MYOGLOBIN ASSAY FOR THE TECHNICON IMMUNO 1 SYSTEM (IN-VITRO DIAGNOSTIC SYSTEM)
Applicant
Bayer Corp.
Product Code
DDR · Immunology
Decision Date
Aug 15, 1996
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 866.5680
Device Class
Class 2
Indications for Use
This in vitro diagnostic procedure is a solid phase immunoassay intended for the quantitative determination of Myoglobin in human serum or heparin plasma on the Technicon Immuno 1 system. When used in combination with other clinical data such as presenting symptoms and EKG values, measurement of Myoglobin aides in the early phase diagnosis of Myocardial Infarctions.
Device Story
The Technicon Immuno 1 Myoglobin assay is an in vitro diagnostic enzyme-labeled sandwich immunoassay. It utilizes a monoclonal mouse capture antibody and a polyclonal goat detector antibody. The capture antibody is labeled with fluorescein, and the detector antibody is labeled with alkaline phosphatase (ALP). The solid phase consists of magnetizable particles coated with anti-fluorescein antibodies. Patient serum or heparin plasma samples are mixed with reagents and incubated at 37°C. Myoglobin forms a fluorescein-conjugate/Myoglobin/ALP-conjugate complex, which is captured by the magnetic particles. An external magnetic field precipitates the particles; after washing, p-Nitrophenylphosphate substrate is added. The resulting increase in absorbance is measured spectrophotometrically at 405 and 450 nm. The system calculates results using a Cubic Fit Through Zero dose-response curve. The assay is performed on the Technicon Immuno 1 system in a clinical laboratory setting. Results are used by clinicians alongside EKG and symptom data to assist in the early diagnosis of Myocardial Infarction.
Clinical Evidence
Bench testing only. Performance evaluated via imprecision studies (n=160 replicates), linearity testing, and interference testing (bilirubin, albumin, hemoglobin, gamma globulins, triglycerides, heparin, citrate, urea, creatinine, rheumatoid factor). Method comparison against Behring Nephelometer (n=100 samples) showed a correlation of y = 1.02x + 1.05 (r=0.993). Sensitivity (minimum detectable concentration) is 1.8 ng/mL. Normal distribution established using 77 non-AMI individuals.
Technological Characteristics
Solid-phase enzyme-labeled sandwich immunoassay. Components: monoclonal mouse capture antibody (fluorescein-labeled), polyclonal goat detector antibody (ALP-labeled), magnetizable particles coated with anti-fluorescein. Energy source: spectrophotometric measurement at 405/450 nm. Assay range: 0-3000 ng/mL. Calibration: Cubic Fit Through Zero. System: Technicon Immuno 1.
Indications for Use
Indicated for the quantitative determination of Myoglobin in human serum or heparin plasma to aid in the early diagnosis of Myocardial Infarction in patients presenting with symptoms suggestive of cardiac events.
Regulatory Classification
Identification
A myoglobin immunological test system is a device that consists of the reagents used to measure by immunochemical techniques the myoglobin (an oxygen storage protein found in muscle) in serum and other body fluids. Measurement of myoglobin aids in the rapid diagnosis of heart or renal disease.
Predicate Devices
Behring N Latex Myoglobin Reagents
Submission Summary (Full Text)
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Attachment 3
# AUG 15 1996
# Summary of Safety and Effectiveness
K962344
# MYOGLOBIN METHOD FOR THE IMMUNO 1 SYSTEM
Listed below is a comparison of the performance between the Immuno 1 Myoglobin method (T01-3653-51) and a similar device that was granted FDA determination of substantial equivalence: The Behring N Latex Myoglobin Reagents. This reagents are designed to run on the Behring Nephelometer. The information used in this summary of Safety and Effectiveness was extracted from the Myoglobin Method Sheet and from data on file at Bayer Corporation.
# Intended Use
This in vitro diagnostic procedure is a solid phase immunoassay intended for the quantitative determination of Myoglobin in human serum or heparin plasma on the Technicon Immuno 1 system. When used in combination with other clinical data such as presenting symptoms and EKG values, measurement of Myoglobin aides in the early phase diagnosis of Myocardial Infarctions.
# Assay Description
The method described is an enzyme label sandwich assay using a monoclonal (mouse) capture and a polyclonal (goat) detector antibody. The monoclonal antibody is labelled with fluorescein and the polyclonal antibody labelled with alkaline phosphatase (ALP). The two reagents are the active compounds of the R1 and the R2 reagent, respectively. The solid phase consists of a suspension of magnetizable particles coated with antibody to fluorescein (mIMP reagent). Sample or calibrator, R1 and R2 reagent and mIMP reagent are mixed simultaneously and incubated at $37^{\circ}\mathrm{C}$ . In the presence of Myoglobin a fluorescein-conjugate $\equiv$ Myoglobin $\equiv$ ALP-conjugate complex is formed and captured by the antiFluorescein antibodies on the magnetic particles. The particles are precipitated by an external magnetic field, washed and paraNitrophenylphosphate is added as the enzyme substrate. The increase in absorbance due to the formation of p-Nitrophenolate is monitored spectrophotometrically at 405 and $450\mathrm{nm}$ . The response read is directly proportional to the

Final result after 23 minutes
Fig. 1 Schematic representation of the Technicon Immuno 1 Myoglobin Assay
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concentration of Myoglobin in a sample. A Cubic Fit Through Zero is used to calculate the dose response curve. The assay is depicted schematically in fig. 1.
