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VITEK® 2 Gram Positive Amoxicillin for Streptococcus pneumoniae is designed for antimicrobial susceptibility testing of Streptococcus pneumoniae. VITEK 2 Gram Positive Amoxicillin for Streptococcus pneumoniae is a quantitative test. It is intended for use with the VITEK 2 and VITEK 2 Compact Systems as a laboratory aid in the determination of in vitro susceptibility to antimicrobial agents.

The VITEK® 2 Antimicrobial Susceptibility Test (AST) is intended to be used with the VITEK® 2 and VITEK® 2 Compact Systems for the automated quantitative or qualitative susceptibility testing of isolated colonies for the most clinically significant aerobic gramnegative bacilli, Staphylococcus spp., Enterococcus spp., Streptococcus agalactiae, and S. pneumoniae.

VITEK® 2 Gram Positive Amoxicillin for Streptococcus pneumoniae is designed for antimicrobial susceptibility testing of Streptococcus pneumoniae. It is intended for use with the VITEK® 2 and VITEK® 2 Compact Systems as a laboratory aid in the determination of in vitro susceptibility to antimicrobial agents. The antimicrobial presented in VITEK 2 AST Cards is in concentrations equivalent by efficacy to standard method concentrations in mcg/ml. The VITEK 2 AST Cards are essentially miniaturized versions of the doubling dilution technique for determining the minimum inhibitory concentration (MIC) microdilution methodology.

The bacterial isolate to be tested is diluted to a standardized concentration in 0.45% saline before being used to rehydrate the antimicrobial medium within the card. The VITEK 2 System automatically fills, seals and places the card into the incubatorireader. The VITEK 2 Compact has a manual filling and sealing operation. The VITEK 2 monitors the growth of each well in the card over a defined period of time (up to 18 hours). At the completion of the incubation cycle, a report is generated that contains the MC value along with the interpretive category result for each antibiotic contained on the card.

Here's a breakdown of the acceptance criteria and study details for the VITEK® 2 Gram Positive Amoxicillin for Streptococcus pneumoniae:

Acceptance Criteria and Device Performance

Note: The FDA Class II Special Controls Guidance Document: Antimicrobial Susceptibility Test (AST) Systems; Guidance for Industry and FDA, Issued Feb. 5, 2003, would contain the specific numerical acceptance criteria for Category Agreement, Essential Agreement, and potentially other metrics, which are not detailed in this 510(k) summary directly. The summary only states that performance was "acceptable" as defined by this guidance.

Study Details

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: The document mentions "fresh and stock clinical isolates and stock challenge strains." It does not provide a specific numerical sample size for the test set.
• Data Provenance: Not explicitly stated (e.g., country of origin). The study involved "external evaluation," which implies real-world clinical isolates. It is a prospective comparison against a reference method.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• This information is not provided in the document. For antimicrobial susceptibility testing, the "ground truth" is typically established by a reference method, not by human expert interpretation of raw data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• This information is not applicable in the context of AST device validation as described. Adjudication methods like "2+1" or "3+1" are typical for image-based diagnostic studies requiring radiologists or pathologists to resolve discrepancies. For AST, the device's results are directly compared to the reference method (CLSI broth microdilution); discrepancies are analyzed, but not resolved by expert adjudication in the same way.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• No, a MRMC comparative effectiveness study was not done. This type of study applies to diagnostic devices that involve human interpretation, often assisted by AI. The VITEK® 2 system is an automated device designed to determine antimicrobial susceptibility, not to assist human readers in interpreting images or other complex data.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Yes, this was a standalone (algorithm only) performance study. The VITEK 2 system automatically processes the antimicrobial medium, monitors growth, and generates results (MIC value and interpretive category). The comparison is between the automated VITEK 2 system and the CLSI broth microdilution reference method. There is no human-in-the-loop performance component being evaluated here.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Reference Method: The ground truth was established by the CLSI broth microdilution reference method. This is a standardized, laboratory-based method for determining minimum inhibitory concentrations (MICs) of antimicrobial agents.

8. The sample size for the training set

• The document does not specify a separate "training set" or its sample size. For in vitro diagnostic devices like AST systems, the development often involves internal testing and refinement, but explicit separate "training" and "test" sets in the machine learning sense are not typically detailed in 510(k) summaries unless a de novo algorithm is being validated. The submission focuses on the external validation of the finalized device.

9. How the ground truth for the training set was established

• Not applicable/details not provided, as a distinct "training set" and its ground truth establishment are not discussed in the provided text. The device's underlying principles are based on established microbiological methods (miniaturized doubling dilution) rather than a machine learning model trained on a specific dataset.

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