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510(k) Data Aggregation
(219 days)
ARCHITECT B12 REAGENTS, ARCHITECT B12 CALIBRATORS, AND ARCHITECT B12 CONTROLS
The ARCHITECT B12 assay is a chemiluminescent microparticle Intrinsic Factor assay for the quantitative determination of vitamin B12 in human serum on the ARCHITECT i System. Measurements obtained by this device are used in the diagnosis and treatment of anemias of gastrointestinal malabsorption.
The ARCHITECT B12 Controls are used for the estimation of test precision and the detection of systematic analytical deviations of the ARCHITECT i System when used for the quantitative determination of vitamin B12 in human serum when using the ARCHITECT B12 Reagent Kit.
The ARCHITECT B12 Calibrators are used to calibrate the ARCHITECT i System when the system is used for the quantitative determination of vitamin B12 in human serum using the ARCHITECT B12 Reagent Kit.
The ARCHITECT B12 assay is a two-step assay with an automated sample pretreatment, for determining the presence of B12 in human serum using chemiluminescent microparticle immunoassay (CMIA) technology with flexible assay protocols, referred to as Chemiflex.
Sample and Pre-Treatment Reagent 1, Pre-Treatment Reagent 2, and Pre-Treatment Reagent 3 are combined. An aliquot of the pre-treated sample is aspirated and transferred into a new reactions vessel (RV). The pre-treated sample, assay diluent, and intrinsic factor coated paramagnetic microparticles are combined. B12 present in the sample binds to the intrinsic factor coated microparticles. After washing, B12 acridinium-labeled conjugate is added in the second step. Pre-Trigger and Trigger Solutions are then added to the reaction mixture; the resulting chemiluminescent reaction is measured as relative light units (RLUs). An inverse relationship exists between the amount of B12 in the sample and the RLUs' detected by the ARCHITECT i System optics.
Acceptance Criteria and Device Performance Study for ARCHITECT B12 Assay
This document describes the acceptance criteria and the study conducted to demonstrate the substantial equivalence of the ARCHITECT B12 assay to a legally marketed predicate device.
1. Table of Acceptance Criteria and Reported Device Performance
The provided 510(k) summary does not explicitly state pre-defined acceptance criteria in terms of specific performance metrics (e.g., target accuracy, precision thresholds) that the ARCHITECT B12 assay needed to meet. Instead, the study aimed to demonstrate substantial equivalence to a predicate device. The performance was assessed through a comparison study.
Performance Metric (Implied) | Reported Device Performance |
---|---|
Agreement with Predicate Device (Correlation) | Equivalence demonstrated using current commercially available Roche Elecsys E170 Vitamin B12 reagents with 172 patient samples covering the range of 83 pg/mL to 2000 pg/mL. |
Note: The 510(k) summary focuses on demonstrating equivalence rather than meeting specific numerical acceptance criteria for a new device type. The key acceptance was the FDA's determination of "substantial equivalence."
2. Sample Size and Data Provenance for the Test Set
- Sample Size: 172 patient samples.
- Data Provenance: Not explicitly stated, but based on the context of a medical device submission, these would typically be human serum samples. The geographical origin (e.g., country) is not specified. The study is implicitly retrospective as it involves testing archived patient samples against an existing predicate device.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Not applicable. The ground truth for this type of assay comparison is established by the results of the legally marketed predicate device, not by expert interpretation.
- Qualifications of Experts: Not applicable.
4. Adjudication Method for the Test Set
- Adjudication Method: Not applicable. The comparison is objective, based on quantitative measurements from two different assay systems, rather than subjective interpretation requiring adjudication.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study: No, an MRMC comparative effectiveness study was not done. This type of study is typically used for diagnostic imaging devices where human readers interpret images. The ARCHITECT B12 assay is a quantitative in-vitro diagnostic test.
- Effect Size of Human Reader Improvement with AI: Not applicable, as no MRMC study was performed.
6. Standalone (Algorithm Only) Performance Study
- Standalone Study: Yes, the study described is a standalone performance study in the context of an in-vitro diagnostic device. The ARCHITECT B12 assay's performance was evaluated independently by comparing its results to a predicate device, without direct human-in-the-loop interpretation of the assay results themselves. The "algorithm" here refers to the entire assay system and its methodology.
7. Type of Ground Truth Used
- Type of Ground Truth: The ground truth was established by the results obtained from the legally marketed predicate device, the Roche Elecsys E170 Vitamin B12 assay (K060755). This is considered the "gold standard" for comparison in demonstrating substantial equivalence for an in-vitro diagnostic device.
8. Sample Size for the Training Set
- Sample Size: The 510(k) summary does not provide specific details about a "training set" for the ARCHITECT B12 assay in the context of machine learning or AI. In the development of an in-vitro diagnostic, calibration and validation samples are used during the development and manufacturing process, but these are distinct from a "training set" in an AI/ML context. The information provided focuses on the pivotal study for regulatory submission.
9. How Ground Truth for the Training Set Was Established
- How Ground Truth Was Established (Training Set): As no specific training set in the AI/ML sense is described, the method for establishing its ground truth is not applicable in this document portion. For the overall assay, calibration materials are used, and their values are typically traceable to reference methods or primary standards, but this is a different concept than ground truth for a machine learning model.
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