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510(k) Data Aggregation
(51 days)
Abuscreen ONLINE for Cannabinoids is an in vitro diagnostic test for the qualitative and semiquantitative detection of cannabinoids in human urine on automated clinical chemistry analyzers at cutoff concentrations of 20 ng/mL, 50 ng/mL, and 100 ng/mL. Semiquantitative test results may be obtained that permit laboratories to assess assay performance as part of a quality control program. Measurements obtained by this device are used in the diagnosis of cannabinoid use or abuse.
Abuscreen ONLINE Cannabinoids is an in vitro diagnostic test for the qualitative and semiquantitative detection of cannabinoids and its metabolites in human urine on automated clinical chemistry analyzers at cutoff concentrations of 20 ng/mL, 50 ng/mL, and 100 ng/mL. Semiquantitative test results may be obtained that permit laboratories to assess assay performance as part of a quality control program. Measurements obtained by this device are used in the diagnosis of cannabinoid use or abuse.
The proposed Abuscreen ONLINE Cannabinoids test kit is specifically intended for use on the Hitachi 917 Analyzer and future similar analyzer models. It was adapted from the currently marketed Abuscreen ONLINE Cannabinoids test kit. The labeling and packaging have been changed for use on the Hitachi 917 Analyzer as well as an addition of a surfactant to the diluent. This modified test kit is not a replacement to the currently marketed kit.
The Hitachi 917 Analyzer System is a fully automatic, computer-controlled system for clinical chemistry. It was conceived for both quantitative and qualitative in vitro determination using a large variety of tests for analysis, e.g. in serum and urine. Integrated in the system is an ion-selective unit for determination of electrolytes. The throughout per hour is 800 tests for clinical chemistry (1200 with electrolytes). The system consists of the analyzer which performs all functions required for fully automatic sample and test processing. Beginning with the automatic recording of patient samples - provided that they are supplied in barcode-labeled vessels - up to the photometric measurement and results transmission to the computer unit.
The Abuscreen ONLINE Cannabinoids device meets its acceptance criteria through various performance characteristics, primarily precision (qualitative and quantitative) and accuracy studies across different cutoff concentrations (20 ng/mL, 50 ng/mL, and 100 ng/mL).
Here's a breakdown of the requested information:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implicitly defined by performing "Precision Qualitative" where >95% negative at a concentration below the cutoff and >95% positive at a concentration above the cutoff should be observed. Precision is also assessed by coefficients of variation (CV%) for both within-run and day-to-day measurements of optical density (OD) and concentration (ng/mL). Accuracy is assessed by comparing the device's qualitative results with confirmed positive samples.
| Performance Characteristic | Acceptance Criteria (Implicit from Predicate/Standard Practice) | Reported Device Performance (Proposed Device) |
|---|---|---|
| Precision Qualitative (20 ng/mL Cutoff) | ||
| at 15 ng/mL (Negative) | >95% negative | >95% negative |
| at 25 ng/mL (Positive) | >95% positive | >95% positive |
| Within Run CV% (OD) | Low CV% (e.g., <5%) | 0.9% (15ng/mL) to 2.0% (60ng/mL) |
| Day-to-Day CV% (OD) | Low CV% (e.g., <10%) | 1.3% (15ng/mL) to 2.9% (60ng/mL) |
| Precision Qualitative (50 ng/mL Cutoff) | ||
| at 40 ng/mL (Negative) | >95% negative | >95% negative |
| at 60 ng/mL (Positive) | >95% positive | >95% positive |
| Within Run CV% (OD) | Low CV% (e.g., <5%) | 1.1% (40ng/mL) to 2.9% (25ng/mL) |
| Day-to-Day CV% (OD) | Low CV% (e.g., <10%) | 1.1% (25ng/mL) to 4.2% (75ng/mL) |
| Precision Qualitative (100 ng/mL Cutoff) | ||
| at 80 ng/mL (Negative) | >95% negative | >95% negative |
| at 120 ng/mL (Positive) | >95% positive | >95% positive |
| Within Run CV% (OD) | Low CV% (e.g., <5%) | 1.2% (100ng/mL) to 2.9% (150ng/mL) |
| Day-to-Day CV% (OD) | Low CV% (e.g., <10%) | 1.6% (50ng/mL) to 3.8% (100ng/mL) |
| Precision Quantitative (20 ng/mL Cutoff) | ||
