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XN CHECK is used for control and calibration verification of Sysmex XN series (XN-10, XN-20) analyzers. It is not, however, intended for actual calibration of these analyzers. Assayed parameters include:

RBC(108/μL), HGB(g/dL), HCT(%), MCV(fL), MCH(pg), MCHC(g/dL), PLT(103/μL), PLT-F(102/pL), RDW-SD(fL), RDW-CV(%), MPV(fL), WBC(103/pL), NEUT(%), LYMPH (%), MONO(%), EO(%), BASO(%), IG(%), NEUT#(103)µL), LYMPH#(103/pL), MONO# (10°/µL), EO#(10°/µL), BASO#(103/pL), IG#(103/pL), RET#(10°/μL), RET%, IRF%, RET-HE(pg), NRBC#(10°/μL), NRBC% (/100 WBC)

XN CHECK™ is an in-vitro diagnostic product that contains the following: stabilized red blood cell component(s), stabilized white blood cell component(s), stabilized platelet component(s), and stabilized nucleated red blood cell component(s) in a preservative medium. The product is packaged in polypropylene plastic vials with screw caps containing 3 ml. The vials will be packaged in (4) welled vacuum formed clamshell container with the Instructions for Use / assay sheet. The product must be stored at 2 - 8° C.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state numerical acceptance criteria for performance metrics (like accuracy, sensitivity, specificity, etc.) for the XN CHECK™. Instead, the performance objective was to be "consistently equivalent and effective" to the predicate product. The studies aimed to demonstrate stability (open and closed vial) and overall performance comparable to the predicate.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify the exact sample size for the test set or the data provenance (e.g., country of origin of the data, retrospective or prospective). It only states that "The following studies were conducted to establish performance of XN CHECK™" and mentions "All testing" for stability. Given the nature of a hematology control product, the "test set" would likely refer to batches of the control material tested against the predicate, rather than patient data.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided because this type of device (a quality control mixture) does not typically involve human expert interpretation for establishing ground truth in the same way an imaging AI diagnostic might. The ground truth for a control material is typically established through analytical methods and comparisons to a known standard or predicate device.

4. Adjudication Method for the Test Set

Not applicable for this type of device. The evaluation of a hematology control mixture relies on analytical measurements against established values and comparison to a predicate, not expert adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic tools that assist human readers (e.g., radiologists, pathologists). The XN CHECK™ is a quality control product, not a diagnostic tool that interacts with human readers in that capacity.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The XN CHECK™ is a physical control material, not an algorithm. Therefore, the concept of "standalone (algorithm only)" performance does not apply. Its performance is assessed by how well it maintains its specified parameter values and how consistently it performs across analyzers, similar to a known reference standard.

7. The Type of Ground Truth Used

The ground truth for the XN CHECK™ would be the expected assayed values for each parameter (RBC, HGB, etc.) as determined by the manufacturer, and its performance is evaluated by demonstrating conformance to these expected values and equivalency to the predicate device. The predicate device (e-CHECK (XE)™) itself serves as a reference point for performance comparison.

8. The Sample Size for the Training Set

This product is a physical control material and not an AI algorithm, so the concept of a "training set" in the context of machine learning does not apply. Its manufacturing process and formulation are developed through R&D, and its performance is validated through testing batches of the product, not by training a model on a dataset.

9. How the Ground Truth for the Training Set Was Established

Given that this is not an AI algorithm, there is no "training set" or ground truth established in that manner. The "ground truth" for the development of such a control would involve rigorous analytical methods, characterization of its components, and extensive testing to ensure stability and accuracy across different batches and on the target analyzers.

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