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510(k) Data Aggregation
(45 days)
The Stratus® CS Acute Care™ NT-proBNP method (pBNP) is an in vitro diagnostic assay for the quantitative determination of N-terminal pro-brain natriuretic peptide (NT-proBNP) in human plasma. In individuals suspected of having congestive heart failure (CHF), measurements of NTproBNP are used as an aid in the diagnosis and assessment of severity. The test is further indicated for the risk stratification of patients with acute coronary syndrome and heart failure. This method is for use by trained health care professionals on the Stratus® CS Stat Fluorometric Analyzer in the clinical laboratory and point of care (POC) settings.
The Stratus® CS Acute Care™ NT-proBNP method is a two-site sandwich assay based upon solid phase Radial Partition Immunoassav (RPIA) technology. In this procedure, dendrimer linked monoclonal antibody is added to the center portion of a square piece of glass fiber paper in the pBNP TestPak. This antibody recognizes a distinct antigenic site on the NT-proBNP molecule. Sample is then added onto the paper where it reacts with the immobilized antibody. After a short incubation, a conjugate consisting of enzyme-labeled monoclonal antibody directed against a second distinct antigenic site on the NT-proBNP molecule is pipetted onto the reaction zone of the paper. During this second incubation period, enzyme-labeled antibody reacts with the bound NT-proBNP, forming an antibody-antigen-labeled antibody sandwich. The unbound labeled antibody is later eluted from the field of view of the Stratus® CS analyzer by applying a substrate wash solution to the center of the reaction zone. By including substrate for the enzyme within the wash solution, initiation of enzyme activity occurs simultaneously with the wash. The enzymatic rate of the bound fraction increases directly with the concentration of NT-proBNP in the sample. The reaction rate can then be measured by an optical system that monitors the reaction rate via front surface fluorescence: All data analysis functions are performed by the microprocessor within the analyzer.
Acceptance Criteria and Device Performance for Stratus® CS Acute Care™ NT-proBNP (pBNP) TestPak (K071834)
The Stratus® CS Acute Care™ NT-proBNP (pBNP) TestPak is an in vitro diagnostic test for the quantitative measurement of N-terminal pro-brain natriuretic peptide (NT-proBNP) in heparanized plasma. It is intended to aid in the diagnosis and assessment of severity of congestive heart failure (CHF) and for risk stratification of patients with acute coronary syndrome and heart failure.
This 510(k) submission (K071834) seeks to demonstrate substantial equivalence to the previously cleared Dade Behring Stratus® CS Acute Care™ NT-proBNP (pBNP) (K043476/K060548) predicate device, primarily addressing a change in the antibody used (from polyclonal to monoclonal). The acceptance criteria and supporting studies are therefore focused on demonstrating comparable performance to the predicate device.
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implicitly defined by the demonstration of "good agreement" and "very good agreement" with the predicate device, as well as meeting specific analytical performance characteristics.
| Acceptance Criteria / Performance Characteristic | Predicate Device Performance (Implicit Acceptance) | Revised Device Performance (Reported and Meets Acceptance) |
|---|---|---|
| Method Comparison (vs. Predicate) | - | Correlation: |
| Slope | - | 1.02 |
| Intercept (pg/mL) | - | 8.2 |
| Correlation Coefficient | - | 0.99 |
| Lithium Heparin vs. Sodium Heparin Comparison | - | Agreement: |
| Slope | - | 1.06 |
| Intercept (pg/mL) | - | -75 |
| Correlation Coefficient | - | 0.997 |
| Analytical Sensitivity | ≤15 pg/mL | ≤15 pg/mL |
| Functional Sensitivity | ≤ 50 pg/mL | ≤ 50 pg/mL |
| Analytical Specificity (Natrecor®) | No significant cross reactivity at 0 and 125 pg/mL NT-proBNP | No significant cross reactivity at 0 and 125 pg/mL NT-proBNP |
| Analytical Specificity (Other substances) | Sixteen other substances show no significant cross reactivity | Sixteen other substances also show no significant cross reactivity |
| Interferences (Bilirubin, Hemoglobin, Lipemia, Rheumatoid Factors) | No significant interference within specified limits (e.g., bilirubin up to 60 mg/dL, hemoglobin up to 1000 mg/dL, triglycerides up to 3000 mg/dL, rheumatoid factors up to 750 IU/mL) | No significant interference within specified limits (e.g., bilirubin up to 60 mg/dL, hemoglobin up to 1000 mg/dL, lipemia up to 3000 mg/dL, rheumatoid factors up to 500 IU/mL) |
| Hook Effect | No high dose effect (up to 1,400,000 pg/mL) | No high dose effect (up to 833,585 pg/mL) |
| Reportable Range | 15- 20,000 pg/mL | 15- 20,000 pg/mL |
| Cut-off | 125 pg/mL (=75 years) | 125 pg/mL (=75 years) |
2. Sample Sizes and Data Provenance for Test Set
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Method Comparison Test Set:
- Sample Size: 148 patient samples.
- Data Provenance: Not explicitly stated, but clinical samples would typically be from the country where the study was conducted (presumably the US, given the FDA submission). The samples were "heparinized plasma patient samples," implying they were retrospective or prospective samples collected from patients. The document does not specify if it was retrospective or prospective.
-
Lithium Heparin versus Sodium Heparin Comparison Test Set:
- Sample Size: 51 samples, tested in duplicate.
- Data Provenance: Not explicitly stated, but likely from a similar clinical setting as the method comparison samples. The document does not specify if it was retrospective or prospective.
3. Number of Experts and Qualifications for Ground Truth for the Test Set
Not applicable. The study is a method comparison against a predicate device, not a diagnostic accuracy study where independent expert interpretation of patient conditions would establish ground truth. The "ground truth" for the performance evaluation of the new device is the measurement obtained from the legally marketed predicate device.
4. Adjudication Method for the Test Set
Not applicable. As described above, this is a method comparison study. There is no expert adjudication of clinical outcomes or images. The comparison is between quantitative measurements from two different analytical methods.
5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study
No. This is not an MRMC study. The device is an in vitro diagnostic assay that produces a quantitative result (NT-proBNP concentration), not an imaging device or clinical assessment tool that relies on human reader interpretation. Therefore, the concept of human readers improving with AI vs. without AI assistance does not apply.
6. Standalone Performance Study
Yes, a standalone performance study of the revised algorithm (TestPak) was done. The entire "Method Performance Summary" section details the analytical performance of the new device, comparing it directly to the predicate device and assessing its own characteristics (e.g., sensitivity, specificity, interferences, hook effect).
7. Type of Ground Truth Used
The "ground truth" for these studies is the quantitative measurement provided by the legally marketed predicate device (Dade Behring Stratus® CS Acute Care™ pBNP immunoassay K043476/K060548). For the lithium heparin versus sodium heparin comparison, the ground truth is the measurement from the other heparinized sample type. The intent is to show that the new device produces results comparable to the established device/reference.
8. Sample Size for the Training Set
The document does not specify a separate "training set" in the context of machine learning. This device is an immunoassay, not an AI/ML algorithm that requires a distinct training phase on a large dataset for model development. The development of the monoclonal antibody and assay reagents would involve internal R&D processes and optimization, but not in the sense of an "AI training set."
9. How the Ground Truth for the Training Set Was Established
Not applicable. As noted above, this is an immunoassay, not an AI/ML device that uses a "training set" with established ground truth in the typical machine learning sense. The assay's chemical and biological components are developed and validated through laboratory procedures, not by training on a labeled dataset.
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