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XN CAL PF is used for the calibration and calibration verification of Sysmex XN series (XN-10, XN-11, XN-20, XN-21) analyzers. Assayed parameters include: PLT-F (10^3/ uL)

XN CAL PF contains the following: stabilized red blood cell component(s), and stabilized platelet component(s) in a preservative medium.

This document describes the XN CAL™ PF device, a calibrator for cell indices used with Sysmex XN series analyzers. The primary purpose of the submission (K141955) is to expand the compatible analyzers and extend the closed-vial stability.

Here's an analysis of the provided text in relation to acceptance criteria and study details:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state numerical acceptance criteria for the different studies. Instead, it states that "The resultant data set established that XN CAL PF is safe and effective for its intended use and that the product is stable for the entire product dating. The product fulfills its intended use as instructed in the Instructions for Use." and "Study results show XN CAL PF to be consistently reproducible, substantially equivalent to the predicate products, and stable for the entire product dating."

Based on the studies conducted and the claims made, the inferred performance metrics and outcomes are:

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not provide details on the specific sample sizes used for the Open-Vial Stability, Closed-Vial Stability, and Precision Performance studies. It also does not specify the country of origin of the data or whether the studies were retrospective or prospective. It only states that "Streck collected product performance data for the following studies".

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

This information is not applicable to this device. XN CAL™ PF is a calibrator for laboratory analyzers, not an diagnostic imaging or AI-driven decision support tool that requires expert human interpretation to establish ground truth for a test set in the traditional sense. The "truth" or reference values for a calibrator are established through manufacturing processes and validation against primary reference methods or standards, not through expert consensus on cases.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

This information is not applicable for the same reasons as #3. Adjudication methods are typically used in clinical studies where human interpretation or diagnoses are being compared, often with an AI system. Ground truth for a laboratory calibrator is determined by its intrinsic properties and manufacturing metrology.

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

An MRMC study is not applicable to this type of device. XN CAL™ PF is a calibrator, not an AI system or software that assists human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This concept is not applicable to XN CAL™ PF. It is a physical calibrator product, not a standalone algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The document does not explicitly state how the "ground truth" or reference values for the calibrator were established. However, for a calibrator, the ground truth would typically be established through:

• Manufacturer's certified reference values: The company manufactures the calibrator to specific concentrations/values (e.g., PLT-F) and validates these values using highly accurate and precise reference methods or instruments, often traceable to international standards.
• Traceability: The values are likely traceable to a recognized standard or method for cell enumeration, ensuring accuracy and consistency across different batches and against a predicate.

8. The sample size for the training set

This information is not applicable. XN CAL™ PF is a physical calibrator product; it does not involve machine learning or a "training set" in the context of AI.

9. How the ground truth for the training set was established

This information is not applicable for the same reasons as #8.

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XN CAL PF is used for calibration and calibration verification of Sysmex XN series (XN-10, XN-20) analyzers. The assayed parameter is:

PLT-F (103/pL)

XN CAL™ PF is an in-vitro diagnostic product that contains the following: stabilized red blood cell component(s), and stabilized platelet 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 (5) welled or (1) 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, while noting the limitations in what can be extracted for a medical device approval that is not based on AI/ML.

Important Note: The provided document is a 510(k) summary for a hematology calibrator (XN CAL™ PF), not an AI/ML powered medical device. Therefore, many of the requested points related to AI/ML (like training sets, experts for ground truth, MRMC studies, standalone performance) are not applicable to this type of device and will not be found in the document. This device is an in-vitro diagnostic product used for instrument calibration.

Acceptance Criteria and Reported Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a quantitative table format for performance metrics. Instead, it relies on demonstrating "substantial equivalence" to a predicate device and stability studies.

Study Details (Applicable to XN CAL™ PF)

2. Sample size used for the test set and the data provenance:

• Sample Size for Test Set: The document does not specify a numerical sample size for the test sets used in the Open-Vial Stability, Closed-Vial Stability, and Precision Performance studies. It only states that "all testing showed" the results.
• Data Provenance: Not specified in the document. The studies were likely conducted internally by Streck.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not Applicable. This is a calibrator for a hematology analyzer, not a diagnostic device requiring human expert ground truth for interpretation of images or patient data. Its "ground truth" is its own verified composition and stability, and its performance is evaluated against the instrument's measurements.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not Applicable. No human adjudication is involved for this type of device.

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 is not an AI/ML-powered device or one that involves human readers interpreting cases.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Not Applicable. This is not an algorithm. The "performance" of the device is its ability to properly calibrate the instrument and remain stable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

• For this device, the "ground truth" for its performance evaluation would likely be:
  • Defined values/concentrations for the stabilized red blood cell and platelet components (for calibration accuracy).
  • Time-based measurements against predefined stability limits (for open and closed vial stability).
  • Repeatability measurements (for precision performance).
  • These are internal quality control and validation metrics, not external clinical ground truth derived from human experts or pathology.

8. The sample size for the training set:

• Not Applicable. This is not an AI/ML device that requires a "training set."

9. How the ground truth for the training set was established:

• Not Applicable. As there is no training set for an AI/ML model, this question is not relevant.

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