(133 days)
Reagent: UniCel DxC SYNCHRON Systems HDL Cholesterol reagent (HDL), when used in conjunction with UniCel DxC 600/800 SYNCHRON System(s) and UniCel DxC SYNCHRON Systems HDL Calibrator, is intended for quantitative determination of HDL cholesterol in the high density lipoprotein fraction of human serum or plasma.
A lipoprotein test system is a device intended to measure lipoprotein in serum and plasma. Lipoprotein measurements are used in the diagnosis and treatment of lipid disorders mellitus), atherosclerosis, and various liver and renal diseases.
Calibrator: The UniCel DxC SYNCHRON Systems HDL Calibrator is designed to provide suitable calibration levels for Beckman Coulter UniCel DxC 600/800 SYNCHRON Systems employing the quantitative UniCel DxC SYNCHRON Systems HDL Cholesterol reagent (HDL).
Not Found
This is a 510(k) premarket notification for in vitro diagnostic devices (UniCel DxC SYNCHRON Systems HDL Cholesterol Reagent and Calibrator), not an AI/ML medical device. Therefore, many of the requested criteria regarding AI model performance, ground truth, experts, and training/test sets are not applicable or typically documented in this type of submission.
However, I can extract and present information relevant to device performance and substantiation that is typically found in such a document for an IVD.
Here's an interpretation based on the provided text, acknowledging the limitations for an IVD device:
Device: UniCel DxC SYNCHRON Systems HDL Cholesterol Reagent (HDL) and UniCel DxC SYNCHRON Systems HDL Calibrator.
Intended Use: Quantitative determination of HDL cholesterol in the high density lipoprotein fraction of human serum or plasma.
1. Table of Acceptance Criteria and Reported Device Performance
For an IVD such as this, "acceptance criteria" and "device performance" typically refer to analytical performance characteristics demonstrated through validation studies (e.g., accuracy, precision, linearity, interference, method comparison). While the provided 510(k) summary (which is typically a brief overview) doesn't contain a detailed table of specific criteria and results, it implies that performance data was submitted and found acceptable for substantial equivalence to a predicate device.
To illustrate, here's a hypothetical table based on common IVD performance metrics that would be assessed for a device like this, as the actual data is not present in the provided snippet:
| Performance Characteristic | Acceptance Criteria (Hypothetical) | Reported Device Performance (Hypothetical / Implied) |
|---|---|---|
| Accuracy (Method Comparison) | Correlation (r) ≥ 0.95 vs. Predicate or Reference Method | Implied to meet or exceed; not explicitly stated. |
| Bias | Bias ≤ 5% at medical decision points | Implied to meet or exceed; not explicitly stated. |
| Precision (Within-run, Total) | %CV ≤ 5% at medical decision points | Implied to meet or exceed; not explicitly stated. |
| Linearity | Linear throughout the reportable range | Implied to meet or exceed; not explicitly stated. |
| Interference | No significant interference from common interferents | Implied to meet or exceed; not explicitly stated. |
Note: The FDA's issuance of a determination of substantial equivalence (as stated in the letter) means that sufficient data was provided to demonstrate the device performs as intended and is as safe and effective as a legally marketed predicate device. This data would include studies addressing the above performance characteristics.
2. Sample Size Used for the Test Set and Data Provenance
The provided text from the 510(k) notification does not specify the sample size for any test set or the provenance of the data. For IVD devices, clinical samples (serum/plasma), control materials, and calibrators are used in analytical validation studies. These studies are typically conducted by the manufacturer, often at multiple sites (laboratories), and typically use samples from diverse populations to ensure generalizability. The studies would be considered prospective in terms of data collection for the specific validation protocols.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This question is not applicable to an in vitro diagnostic reagent and calibrator like the one described. "Ground truth" in the context of an AI/ML medical device typically refers to expert annotations or conclusive diagnoses. For an IVD, the "ground truth" for evaluating performance would be established by:
- Reference Methods: Using a well-established, often CDC-certified or industry-standard reference method for HDL cholesterol measurement, which may itself involve highly skilled laboratory professionals.
- Certified Reference Materials: Using materials with established true values (e.g., NIST traceable materials).
The performance of the device is assessed against these established methods or materials, not against expert consensus on images or similar diagnostic interpretations.
4. Adjudication Method for the Test Set
This concept is not typically applicable to the validation of an IVD reagent and calibrator. Adjudication methods (like 2+1 or 3+1) are used in AI/ML performance studies where multiple human experts individually assess a case, and discrepancies are resolved. For an IVD, analytical results are quantitative and compared against established methods or values, not adjudicated expert opinions.
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 question is not applicable. MRMC studies are used to evaluate the diagnostic accuracy of imaging devices or AI algorithms (often with human readers). This device is a quantitative a chemical reagent and calibrator for an automated laboratory system, not an imaging device or an AI diagnostic tool that assists human readers in interpretation.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
This question is not applicable. The device is a chemical reagent and calibrator system used on an automated analyzer (UniCel DxC 600/800 SYNCHRON System). Its performance is inherently "standalone" in the sense that the instrument processes the sample and yields a quantitative result without direct "human-in-the-loop" interpretation for each test result in the way an AI diagnostic tool might require. The "algorithm" here is the chemical reaction and photometric measurement, not a computational AI model.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
For this IVD, the "ground truth" would be established by:
- Reference Measurement Procedures: Highly accurate and precise laboratory methods (e.g., CDC reference methods) for determining HDL cholesterol levels in samples.
- Certified Reference Materials: Materials with an accurately assigned value for HDL cholesterol.
Essentially, it's a comparison to established, highly accurate analytical methods rather than subjective expert consensus or pathology.
8. The sample size for the training set
This question is not applicable. This device is not an AI/ML model that requires a "training set" in the computational sense. The reagents are developed and optimized through chemical formulation and analytical studies, not statistical model training.
9. How the ground truth for the training set was established
This question is not applicable, as there is no "training set" in the AI/ML context for this type of IVD device. The "ground truth" (reference values) established for validation studies (as in #7) would be used to assess the accuracy and performance of the finalized reagent and calibrator system.
§ 862.1150 Calibrator.
(a)
Identification. A calibrator is a device intended for medical purposes for use in a test system to establish points of reference that are used in the determination of values in the measurement of substances in human specimens. (See also § 862.2 in this part.)(b)
Classification. Class II (special controls). The device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9.