FDA 510(k) Database – Cleared Devices, Predicates, Product Codes & Real-World Regulatory Intelligence

This topic is part of the SG Systems Global medical device regulatory, quality systems & document-control glossary.

Updated December 2025 • 510(k) Clearance, Substantial Equivalence, Predicate Devices, Product Codes, 21 CFR Part 807, 21 CFR Part 820, QMSR, ISO 13485, ISO 14971, Design History File (DHF), Device Master Record (DMR), Device History Record (DHR), Document Control, Data Integrity

The FDA 510(k) Database is the public, searchable catalogue of medical devices cleared through the US 510(k) premarket notification pathway. It’s where you validate whether a device has been cleared, identify likely predicates, map the right product code and classification regulation, and pull the basic “public story” of a clearance: who submitted it, what the device is called, when FDA made the decision, and what documents were released.

Used properly, the 510(k) Database is regulatory intelligence: a fast way to reduce uncertainty, challenge assumptions, and anchor your strategy in what FDA has actually cleared before. Used poorly, it becomes cargo-cult compliance: teams cherry-pick a “similar looking” entry, assume “cleared = approved”, and build a product plan on thin ice.

“If your predicate research is ‘whatever came up first in the 510(k) database’, you don’t have a regulatory strategy — you have a browser history.”

1) What Is the FDA 510(k) Database?

The FDA’s 510(k) Database is the public-facing index of 510(k) submissions that FDA has processed and cleared (or otherwise decided). In practice, it’s used for four high-value tasks:

• Clearance verification: “Is this device actually cleared?” and “When?”
• Regulatory mapping: “What panel, product code, and regulation number does FDA associate with this device type?”
• Predicate scouting: “What existing cleared devices look closest to our intended use and technology?”
• Document capture: “Can we pull releasable summaries/letters to understand the public basis of clearance?”

FDA’s public ecosystem includes both the 510(k) search interface and related databases like the Product Classification database (product codes, regulation numbers, class, review divisions) and the Predicate Devices list (links between cleared submissions and predicate references). In other words: the “510(k) Database” is not one page — it’s a connected set of lookup tools that, together, tell you how FDA sees a device category.

2) 510(k) Basics: Clearance, Not “Approval”

The legal and practical point that repeatedly trips teams up:

• A 510(k) is a Premarket Notification, not a Premarket Approval. The outcome is typically clearance based on substantial equivalence to a predicate device.
• Clearance does not mean “FDA approved”. Even FDA’s regulations explicitly warn that an FDA determination of substantial equivalence is not an “official approval” of the device.
• You generally cannot market the device until you receive an order (clearance) letter. “We submitted” is not “we can ship.”

That distinction matters because teams routinely over-interpret a database entry. A cleared device can still be poorly designed, poorly manufactured, or poorly supported — those failures show up later as complaints, recalls, or enforcement actions. The database tells you what FDA cleared at a point in time, not whether your organization has a robust QMS capable of delivering a consistent device afterwards.

Practically, the “clearance vs approval” distinction should be hard-baked into labeling, marketing claims review, sales training, and your QMS controls. If your commercial team talks like a 510(k) is an FDA endorsement, you’re creating avoidable regulatory risk and credibility damage.

3) What You Actually Get From a 510(k) Record

A 510(k) database result typically gives you structured metadata plus, in many cases, downloadable public documents. The specific data fields can vary by record age and what FDA has released, but the core elements commonly include:

• 510(k) number (the “K-number”): the unique identifier used to reference the submission.
• Applicant / submitter and contact information (historical; may not match current owner).
• Device name (often a generic or legacy naming style, not always marketing brand).
• Decision date and decision (e.g., cleared / NSE / withdrawn, etc.).
• Advisory committee / panel assignment.
• Product code and often the associated regulation number.
• Submission type indicators and other flags that may appear (e.g., third party review, expedited review, combination product).
• Attachments where available: 510(k) summary or statement, decision letter, and sometimes decision memo or supporting PDFs.

Two practical implications:

• Older records can be sparse. If you’re relying on older entries, expect missing documents, incomplete fields, and less consistent naming.
• “Released” is not “complete”. Many details in a full 510(k) file are not publicly accessible, and you should plan accordingly.

So treat database outputs as “public footprint” evidence — useful and fast, but not the entire regulatory record.