The assay has a range of 0 to 3000 ng/mL with a sensitivity of 1.8 ng/mL; six calibrators with Myoglobin concentrations of 0, 60, 180, 600, 1500 and 3000 ng/mL are provided.
A dose response curve is shown in fig. 2.

Fig. 2 Calibration Curve of the Technicon Immuno 1 Myoglobin Assay
Data pairs measured:
0 ng/mL: 0.0019 AU/min
60 ng/mL: 0.0645 AU/min
180 ng/mL: 0.1937 AU/min
600 ng/mL: 0.6805 AU/min
1500 ng/mL: 1.7656 AU/min
3000 ng/mL: 3.1391 AU/min
# ASSAY PERFORMANCE
## Imprecision
Total imprecision data was obtained by analyzing human serum controls on two Immuno 1 instruments on 20 different days. Two separate lots of reagents and calibrators were used. Both reagent and calibrator combinations on both instruments were tested with two different lots of magnetic particles. The total number of replicates for each level was 160. The calibration was only performed when a new reagent/ calibrator lot or particle lot combination was implemented on a machine.
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| Table 1: Imprecision of Immuno 1 Myoglobin Assay Data was collected on two Systems over twenty days with four replicates on each day on each system | | | | | |
| --- | --- | --- | --- | --- | --- |
| Specimen | Total Imprecision (n= 160) | | | Within run imprecision (mean) | |
| | Average [ng/mL] | Std Dev [ng/mL] | CV [%] | Std Dev [ng/mL] | CV [%] |
| Sample 1 | 14.8 | 0.8196 | 5.5 | 0.3081 | 2.1 |
| Sample 2 | 52.6 | 1.9084 | 3.6 | 0.77925 | 1.5 |
| Sample 3 | 75.6 | 2.9831 | 3.9 | 1.06855 | 1.4 |
| Sample 4 | 131.3 | 4.6636 | 3.6 | 2.28195 | 1.7 |
| Sample 5 | 247 | 10.9436 | 4.4 | 4.47045 | 1.8 |
| Sample 6 | 278.1 | 8.2182 | 3.0 | 3.53275 | 1.3 |
| Sample 7 | 639.7 | 23.0701 | 3.6 | 11.3425 | 1.8 |
| Sample 8 | 1557.6 | 56.46 | 3.6 | 21.4224 | 1.4 |
| Sample 9 | 2718.9 | 91.3937 | 3.4 | 44.90395 | 1.7 |
Correlation with Immuno 1 Myoglobin results with Behring Nephelometer A
A total of 100 serum and plasma samples with Behring Nephelometer (BNA) values in the range of 21 to 2660 ng/mL (BNA) were tested with the Behring Nephelometer A and the Immuno 1 Myoglobin assay. The correlation equation according to Bablock-Passing was
$$
y = 1.02 \times x + 1.05
$$
(y is Immuno 1 Myoglobin assay; x is Behring Nephelometer A Myoglobin assay).
| Calculation of Regression Line : | | | |
| --- | --- | --- | --- |
| Slope (b) : | 1.02 | | Number of Samples : 100 |
| Limits : | 0.98 | 1.06 | Sampletype : all sample codes |
| Intercept (a) : | 1.05 | | |
| Limits : | -3.2 | 4.5 | |
| Confidence of Correlation : | 0.99314 | | |
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Fig. 3 Correlation Plot of Immuno 1 Myoglobin Results versus Behring Nephelometer A results
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31-JUL-1996 16:42
BAYER AG , ABT. ZF-F
+49 2151 887703 S.02
TEST.XLG (METHOD.XLD)
# Method Comparison acc. to Bablok-Passing
Methods: Immuno 1 Myoglobin vs. Behring N-Latex Reagents (BNA)
Serum and Plasma Samples

♦ Values
— Regression Line
— Identity Line
Calculation of Regression Line:
| Slope (b) : | 1.04110 | Number of Samples : | 54 |
| --- | --- | --- | --- |
| Limits : | 0.96763 | 1.12364 | Sampletype : all sample codes |
| Intercept (a) : | -0.98356 | | |
| Limits : | -5.79055 | 4.21007 | |
| Confidence of Correlati- | 0.96814 | | |
Bayer Leverkusen, Dr. Petry
4a
PEY 31.07.1996
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# Interference
For all interference measurements a +2 Pool and a -2 Pool was prepared. The +2 Pool was made from a solution of Myoglobin in serum with a concentration of approximately 180 µg/l (double of the concentration at the medical decision point) being diluted 1+1 with a solution of the potentially interfering substance in Myoglobin-stripped serum at a concentration twice as high as required. This yields a Myoglobin concentration at the medical decision level with the required interferant-concentration. The -2 Pool was made up from the same Myoglobin solution, but this time being diluted 1+1 with stripped serum containing no interferant. So two solutions of exactly identical Myoglobin concentration were obtained, one containing no interferent, the other with the required high concentration. The 0-Pool was obtained mixing equivalent amounts of the +2- and the -2-Pool while the -1- and +1-Pool were prepared from equal quantities of the 0-Pool and the -2- respectively the +2-Pool.
