.