4) How to Search Like You Mean It

Most teams use the 510(k) Database in the most naive way possible: type a brand name, skim the first result, stop thinking. If you want high-quality answers, use a structured search approach:

• If you have a K-number, use it. It’s the fastest route to the exact record.
• If you don’t, use product code + one distinctive device term. Product codes reduce noise massively.
• Use generic device words, not branding. FDA’s naming in the database often tracks generic categories.
• Try variants and synonyms. “Catheter” vs “cannula”, “software” vs “algorithm”, “test” vs “assay”, etc.
• Search by panel when the market is crowded. If the device family is broad, the panel filter can keep you out of irrelevant device types.
• Download bulk files when you need scale. When you’re building a predicate library, ad hoc clicking is slow and inconsistent. Use bulk downloads or structured extraction as inputs to a controlled library.

One more reality check: the database is a starting point, not the finish line. Your job is not to “find a similar device.” Your job is to prove that your device’s intended use and technological characteristics fit a defensible regulatory path and that your evidence package meets the bar.

5) Predicates: The Database Can Help, But It Won’t Do Your Thinking

The 510(k) pathway is built on substantial equivalence. That means predicate selection is not a trivia contest — it’s the spine of your regulatory logic.

The database helps you find candidate predicates. It does not tell you whether they are defensible. A disciplined predicate workflow looks like this:

• Start from intended use. If the intended use doesn’t align, you’re already in trouble. Don’t force-fit it.
• Compare technological characteristics. Same general technology? Same key mechanisms? Same energy delivery? Same interfaces?
• Identify what’s “new”. Any difference that raises new questions of safety/effectiveness triggers evidence requirements — or pushes you toward a different pathway.
• Map claims to evidence. If your planned labeling claims are stronger than what predicates support, expect additional testing or a strategy rethink.

For deeper predicate mapping, FDA’s Predicate Devices database can show links between submissions and predicates. It’s useful, but it comes with sharp edges: search behavior is strict, and results can be sensitive to formatting. It is not a “Google-style” search tool; it’s a structured database. If your team expects fuzzy search and gets frustrated, that’s not FDA’s problem — it’s a training and process problem in your regulatory workflow.

Finally, don’t fall for the “split predicate” trap as a default approach. Combining one predicate for intended use and another for technology can be valid in certain contexts, but used casually it creates a story that’s hard to defend and easy to attack in review. If you need split predicates, make sure your logic is tight and your evidence plan is built for scrutiny.

6) Product Codes: The Three Letters That Change Everything

In US device regulation, medical device product codes (classification product codes) are a core organizing mechanism. They’re not just tags — they connect classification, review assignment, database grouping, and often the practical “family tree” of similar devices.

Here’s why product codes matter:

• They narrow the search space. Instead of browsing thousands of “similar sounding” devices, you target the regulatory category FDA uses internally.
• They align you to relevant controls. Product code families often map to specific guidances, standards expectations, and special controls.
• They influence review routing. Product code selection is tied to which FDA group reviews the submission and what evidence patterns they expect.
• They keep your predicate selection honest. Choosing a predicate with the same or closely aligned product code is often more defensible than a “looks similar” predicate in a different code family.

Practical workflow: use the Product Classification database to identify likely product codes and regulation numbers for your device type. Then use those codes to pull a curated candidate set from the 510(k) Database. This is how you avoid wasting weeks chasing irrelevant devices with similar marketing language.

Also note: product codes can evolve over time. FDA may create new codes for subgroups or reassign devices as technology and policy shift. That means your predicate library should be maintained like any other controlled knowledge asset: versioned, reviewed, and updated, not left to rot in a spreadsheet nobody trusts.

7) The Big Limitations (and the Lies People Tell Themselves)

Here are the most common, expensive misunderstandings about the 510(k) Database:

• “If it’s in the database, it’s the current device on the market.” Not necessarily. The record reflects what was submitted and cleared. Ownership, trade names, and versions may have changed.
• “Cleared means it’s safe.” Clearance is not a lifetime guarantee. Safety depends on design, manufacturing, labeling, postmarket performance, and the company’s QMS maturity.
• “If we match a predicate, we’re done.” No. You still need a defensible evidence package, correct labeling, and a validated production system aligned with 21 CFR 820/QMSR and ISO 13485 requirements.
• “The public documents tell the full story.” They don’t. Public summaries are not full submissions. Don’t pretend they are.
• “We can borrow claims from cleared competitors.” If your device isn’t equivalent in intended use, technological characteristics, and performance, copying claims is how you earn a refusal, a warning letter, or a credibility crater.

A mature organization treats database findings as hypotheses that must be tested: classification confirmation, predicate fit analysis, risk assessment, and a requirements-driven V&V plan. Anything else is roleplay.

8) Bringing 510(k) Intelligence Under Document Control (Yes, Really)

Most companies treat 510(k) database research as “informal”: screenshots in emails, links in chat threads, and a mess of PDFs on someone’s desktop. That is a governance failure.

