Search Results

Sigma Diagnostics ALEXIN™ LS is a device used for primary screening for coagulation Shina Diagnest of evaluation of the effect of therapy on procoagulant disorders, and as able mail.com factor deficiencies of the intrinsic coagulation pathway.

Sigma Diagnostics ALEXIN™ LS contains purified rabbit brain and sov phospholipids with an ellagic acid activator for the determination of the activated partial thromboplastin time and related coagulation procedures in citrated plasma. This reagent has increased sensitivity to lupus anticoagulants.

The provided document describes the safety and effectiveness study for the Sigma Diagnostics ALEXIN™ LS device, demonstrating its substantial equivalence to the Dade Actin-FSL device.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance:

The primary acceptance criteria for this device appear to be a strong correlation and agreement with a legally marketed predicate device (Dade Actin-FSL). The precision data also represents performance within expected ranges for such assays.

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

• Sample Size:
  • Substantial Equivalence Testing: 153 random frozen patient samples were used for comparison between ALEXIN™ LS and Dade Actin-FSL.
  • KC 4A Analyzer Correlation: 98 random frozen samples were used for correlation on the KC 4A analyzer.
  • Precision Testing: Not explicitly stated, but "plasma controls at three different APTT levels" were used. The number of replicates for each control is not specified.
• Data Provenance: The samples were "random frozen patient samples." The country of origin is not specified, and it's implied these were retrospective samples as they were "frozen."

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

This type of study (comparative performance of a diagnostic reagent) typically does not rely on expert adjudication to establish a "ground truth" in the way an imaging AI algorithm might. Instead, the "ground truth" for the comparative effectiveness portion is established by an existing, legally marketed predicate device (Dade Actin-FSL), which is assumed to provide accurate results. The precision testing uses established control materials with known target values. Therefore, this question is not directly applicable in the same way it would be for a typical AI medical device. There were no human experts adjudicating individual patient cases in this study.

4. Adjudication Method for the Test Set:

Not applicable. As explained above, the "truth" for the comparative portion was the result produced by the predicate device, not an adjudicated expert consensus.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No, an MRMC comparative effectiveness study was not done. This study is focused on the analytical performance of a diagnostic reagent, not on the impact of a device on human reader performance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

Yes, in a sense, this is a standalone performance study of the ALEXIN™ LS reagent. Its performance is measured directly against a predicate device and against established precision metrics, without active human-in-the-loop interaction beyond running the assay and interpreting the numerical output. The device itself (the reagent) is the "algorithm" and its output is a direct assay result.

7. Type of Ground Truth Used:

• For Substantial Equivalence: The "ground truth" was the results obtained from the legally marketed predicate device, Dade Actin-FSL. This is a common approach for demonstrating substantial equivalence for in vitro diagnostic devices.
• For Precision Studies: The "ground truth" (or target values) were derived from plasma controls at three different APTT levels. These controls are manufactured to have specific, known ranges for APTT.

8. Sample Size for the Training Set:

This information is not provided because this is a diagnostic reagent, not an AI/ML algorithm that requires a "training set" in the conventional sense. The "development" of the reagent would involve chemical formulation and optimization, not machine learning training.

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

Not applicable, as there is no "training set" for this type of device.

Ask a specific question about this device