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510(k) Data Aggregation
(128 days)
The IMMAGE Immunochemistry System Digoxin (DIG) Reagent, in conjunction with Beckman Drug Calibrator 2, is intended for use in the quantitative determination of digoxin in human serum and plaama samples by turbidimetric immunossay. This assay is designed for use with the IMMAGE Immunochemistry System.
The IMMAGE System Digoxin (DIG) Reagent is designed for optimal performance on the IMMAGE Immunochemistry System. It is intended for use in the quantitative determination of digoxin concentrations in human serum and plasma semples.
Here's a breakdown of the acceptance criteria and study information for the IMMAGE™ Immunochemistry System Digoxin (DIG) Reagent, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't explicitly state "acceptance criteria" for each performance metric in a standardized table. However, it implicitly defines acceptable performance by comparing the IMMAGE DIG Reagent to the predicate device, Abbott TDx Digoxin II Reagent, and by presenting its own performance characteristics. I will infer the implied acceptance criteria based on these comparisons and performance data.
| Performance Metric | Acceptance Criteria (Implied) | Reported Device Performance (IMMAGE DIG Reagent) |
|---|---|---|
| Intended Use | Quantitative determination of digoxin in human serum and plasma. | Same as predicate (Abbott TDx Digoxin II Reagent). |
| Measurement Temperature | 37°C | Same as predicate (Abbott TDx Digoxin II Reagent). |
| Sample Type | Human plasma and serum samples. | Same as predicate (Abbott TDx Digoxin II Reagent). |
| Analytic Range | 0.2 to 5 ng/mL. | Same as predicate (Abbott TDx Digoxin II Reagent). |
| Assay Technology | Must be suitable for digoxin determination. | Turbidimetric inhibition immunoassay technology. (Different from predicate's fluorescence polarization immunoassay, but acceptable as demonstrated by method comparison). |
| Sample Pretreatment | Must be efficient for clinical use. | Does not require sample pretreatment. (Improved over predicate, which requires pretreatment). |
| Method Comparison | Substantial equivalence to predicate. | Regression Analysis (IMMAGE DIG vs. Abbott Digoxin II): Slope = 1.011 + 0.037, Intercept = 0.047 + 0.0016. (Indicates strong agreement and substantial equivalence to the predicate). |
| Shelf-Life Stability | Sufficient for clinical and commercial use. | 18 months shelf-life. |
| On-Board Stability | Sufficient for clinical use. | 14-day on-board stability. |
| Calibrator Shelf-Life | Sufficient for clinical and commercial use. | 24 months shelf-life (Drug Calibrator 2). |
| Calibrator On-Board Stability | Sufficient for clinical use. | 14-day calibration stability (Drug Calibrator 2). |
| Within-Run Imprecision | Low variability for accurate results. | Level 1 (Mean 0.87 U/mL): SD 0.042, %CV 4.8. |
| Level 1 (Mean 1.32 U/mL): SD 0.068, %CV 5.1. | ||
| Level 2 (Mean 2.63 U/mL): SD 0.079, %CV 3.0. | ||
| Level 3 (Mean 4.19 U/mL): SD 0.095, %CV 2.3. (All reported %CVs are low, indicating good precision). |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Size: The document does not explicitly state the total number of samples or patients used for the "Method Comparison Study." However, it mentions "Number of Results" for Imprecision studies:
- Level 1 (0.87 U/mL): 15 results
- Level 1 (1.32 U/mL): 80 results
- Level 2 (2.63 U/mL): 80 results
- Level 3 (4.19 U/mL): 80 results
- Data Provenance: Not specified. The document does not mention the country of origin of the data or whether it was retrospective or prospective.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts
- This information is not applicable to this type of device and study. The IMMAGE DIG Reagent is an in-vitro diagnostic (IVD) device for quantitative biochemical measurement. Its ground truth is established through comparison to a recognized predicate device (Abbott TDx Digoxin II Reagent), which itself is validated against established analytical methods, and through its own analytical performance (imprecision, stability). There are no "experts" in the human diagnostic interpretation sense establishing a "ground truth" for each sample.
4. Adjudication Method for the Test Set
- Not applicable. As stated above, this is an IVD device measuring a biochemical analyte. Adjudication methods like 2+1 or 3+1 are typically used in imaging or clinical diagnostic studies where human interpretation of medical data is involved. The "ground truth" for the test set is based on the quantitative results from the predicate device (Abbott Digoxin II) and the analytical characteristics of the IMMAGE DIG Reagent itself.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- No, an MRMC study was not done. MRMC studies are relevant for devices where human readers interpret data, such as in medical imaging. This device is an automated immunoassay system.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
- Yes, this is essentially a standalone performance study. The IMMAGE Immunochemistry System Digoxin (DIG) Reagent is an automated in-vitro diagnostic test. Its performance data (method comparison, stability, imprecision) represent its direct analytical capability without direct human intervention in the result generation or interpretation workflow that would be considered "human-in-the-loop" for an AI device. The system provides a quantitative measurement.
7. The Type of Ground Truth Used
- The ground truth for validating the IMMAGE DIG Reagent's performance in the method comparison study was the results obtained from the predicate device, Abbott TDx Digoxin II Reagent.
- For the stability and imprecision studies, the ground truth is based on the inherent analytical properties and precision of the IMMAGE system itself and reference materials/controls.
8. The Sample Size for the Training Set
- Not applicable. This document describes the validation of an in-vitro diagnostic reagent and system, not a machine learning or AI model that requires a "training set" in the conventional sense. The "training" for such a system would involve optimizing chemical formulations and system parameters during development, not learning from a labeled dataset.
9. How the Ground Truth for the Training Set Was Established
- Not applicable. As explained in point 8, there isn't a "training set" or "ground truth" as typically defined for AI/ML models. The development process would involve optimizing reagents and instrument settings to achieve desired analytical performance characteristics.
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