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510(k) Data Aggregation
(130 days)
The MarDx ENA IgG ImmunoStripe Test System is intended for use in testing human serum for the presence of human IgG to extractable nuclear antigens, as an aid in the diagnosis of autoimmune disease, such as systemic lupus erythematosus. Siogren syndrome, scieroderma, and myositis.
The MarDx ENA IgG Immunostripe Test System is an enzyme-linked immunosorbent assay (ElA) for the detection of IgG to extractable nuclear antigens in human serum.
The MarDx ENA IgG Immunostripe Test System is an enzyme-linked immunosorbent assay (EIA) for the detection of IgG to extractable nuclear antigens (SSA, SSB, Sm, RNP, Jo-1, Scl-70) in human serum. It is intended for use as an aid in diagnosing autoimmune diseases such as systemic lupus erythematosus, Sjogren's syndrome, scleroderma, and myositis.
The device's performance was evaluated by comparing it against two predicate devices: the INOVA ENA's for SSA, SSB, Sm, RNP, Jo-1, Scl-70, and the MarDx ENA EIA 6 in 1 Test System.
1. Table of Acceptance Criteria and the Reported Device Performance
Although explicit "acceptance criteria" values were not formally stated as thresholds in the provided text, the study aimed to demonstrate "substantial equivalence" to predicate devices, implying that performance metrics (sensitivity and specificity) should be comparable. Based on the study results, the implied acceptance criteria were successfully met.
| Metric (Antigen / Condition) | Implied Acceptance Criteria (via predicate equivalence) | Reported Device Performance (MarDx Immunostripe) |
|---|---|---|
| Specificity (vs INOVA EIA) | High specificity (comparable to INOVA) | 100% (40/40) |
| Specificity (vs MarDx 6-in-1 EIA) | High specificity (comparable to MarDx 6-in-1) | 100% (139/139) |
| Sensitivity (SSA vs INOVA SSA EIA) | High sensitivity (comparable to INOVA SSA) | 95% (35/37) |
| Sensitivity (SSB vs INOVA SSB EIA) | High sensitivity (comparable to INOVA SSB) | 94% (16/17) |
| Sensitivity (Sm vs INOVA Sm EIA) | High sensitivity (comparable to INOVA Sm) | 100% (20/20) |
| Sensitivity (RNP vs INOVA RNP EIA) | High sensitivity (comparable to INOVA RNP) | 89% (33/37) |
| Sensitivity (Jo-1 vs INOVA Jo-1 EIA) | High sensitivity (comparable to INOVA Jo-1) | 100% (2/2) |
| Sensitivity (Scl-70 vs INOVA Scl-70 EIA) | High sensitivity (comparable to INOVA Scl-70) | 100% (7/7) |
| Sensitivity (Combined ENA vs INOVA) | High sensitivity (comparable to combined INOVA ENA) | 94% (113/120) |
| Sensitivity (vs MarDx 6-in-1 ENA EIA) | High sensitivity (comparable to MarDx 6-in-1) | 97% (67/69) |
| Overall Combined Sensitivity | Not explicitly stated, but high | 95% |
| Overall Combined Specificity | Not explicitly stated, but high | 100% |
| Precision | High precision | 100% (within and between assays) |
| Cross-Reactivity (to dsDNA) | No cross-reactivity | 0% (7/7 negative) |
2. Sample Size Used for the Test Set and the Data Provenance
A total of 264 sera were used in the comparative study.
- 139 sera were from normal individuals.
- 125 sera were from patients "thought to have autoimmune disease and submitted as blind specimens for ENA antigen detection."
The data provenance is not explicitly stated in terms of country of origin. The study was conducted by the R&D laboratory at MarDx. The nature of the "blind specimens" suggests a prospective collection or at least blinding of historical samples for the purpose of the study. The phrasing "sera from patients thought to have autoimmune disease" indicates these were clinical samples.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts
No experts were used to establish the ground truth in this study. The ground truth was established by the predicate devices (INOVA ELISA for ENA antibodies and MarDx 6 in 1 ENA EIA), which served as the reference standards.
4. Adjudication Method for the Test Set
No adjudication method was used for the test set. The results of the predicate devices were directly treated as the reference standard ("ground truth") against which the MarDx ENA IgG Immunostripe Test System was compared.
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 was not a multi-reader multi-case (MRMC) comparative effectiveness study, nor did it involve AI. The device is a diagnostic assay, and its performance was evaluated against established predicate assays. Therefore, no effect size related to human reader improvement with or without AI assistance is applicable or reported.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
This study evaluates the performance of a diagnostic assay (MarDx ENA IgG Immunostripe Test System) in a standalone manner, comparing its results directly against those of predicate assays. The "human-in-the-loop" aspect for reading the strip and interpreting the results is inherently part of the assay's use, but the reported performance metrics (sensitivity, specificity, precision, cross-reactivity) refer to the device's ability to correctly identify the presence or absence of antibodies as determined by the assay itself, not an algorithm's interpretation without human oversight. The "manufacturer's package insert" states how the assays were performed, implying a standard, non-automated reading of the strip results.
7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)
The ground truth used was the results obtained from established predicate devices:
- INOVA ELISA for ENA antibodies (for SSA, SSB, Sm, RNP, Jo-1, Scl-70)
- MarDx ENA EIA 6 in 1 Test System
These predicate devices serve as the reference standard due to their prior clearance or established clinical use for detecting these specific antibodies.
8. The Sample Size for the Training Set
No explicit training set is mentioned as this is a traditional diagnostic assay validation, not a machine learning model. The assay itself (reagent formulation, stripe design) would have been developed and optimized, but the study focuses on the validation of the final product with a test set of clinical samples.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as there was no explicit separate training set for a machine learning model. The development of the assay itself would have involved internal validation and optimization, likely using characterized positive and negative control samples, but this information is not detailed in the summary.
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