| Within Run CV% (ng/mL) | Low CV% (e.g., <10%) | 1.3% (60ng/mL) to 5.0% (15ng/mL) |
| Day-to-Day CV% (ng/mL) | Low CV% (e.g., <15%) | 3.3% (50ng/mL) to 9.1% (15ng/mL) |
| Precision Quantitative (50 ng/mL Cutoff) | ||
| Within Run CV% (ng/mL) | Low CV% (e.g., <10%) | 1.1% (40ng/mL) to 2.9% (25ng/mL) |
| Day-to-Day CV% (ng/mL) | Low CV% (e.g., <15%) | 3.3% (50ng/mL) to 5.8% (60ng/mL) |
| Precision Quantitative (100 ng/mL Cutoff) | ||
| Within Run CV% (ng/mL) | Low CV% (e.g., <10%) | 1.5% (150ng/mL) to 2.3% (50ng/mL) |
| Day-to-Day CV% (ng/mL) | Low CV% (e.g., <15%) | 2.6% (80ng/mL) to 3.8% (100ng/mL) |
| Accuracy (20 ng/mL Cutoff) | All confirmed positives detected | N=69 Confirmed Positives, 69 Pos., 0 Neg. |
| Accuracy (50 ng/mL Cutoff) | All confirmed positives detected above cutoff; appropriate negatives below cutoff | N=69 Confirmed Positives, 53 Pos., 16 Neg. |
| Accuracy (100 ng/mL Cutoff) | All confirmed positives detected above cutoff; appropriate negatives below cutoff | N=69 Confirmed Positives, 37 Pos., 32 Neg. |
| Limit of Detection | Comparable to predicate or established standard | 7 ng/mL |
2. Sample size used for the test set and the data provenance
- Test Set Sample Size:
- Accuracy: N = 69 confirmed positive samples were used across all three cutoff concentrations.
- Precision (Qualitative and Quantitative): Various concentrations (e.g., 15, 20, 25, 50, 60 ng/mL for 20 ng/mL cutoff) were tested for within-run and day-to-day precision. The exact number of replicates or runs for each concentration is not explicitly stated, but the presence of Mean (OD/ng/mL) and CV% values implies multiple measurements.
- Data Provenance: Not specified in the provided text (e.g., country of origin, retrospective or prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- The document implies that "Confirmed Positives" were used as ground truth for accuracy studies. However, the number of experts, their qualifications, or the method of confirmation are not specified in the provided text.
4. Adjudication method for the test set
- The document does not describe an adjudication method for the test set. The term "Confirmed Positives" suggests a definitive reference method was used, but the process of arriving at this confirmation (e.g., 2+1, 3+1, or a single expert) is not detailed.
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
- This is a diagnostic device for in vitro detection of cannabinoids in urine, not an imaging-based AI system requiring human interpretation or multi-reader studies. Therefore, an MRMC comparative effectiveness study is not applicable and was not performed.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- The device is an automated clinical chemistry analyzer test kit. The performance described (precision and accuracy) is inherently the standalone performance of the device (reagents and analyzer) without human-in-the-loop diagnostic interpretation in the sense of AI-assisted diagnosis. The test itself is an "algorithm only" performance, where the algorithm is the chemical reaction and photometric measurement logic of the analyzer.
7. The type of ground truth used
- The ground truth for the accuracy studies was "Confirmed Positives." This implies a reference method, likely Gas Chromatography-Mass Spectrometry (GC-MS), which is typically considered the gold standard for confirming drug concentrations in toxicology. The text does not explicitly state GC-MS, but "Confirmed Positives" for drug testing generally refers to such highly accurate analytical methods.
8. The sample size for the training set
- This device is an in vitro diagnostic assay, not a machine learning model that requires a "training set" in the conventional sense of AI. There is no mention of a training set for an algorithm. The development of such assays involves method validation and optimization by the manufacturer, but this is a different process than training a predictive AI model.
9. How the ground truth for the training set was established
- As explained in point 8, the concept of a "training set" and associated ground truth establishment is not applicable to this type of in vitro diagnostic device.
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