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The MicroScan® Dried Gram Negative Panel is designed for use in the Confirmation of Extended-Spectrum Beta-Lactamase (ESBL) production in Escherichia coli, Klebsiella spp. and Proteus mirabilis.

After inoculation, panels are incubated for 16-20 hours at 35°C +/- 1°C in a non-CO2 incubator and read either visually or with MicroScan® instrumentation, according to the Package Insert.

This particular submission is for the use of cefotaxime (2, 16 µg/ml), cefotaxime/clavulanic acid (0.5/4, 4/4 µg/ml), ceftazidime (1, 8 µg/ml) and ceftazidime/clavulanic acid (0.25/4, 2/4 µg/ml) for ESBL confirmatory testing on MicroScan® Dried Gram Negative MIC/Combo Panels and the addition of automated read methods.

The Gram Negative organisms which may be used for confirmation of ESBL production in this panel are:

Escherichia coli
Klebsiella oxytoca
Klebsiella pneumoniae
Proteus mirabilis

MicroScan® Dried Gram Negative MIC/Combo Panels are designed for use in determining quantitative and/or qualitative antimicrobial agent susceptibility for Gram Negative organisms and screening for suspected ESBL production in E. coli, Klebsiella spp. and P. mirabilis.

The antimicrobial susceptibility tests are miniaturizations of the broth dilution susceptibility test that have been diluted in broth and dehydrated. Various antimicrobial agents are diluted in broth to concentrations bridging the range of clinical interest. Panels are rehydrated with water, after inoculation with a standardized suspension of the organism. After incubation in a non-CO2 incubator at 35 °C for a minimum of 16 hours, the minimum inhibitory concentration (MIC) for the test organism is determined by observing the lowest antimicrobial concentration showing inhibition of growth.

The provided 510(k) summary focuses on the MicroScan® Dried Gram Negative MIC/Combo Panels for confirming Extended-Spectrum Beta-Lactamase (ESBL) production. The study details are specific to this device.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size Used for the Test Set and Data Provenance:

• Sample Size: The document does not explicitly state a precise numerical sample size for the "test set" (referred to as "stock challenge strains" within "Design Validation studies"). It mentions "ESBL and non-ESBL-producing strains." Without a specific number, it is impossible to definitively detail the sample size.
• Data Provenance: The data appears to be prospective as it's generated from "Challenge studies" and "Design Validation studies" specifically conducted to confirm the acceptability of the streamlined dilutions. The country of origin of the data is not specified, but given the manufacturer (Dade Behring Inc.) and the FDA submission, it's likely primarily US-based or international data submitted to the FDA for US market clearance.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:

• The ground truth for the test set was established by "molecular characterization." This implies laboratory-based genetic or biochemical testing, not human expert interpretation of images or clinical findings. Therefore, the concept of "number of experts" and their "qualifications" in the traditional sense (e.g., radiologists) is not applicable here. The ground truth relies on the accuracy and established validity of the molecular characterization methods themselves.

4. Adjudication Method for the Test Set:

• Since the ground truth was established by "molecular characterization," the concept of an "adjudication method" involving multiple human readers (like 2+1, 3+1) is not applicable. The molecular characterization itself serves as the definitive reference, and the device's results are compared directly against it.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• No, an MRMC comparative effectiveness study was not explicitly mentioned or performed in the context typically seen for diagnostic imaging tools involving human readers. This device is an in-vitro diagnostic (IVD) for bacterial susceptibility testing, where the "reader" can be manual or an automated instrument. The study did assess "reproducibility" across "manual, WalkAway® and autoSCAN®-4 instruments," which addresses variation in reading methods but not human diagnostic improvement with AI assistance.
• Therefore, the effect size of human readers improving with AI vs without AI assistance is not applicable here because it's not an AI-assisted diagnostic task for visual interpretation by humans.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

• Yes, a standalone performance study was conducted. The "Overall Agreement of > 93% with ESBL and non-ESBL-producing strains" was determined by comparing the "panel confirmation result" (the device's output) directly against the "molecular characterization result" (the ground truth). This is a standalone assessment of the device's accuracy without human intervention influencing the final classification, other than the initial setup and reading of the panel, which can be automated.

7. Type of Ground Truth Used:

• The ground truth used was molecular characterization (described as "molecular characterization result"). This refers to laboratory techniques (e.g., PCR, sequencing, enzyme assays) that identify the presence or absence of ESBL-producing genes or enzymes, providing a definitive biological determination.

8. Sample Size for the Training Set:

• The document does not explicitly state a sample size for a "training set." This type of device (microdilution panels) is typically developed and validated based on established microbiological principles and chemical formulations rather than machine learning models that require distinct training and test sets. The studies described are validation and challenge studies, which assess the performance of the final device, not the iterative training of an algorithm.

9. How the Ground Truth for the Training Set Was Established:

• As there's no mention of a "training set" in the context of machine learning, the question of how its ground truth was established is not applicable. The development of such panels relies on a priori chemical and biological knowledge, and subsequent validation against well-characterized reference strains and clinical isolates. The "molecular characterization" served as the gold standard for the validation/test set.

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