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For the quantitative in vitro determination of LDL-cholesterol concentration in human plasma and serum. Lipoprotein measurements are used in the diagnosis and treatment of lipid disorders mellitus), atherosclerosis and various liver and renal diseases.

This in vitro diagnostic device is intended for prescription use only.

The LDL Cholesterol kit assay consists of ready to use reagent solutions. CATALOGUE NUMBER: CH8312

R1. Enzyme Reagent 1 4 x 20 mL R2. Enzyme Reagent 2 4 x 9 mL

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Reported Device Performance

Device Name: Direct LDL Cholesterol (LDL)

Study Details

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

   • Precision (Analytical Performance): 80 determinations for each of 7 pools/QC levels (total of 560 determinations). The samples included control material and "unaltered human serum samples that were spiked with LDL cholesterol concentrations or diluted to achieve concentrations based on established ranges" (e.g., <100mg/dl, 130-159mg/dL, etc.). Data provenance is unspecified, but implied to be from a laboratory setting (prospective testing) rather than patient cohorts, using human serum.
   • Linearity/Assay Reportable Range: 11 levels, run in replicates of five on two lots of reagent. This means 11 * 5 * 2 = 110 individual measurements. The samples were prepared using "low and high serum pools." Data provenance is unspecified, but implied to be laboratory-prepared (prospective testing) using serum.
   • Detection Limit: 240 determinations (across 4 low-level samples) for LoD. Data provenance is unspecified, but implied to be laboratory-based (prospective testing).
   • Analytical Specificity: Two LDL Cholesterol concentrations (96.75 mg/dl and 193.5 mg/dl) were tested against various interferent levels. Each interferent was tested. The specific number of replicates or total determinations per interferent is not specified, but inferred to be multiple to allow for mean value calculation. Data provenance is unspecified, but implied to be laboratory-based (prospective testing).
   • Method Comparison with Predicate Device: 139 serum patient samples. Data provenance is unspecified, but these are patient samples, making this a retrospective or prospectively collected patient sample study. The country of origin for these patients is not specified.
   • Matrix Comparison: 70 matched patient sample pairs (serum and lithium heparin plasma). Data provenance is unspecified, but these are patient samples, making this a retrospective or prospectively collected patient sample study. The country of origin for these patients is not specified.

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

   • For this type of in vitro diagnostic device (quantitative measurement of a biomarker), "ground truth" is typically established by reference methods or highly characterized calibrators/control materials, rather than expert human interpretation.
   • Precision, Linearity, Detection Limit, Analytical Specificity: Ground truth is based on the known concentrations of analytes in control materials, spiked samples, or reference preparations.
   • Method Comparison and Matrix Comparison: The ground truth for comparative studies is the measurement obtained from the "predicate device" or the paired sample (e.g., serum for matrix comparison). This is an analytical comparison rather than a clinical ground truth requiring expert consensus on a diagnosis.
   • Therefore, the concept of "number of experts" and "qualifications of experts" used for establishing ground truth as it would apply to imaging or diagnostic interpretation is not applicable here.

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

   • Given that this is an in vitro diagnostic device for quantitative measurement, adjudication methods typically used for subjective interpretations (like medical imaging diagnosis) are not applicable. The device provides a numerical output.
   • Results are compared to established reference values or predicate device measurements directly. Any discrepancies would be investigated through re-testing or troubleshooting, not through a formal adjudication process between experts primarily for the test set results.

4. 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:

   • No, an MRMC comparative effectiveness study was not done. This type of study applies to devices that assist human readers (e.g., radiologists interpreting images). The "Direct LDL Cholesterol (LDL)" device is an in vitro diagnostic assay that generates a quantitative result directly from a patient sample; it does not involve human "readers" in the context of interpretation that an MRMC study would assess.

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

   • Yes, this entire submission describes the standalone performance of the Direct LDL Cholesterol (LDL) assay system. It is an automated in vitro diagnostic test that provides a measurement result without human intervention for interpretation beyond operating the instrument and reviewing the numerical output.

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

   • The primary ground truth used is measurements from reference methods, predicate devices, or traceable calibrator/control values.
   • For method comparison, the predicate device's measurements serve as the comparator.
   • For precision, linearity, and detection limit, the "ground truth" refers to the known concentrations of the analyte in the prepared samples and controls.
   • The device mentions traceability to an "internal master reference material" for calibrators, which is a form of analytical ground truth.

7. The sample size for the training set:

   • The document does not provide details on a "training set" in the context of machine learning, because this device is a chemical reagent-based assay, not an AI/ML algorithm.
   • The study describes the validation of the analytical performance of the reagent system and its associated instrument for quantitative measurement. The concept of training data, as used in AI, does not apply here.

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

   • As there is no "training set" in the AI/ML sense, this question is not applicable. The analytical performance of the assay is based on chemical reactions and spectrophotometric measurements, calibrated against materials with known LDL Cholesterol concentrations.

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For the quantitative in vitro determination of HDL Cholesterol in serum and plasma. Such measurements are used in the diagnosis and treatment of lipid disorders (such as diabetes mellitus), atherosclerosis and renal diseases and for the assessment for the risk of developing cardiovascular disease.

