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The AmpliChip CYP450 test is intended to identify a patient's CYP2C19 genotype from genomic DNA extracted from a whole blood sample. Information about CYP2C19 genotype may be used as an aid to clinicians in determining therapeutic strategy and treatment dose for therapeutics that are metabolized by the CYP2C19 gene product.

The AmpliChip CYP450 Test is based on five major processes: PCR amplification of purified DNA: fragmentation and labeling of the amplified products; hybridization of the amplified products to a microarray and staining of the bound products; scanning of the microarray; and determination of the CYP450 genotype and predicted phenotype.

The AmpliChip CYP450 Test is designed to identify specific nucleic acid sequences and query for the presence of known sequence polymorphisms through analysis of the pattern of hybridization to a series of probes that are specifically complementary either to wildtype or mutant sequences. Microarrays of oligonucleotide probes synthesized on a glass substrate are utilized for the analysis.

The provided document describes the performance of the AmpliChip CYP450 Test for CYP2C19. Here's an analysis based on your request:

Acceptance Criteria and Device Performance

The document doesn't explicitly state "acceptance criteria" as a separate section with predefined thresholds. Instead, it presents performance data from non-clinical studies (Limit of Detection, Specificity, Genotype Detection, Whole System Failure, Cross Contamination, Reproducibility, Interference Studies) and implicitly demonstrates that the device meets the expected performance for a diagnostic tool of this nature. The "Percent Agreement" values (often ≥99%) and "Positivity Rate" (100% at relevant DNA concentrations) serve as de facto performance targets that the device achieved.

Here's a table summarizing the reported device performance, with implicit acceptance criteria derived from the presented results:

Study Details

Here's the breakdown of the study details according to your requested points:

1. A table of acceptance criteria and the reported device performance:
   (Provided above)

2. Sample size used for the test set and the data provenance:

   • Limit of Detection: 144 arrays per DNA concentration (two genomic DNA samples).
   • Specificity: 270 samples (*1/*1 genotype).
   • Genotype Detection (Sequencing Concordance): 123 samples (246 alleles in total analyzed by sequencing).
   • Genotype Detection (Overall): 399 unique samples (796 unique alleles)
   • Whole System Failure: 100 replicates of genomic DNA from one whole blood specimen.
   • Cross Contamination: 5 runs of alternating two specimens of distinct genotype.
   • Reproducibility: 6-member panel tested in triplicate for 5 runs by one operator at each of 3 sites, using 3 lots of reagents. (Total 809 results).
   • Interference Studies: 10 random whole blood specimens collected in EDTA.

   Data Provenance: The document does not explicitly state the country of origin for the samples. It mentions "genomic DNA purified from a whole blood specimen" and "cell lines," implying human biological samples. It is a non-clinical study, focusing on assay performance rather than patient outcomes, and therefore likely based on retrospective samples or controlled-condition samples for analytical validation.

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):

   The ground truth for the test set was established using established molecular methods, not human expert consensus.

   • Reference methods included: Bi-directional DNA sequencing, allele-specific PCR, and PCR-RFLP.
   • The document does not specify the number or qualifications of individuals who performed these reference methods. This is typical for molecular diagnostic device submissions, where the "ground truth" is analytical (e.g., precise sequence determination) rather than subjective human interpretation.

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

   No adjudication method involving multiple human readers is mentioned as the ground truth was established by definitive molecular methods (sequencing, PCR-RFLP). Comparisons were made between the AmpliChip results and these reference methods. For example, in Genotype Detection, "DNA sequence analysis for genotype confirmation was performed" and "additional samples were evaluated by established methods including allele-specific PCR and PCR-RFLP in order to determine the reference CYP2C19 genotype."

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 multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or described. This device is a molecular diagnostic assay for genotyping, not an image-based AI system that would assist human readers in interpretation. Therefore, the concept of "human readers improve with AI vs without AI assistance" does not apply to this type of device.

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

   Yes, the performance studies described are essentially standalone algorithm (device) performance. The AmpliChip CYP450 Test is an automated assay system (PCR amplification, hybridization to microarray, scanning, genotype determination). Its performance metrics (Limit of Detection, Specificity, Genotype Detection, etc.) are evaluating the device's ability to accurately determine genotypes independently. There is no human "in the loop" performing interpretation that the device is assisting; rather, the device is providing the direct result.

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

   The ground truth used was based on molecular reference methods, specifically:

   • Bi-directional DNA sequencing
   • Allele-specific PCR
   • PCR-RFLP (Restriction Fragment Length Polymorphism)

   These are considered definitive analytical methods for determining genetic sequences and polymorphisms.

8. The sample size for the training set:

   This document describes a 510(k) submission for a diagnostic device, which is typically based on pre-analytical validation, not machine learning model training. Therefore, there is no mention or concept of a "training set" in the context of machine learning. The studies described are performance validation tests.

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

   Since there is no training set in the machine learning sense, this question is not applicable. The device's underlying principles are based on molecular biology (hybridization to probes), not an algorithm that learns from a training dataset. The "ground truth" for the analytical validation was established by the definitive molecular reference methods listed in point 7.

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