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510(k) Data Aggregation
(89 days)
AMEDICA DRUG SCREEN METHAPHETAMINE TEST
The Amedica Drug Screen Methamphetamine Test is a immunochromatographic assay for the rapid detection of methamphetamine in human urine at a cutoff concentration of 1000 ng/ml. This assay has not been evaluated at point-of-care locations and is intended for use by healthcare professionals. This assay provides only a preliminary result. A more specific alternative chemical method is needed to obtain a confirmed result.
Membrane based one-step, lateral flow, competitive immunoassay use colloidal gold for visual detection. The test cutoff is 1000 ng/ml.
Here's an analysis of the provided text regarding the Amedica Drug Screen Methamphetamine Test, structured to address your specific points:
Acceptance Criteria and Study Details for Amedica Drug Screen Methamphetamine Test
The information provided is from a 510(k) premarket notification for a medical device. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than establishing de novo performance criteria against a predefined set of acceptance values. Therefore, the "acceptance criteria" for this device are implicitly tied to demonstrating performance comparable to the predicate device and achieving a high level of agreement with a confirmatory method (GC/MS).
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria (Implied) | Reported Device Performance |
---|---|
High agreement with GC/MS (confirmatory method) | "> 94% agreement with GC/MS results" |
Usability by professionals at clinical sites | "demonstrated that Amedica Biotech Drug Screen Methamphetamine Test can be use by professionals to obtain a visual, qualitative detection of drugs of abuse." |
Substantial equivalence to predicate device | "comparison with Rapid Diagnostics methamphetamine test demonstrated that Amedica Biotech Drug Screen Methamphetamine Test is substantially equivalent to the predicate kit." |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: The text mentions a "correlation study using blind-labeled clinical specimens" and a "clinical site study." However, the specific number of samples (sample size) for these test sets is not explicitly stated in the provided document.
- Data Provenance:
- Country of Origin: Not specified.
- Retrospective or Prospective: Not specified. The use of "clinical specimens" implies a real-world context, but whether they were collected specifically for this study (prospective) or were pre-existing (retrospective) is not detailed.
3. Number of Experts Used to Establish Ground Truth and Qualifications
- The document implies that the ground truth for the correlation study was established by Gas Chromatography/Mass Spectrometry (GC/MS) results. GC/MS is an analytical chemistry technique, not a human expert. Therefore, the concept of "number of experts" is not directly applicable in this context.
- For the "clinical site study," human professionals (implied to be healthcare professionals working in certified laboratories) used the device. However, their role was to use the device, not to establish a separate ground truth against which the device was compared. The ground truth for this validation would still likely refer back to objective analytical methods or the predicate device's performance.
- Qualifications of Experts: Not applicable, as GC/MS is an instrumental method. For the clinical site study, the "professionals" were working in "two certified laboratories," implying they were qualified given the laboratory accreditation.
4. Adjudication Method for the Test Set
- None specified. The "correlation study" used GC/MS as the reference standard, which is an objective analytical method. There's no mention of multiple human readers or an adjudication process for the GC/MS results themselves or for the visual test results from the Amedica device to establish a consensus ground truth. The agreement was likely a direct comparison of the device's qualitative result (positive/negative) against the quantitative GC/MS result relative to the 1000 ng/ml cutoff.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No, an MRMC comparative effectiveness study was not explicitly mentioned or clearly conducted. The focus was on the device's performance against GC/MS and its substantial equivalence to a predicate device. While a "clinical site study" was performed, it describes the device being "use by professionals" rather than a formal MRMC study comparing human readers with and without AI assistance to measure an effect size.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
- Yes, in essence, the "correlation study" is a standalone evaluation of the device's analytical performance. The "Amedica Drug Screen Methamphetamine Test" is a lateral flow immunoassay that provides a visual qualitative result. While a human physically interprets the lines on the strip, the device itself is a "standalone" diagnostic rather than an AI algorithm that augments human interpretation. The reported agreement of "> 94% agreement with GC/MS results" directly reflects this standalone performance.
7. Type of Ground Truth Used
- Expert Consensus: No.
- Pathology: No (this is for drug detection, not tissue pathology).
- Outcomes Data: No.
- Other: The primary ground truth for evaluating the device's accuracy was Gas Chromatography/Mass Spectrometry (GC/MS), which is a highly specific and sensitive analytical method considered the gold standard for drug confirmation.
8. Sample Size for the Training Set
- Not applicable / Not specified. This device is a competitive immunoassay, not an AI/machine learning algorithm that requires a "training set" in the conventional sense. The "training" in this context would refer to the development and optimization of the immunoassay reagents and manufacturing process, which doesn't typically involve a distinct data "training set" like AI models do.
9. How the Ground Truth for the Training Set was Established
- Not applicable. As mentioned above, there isn't a "training set" in the machine learning sense for this type of device. The immunoassay's specificity and sensitivity are inherent in its chemical design and production, not learned from a dataset.
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