If predicate selection and classification decisions are part of your regulatory rationale, they should be managed like the high-impact quality records they are:

• Controlled capture: store the database outputs (PDFs, summaries, decision letters) in a controlled repository with defined metadata: date captured, search terms used, and context (“used for predicate shortlist for Project X”).
• Traceable rationale: link the chosen predicate(s) to the DHF and to your regulatory strategy document, not to a person’s memory.
• Change management: if your predicate changes, that’s not a casual edit — it is an explicit change-control event with downstream impacts to requirements, risk, testing, and labeling.
• Data integrity: ensure “what you relied on” is preserved and reviewable later under ALCOA+ data integrity expectations, including audit trail for key decisions.
• Retention: tie regulatory intelligence records into your record retention & archival rules so you can reproduce the rationale years later.

Why be this disciplined? Because when a reviewer, auditor, partner, or acquirer asks, “Why did you choose this pathway and predicate?”, you either have a clean, controlled narrative — or you have chaos and excuses.

9) A Practical “510(k) Database Workflow” for Real Teams

Here is a workflow that avoids the two classic failure modes (analysis paralysis and reckless shortcutting):

• Step 1: Define the device at the right level. Write a one-page definition: intended use, indications, key technological characteristics, novelty points, and intended labeling claims. Treat this as a controlled input.
• Step 2: Identify candidate product codes. Use the Product Classification database to shortlist plausible product codes and regulation numbers.
• Step 3: Pull a curated clearance set. Search the 510(k) Database by product code + a key term. Export or capture the relevant results with dates.
• Step 4: Build a predicate shortlist. Pick candidates that align on intended use and technology. Don’t overfit. Don’t pick “cool” competitors. Pick defensible predicates.
• Step 5: Extract the public evidence. Grab summaries/letters where available; capture them as controlled documents.
• Step 6: Perform a structured equivalence gap analysis. Map intended use, tech characteristics, and performance evidence needs. Feed the outputs into ISO 14971 risk management and your V&V plan.
• Step 7: Lock the decision under QMS governance. Formalize pathway and predicate selection in your QMS, linked to DHF and labeling control.

This is not “extra bureaucracy.” It’s what keeps you from waking up six months later realizing your predicate doesn’t match your claims and your entire evidence package is misaligned.

10) Database Signals You Should Not Ignore

The database can reveal early warning signals — if you pay attention:

• Rapidly changing product code families: if you see many similar devices with shifting product codes or novel subgroups, the category may be evolving and reviewers may be tightening expectations.
• Clusters of “new” technology terms: (software-driven claims, AI, novel energy delivery, new materials). That often correlates with more stringent evidence expectations.
• Unclear or inconsistent naming: if the same generic device name maps to multiple product codes, stop and resolve classification properly before you commit.
• Decision patterns over time: a dense wave of clearances can mean a mature pathway; sparse, irregular patterns can mean a more nuanced space.

Do not overinterpret these signals, but also don’t ignore them. They are a prompt to do smarter homework: deeper classification analysis, targeted standards review, and higher-quality evidence planning.

11) What This Means for V5

Most companies treat 510(k) database research as “outside the QMS.” That’s backwards. The database itself is external, but the decisions you make from it are quality-critical: classification, predicate selection, claims, evidence planning, and postmarket obligations. V5 is valuable when it turns this from “emails and spreadsheets” into a controlled, traceable system of record.

• V5 Solution Overview – Provides a unified backbone so regulatory intelligence, design history, quality events and operational execution can point to the same objects (products, variants, documents, lots, suppliers, customers) rather than living in separate silos.
• V5 QMS – Quality Management System – Where 510(k) database outputs become governed knowledge:
  • Document control: store predicate PDFs, clearance letters and classification evidence as controlled documents with versioning, review/approval workflows, and clear ownership (instead of “someone’s folder”).
  • Traceable rationale: link predicates and pathway decisions directly to the DHF, labeling controls and risk files, so changes are auditable and defensible.
  • Change control: manage predicate or classification changes as formal change-control events with impact assessment and required downstream updates.
  • Training & competence: ensure regulatory, R&D and quality teams are trained on “clearance vs approval”, claims control, and predicate discipline using a controlled training matrix.
• V5 Connect API – Enables integration patterns that reduce manual re-entry:
  • Pull or push structured metadata (K-number, product codes, predicate lists, internal project IDs) into your controlled “predicate library” objects.
  • Connect regulatory intelligence artifacts to PLM/requirements tools, eQMS workflows, or analytics without turning everything into fragile spreadsheets.
  • Support controlled sharing with partners/CMOs where the “why” behind pathway and evidence decisions must travel with the device program.
• V5 MES – Manufacturing Execution System – Turns “we cleared it” into “we can consistently build it”:
  • Connect design outputs and quality requirements to executed manufacturing steps, capturing evidence that production follows the defined process.
  • Strengthen traceability from product definition to shop-floor execution, improving the credibility of your overall quality system under 21 CFR 820 expectations.
• V5 WMS – Warehouse Management System – Keeps the “as-built” and “as-distributed” story aligned:
  • Support lot and material genealogy that complements device history expectations and strengthens complaint/investigation response.
  • Help maintain labeling and UDI-controlled inventory flows when products, variants, and market configurations are complex.

