K210585 · Becton, Dickinson and Company · QEP · May 10, 2022 · Microbiology
Device Facts
Record ID
K210585
Device Name
BD CTGCTV2
Applicant
Becton, Dickinson and Company
Product Code
QEP · Microbiology
Decision Date
May 10, 2022
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 866.3393
Device Class
Class 2
Indications for Use
The BD CTGCTV2 assay incorporates automated DNA extraction and real-time polymerase chain reaction (PCR) for the direct, qualitative detection of DNA from: Chlamydia trachomatis (CT) Neisseria gonorrhoeae (GC) Trichomonas vaginalis (TV) The assay may be used for detection of CT, GC and/or TV DNA in patient- or clinician-collected vaginal swab specimens (in a clinical setting) and male and female urine specimens. The assay may also be used for the detection of CT and GC DNA in endocervical swab and Liquid-Based Cytology (LBC) specimens in ThinPrep PreserveCyt Solution using an aliquot that is removed prior to processing for the ThinPrep Pap test. The assay is indicated for use with asymptomatic and symptomatic individuals to aid in the diagnosis of chlamydial urogenital disease, gonococcal urogenital disease and/or trichomoniasis. The BD CTGCTV2 assay is available for use with the BD MAX System or the BD COR System.
Device Story
High-volume molecular testing system (BD COR) for automated processing of clinical samples; migrates BD CTGCTV2 assay from BD MAX to BD COR. Input: vaginal swabs, urine, endocervical swabs, LBC specimens. PX pre-analytical module performs automated sample vortexing, aliquoting, pre-warming, cooling, and transport to MX analyzer. MX analyzer performs magnetic affinity DNA extraction, real-time PCR amplification, and detection. Output: qualitative results for CT, GC, and TV DNA. Used in clinical laboratories; operated by laboratory personnel. System automates manual steps of BD MAX; increases throughput to 72 samples per run. Output used by clinicians to diagnose STIs; aids in patient management and treatment decisions.
Clinical Evidence
Clinical agreement study compared BD COR results to BD MAX (reference) using 433 independent panel members (urine and spiked clinical specimens). PPA and NPA were calculated across three sites. For CT, PPA was 100% and NPA was 100%. For GC, average PPA was 97.6% and NPA was 100%. For TV, average PPA was 99.7% and NPA was 98.5%. Weighted Deming regression showed strong correlation (Pearson's r 0.934–0.975) between Ct scores on both systems.
Technological Characteristics
Qualitative real-time PCR; magnetic affinity DNA extraction. Automated pre-analytical processing (PX module) and high-throughput molecular analysis (MX module). Connectivity to LIS. Software algorithm for result interpretation is identical to predicate. Consumables include reagent plates (up to 96 samples) and PCR cartridges.
Indications for Use
Indicated for asymptomatic and symptomatic individuals to aid in the diagnosis of chlamydial urogenital disease, gonococcal urogenital disease, and/or trichomoniasis.
Regulatory Classification
Identification
A device to detect nucleic acids from non-viral microorganism(s) causing sexually transmitted infections and associated resistance marker(s) is an in vitro diagnostic device intended for the detection and identification of nucleic acids from non-viral microorganism(s) and their associated resistance markers in clinical specimens collected from patients suspected of sexually transmitted infections. The device is intended to aid in the diagnosis of non-viral sexually transmitted infections in conjunction with other clinical and laboratory data. These devices do not provide confirmation of antibiotic susceptibility since mechanisms of resistance may exist that are not detected by the device.
Special Controls
A device to detect nucleic acids from non-viral microorganism(s) causing sexually transmitted infections and associated resistance marker(s) must comply with the following special controls: (1) The intended use for the 21 CFR 809.10 labeling must include a detailed description of targets the device detects, the results provided to the user, the clinical indications appropriate for test use, and the specific population(s) for which the device is intended. (2) Any sample collection device used must be FDA-cleared, -approved, or -classified as 510(k) exempt (standalone or as part of a test system) for the collection of specimen types claimed by this device: alternatively, the sample collection device must be cleared in a premarket submission as a part of this device. (3) The 21 CFR 809.10(b) labeling must include: (i) A detailed device description, including reagents, instruments, ancillary materials, all control elements, and a detailed explanation of the methodology, including all pre-analytical methods for processing of specimens; (ii) Detailed discussion of the performance characteristics of the device for all claimed specimen types based on analytical studies, including, but not limited to. Limit of Detection, inclusivity, cross-reactivity, interfering substances, competitive inhibition, carryover/cross contamination, specimen stability, with-in lab precision, and reproducibility, as appropriate; (iii) Detailed descriptions of the test procedure, the interpretation of test results for clinical specimens, and acceptance criteria for any quality control testing. (iv) Limiting statements indicating that: (A)a negative test result does not preclude the possibility of infection; (B) the test results should be interpreted in conjunction with other clinical and laboratory data available to the clinician; (C) reliable results are dependent on adequate specimen collection, transport, storage, and processing. Failure to observe procedures in any one of these steps can lead to incorrect results; and (D)if appropriate (e.g., recommended by CDC, by current well-accepted clinical guidelines, or by published peer reviewed research), that the clinical performance is inferior in a specific clinical subpopulation or for a specific claimed specimen type. (v) If the device is intended to detect antimicrobial resistance markers, limiting statements, as appropriate, indicating that: (A)negative results for claimed resistance markers do not indicate susceptibility of detected microorganisms, as resistance markers not measured by the assay or other potential mechanisms of antibiotic resistance may be present; (B) detection of resistance markers cannot be definitively linked to specific microorganisms and the source of a detected resistance marker may be an organism not detected