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Automated immunoassay analyzer designed specifically for in vitro diagnostic use in a clinical laboratory. The assay analysis is based on chemiluminescent technology. The system provides results for both direct measurements and calculated parameters.

The AcuStar is an automated, bench-top system for lab use that measures the analyte amount in samples by: Subjecting the sample to reagents that cause a reaction with an antigen or antibody in the sample. Placing the cuvettes in a controlled environment to let the reactants bind into a complex. Separating out the complex from unused reactants. Treating this complex with a chemical that produces light in proportion to the analyte concentration. Measuring the light output to determine the amount of antibodies or antigens that were in the sample.

This document is a 510(k) premarket notification for a software update to an existing medical device, the ACL AcuStar™. The submission qualifies as a "Special 510(k)" because the modification (updating the operating system from Windows XP to Windows 7) does not change the indications for use, operating principle, labeled performance claims, hardware, data reduction software, fluidic design, test parameters, calibration, quality control, consumables, or reagents. Therefore, the information provided focuses on demonstrating that the updated device is substantially equivalent to the predicate device and does not involve a new clinical study to establish acceptance criteria or device performance in the same way a de novo submission would.

Given the nature of this Special 510(k), the prompt's questions regarding acceptance criteria, device performance, sample sizes, expert ground truth, adjudication methods, MRMC studies, and standalone performance are not directly applicable in the traditional sense for a new device evaluation. Instead, the "acceptance criteria" here relate to demonstrating that the change did not negatively impact the device's original validated performance.

Here's how the information aligns with the prompt's request, interpreted in the context of a Special 510(k) for a software update:

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

The document does not present a table of specific performance acceptance criteria (e.g., sensitivity, specificity, accuracy) for the device itself because it's a software update to an already cleared device. Instead, the implicit acceptance criterion is that the device's performance remains unchanged despite the operating system upgrade.

The "reported device performance" in this context is that the updated device is substantially equivalent to the predicate device, meaning its performance characteristics relevant to its intended use have not been altered.

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 detail a specific "test set" or its sample size in the context of clinical performance evaluation for this Special 510(k). The focus is on demonstrating that the software change itself does not affect the existing device's validated performance. For a software update of this nature, testing would typically involve verification and validation (V&V) activities against the original design specifications and potentially comparing outputs from the Windows XP and Windows 7 versions using a set of representative samples, but these details are not provided in this summary. There is no mention of country of origin or retrospective/prospective data for a new performance study.

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 is not applicable to this Special 510(k). The regulatory submission for a software operating system update does not involve establishing new diagnostic ground truth with medical experts. The ground truth for the underlying assays was established during the initial clearance of the predicate device (K083518).

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

This is not applicable to this Special 510(k) as no new clinical test set requiring expert adjudication is described.

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

This is not applicable. The ACL AcuStar™ is an automated immunoassay analyzer, not a device involving human interpretation of images or data that would benefit from AI assistance in the way described for an MRMC study.

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

The ACL AcuStar™ is an automated system that performs tests in a standalone manner (without continuous human intervention for each test analysis beyond loading samples and reagents). The original predicate device's performance was established as a standalone automated system. The current submission simply states that the software update does not change the operating principle, performance claims, or data reduction software, implying the standalone performance remains identical to the predicate.

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

The ground truth for the diagnostic assays performed by the ACL AcuStar™ would have been established during the original clearance of the predicate device (K083518), likely through comparison to reference methods, clinical diagnosis, or other accepted gold standards for in vitro diagnostic tests. This Special 510(k) does not provide details on the ground truth for the original assays, as it is assumed to be established with the predicate.

8. The sample size for the training set

This is not applicable. The device is not an AI/ML algorithm that requires a training set in the common understanding of an "AI device." It's an automated immunoassay analyzer with a software operating system update.

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

This is not applicable for the same reason as point 8.

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ACL AcuStar: Automated immunoassay analyzer designed specifically for in vitro diagnostic use in a clinical laboratory. The assay analysis is based on chemiluminescent technology. The system provides results for both direct measurements and calculated parameters.
HemosIL AcuStar D-Dimer: Fully automated chemiluminescent immunoassay for the quantitative determination of D-Dimer in human citrated plasma on the ACL AcuStar as an aid in the diagnosis of venous thromboembolism (VTE) [deep vein thrombosis (DVT) and pulmonary embolism (PE)].
HemosIL AcuStar D-Dimer Controls: For the quality control of D-Dimer assay performed on the ACL AcuStar.

