Search Results

For in vitro diagnostic use only. The extendSURE™ lyophilized hemoglobin A1c linearity controls are intended to verify the linearity of HbA1c assays across the patient reportable range (4 to 18%, NGSP aligned) using protocols established in individual laboratories.

The extendSURE™ Lyophilized Hemoglobin A1c Linearity Controls comprises one vial each of 5 equally spaced levels of HbA1c control, 1 vial of reconstitution fluid and a product information sheet. The product is provided in a lyophilized form and each vial is reconstituted with 0.25 mL of reconstitution fluid (0.09% sodium azide) prior to use.

Here's a summary of the acceptance criteria and the study details for the extendSURE™ Lyophilized Hemoglobin A1c Linearity Controls, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state a "test set" in the context of typical AI/ML device validation where a distinct, prospectively collected, or independently curated dataset is used to evaluate the final performance of an algorithm against ground truth.

Instead, the performance characteristics are related to stability studies, which involve testing the device itself over time on various laboratory instruments.

• Sample Size: The document does not specify the number of individual control vials or measurements used in the stability studies. It simply mentions "Stability studies...were performed."
• Data Provenance: The studies were conducted by Canterbury Scientific Ltd. It's an internal study presumably performed in New Zealand based on the company's location. The data is retrospective in the sense that the stability studies were completed and then reported for the 510(k) submission.

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

Not applicable. This device is a linearity control, not an AI/ML algorithm that generates a diagnostic output requiring expert interpretation for ground truth. The "ground truth" here is the chemical stability and performance of the control material itself when measured by laboratory instruments. The "truth" is established by the known concentration ranges of HbA1c in the control material and its linearity when tested on specific instruments.

4. Adjudication Method for the Test Set

Not applicable, for the same reasons as above.

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 not an AI/ML diagnostic aid. It is a quality control material.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. This is not an AI/ML algorithm.

7. The Type of Ground Truth Used

The ground truth used for evaluating the performance of the linearity controls is:

• Analytical Performance: The inherent chemical stability of the HbA1c levels within the control material over time and under various storage conditions.
• Instrument Measurements: The ability of the control material to demonstrate linearity when measured by established and validated laboratory instruments (e.g., Bayer DCA 2000, BioRad Variant Classic). The "truth" is whether the measured values on these instruments fall within expected ranges and demonstrate
  linearity across the specified range.

8. The Sample Size for the Training Set

Not applicable. This is not an AI/ML algorithm that requires a training set.

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

Not applicable, for the same reasons as above. The "ground truth" for the linearity controls themselves is established during their manufacturing process, where specific concentrations of HbA1c are formulated, and then confirmed through extensive stability and performance testing against known analytical methods on various laboratory platforms.

Ask a specific question about this device

The Liquid Stable Hemoglobin A1c Control is intended for use as a quality control material to monitor the performance of laboratory testing procedures for HbA1c quantitation. The control is designed for use with lon exchange HPLC assays, Immunoassay based analyses and Boronate Affinity based assays.

The use of quality control materials is indicated as an objective assessment of the precision and bias of methods and techniques in use and is an integral part of good laboratory practices. The two levels of controls allow performance monitoring within the clinical range.

The controls are for in vitro diagnostic use only and should not be used past the expiry date.

The Liquid Stable HbA1c Control is a bi-level liquid HbA1c control that is prepared from human red cells. The control should be treated the same as patient specimens and tested following the instructions included with the instrument, kit or reagent being used.

The provided text describes a 510(k) summary for a "Liquid Stable HbA1c Control" device. This device is a quality control material, not an AI-powered diagnostic tool. Therefore, many of the typical acceptance criteria and study elements associated with AI diagnostic devices (such as sensitivity, specificity, MRMC studies, ground truth establishment by experts, and training/test sets) are not applicable here.

Instead, the "acceptance criteria" for a quality control material primarily revolve around its stability and performance characteristics in monitoring laboratory testing procedures. The study described focuses on validating these stability claims.

Here's an adaptation of the request to fit the context of a quality control material:

1. Table of Acceptance Criteria (or Performance Claims) and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Test Sample: The "test set" in this context refers to the Liquid Stable HbA1c Control material itself, which was subjected to stability testing. The document does not specify a "sample size" in terms of individual patient samples or measurements beyond the stability data.
• Data Provenance: The document does not specify the country of origin for any human data, as it's a quality control material. The data provenance relates to the stability testing done on the control material itself.
  • Specificity: Stability studies were performed on various laboratory instruments (Bio-Rad Variant, Bayer DCA200, Roche Hitachi 917, Primus CLC385, Bayer DCA2000). The context implies these were conducted in a laboratory setting by the manufacturer, Canterbury Scientific Ltd.
  • Retrospective or Prospective: The stability studies were prospective, as they involved real-time and accelerated (20°-25°C) testing over time to determine shelf life.

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

• This is not applicable for a quality control material. "Ground truth" in the context of diagnostic AI typically refers to a definitive diagnosis or measurement for a patient sample. For a quality control material, its "truth" is its stability and its ability to consistently perform within expected ranges when used to monitor assays. The "truth" is established through documented laboratory methods and instrument validations.

