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For in vitro diagnostic use only. VITROS Chemistry Products dHDL Slides are used to quantitatively measure HDL cholesterol (HDLC) concentration in serum and plasma using VITROS 250/350/950/5, I FS and 4600 Chemistry Systems and the VITROS 5600 Integrated System. High Density Lipoprotein (HDL) cholesterol is used to evaluate the risk of developing coronary heart disease (CHD). The risk of CHD increases with lower HDL cholesterol concentrations.

The VITROS dHDL assay is performed using the VITROS Chemistry Products dHDL Slide and the VITROS Chemistry Products Calibrator Kit 25 on the VITROS Chemistry Systems. The VITROS dHDL Slide is a multi-layered analytical element coated on a polyester support. The method is based on a non-HDL precipitation method followed by an enzymatic detection. All reactions necessary for a single quantitative measurement of HDLC take place within the multi-layered analytical element of a VITROS Chemistry Products dHDL Slide. A drop of sample fluid is metered onto the slide and a reaction occurs which ultimately generates a colored dye. The density of dye formed is proportional to the HDL Cholesterol concentration present in the sample and is measured by reflectance spectrophotometry.

The provided document does not contain information about acceptance criteria or a study that proves the device meets specific acceptance criteria in the context of clinical performance metrics like sensitivity, specificity, or reader studies for an AI device.

This document describes a Special 510(k) submission for a modification to an existing in vitro diagnostic (IVD) device, the VITROS Chemistry Products dHDL Slide, used to measure HDL cholesterol. The modification primarily involves a reduction in the physical size of the slide and consequently a reduction in the reagents per slide and the sample volume required per test, without changing the fundamental technology or intended use.

Therefore, the requested information elements related to AI device performance evaluation (e.g., sample sizes for test sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, training set details) are not applicable to this type of device and submission.

The "study" conducted for this submission would have focused on demonstrating that despite the physical changes, the modified device performs equivalently to the predicate (unmodified) device in terms of analytical performance. This would typically involve analytical studies like method comparison, precision, linearity, and interference testing, not clinical performance studies as would be conducted for a new AI-powered diagnostic.

Here's what can be extracted and inferred from the document regarding the device and its evaluation:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria (Inferred from a Special 510(k) for device modification): The primary acceptance criterion for this type of submission is demonstrating substantial equivalence to the predicate device. This means the modified device must perform comparably to the predicate device for its intended use, with no new questions of safety or effectiveness raised. Specifically, for an IVD, this generally means analytical performance (e.g., accuracy, precision, linearity, measuring range) should be equivalent. The document indicates that the concentration of ingredients per slide has changed, but the concentration of ingredients in the assay reaction remains the same, which is key to maintaining performance.
• Reported Device Performance: The document provides a summary stating: "The information presented in the premarket notification provides a reasonable assurance that the VITROS Chemistry Products dHDL Slides (modified) for use with human serum and plasma is substantially equivalent to the predicate (unmodified VITROS dHDL Slide) and is safe and effective for the stated intended use." This statement is the ultimate "reported performance" in the context of a 510(k) submission, confirming that the device meets the regulatory standard of substantial equivalence. Specific analytical performance metrics (e.g., bias, correlation coefficients between modified and predicate device results) are not detailed in this summary but would have been part of the full 510(k) submission to demonstrate equivalence.

2. Sample size used for the test set and the data provenance:
* Not applicable / Not explicitly stated in this summary. This document is a summary of a Special 510(k), which focuses on modifications to an existing device. The "test set" in this context refers to samples used for analytical verification and validation (e.g., method comparison, precision studies), not a clinical test set for AI performance. The details of these analytical studies (number of samples, study design) are not included in this summary document.
* Data Provenance: Not specified in this summary.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
* Not applicable. This is an in vitro diagnostic device for quantitative measurement of HDL cholesterol. The "ground truth" for such a device is established by qualified laboratory methods (e.g., CDC reference methods, other validated clinical laboratory analyzers) or reference materials, not by human expert interpretation of images or other subjective data. Human experts are typically involved in interpreting the results in a clinical context, but not in establishing the "ground truth" for the device's measurement performance itself.

4. Adjudication method for the test set:
* Not applicable. See point 3.

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 device is an automated in vitro diagnostic assay, not an AI-powered image analysis or diagnostic support system for human readers.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
* Not applicable. This is an automated laboratory test, not an AI algorithm. Its performance is inherently "standalone" in the sense that it provides a quantitative result without human subjective interpretation of an image or pattern.