ACL AcuStar: The AcuStar is an automated, bench-top system for lab use that measures the analyte amount in blood samples by: Subjecting the blood sample to reagents that cause a reaction with an antigen or antibody in the sample. Placing the cuvettes in a controlled environment to allow the reactants to bind into a complex. Separating out the complex from unused reactants. Treating this complex with a chemical that produces light in proportion to the analyte concentration. Measuring the light output to determine the amount of antibodies or antigens that were in the sample.

HemosIL AcuStar D-Dimer: The HemosIL AcuStar D-Dimer assay is a two-step immunoassay to quantify D-Dimer in human citrated plasma using magnetic particles as solid phase and a chemiluminescent detection system. In the first step, sample, anti-D-Dimer antibody coated magnetic particles, and assay buffer are combined, and the fibrin soluble derivatives containing the D-Dimer domain present in the sample bind to the anti-D-Dimer antibody coated magnetic particles. After magnetic separation and washing, an anti-XDP antibody labeled with isoluminol is added and incubated in a second step. After a new magnetic separation and washing, two triggers are added and the resulting chemiluminescent reaction is measured as relative light units (RLUs) by the ACL AcuStar optical system. The RLUs are directly proportional to the D-Dimer concentration in the sample. The ACL AcuStar D-Dimer assay utilizes a 4 Parameter Logistic Curve (4PLC) fit data reduction method to generate a Master Curve. The Master Curve is predefined lot dependent, and is stored in the instrument through the cartridge barcode. With the measurement of calibrators, the predefined Master Curve is transformed to a new, instrument specific 4PLC Working Curve. The concentration values of the calibrators are included in the calibrator plastic tube barcodes.

HemosIL AcuStar D-Dimer Controls: The Low, High, and Very High D-Dimer Controls are prepared by means of a dedicated process and contain different concentrations of partially purified D-Dimer obtained by digestion of Factor XIIIa cross-linked human fibrin with human plasmin.

Here's a breakdown of the acceptance criteria and the study details for the ACL™ AcuStar™ HemosIL™ AcuStar™ D-Dimer, based on the provided 510(k) summary:

Acceptance Criteria and Reported Device Performance

Study Information

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

• Test Set Sample Size (Clinical Management Study): 344 frozen citrated plasma samples.
• Data Provenance: From patients admitted to an emergency unit with suspected PE or DVT. The country of origin is not explicitly stated, but the context generally implies a clinical setting in a developed country (e.g., US or EU) for regulatory submissions of this type. The samples were retrospective, as they were "frozen citrated plasmas from patients."

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

• The document does not explicitly state the number of experts or their qualifications.
• Ground Truth Establishment: "97 were confirmed as VTE positive (64 PE and 33 DVT) by standard objective tests and the remaining 247 were confirmed as negative." The "standard objective tests" would typically be interpreted and confirmed by medical specialists (e.g., radiologists for imaging, clinical physicians for diagnosis confirmation), but this is not detailed.

4. Adjudication method for the test set:

• The document does not specify an explicit adjudication method (e.g., 2+1, 3+1). The ground truth was established by "standard objective tests" and clinical confirmation.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No, an MRMC comparative effectiveness study was not done. This submission focuses on the performance of a diagnostic instrument and assay (ACL AcuStar D-Dimer) as a standalone test, and its clinical utility for exclusion of VTE, rather than human reader improvement with AI assistance.

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

• Yes, the performance data presented is for the standalone device (ACL AcuStar D-Dimer assay and instrument) without human-in-the-loop assistance in its interpretation. The results (sensitivity, specificity, NPV) directly reflect the device's diagnostic capability.

7. The type of ground truth used:

• The ground truth was established by outcomes data and expert diagnosis based on standard objective tests:
  • VTE positive cases (PE and DVT) were "confirmed ... by standard objective tests." These typically include imaging studies (e.g., CT pulmonary angiography for PE, ultrasound for DVT) and clinical assessment.
  • VTE negative cases were "confirmed as negative" also presumably by the absence of findings on standard objective tests and clinical follow-up.

8. The sample size for the training set:

• The document does not specify a separate "training set" in the context of machine learning. This is a traditional in-vitro diagnostic device submission for an immunoassay. The concept of a training set for an algorithm is not directly applicable here. The "training" in this context refers to the development and optimization of the assay and its reagents during product development, which is typically done with various in-house samples and analytical studies.

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

• As noted above, the concept of a separate "training set" with ground truth in the machine learning sense is not explicitly present. For an immunoassay, the "ground truth" during development (analogous to training) would involve using characterized samples with known D-Dimer concentrations or clinical status, and optimizing the assay's reagents and calibration curve to achieve accurate measurements and clinical cut-offs. This development process for assays does not typically involve the formal "ground truth establishment" and "adjudication" methods seen in AI/ML validation studies.

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