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The Diazyme Lipoprotein (a) Calibrator Set is intended for use in establishing the calibration curve for the Diazyme Lipoprotein (a) Assay reagents by turbidimetry. For in vitro diagnostic use only.

The Diazyme Lipoprotein (a) Control Set is intended for use in monitoring the quality control of results obtained with the Diazyme Lipoprotein (a) Assay reagents by turbidimetry. For in vitro diagnostic use only.

The Diazyme Lipoprotein (a) Assay is based on a latex enhanced immunoturbidimetric assay. Lp(a) in the sample binds to specific anti-Lp(a) antibody, which is coated on latex particles, and causes agglutination. The degree of the turbidity caused by agglutination can be measured optically and is proportional to the amount of Lp(a) in the sample.

Diazyme Lp(a) standard is intended for use in establishing the calibration curve for the Diazyme Lp(a) reagents by turbidimetry. The measurement of Lp(a) is useful in evaluating lipid metabolism disorders and assessing atherosclerotic cardiovascular diseases in specific populations, when used in conjunction with clinical evaluation.

Here's an analysis of the acceptance criteria and study information for the Diazyme Lipoprotein (a) Calibrator Set and Control Set, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on demonstrating the substantial equivalence of the modified device (liquid stable calibrators/controls) to the predicate device (lyophilized calibrators/controls) using various performance studies. Rather than explicit "acceptance criteria" presented as pass/fail thresholds for each metric, the document describes the analytical performance characteristics that were verified and validated to show equivalence. The "reported device performance" are the results from these verification/validation activities, demonstrating that the modified device maintains the performance characteristics of the predicate.

Given this, I'll present the key performance metrics and their reported values for both the predicate and the new device where available, implicitly showing that the new device's performance aligns with or is very similar to the predicate, thus "meeting" the unstated acceptance criteria of maintaining similar performance.

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

The document does not explicitly state the sample sizes for the "test sets" for each specific study (e.g., how many samples were used for linearity, method comparison, etc.). It mentions that the studies were performed as "verification and validation activities."

• Data Provenance: The studies were conducted by Diazyme Laboratories, likely in their facilities (Poway, CA). The data is retrospective in the sense that it evaluates the performance of a modified device against a previously cleared predicate device. The samples used (human serum, plasma) are laboratory materials. There is no mention of country of origin for these samples or if they were from a specific clinical population.

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

This submission is for calibrators and controls, which are used to standardize an assay and monitor its quality. The "ground truth" here isn't established by human experts in a clinical context (like radiologists reading images). Instead, the ground truth for the values assigned to the calibrators and controls is established through:

• Value Transfer: The "Value Transfer from predicate device Diazyme Lp(a) Reagent and calibrator (K082488) to reference lot of liquid stable calibrators." This implies the predicate device and its established values serve as the primary reference.
• Analytical Measurement: The calibrator materials were "assayed as samples in triplicate on Beckman AU400 Analyzer." Mean values from these analytical measurements become the assigned values.

Therefore, the "ground truth" is based on the analytical performance and established traceability to the predicate device, not on expert consensus.

4. Adjudication Method for the Test Set:

Not applicable in the human expert sense. The "adjudication" for the values of the calibrators and controls is based on standard laboratory practices for value assignment and verification, involving multiple analytical runs (e.g., in triplicate) and statistical averaging.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, an MRMC study was not done. This type of study is typically performed for diagnostic devices (like imaging software) where human readers (e.g., radiologists) interpret cases with and without AI assistance, and their performance is compared. This submission is for laboratory calibrators and controls, which are reagents for an automated assay, not a device requiring human interpretation in this manner.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

This concept also doesn't directly apply here in the way it does for AI algorithms. The device itself (the calibrator and control set) is a standalone reagent set designed to work with an automated analytical instrument (Olympus AU400 in this case). The performance metrics (linearity, precision, accuracy, etc.) are all a measure of the analytical performance of the reagents within the context of the assay and instrument, without direct human intervention in the measurement process (beyond setting up the analyzer and quality control).

7. The Type of Ground Truth Used:

The ground truth used for the calibrator and control values is analytical reference/traceability to the predicate device's established values and results from multiple analytical measurements on a reference instrument.

8. The Sample Size for the Training Set:

This document does not describe a "training set" in the context of machine learning or AI development. Since this is a submission for diagnostic reagents (calibrators and controls), there is no AI algorithm being trained. The "training" in a laboratory context refers to the initial process of value assignment for reference lots, typically based on multiple measurements against a hierarchy of reference materials. The document states:

• "Diazyme Lp(a) master lot of calibrator materials were assigned values... assayed as samples three times in triplicate on the Hitachi or Olympus Analyzer."
• "Lp(a) Liquid Stable Calibrator Value Verification Reference lot of the Lp(a) liquid stable calibrator with the target values assigned are used to test library samples assigned with predicate device and trueness controls."

The exact number of "master lot" or "reference lot" materials used for this initial value assignment is not specified, but it would typically involve several levels of calibrators/controls run numerous times.

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

As elaborated above, there isn't a "training set" in the AI sense. The "ground truth" (i.e., the assigned values) for the calibrators and controls were established through a process of:

1. Value Transfer from the Predicate Device: The current Diazyme Lp(a) Assay reagent and calibrators (K082488) were used as the reference.
2. Repetitive Analytical Measurements: Calibrator materials were assayed "three times in triplicate" (total of 9 measurements per calibrator level) on a clinical chemistry analyzer (Hitachi or Olympus for the predicate, Beckman AU400 for the new device).
3. Calculation of Mean Values: "For each calibrator level, mean values were calculated from the data points and assigned as the calibrator value."
4. Verification: The reference lot of liquid stable calibrator was then used to "test library samples assigned with predicate device and trueness controls" to verify the assigned values.

This process ensures the new liquid stable calibrators and controls have values traceable to the established predicate device and are analytically sound.

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