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For in vitro diagnostic use only. VITROS Chemistry Products mALB Reagent is used to quantitatively measure albumin concentration in human urine (mALB). Measurement of urinary albumin aids in the diagnosis of diabetic nephropathy, hypertension and cardiovascular disease.

For in vitro diagnostic use. VITROS Chemistry Products Calibrator Kit 24 is used to calibrate VITROS 5,1 FS Chemistry Systems for the quantitative measurement of albumin in urine (mALB).

For in vitro diagnostic use only. VITROS Chemistry Products mALB Performance Verifiers are assayed controls used to monitor the performance of mALB Reagent on VITROS 5,1 FS Chemistry Systems.

The VITROS 5,1 FS Chemistry System is a fully automated clinical chemistry analyzer intended for use in the in vitro determination of various analytes in human specimens (serum, plasma, urine, and cerebrospinal fluid). The VITROS 5,1 FS System is designed for use with VITROS Chemistry Products MicroTip and Thin Film assays.

The system is comprised of four main elements:

1. The VITROS 5,1 FS Chemistry System instrumentation, which provides automated use of the chemistry reagents.
2. The VITROS Chemistry Products MicroTip range of liquid reagent products (in this case VITROS Chemistry Products mALB Reagent, VITROS Chemistry Products Calibrator Kit 24 and VITROS Chemistry Products mALB Performance Verifiers I and II), which are combined by the VITROS 5,1 FS Chemistry System to perform the VITROS mALB assay.
3. The VITROS Chemistry Products Thin Film range of dry products, which are dry, multilayered, analytical elements, coated on polyester supports.
4. Common reagents used by multiple assays on the VITROS System (in this case, VITROS Chemistry Products FS Diluent Pack 2).

The VITROS System and reagents are designed specifically for use with the VITROS Chemistry Products range of products.

The provided text describes the 510(k) submission for the VITROS Chemistry Products mALB Reagent, Calibrator Kit 24, and Performance Verifiers. The submission focuses on demonstrating substantial equivalence to a predicate device, rather than defining and proving acceptance criteria with a standalone study against a ground truth as would be typical for an AI/ML device.

Therefore, many of the requested categories (e.g., sample size for test set, number of experts, adjudication method, MRMC study, training set details) are not directly applicable or explicitly mentioned in the context of this 510(k) submission. This document describes a traditional in-vitro diagnostic device, not an AI/ML device.

However, I will extract the available information and indicate where data is not provided or applicable given the nature of the submission.

1. Table of Acceptance Criteria and Reported Device Performance

For the VITROS Chemistry Products mALB Reagent and Calibrator Kit 24, the primary method for demonstrating performance and equivalence was a correlation study against a predicate device. For the Performance Verifiers, it was a comparison of device characteristics.

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

• Sample Size for Test Set: Not explicitly stated for the correlation study. The document mentions "commercially available reagents along with patient samples" were used to demonstrate equivalence, but the number of such samples is not disclosed in the provided text.
• Data Provenance: Not explicitly stated. Given the context of a 510(k) for an in-vitro diagnostic device from a US-based company (Ortho-Clinical Diagnostics, Inc. Rochester, New York), it's likely the studies were conducted in the US, but this is not confirmed.
• Retrospective or Prospective: Not explicitly stated. For a correlation study of this nature, samples could be collected either retrospectively or prospectively.

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

This concept is not applicable in the context of this 510(k) submission. The "ground truth" for the performance evaluation of the new device (VITROS mALB assay) was essentially the results obtained from the predicate device (N Antiserum to Human Albumin assay on the Dade Behring BN ProSpec System). The performance of the predicate device itself would have been established through its own validation studies, often involving clinical correlation and robust analytical performance, rather than expert consensus on individual cases.

4. Adjudication method for the test set

Not applicable. There was no expert "adjudication" in the sense of reviewing disagreements on a test set, as the performance was assessed analytically by comparing results to a predicate 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 an In-Vitro Diagnostic device for measuring albumin levels in urine, not an AI/ML-driven imaging or diagnostic tool requiring human reader studies.

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

This refers to the independent analytical performance of the new device (VITROS mALB assay). Yes, standalone studies were performed as indicated by the mention of studies for:

• Precision
• Analytical sensitivity
• Specificity
• Expected values

The results of these studies are stated to be in the "VITROS mALB Reagent Instructions For Use," which is not part of the provided text. The correlation study against the predicate device also represents a standalone performance assessment of the new device against another established method.

7. The type of ground truth used

The primary "ground truth" for demonstrating substantial equivalence for the VITROS mALB assay was the results obtained from the legally marketed predicate device (N Antiserum to Human Albumin assay on the Dade Behring BN ProSpec System). This is a well-established method for demonstrating equivalence for IVD devices. The predicate device's own performance would have been validated against clinical outcomes or reference methods.

8. The sample size for the training set

Not applicable. This is not an AI/ML device, so there is no concept of a "training set" in the context of machine learning model development. The development and optimization of the reagent and assay method would have involved various experimental batches and iterative testing, but this is distinct from an AI training set.

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

Not applicable, as there is no training set for this type of device.

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