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510(k) Data Aggregation
(255 days)
SPOTCHECK Blood Spot Controls are used for monitoring assay performance during in vitro diagnostic newborn screening for deficient Galactose-1-phosphate UridyItransferase (GALT) and/or Biotinidase enzyme activity. Enzyme response quantitation is provided in the product insert.
SPOTCHECK Blood Spot Controls. Part No. 80-0900P4K, Blood Spot Controls, Deficient; 4 cards. Part No. 80-0901P4K, Blood Spot Controls, Normal; 4 cards. The controls are prepared with mixtures of human serum and human red blood cells. adjusted to approximately 55% hematocrit. Enzyme activity in the Deficient Control is decreased by heating. Enzyme activity in the Normal Control is supported by the addition of dithioerythritol (DTE). The mixtures are spotted on Whatman 903A filter paper and allowed to air dry at room temperature. The suppliers of serum and red blood cells certify that the materials have been tested using FDA-approved assays and shown to be negative for infectious disease agents. The SPOTCHECK Blood Spot Controls provide an ongoing indication of the assay performance. The Deficient Control responds below the assay cutoff, and the Normal Control responds above the assay cutoff within normal limits.
The provided 510(k) summary (K090940) describes a medical device, the SPOTCHECK® Blood Spot Controls, which are quality control materials for newborn screening assays. The acceptance criteria and the study proving the device meets these criteria are detailed below.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Normal Control: Contains sufficient enzyme activity to be labeled as "normal." | Each manufactured lot of Normal Control is tested using FDA-cleared SPOTCHECK Biotinidase and Uridyl Transferase 50-Hour Reagent Kits. The Normal Control responds above the assay cutoff, within normal limits, as verified in Astoria-Pacific's laboratory. |
| Deficient Control: Has clinically deficient enzyme activity. | Each manufactured lot of Deficient Control is tested using FDA-cleared SPOTCHECK Biotinidase and Uridyl Transferase 50-Hour Reagent Kits. The Deficient Control responds below the assay cutoff, as verified in Astoria-Pacific's laboratory. |
| Stability: Stable for a minimum of 2 years from the manufacture date when stored at < -10 degrees Celsius and desiccated. | Stored at < -10 degrees Celsius and desiccated, the controls are stable for a minimum of 2 years from the manufacture date. |
| Material Safety: Human serum and red blood cells used for preparation are certified negative for infectious disease agents. | Suppliers of serum and red blood cells certify that the materials have been tested using FDA-approved assays and shown to be negative for infectious disease agents. |
2. Sample size used for the test set and the data provenance
The document specifies that "Each manufactured lot of blood spot controls is analyzed" to verify performance. However, it does not provide a specific numerical sample size for the test set or the number of controls per lot that are tested.
Data Provenance: The device performance verification is conducted at Astoria-Pacific's laboratory on dedicated SPOTCHECK systems. The data is prospective, as each newly manufactured lot is tested. The country of origin for the data is implied to be the United States (Oregon), where Astoria-Pacific, Inc. is located.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not specify the use of external experts to establish the ground truth for the test set for the performance verification of these controls. The verification is performed internally by Astoria-Pacific's laboratory personnel, who are implicitly considered qualified to conduct these tests using FDA-cleared reagent kits and dedicated systems.
4. Adjudication method for the test set
The document does not describe an adjudication method involving multiple readers or experts for the test set's ground truth. The performance verification appears to be based on direct measurement of enzyme activity against established assay cutoffs, as assessed internally by Astoria-Pacific's laboratory.
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
This is not applicable. The SPOTCHECK® Blood Spot Controls are quality control materials, not an AI-powered diagnostic device. Therefore, no MRMC comparative effectiveness study involving human readers or AI assistance was conducted or reported in this 510(k) summary.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This is not applicable. The SPOTCHECK® Blood Spot Controls are not an algorithm or an AI system. They are physical quality control materials used in laboratory assays. The "standalone performance" in this context refers to the intrinsic performance of the control materials themselves, which is verified by enzyme activity measurements as described in Section 1.
7. The type of ground truth used
The ground truth used for verifying the SPOTCHECK® Blood Spot Controls is based on direct measurement of enzyme activity using FDA-cleared SPOTCHECK Biotinidase and Uridyl Transferase 50-Hour Reagent Kits. The "normal" and "deficient" classifications are determined by whether the measured enzyme activity falls above or below established assay cutoff values, respectively. This is a form of empirical measurement against a predefined standard.
8. The sample size for the training set
This is not applicable. The SPOTCHECK® Blood Spot Controls are quality control materials, not a machine learning algorithm. Therefore, there is no "training set" in the context of AI or algorithm development. The phrase "training set" is usually associated with supervised learning scenarios where an algorithm learns from labeled data.
9. How the ground truth for the training set was established
This is not applicable for the same reasons as outlined in Section 8. As there is no training set for an algorithm, there is no ground truth established for such a set.
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