This in vitro diagnostic device is intended for Rx Only.

The Direct HDL Cholesterol (HDL) kit assay consists of ready to use reagent solutions.

CATALOGUE NUMBER: CH8311

R1. Enzyme Reagent 1 4 x 20 ml
R2. Enzyme Reagent 2 4 x 9 ml

REAGENT COMPOSITION

R1. Enzyme Reagent 1 N,N-Bis(2-hydroxyethyl)- 2-aminoethanesulfonic acid N-(2-hydroxy-3-Sulfopropyl)- 3,5-dimethoxyaniline, sodium salt (HDAOS) Cholesterol Esterase [E.C.3.1.1.13. Microorganism] Cholesterol Oxidase [E.C.1.1.3.6. Streptomyces sp] Catalase [E.C.1.11.1.6. Microbial] Ascorbate oxidase [EC.1.10.3.3. Acremonium sp.] Initial Concentration of Solution 100 mM, pH 6.6 (+25 °C) 0.7 mM ≥800 U/L ≥500 U/L ≥300 KU/L ≥3000 U/L
R2. Enzyme Reagent 2 N,N-Bis(2-hydroxyethyl)- 2-aminoethanesulfonic acid 4-Aminoantipyrine Peroxidase [E.C.1.11.1.7, Horse Radish, +25°C] Sodium Azide Surfactants Initial Concentration of Solution 100 mM, pH 7.0 (+25 °C) 4.0 mM ≥3500 U/L 0.05 w/v % 1.4 % w/v %

This looks like a 510(k) summary for an in vitro diagnostic (IVD) device, specifically for a Direct HDL Cholesterol (HDL) test system. Since IVD devices, especially Class I, do not typically involve AI or machine learning components as described in the prompt's questions, many of the requested fields (such as "number of experts used to establish ground truth", "adjudication method", "MRMC study", "standalone performance", "training set size", and "how ground truth for training set was established") are not applicable.

However, I can extract the relevant information regarding acceptance criteria and performance from the document.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Precision/Reproducibility:
  • Control material and human serum samples: 80 determinations per control level/serum pool (2 replicates/run x 2 runs/day x 20 days).
  • Provenance: "unaltered human serum samples" and "control material." The documentation does not specify the country of origin of the human serum samples. The study design (testing over 20 non-consecutive days) suggests it's a prospective study for data collection, but the samples themselves could be retrospective.
• Linearity/Reportable Range:
  • Samples: 11 levels, each run in replicates of five.
  • Provenance: Low and high serum pool samples. Not specified for country of origin or retrospective/prospective.
• Detection Limit:
  • Samples: 240 determinations (4 low-level samples) for LoD.
  • Provenance: Not specified for country of origin or retrospective/prospective.
• Analytical Specificity (Interference):
  • Samples: Spiked samples at 34.8 mg/dL and 70 mg/dL HDL Cholesterol concentrations. The number of individual samples is not explicitly stated.
  • Provenance: Not specified for country of origin or retrospective/prospective.
• Method Comparison:
  • Patient Samples: 103 serum patient samples.
  • Provenance: Not specified for country of origin or retrospective/prospective.
• Matrix Comparison:
  • Patient Samples: 45 matched patient sample pairs (serum and lithium heparin plasma).
  • Provenance: Not specified for country of origin or retrospective/prospective.

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

• Not applicable as this is an in vitro diagnostic (IVD) device for quantitative biochemical analysis, not an AI/image-based diagnostic device requiring expert interpretation for ground truth. Ground truth is typically established by reference methods or validated laboratory measurements.

4. Adjudication Method for the Test Set:

• Not applicable for this type of IVD device.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:

• Not applicable as this is an IVD device, not an AI-assisted diagnostic system involving human readers.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

• This device is a standalone in the sense that its performance characteristics (precision, linearity, interference, method comparison) are evaluated for the device itself as an assay system. There is no "algorithm only" vs. "human-in-the-loop" distinction because it's a quantitative chemical assay.

7. The Type of Ground Truth Used:

• For precision, linearity, and detection limit: The "ground truth" is implied to be the actual concentration of HDL Cholesterol, which is determined by the preparation of controls, spiked samples, and dilutions, or by the inherent properties of the samples themselves, and measured by the device and predicate.
• For analytical specificity (interference): The ground truth is the presence/absence of interferents at specific concentrations and their impact on the HDL measurement.
• For method comparison: The "ground truth" for comparison is the measurement obtained from the predicate device (Randox Laboratories Ltd, Direct HDL Cholesterol reagent, K982341). This represents a legally marketed device against which equivalence is demonstrated.
• For matrix comparison: The ground truth for comparison is the measurement obtained from serum samples when comparing to plasma samples.

8. The Sample Size for the Training Set:

• Not applicable in the context of machine learning/AI where "training set" has a specific meaning. For an IVD, the development and optimization of the reagent formulation and assay parameters would be an analogous "training" phase, but it doesn't involve a distinct "training set" of patient data in the AI sense.

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

• Not applicable for the same reasons as above. The "ground truth" for developing the assay itself would be established through chemical principles, optimization experiments, and validation against known standards and reference materials.

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