7. The type of ground truth used:
* Inferred: For an IVD measuring HDL cholesterol, the ground truth for establishing analytical performance typically comes from:
* Reference methods: Comparability to established, validated reference methods (e.g., CDC-certified reference methods for lipid measurements).
* Certified reference materials: Materials with known, traceable concentrations of the analyte.
* Standard clinical laboratory analyzers: Comparison to existing, cleared devices in a method comparison study.

8. The sample size for the training set:
* Not applicable. This is not a machine learning or AI device that requires a "training set."

9. How the ground truth for the training set was established:
* Not applicable. This is not a machine learning or AI device.

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For in vitro diagnostic use only. VITROS dHDL Slides are used to quantitatively measure HDL cholesterol (HDLC) concentration in serum and plasma. High Density Lipoprotein (HDL) cholesterol is used to evaluate the risk of developing coronary heart disease (CHD). The risk of CHD increases with lower HDL cholesterol concentrations.

For in vitro diagnostic use. VITROS Chemistry Products Calibrator Kit 25 is used to calibrate VITROS Chemistry Systems for the quantitative measurement of HDL cholesterol using VITROS Chemistry Products dHDL Slides.

For in vitro diagnostic use only. VITROS Chemistry Products Performance Verifiers I and II are assayed controls used to monitor performance on VITROS Chemistry Systems.

The VITROS Chemistry Systems are fully automated clinical chemistry analyzers intended for use in the in vitro determination of various analytes in human specimens (serum, plasma, urine, and cerebrospinal fluid).

The system is comprised of four main elements:

1. The VITROS Chemistry System - instrumentation, which provides automated use of the chemistry reagents.
2. The VITROS Chemistry Products MicroSlides range of products, in this case the VITROS Chemistry Products dHDL Slides, VITROS Chemistry Products Calibrator Kit 25 and VITROS Chemistry Products Performance Verifier I and II which are combined by the VITROS Chemistry Systems to perform the VITROS dHDL Slide assay.
3. The VITROS Chemistry Products MicroTip range of liquid reagent products that are formulated for use only on the VITROS 5,1 FS Chemistry System.
4. Common reagents used by multiple assays on the VITROS Chemistry Systems, in this case VITROS Chemistry Products 7% BSA.

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

The provided document is a 510(k) summary for in-vitro diagnostic devices (VITROS Chemistry Products dHDL Slides, Calibrator Kit 25, and Performance Verifiers I and II) and therefore does not include the detailed acceptance criteria and study information typically found for AI/ML-driven medical devices. The document focuses on demonstrating substantial equivalence to a predicate device.

However, I can extract the information provided regarding the comparison study.

Here's a breakdown of the available information based on your request categories:

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

The document doesn't explicitly state "acceptance criteria" in the way an AI/ML study would (e.g., target specificity, sensitivity thresholds). Instead, it relies on demonstrating substantial equivalence to a predicate device. The performance is reported as a correlation between the new device and the predicate device.

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

• Sample Size: The document states that "patient samples" were used, but the specific number is not provided.
• Data Provenance: Not specified (e.g., country of origin).
• Retrospective/Prospective: Not specified.

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

This information is not applicable as this is an in-vitro diagnostic device measuring HDL cholesterol. The "ground truth" would be established by reference methods or comparison to the predicate device, not by expert interpretation of images or other subjective data. No experts for ground truth establishment are mentioned.

4. Adjudication method for the test set:

Not applicable for this type of device. The comparison is between quantitative measurements from two different analytical systems.

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 assay, not an AI-assisted diagnostic tool that would involve human readers interpreting results.

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

The device is a standalone in-vitro diagnostic assay. Its performance is evaluated intrinsically and through comparison to the predicate device. It operates without human-in-the-loop performance in the interpretative sense of AI.

7. The type of ground truth used:

The "ground truth" for this study is essentially the quantitative measurement of HDL cholesterol obtained from the predicate device, the AHDL Flex® reagent cartridge for use with the Dade Behring Dimension® analyzer. The study aims to show that the new device's measurements are highly correlated with those of the predicate device.

8. The sample size for the training set:

The document describes a comparison study, not a machine learning study with distinct training and test sets in the typical sense. Therefore, "training set" is not applicable in this context. The study utilized patient samples for the correlation analysis.

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

Not applicable, as there's no "training set" in the context of an ML model. The comparison is against the predicate device's measurements.

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