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Precinorm ® U/ Precipath ® U is for use in quality control by monitoring accuracy and precision for the quantitative methods as specified in the enclosed value sheet.

Precinorm ® U/ Precipath ® U is a two level quality control product consisting of lyophilized human sera with constituents added as required to obtain desired component levels

This document is a 510(k) summary for the Precinorm® Universal and Precipath® Universal Control Sera. It is a submission for a multi-analyte control product used for quality control in quantitative laboratory methods. The submission declares substantial equivalence to a previously marketed device (K992900).

Here's the breakdown of the information requested, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

It's important to note that this 510(k) submission is for a quality control product, not a diagnostic device with specific performance metrics like sensitivity or specificity. The "acceptance criteria" here relate to demonstrating substantial equivalence to a predicate device, primarily by showing that the modified device's characteristics (intended use, format, levels, stability) are the same, and the change in analyte source does not compromise performance.

Study Proving Acceptance Criteria:

The document describes a submission seeking substantial equivalence to a predicate device (K992900). The "study" here is essentially a comparison study demonstrating that the characteristics of the modified device Precinorm U/ Precipath U are either identical to the predicate or that any differences (specifically the addition of bovine plasma albumin as an analyte source for Precinorm U) do not alter the fundamental safety or effectiveness of the device as a quality control material. The basis for substantial equivalence is listed as the currently marketed Roche Diagnostics Precinorm® Universal and Precipath® Universal Control Sera (K992900).

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

This information is not provided in the given text. For a quality control product, a "test set" in the traditional sense of patient samples for diagnostic performance is not applicable. The comparison likely involved testing the modified control material against the predicate material over a range of analytes and instruments to ensure equivalent performance, but no details on sample size, data origin, or study design are given.

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 provided and is generally not applicable for a quality control product submission. The "ground truth" for a control material would be its assigned target values and acceptable ranges, typically established through extensive reference methods and laboratory consensus across multiple instruments and laboratories, not by a small number of experts interpreting results.

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

This information is not provided and is not applicable for this type of submission. Adjudication methods are used in studies involving human interpretation or clinical outcomes, not typically for the characterization of a quality control product.

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

A multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned and is not relevant for this device. This type of study is specific to diagnostic imaging devices, often involving AI, where human readers interpret medical images. This submission is for a laboratory quality control material.

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

This information is not applicable for this device. This device is a biochemical control material, not a software algorithm or a device that performs a diagnostic task independently. Its "performance" is its ability to consistently produce target values and reflect the accuracy and precision of the analytical systems it is used to control.

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

The "ground truth" for a quality control product like Precinorm U/ Precipath U is typically established by:

• Assigned Target Values: These are derived from extensive testing using recognized reference methods and inter-laboratory studies across a range of instruments.
• Acceptable Ranges: Determined statistically from these studies to encompass expected variation for different analytical platforms.

The document does not explicitly state how the target values and ranges for these specific control sera were established, but this is the general method for such products. It certainly isn't expert consensus on clinical findings, pathology, or outcomes data, as this product is not a diagnostic test for patient samples.

8. The sample size for the training set

This is not specified and is not directly applicable in the terms of a "training set" for a machine learning algorithm. For a quality control material, the equivalent would be the number of lots, replicates, and instruments used in the characterization studies to set target values and stability claims. This detail is not provided.

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

As in point 7 and 8, the concept of a "training set" and its "ground truth" is not directly relevant to this non-AI, non-diagnostic quality control product. The "ground truth" (i.e., the expected performance characteristics of the control material over its shelf-life) would be established through rigorous analytical testing and characterization studies, often involving multiple laboratories and instruments, to assign values and validate stability under various conditions. The document does not provide details on these specific studies, beyond stating the product's stability claims.

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For quality control in the quantitative determination of substrates, electrolytes, lipids, enzymes, proteins, and drugs. The control is used for monitoring accuracy or precision for manual techniques and assays from Roche on automated clinical chemistry analyzers.

Roche Diagnostics Precinorm® Universal and Precipath® Universal Control Sera is a two level quality control product prepared from lyophilized human serum with addition of constituent analytes as required to obtain normal and pathological levels.

The provided 510(k) summary for "Precinorm® Universal and Precipath® Universal Control Sera" describes a quality control product for laboratory assays. However, it does not include detailed acceptance criteria or a specific study proving the device meets those criteria in the way typically expected for a diagnostic or AI-driven medical device.

Instead, the submission focuses on establishing substantial equivalence to an already marketed predicate device (K811832). This means the primary "study" presented is a comparison to demonstrate that the modified device performs similarly to the predicate, with the main modification being the inclusion of additional analytes.

Therefore, many of the requested points cannot be directly extracted from the provided text because they pertain to a different type of device evaluation (e.g., performance study for a diagnostic algorithm, clinical trial for a treatment).

Here's a breakdown based on the provided information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't provide a table of acceptance criteria with specific numerical targets. Instead, it relies on demonstrating equivalence to a predicate device. The performance is implied to be similar to the predicate device, which is an already marketed and cleared product.

The "acceptance criteria" here are essentially "performs sufficiently well as a quality control material for the listed analytes on clinical chemistry analyzers, similar to the predicate device, and includes values for additional analytes." There are no specific performance metrics like sensitivity, specificity, or AUC as one might see for an AI diagnostic.

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

This information is not provided. The submission is a regulatory filing for a quality control product, not a performance study for a diagnostic. The "test set" would implicitly be the demonstration that the control material can be used to monitor accuracy and precision on various analyzers, but no specific data or sample sizes for this demonstration are detailed.

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

Not applicable. For a quality control material, "ground truth" would be the known concentration of analytes within the control serum, established through validated reference methods and internal quality assurance processes by the manufacturer. This isn't a diagnostic device requiring expert interpretation of patient data.

4. Adjudication Method for the Test Set:

Not applicable for a quality control material.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

No. This is a quality control product, not an AI-assisted diagnostic tool for human readers.

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

Not applicable. This is a physical control serum, not an algorithm.

7. The Type of Ground Truth Used:

For a quality control material, the "ground truth" for the assigned values of the analytes within the sera is established by the manufacturer through rigorous analytical testing using reference methods and internal standardization processes. This is implicit in the manufacturing of such products.

8. The Sample Size for the Training Set:

Not applicable. This is not an AI/machine learning device that requires a training set. The "training" for such a product involves its manufacturing process and validation of its stability and assigned values.

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

Not applicable. This is not an AI/machine learning device. The "ground truth" for its inherent properties (analyte concentrations, stability) would be established through a manufacturer's internal quality control and validation procedures based on analytical chemistry principles.

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