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510(k) Data Aggregation
(63 days)
The Sensititre Haemophilus/Streptococcus pneumoniae (HP) MIC Susceptibility plate is an in vitro diagnostic product for clinical susceptibility testing of Streptococcus pneumoniae and Haemophilus influenzae. This 510(k) is for the addition of Moxifloxacin in the dilution range of 0.004 - 8 ug/ml to the Sensititre Haemophilus/Streptococcus pneumoniae MIC panel for testing Streptococcus pneumoniae and Haemophilus influenzae isolates. The approved primary "Indications for Use" and clinical significance of Moxilloxacin is for: Streptococcus pneumoniae (penicillin-susceptible strains only) In vitro data, without clinical correlation is provided for: Streptococcus pneumoniae (penicillin- resistant strains)
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The provided text is a 510(k) approval letter and associated documentation for a medical device called "Sensititre™ Haemophilus/Streptococcus pneumoniae (HP) MIC Susceptibility Plates," specifically for the addition of Moxifloxacin. It does not contain the detailed information necessary to complete most of the requested fields in the table, as it is a regulatory approval rather than a scientific study report.
Here's a breakdown of what can and cannot be extracted:
- Acceptance Criteria and Reported Device Performance: This information is typically found in the clinical or analytical study section of a 510(k) summary, which is not fully provided here. The document mentions "clinical susceptibility testing" but doesn't detail performance metrics like sensitivity, specificity, accuracy, or specific MIC agreement rates against a reference method (which would be the common acceptance criteria for such a device).
- Sample Size and Data Provenance: Not available in this document.
- Number of Experts and Qualifications: Not available. This primarily applies to diagnostic imaging or interpretation tasks, not usually to automated susceptibility testing.
- Adjudication Method: Not applicable or available.
- MRMC Comparative Effectiveness Study: Not applicable to this type of device (an in vitro diagnostic susceptibility plate). This is relevant for devices providing interpretations for human readers (e.g., AI in radiology).
- Standalone Performance: The general nature of an in vitro diagnostic susceptibility plate is that it's a standalone test. However, specific performance metrics (accuracy, etc.) are not detailed.
- Type of Ground Truth: For antimicrobial susceptibility testing, the ground truth is typically established by a reference method, such as broth microdilution or agar dilution, performed according to CLSI (Clinical and Laboratory Standards Institute) guidelines. This document doesn't explicitly state the reference method used.
- Training Set Sample Size and Ground Truth: Not available, as the document describes a device, not an AI/ML algorithm that requires a training set.
Based on the provided text, here is what can be inferred or stated:
1. Table of Acceptance Criteria and Reported Device Performance
| Metric | Acceptance Criteria (Not explicitly stated in text, typical for AST devices) | Reported Device Performance (Not explicitly stated in text) |
|---|---|---|
| Essential Agreement (EA) | Typically >90-95% for each drug-bug combination | Not provided in the document |
| Category Agreement (CA) | Typically >90-95% for each drug-bug combination | Not provided in the document |
| Major Discrepancies (MD) | Typically <3% | Not provided in the document |
| Very Major Discrepancies (VMD) | Typically <3% | Not provided in the document |
Note: The acceptance criteria and performance metrics for in vitro diagnostic antimicrobial susceptibility tests (AST) like the Sensititre™ Haemophilus/Streptococcus pneumoniae (HP) MIC Susceptibility Plates are typically defined by regulatory guidance (e.g., FDA guidance for AST devices) and industry standards (e.g., CLSI guidelines). These generally involve demonstrating high percentages of essential agreement (agreement between the MIC values of the device and a reference method within one doubling dilution) and categorical agreement (agreement on whether the organism is susceptible, intermediate, or resistant). The provided document is an FDA approval letter and does not contain the detailed study results that would report these specific performance metrics or the explicit acceptance criteria used by the manufacturer in their submission.
2. Sample size used for the test set and the data provenance:
- Sample Size for Test Set: Not mentioned in the provided text.
- Data Provenance: Not mentioned in the provided text.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable/Not mentioned. The ground truth for antimicrobial susceptibility testing is typically established by laboratory reference methods, not human expert consensus, for this type of device.
4. Adjudication method for the test set:
- Not applicable/Not mentioned.
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:
- Not applicable. This device is an in vitro diagnostic test for antimicrobial susceptibility, not an AI/ML device intended for human interpretation or assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, this device is inherently standalone in its function as an in vitro diagnostic susceptibility plate that determines MIC values. However, specific quantitative standalone performance metrics (e.g., accuracy, agreement rates) are not provided in this document.
7. The type of ground truth used:
- Based on the context of an antimicrobial susceptibility plate, the ground truth would typically be established by a broth microdilution or agar dilution reference method performed according to recognized standards (e.g., Clinical and Laboratory Standards Institute - CLSI guidelines). The document itself does not explicitly state the ground truth method.
8. The sample size for the training set:
- Not applicable. This device is an in vitro diagnostic product, not an AI/ML algorithm that requires a training set.
9. How the ground truth for the training set was established:
- Not applicable.
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