4. Adjudication method for the test set

• Not applicable. Adjudication methods like 2+1 or 3+1 are used for resolving discrepancies in expert interpretations of patient data (e.g., medical images). For stability testing of a control material, the results are quantitative measurements against predefined criteria, not subjective interpretations 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 device is a quality control material, not an AI-powered diagnostic tool, and it does not involve human readers interpreting cases or AI assistance.

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

• Not applicable. This is not an algorithm or AI device. It's a laboratory reagent.

7. The type of ground truth used

• For the stability studies, the "ground truth" implicitly refers to the established scientific principles of chemical and biological stability and the performance of the control material over time when stored under specified conditions and tested on validated laboratory instruments. The "truth" is that the material retains its expected properties (e.g., target HbA1c values) within acceptable variation over the claimed stability period.

8. The sample size for the training set

• Not applicable. This is not an AI/machine learning device that uses training sets.

9. How the ground truth for the training set was established

• Not applicable. No training set exists for this device.

Ask a specific question about this device

The Hemoglobin A1c Control contains both Normal and Abnormal level Controls. They are intended for use as a quality control material to monitor the precision of laboratory testing procedures for HbA1c quantitation.

The Hemoglobin A1c Control contains both Normal and Abnormal level Controls. They are intended for use as a quality control material to monitor the precision of laboratory testing procedures for HbA1c quantitation. They are prepared from normal adult human blood The source blood is tested and found to be non-reactive for Hepatitis B surface antigen, Hepatitis C antibody, antibodies against human immunodeficiency virus (HIV) types 1 & 2, and Syphilis (RPR and TPHA). The Abnormal Level Control is prepared by controlled glycation of normal non-diabetic hemolysate. The controls are reconstituted with distilled water or a reconstitution solution comprising the biocide sodium azide (0.09%). The reconstitution volume is between 0.25 mL and 0.5 mL. The controls reconstitute in 15 minutes with occasional swirling, and are stored at 2° - 8°C.

This is a 510(k) summary for a Hemoglobin A1c Control device, which is a quality control material used to monitor the precision of laboratory testing procedures for HbA1c quantitation. It is not an AI/ML device for diagnosis or treatment. The provided text refers to a traditional in vitro diagnostic device. Therefore, many of the typical questions for AI/ML device studies (like expert consensus, adjudication methods, multi-reader multi-case studies, and AI effect size) are not applicable to this type of submission.

The "study" here is a comparison of technological characteristics and performance between the new device and a legally marketed predicate device to demonstrate substantial equivalence, rather than a clinical study evaluating diagnostic accuracy.

Here's the breakdown of the available information:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for a quality control device typically revolve around its stability, composition, and its ability to perform similarly to predicate devices across different measurement methods. The reported performance is primarily a comparison against a predicate device.

The "study" demonstrating these meet the criteria is presented as a "Comparison of Technological Characteristics with Predicate Device" shown in the table. The primary evidence for acceptance is the demonstration of substantial equivalence to the predicate device (Lyphochek Diabetes Control Level 1 & 2, Bio-Rad Laboratories, K831478) based on these characteristics.

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

• Sample Size: Not explicitly stated as a "test set" in the context of an AI/ML algorithm. The comparison is based on the characteristics of the manufactured control materials themselves. For quality control materials, "sample size" might refer to the number of lots manufactured and tested or the replicates of aliquots tested to determine stability and target values. This information is not detailed in the provided summary.
• Data Provenance: Not applicable in the sense of patient data. The product is a manufactured in vitro diagnostic control. The source material is normal adult human blood, tested for pathogens. The device is manufactured by Canterbury Scientific Limited in Christchurch, New Zealand.

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

Not applicable. This is a quality control material, not a diagnostic device requiring expert interpretation of images or clinical data to establish a "ground truth" in that sense. The "ground truth" for the control material is its chemical composition and its measured HbA1c values, which are determined by established laboratory methods and validated against known standards, not by expert consensus in a clinical context.

4. Adjudication Method for the Test Set

Not applicable. There is no "test set" in the context of clinical interpretation 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 not an AI/ML device.

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

Not applicable. This is not an AI/ML device.

7. The Type of Ground Truth Used

The "ground truth" for this device (a quality control material) is its chemical composition, stability, and the target HbA1c values, established through:

• Analytical Testing: Rigorous laboratory testing using recognized methods (e.g., HPLC, latex immunoagglutination inhibition) to determine the precise HbA1c percentage, other hemoglobin fractions, and stability over time.
• Reference Standards: Calibration against international or national reference standards for HbA1c measurement.
• Manufacturing Specifications: Adherence to defined manufacturing processes to ensure consistent product attributes.
• Predicate Device Comparison: Used as a benchmark for performance characteristics.

8. The Sample Size for the Training Set

Not applicable. This is not an AI/ML device.

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

Not applicable. This is not an AI/ML device.

Ask a specific question about this device