by the assay, including colonizing flora; (C) detection of antibiotic resistance markers may not correlate with phenotypic gene expression; and (D) therapeutic failure or success cannot be determined based on the assay results, since nucleic acid may persist following appropriate antimicrobial therapy. (4) Design verification and validation must include: (i) Detailed device description documentation, including, but not limited to, methodology from obtaining sample to result, design of primer/probe sequences, rationale for target sequence selection, and computational path from collected raw data to reported result (e.g., how collected raw signals are converted into a reported result). (ii) Detailed documentation of analytical studies including but not limited to, Limit of Detection, inclusivity, cross-reactivity, microbial interference, interfering substances, competitive inhibition, carryover/cross contamination, specimen stability, with-in lab precision, and reproducibility, as appropriate. (iii) Detailed documentation and performance results from a clinical study that includes prospective (sequential) samples for each claimed specimen type and, when determined to be appropriate by FDA, additional characterized clinical samples. The study must be performed on a study population consistent with the intended use population and compare the device performance to results obtained from FDA accepted comparator methods. Documentation from the clinical studies must include the clinical study protocol (including a predefined statistical analysis plan) study report, testing results, and results of all statistical analyses. (iv) A detailed description of the impact of any software, including, but not limited to, software applications and hardware-based devices that incorporate software, on the device's functions.
*Classification.* Class II (special controls). The special controls for this device are:(1) The intended use for the labeling required under § 809.10 of this chapter must include a detailed description of targets the device detects, the results provided to the user, the clinical indications appropriate for test use, and the specific population(s) for which the device is intended.
(2) Any sample collection device used must be FDA-cleared, -approved, or -classified as 510(k) exempt (standalone or as part of a test system) for the collection of specimen types claimed by this device; alternatively, the sample collection device must be cleared in a premarket submission as a part of this device.
(3) The labeling required under § 809.10(b) of this chapter must include:
(i) A detailed device description, including reagents, instruments, ancillary materials, all control elements, and a detailed explanation of the methodology, including all pre-analytical methods for processing of specimens;
(ii) Detailed discussion of the performance characteristics of the device for all claimed specimen types based on analytical studies, including Limit of Detection, inclusivity, cross-reactivity, interfering substances, competitive inhibition, carryover/cross contamination, specimen stability, within lab precision, and reproducibility, as appropriate;
(iii) Detailed descriptions of the test procedure, the interpretation of test results for clinical specimens, and acceptance criteria for any quality control testing;
(iv) Limiting statements indicating that:
(A) A negative test result does not preclude the possibility of infection;
(B) The test results should be interpreted in conjunction with other clinical and laboratory data available to the clinician;
(C) Reliable results are dependent on adequate specimen collection, transport, storage, and processing. Failure to observe proper procedures in any one of these steps can lead to incorrect results; and
(D) If appropriate (
*e.g.,* recommended by the Centers for Disease Control and Prevention, by current well-accepted clinical guidelines, or by published peer reviewed research), that the clinical performance is inferior in a specific clinical subpopulation or for a specific claimed specimen type; and(v) If the device is intended to detect antimicrobial resistance markers, limiting statements, as appropriate, indicating that:
(A) Negative results for claimed resistance markers do not indicate susceptibility of detected microorganisms, as resistance markers not measured by the assay or other potential mechanisms of antibiotic resistance may be present;
(B) Detection of resistance markers cannot be definitively linked to specific microorganisms and the source of a detected resistance marker may be an organism not detected by the assay, including colonizing flora;
(C) Detection of antibiotic resistance markers may not correlate with phenotypic gene expression; and
(D) Therapeutic failure or success cannot be determined based on the assay results, since nucleic acid may persist following appropriate antimicrobial therapy.
(4) Design verification and validation must include:
(i) Detailed device description documentation, including methodology from obtaining sample to result, design of primer/probe sequences, rationale for target sequence selection, and computational path from collected raw data to reported result (
*e.g.,* how collected raw signals are converted into a reported result).(ii) Detailed documentation of analytical studies, including, Limit of Detection, inclusivity, cross-reactivity, microbial interference, interfering substances, competitive inhibition, carryover/cross contamination, specimen stability, within lab precision, and reproducibility, as appropriate.
(iii) Detailed documentation and performance results from a clinical study that includes prospective (sequential) samples for each claimed specimen type and, when determined to be appropriate by FDA, additional characterized clinical samples. The study must be performed on a study population consistent with the intended use population and compare the device performance to results obtained from FDA accepted comparator methods. Documentation from the clinical studies must include the clinical study protocol (including a predefined statistical analysis plan) study report, testing results, and results of all statistical analyses.
(iv) A detailed description of the impact of any software, including software applications and hardware-based devices that incorporate software, on the device's functions.
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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.
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.