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The Atellica™ CH Magnesium (Mg) assay is for in vitro diagnostic use in the quantitative determination of magnesium in human serum, plasma (lithium heparin), and urine using the Atellica™ CH Analyzer. Magnesium measurements are used in the diagnosis and treatment of hypomagnesemia (abnormally low levels of magnesium) and hypermagnesemia (abnormally high levels of magnesium).

The Atellica CH Mg assay is based on the modified xylidyl blue reaction, which was first described by C.K. Mann and J.H. Yoe. 1-2 The reagent was modified to eliminate the use of organic solvents. Magnesium ions react with xylidyl blue in an alkaline medium to form a water-soluble purple-red complex. The increase in absorbance of xylidyl blue at 505/694 nm is proportional to the concentration of magnesium in the sample. Calcium is excluded from the reaction by complexing with EGTA.

This document describes the performance assessment of the Siemens Atellica CH Magnesium (Mg) assay, an in vitro diagnostic device used for the quantitative determination of magnesium in human serum, plasma, and urine.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the performance characteristics demonstrated in the study and compared to the predicate device. The document does not explicitly list "acceptance criteria" as a separate column with pass/fail remarks, but rather presents the study results, implying that these results met the internal validation requirements for substantial equivalence.

2. Sample Sizes Used for the Test Set and Data Provenance

• Detection Limit (LoB/LoD):
  • LoB: 4 samples with no analyte tested (N=5) for 3 days, 1 run/day, 3 reagent lots (Total of 60 measurements for LoB calculation).
  • LoD: 4 low analyte samples tested (N=5) for 3 days, 1 run/day, 3 reagent lots (Total of 60 measurements for LoD calculation).
• Limit of Quantitation (LoQ): 4 low samples processed on 3 reagent lots for 3 days, on 1 instrument, for a total of 60 measurements per lot for both serum/plasma and urine.
• Linearity Study:
  • Serum: 10 samples (prepared by mixing high and low concentration samples). 4 replicates measured for each sample.
  • Urine: 10 samples (prepared by mixing high and low concentration samples). 4 replicates measured for each sample.
• Precision Studies: N = 2 replicates, two times a day for at least 20 days for a total of 80 replicates with controls, serum, and plasma pools on one instrument.
• Interferences: Not explicitly stated as a number of samples, but samples were "fresh sample pools containing either low or high levels of measurand in serum and urine pools." Dilution studies were conducted "as needed for both serum pools."
• Method Comparison:
  • Serum: 108 remnant de-identified samples.
  • Urine: 100 remnant de-identified samples.
  • Lithium heparin plasma (for Matrix Equivalency): 109 samples.
• Data Provenance: The studies were conducted internally by Siemens Healthcare Diagnostic Inc. R&D organization personnel. The samples for method comparison were "remnant de-identified samples," implying a retrospective collection from a clinical laboratory setting. The country of origin of the data is not specified, but Siemens Healthcare Diagnostics Inc. is based in the US (Newark, DE, and Tarrytown, NY), suggesting data from the US or a region with similar clinical practices.

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

• This device is an in vitro diagnostic (IVD) assay for quantitative measurement of magnesium. The "ground truth" for such devices is typically established through a reference method or known concentrations of certified reference materials, not through expert consensus or clinical experts like radiologists.
• The document states that "Magnesium values are traceable to Atomic Absorption reference method which is calibrated with NIST SRM 929 reference material." This indicates that the ground truth is based on an established, highly accurate analytical method and certified reference materials.
• Qualifications of Experts: The personnel conducting the study were "laboratory technicians with training similar to personnel who would conduct the tests in a hospital laboratory setting." They were trained on the operation of both the device and the predicate device. This implies expertise in laboratory procedures and instrument operation, rather than clinical interpretation.

4. Adjudication Method for the Test Set

• Not applicable. As this is a quantitative chemical assay, ground truth is established analytically (reference method, certified materials), not through human adjudication of qualitative findings.

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. This is an in vitro diagnostic device for a quantitative chemical measurement, not an AI-assisted diagnostic imaging or classification system that involves human readers.

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

• This device is a standalone automated chemical analyzer. Its performance, as described, is the standalone performance of the instrument and reagents in determining magnesium concentrations. There is no human-in-the-loop component in the analytical measurement process itself; human involvement is in operating the instrument and interpreting the results.

7. The Type of Ground Truth Used

• Analytical Ground Truth:
  • Reference Method: Traceable to "Atomic Absorption reference method."
  • Certified Reference Materials: Calibrated with "NIST SRM 929 reference material."
  • Clinical Samples: For method comparison studies, "remnant de-identified samples" were used, where the predicate device's measurement served as a comparative reference. For linearity, samples were prepared by "mixing high and low concentration samples" or "spiking native serum or urine pools with magnesium acetate."

8. The Sample Size for the Training Set

• Not applicable in the typical sense of machine learning. This is a traditional IVD chemical assay, not an AI/ML algorithm that requires a "training set" for model development. The development and optimization of the assay (e.g., reagent formulation, reaction conditions) would be based on laboratory R&D, but not in the same way an AI model is trained on a data set.

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. The "ground truth" during the development of the assay would be established through standard analytical chemistry principles, using known concentrations and reference methods to ensure the assay accurately measures magnesium.

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