Simplexa VZV Swab Direct, Simplexa VZV Positive Control Pack
Applicant
Diasorin Molecular, LLC
Product Code
PGI · Microbiology
Decision Date
Nov 26, 2019
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 866.3309
Device Class
Class 2
Attributes
Real-World Evidence, Pediatric
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K192376 · Nov 26, 2019
Simplexa VZV Swab Direct, Simplexa VZV Positive Control Pack
Diasorin Molecular, LLC
Retrospective clinical swab specimens
Retrospective clinical samples were used to evaluate the performance (PPA/NPA) of the Simplexa VZV Swab Direct assay against a composite reference method.
Patients with signs and symptoms of VZV infection (cutaneous and mucocutaneous swab specimens); Sample Size: 180 specimens (60 positive, 120 negative)
Composite Reference Method 2 (CRM 2) consisting of one FDA-cleared NAAT and two validated VZV PCR assays followed by bi-directional sequencing
Positive Percent Agreement (PPA) and Negative Percent Agreement (NPA)
Indications for Use
The DiaSorin Molecular Simplexa™ VZV Swab Direct assay is intended for use on the LIAJSON® MDX instrument for the qualitative detection of varicella-zoster virus (VZV) DNA present in cutaneous and mucocutaneous lesion swabs from patients with signs and symptoms of VZV infection. This test is intended as an aid in the diagnosis of VZV infection. Negative results do not preclude VZV infection and should not be used as the sole basis for treatment or other patient management decisions. The Simplexa™ VZV Positive Control Pack is intended to be used as a control with the Simplexa™ VZV Direct kit and the Simplexa™ VZV Swab Direct kit on the LIAISON® MDX Instrument. It is not intended for use with other assays or systems.
Device Story
Simplexa VZV Swab Direct is a real-time PCR assay for qualitative detection of VZV DNA from unprocessed cutaneous and mucocutaneous lesion swabs. The system uses the LIAISON MDX instrument and Direct Amplification Disc (DAD). It targets a conserved region of the VZV DNA polymerase gene; fluorescent probes and primers amplify VZV and an internal control. The assay eliminates the need for nucleic acid extraction. It is used in clinical laboratory settings by trained personnel. Results aid in diagnosing VZV infection; negative results do not rule out infection. The device provides rapid, automated detection, assisting clinicians in patient management decisions.
Clinical Evidence
Clinical study included 452 prospective, 60 retrospective, and 60 contrived samples. Prospective study sensitivity 97.8% (92.2-99.4% CI), specificity 99.2% (97.6-99.7% CI) against a 3-part composite reference method (DSFA/culture + 2 PCR/sequencing). Retrospective study PPA 98.4%, NPA 99.2%. Contrived study PPA 100%, NPA 100%. Reproducibility evaluated across 3 sites with 90 replicates per panel member; 100% agreement observed.
Technological Characteristics
Real-time PCR assay; targets VZV DNA polymerase gene; uses fluorescent probes and primers; requires LIAISON MDX instrument and Direct Amplification Disc (DAD); no nucleic acid extraction required; internal control included for PCR inhibition monitoring; qualitative output.
Indications for Use
Indicated for qualitative detection of VZV DNA in cutaneous and mucocutaneous lesion swabs from symptomatic patients of all ages (one month to >60 years) suspected of VZV infection. Rx only.
Regulatory Classification
Identification
A herpes virus nucleic acid-based cutaneous and mucocutaneous lesion panel is a qualitative in vitro diagnostic device intended for the simultaneous detection and differentiation of different herpes viruses in cutaneous and mucocutaneous lesion samples from symptomatic patients suspected of Herpetic infections. Negative results do not preclude infection and should not be used as the sole basis for treatment or other patient management decisions. The assay is not intended for use in cerebrospinal fluid samples.
Special Controls
*Classification.* Class II (special controls). The special controls for this device are:(1) Premarket notification submissions must include detailed documentation for the device description, including the device components, ancillary reagents required but not provided, and a detailed explanation of the methodology including primer design and selection.
(2) Premarket notification submissions must include detailed documentation from the following analytical and clinical performance studies: Analytical sensitivity (Limit of Detection), reactivity, inclusivity, precision, reproducibility, interference, cross reactivity, carry-over, and cross contamination.
(3) Premarket notification submissions must include detailed documentation of a clinical study using lesion samples in which Herpes Simplex Virus 1, Herpes Simplex Virus 2, or Varicella Zoster Virus DNA detection was requested. The study must compare the device performance to an appropriate well established reference method.
(4) A detailed explanation of the interpretation of results and acceptance criteria must be included in the device's 21 CFR 809.10(b)(9) compliant labeling.
(5) The device labeling must include a limitation statement that reads: “The device is not intended for use with cerebrospinal fluid or to aid in the diagnosis of HSV or VZV infections of the central nervous system (CNS).”
(6) Premarket notification submissions must include quality assurance protocols and a detailed documentation for device software, including, but not limited to, standalone software applications and hardware-based devices that incorporate software.
(7) The risk management activities performed as part of the manufacturer's 21 CFR 820.10(c) design and development activities must document an appropriate end user device training program that will be offered as part of efforts to mitigate the risk of failure to correctly operate the instrument.
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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.
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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.
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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.
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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.
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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.