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System reagent for the quantitative determination of Triglyceride concentrations in human serum and plasma on OLYMPUS analyzers. Measurements of triglyceride are used in the diagnosis and treatment of patients with diabetes mellitus, nephrosis, liver obstruction, other diseases involving lipid metabolism, or various enocrine disorders, and in the assessment of risk factors for atherosclerosis and coronary artery disease.

Device Description

This Olympus Triglyceride procedure is based on a series of coupled enzymatic reactions. The triglycerides in the sample are hydrolyzed by a combination of microbial lipases to give glycerol and fatty acids. The glycerol is phosphorylated by adenosine triphosphate (ATP) in the presence of glycerol kinase (GK) to produce glycerol-3-phosphate. The glycerol-3-phosphate is oxidized by molecular oxygen in the presence of GPO (glycerol phosphate oxidase) to produce hydrogen peroxide (H2O2) and dihydroxyacetone phosphate. The formed H2O2 reacts with 4-aminophenazone and N,N-bis(4-sulfobutyl)-3,5-dimethylaniline, disodium salt (MADB) in the presence of peroxidase (POD) to produce a chromophore, which is read at 660/800nm. The increase in absorbance at 660/800 nm is proportional to the triglyceride content of the sample.

AI/ML Overview

Here's a summary of the acceptance criteria and study information for the Olympus Triglyceride Test System, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally implied by the comparison to a predicate device, showing "similarities" and specific performance characteristics. The specific thresholds for acceptable performance are not explicitly stated as strict "acceptance criteria" but rather as "Performance Characteristics" which are compared to the predicate device.

Performance Characteristic	Acceptance Criteria (Implied by Predicate)	New Olympus Triglyceride Reported Performance	Predicate Reported Performance
Precision AU400/400e	Comparable to predicate	Sample 1: 2.58% CV	Sample 1: 1.21% CV
		Sample 2: 2.54% CV	Sample 2: 1.67% CV
		Sample 3: 2.41% CV	Sample 3: 1.37% CV
Precision AU600/640/640e	Comparable to predicate	AU600:	AU600:
		Sample 1: 1.65% CV	Sample 1: 1.83% CV
		Sample 2: 1.41% CV	Sample 2: 1.58% CV
		Sample 3: 1.46% CV	Sample 3: 2.80% CV
			Sample 4: 1.13% CV
		AU640/640e:	AU640/640e:
			Sample 1: 1.00% CV
			Sample 2: 1.00% CV
Precision AU2700/5400	Comparable to predicate	Sample 1: 2.00% CV	Sample 1: 2.50% CV
		Sample 2: 1.72% CV	Sample 2: 2.00% CV
		Sample 3: 1.78% CV	Sample 3: 1.50% CV
			Sample 4: 1.20% CV
Assay Range	10 - 1000 mg/dL	10 - 1000 mg/dL	10 - 1000 mg/dL
Method Comparison	Slope ≈ 1, Intercept ≈ 0, R ≈ 1	Intercept: -0.871	Intercept: 3.2
		Slope: 1.011	Slope: 1.010
		R: 1.000	R: 0.999
Interfering Substances	Performance generally comparable to predicate, with limits on interference	AU400/400e/600/640/640e/2700/5400:	AU400/400e:
Ascorbate	≤ 2-10%	≤ 5% up to 20 mg/dL	≤ 2% up to 20mg/dL
Bilirubin	≤ 10%	≤ 3% up to 40 mg/dL	≤ 10% up to 20 mg/dL
Hemolysis	≤ 7-8%	≤ 3% up to 500 mg/dL	≤ 8% up to 500 mg/dL
			AU600/640/640e:
Ascorbate			≤ 1% up to 20mg/dL
Bilirubin			≤ 10% up to 32 mg/dL
Hemolysis			≤ 7% up to 500 mg/dL
			AU2700/5400:
Ascorbate			≤ 2% up to 20mg/dL
Bilirubin			≤ 10% up to 16 mg/dL
Hemolysis			≤ 8% up to 500 mg/dL

Study Details:

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 mentions "samples" for precision and method comparison studies but does not specify the exact number of samples (sample size) used for the test sets. For precision, multiple "samples" (likely control or pooled patient samples) were tested across different Olympus analyzer models. For method comparison, it implies a set of samples were run on both the new and predicate devices, yielding the Intercept, Slope, and R values.
- Data Provenance: Not specified. The document does not indicate the country of origin of the data or whether the studies were retrospective or prospective.
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 a diagnostic reagent for quantitative measurement of triglyceride concentrations, not an imaging device requiring expert interpretation. Therefore, the concept of "experts establishing ground truth" in the interpretive sense does not directly apply. The "ground truth" for such devices is established through reference methods or highly characterized control materials. The document states traceability to College of American Pathology (CAP) Serum Lipid (RM016) #2, which serves as the reference for accuracy.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not applicable as this is a quantitative chemical assay, not an interpretive device requiring adjudication.
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 a chemical diagnostic reagent device, not an AI or imaging device involving human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, the performance data presented (precision, assay range, method comparison, interfering substances) represents the standalone performance of the Olympus Triglyceride Reagent when run on Olympus analyzers. There is no human-in-the-loop component for the direct measurement of triglycerides by this device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- The ground truth for accuracy and calibration is based on traceability to College of American Pathology (CAP) Serum Lipid (RM016) #2. This implies that the device's measurements are referenced against established standards or values obtained from a highly reliable and recognized reference material.
8. The sample size for the training set
- Not applicable. This is a chemical reagent, not a machine learning or AI device that typically requires a "training set." The development would involve chemical optimization and formulation, followed by validation studies.
9. How the ground truth for the training set was established
- Not applicable, as there is no "training set" in the context of an AI/ML algorithm for this type of device. The development and validation relied on established chemical principles and reference materials (like CAP standards).

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