Net effect: V5 helps you operationalize the 510(k) story. Not just “we found a predicate,” but “we controlled the decision, managed the change, verified the claims, and can prove what we shipped.” That’s the difference between regulatory theatre and a real quality system.

12) Common Audit Questions Around 510(k) Research

Auditors, partners, and regulators (directly or indirectly) will probe the quality of your regulatory thinking. Expect questions like:

• “How did you determine the device classification and product code?”
• “What was your predicate selection rationale, and where is it controlled?”
• “How do you prevent marketing from overstating clearance?”
• “How do you handle changes to intended use, technology, or labeling claims?”
• “Show how your DHF, risk file, V&V plan and labeling are aligned with the chosen pathway.”

If your answer is “we looked it up,” that’s not an answer. The defensible answer is a controlled chain: classification evidence, predicate rationale, risk-based evidence plan, and documented change governance.

13) Implementation Roadmap & Practice Tips

If you want your organization to use the 510(k) Database as a strength rather than a sloppy habit, implement it like a system:

• 1. Create a “predicate library” standard. Define what must be captured for each candidate: K-number, device name, product code, decision date, public documents captured, and why it’s relevant.
• 2. Define your equivalence template. A repeatable comparison template for intended use, technology, and performance expectations reduces bias and forces clarity.
• 3. Bring it under document control. Regulatory intelligence that drives design decisions should be versioned and reviewable.
• 4. Train marketing and sales. The “approval” language mistake is a classic self-inflicted wound. Fix it with training and controlled claim review.
• 5. Review periodically. Product codes shift, categories evolve, and your assumptions go stale. Treat predicate research as a living knowledge asset with scheduled review.
• 6. Link to risk and V&V early. Predicate differences should immediately drive risk controls and test planning — not late-stage panic.

The goal is not “more paperwork.” The goal is fewer strategic reversals, fewer “surprise” reviewer objections, and fewer internal arguments caused by weak, undocumented assumptions.

FAQ

Q1. Does a 510(k) database listing mean the device is FDA “approved”?
No. A 510(k) outcome is typically clearance based on substantial equivalence, not premarket approval. Clearance should never be marketed as “FDA approved,” and organizations should control that language through labeling and promotional review.

Q2. Why can’t I find a device I know is on the market?
Common reasons include: it wasn’t cleared under 510(k) (it may be PMA, De Novo, exempt, or under a different framework), the device is listed under a generic name you didn’t search, the record is older and sparse, or the current marketed brand name differs from the original submission naming.

Q3. Can we use any cleared device as a predicate?
Not responsibly. A predicate must be legally marketed and should align in intended use and technological characteristics closely enough to support a substantial equivalence argument. “It looks similar” is not a regulatory rationale; it’s a shortcut that usually backfires.

Q4. What are product codes and why do they matter?
Medical device product codes are FDA’s internal classification identifiers that connect device categories to classification, review assignment, and related databases. Knowing the right product code sharply improves predicate research and evidence planning, and helps avoid chasing irrelevant device types.

Q5. Should 510(k) database research be part of the QMS?
The database itself is external, but the decisions you make from it (classification, predicate selection, claims, evidence requirements) are quality-critical. Mature organizations control those decisions, maintain traceability to the DHF/risk/V&V plan, and manage changes under formal QMS governance.

Related Reading
• US Medical Device Regulation: 21 CFR Part 807 | 21 CFR Part 820 | QMSR | ISO 13485 | Labeling Medical Devices | UDI
• Design Controls & Records: Design History File (DHF) | Device Master Record (DMR) | Device History Record (DHR) | Verification & Validation (V&V)
• Governance & Integrity: Document Control | Document Management System (DMS) | Change Control | Data Integrity | Audit Trail | Record Retention & Archival
• V5 Platform: V5 Solution Overview | V5 QMS | V5 MES | V5 WMS | V5 Connect API