## - Bilirubin
Bilirubin:
- -2 Pool: 0 mg/dL
- +2 Pool: 25 mg/dL
Results:
| Pool | conc. (meas.) | % of -2 pool |
| --- | --- | --- |
| 2- | 90.6 | 100.0 |
| 1- | 90.6 | 100.0 |
| 0 | 91.3 | 100.8 |
| 1+ | 91.5 | 101 |
| 2+ | 90.9 | 100.3 |
## - Albumin
Albumin:
- -2 Pool: 0 mg/mL
- +2 Pool: 6.5 g/dL
Results:
| Pool | conc (meas.) | % of -2 Pool |
| --- | --- | --- |
| 2- | 87.1 | 100.0 |
| 1- | 87.1 | 100.0 |
| 0 | 87.9 | 100.9 |
| 1+ | 88.1 | 101.1 |
| 2+ | 88.4 | 101.5 |
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# - Hemoglobin
Hemoglobin: -2 Pool: 0 mg/mL
+2 Pool: 1 g/dL
Results:
| Pool | conc. (meas) | % of -2 Pool |
| --- | --- | --- |
| 2- | 88 | 100.0 |
| 1- | 89.3 | 101.5 |
| 0 | 89.8 | 102 |
| 1+ | 88.1 | 100.1 |
| 2+ | 89.3 | 101.5 |
# - Gamma Globulins
BGG: -2 Pool: 0 mg/dL
+2 Pool: 5.3 g/dL
Results:
| Pool | conc. (meas.) | % of -2 Pool |
| --- | --- | --- |
| 2- | 82,7 | 100,0 |
| 1- | 84,1 | 101,7 |
| 0 | 84,6 | 102,3 |
| 1+ | 78,6 | 95 |
| 2+ | 85,6 | 103.5 |
# - Triglycerides
Triglyceride Supertrate: -2 Pool: 0 g/dL
+2 Pool: 1.3 g/dL (calculated for Triglycerides)
Results:
| Pool | conc. (meas.) | % of -2 Pool |
| --- | --- | --- |
| 2- | 94,9 | 100,0 |
| 1- | 90,4 | 95,3 |
| 0 | 90 | 94.8 |
| 1+ | 89 | 93.8 |
| 2+ | 99.2 | 104.5 |
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# - Heparin
Heparin: -2 Pool: 0 IU/mL (Serum)
+2 Pool: 65 IU/mL (0.5 mg/mL)
Results:
| Pools | conc. (meas.) | % of -2 Pool |
| --- | --- | --- |
| 2- | 85,9 | 100,0 |
| 1- | 85,8 | 99,9 |
| 0 | 85,8 | 99,9 |
| 1+ | 86,4 | 100,6 |
| 2+ | 87,2 | 101,5 |
# - Citrate
Trisodium Citrate, Dihydrate: -2 Pool: 0 mg/ml
+2Pool: 50 mg/mL
Results:
| Pools | conc. (meas.) | % of -2 Pool |
| --- | --- | --- |
| 2- | 92,4 | 100,0 |
| 1- | 90,8 | 98,3 |
| 0 | 91,5 | 99 |
| 1+ | 91,1 | 98,6 |
| 2+ | 88,7 | 96 |
# - Urea and Creatine
Urea and Creatine: -2 Pool: no Urea, no Creatine
+2 Pool: 200 mg/dL Urea, 2.5 mg/dL Creatine
Results:
| Pools | conc. (meas.) | % of -2 Pool |
| --- | --- | --- |
| 2- | 127,7 | 100,0 |
| 1- | 129,2 | 101,2 |
| 0 | 127,9 | 100,2 |
| 1+ | 127,5 | 99,8 |
| 2+ | 128,4 | 100,5 |
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# - Rheumatory Factor
Rheumatory Factor: -2 Pool: 0 IU/mL
+2 Pool: 567 IU/mL
## Results:
| Pool | conc. (meas.) | % of -2 Pool |
| --- | --- | --- |
| 2- | 95,9 | 100,0 |
| 1- | 96,1 | 100,2 |
| 0 | 98,1 | 102,3 |
| 1+ | 98,8 | 103 |
| 2+ | 99,6 | 103,9 |
## Linearity
All control to check linearity pools were generated in a way, that a serum sample with a high Myoglobin level (+2 Pool) and a low sample (-2 Pool) were mixed in a ratio of 1 + 1. The 0 Pool thus generated was furtherly mixed with the same amount of the +2 respectively the -2 Pool to have the +1 and -1 Pool. By that procedure five equally spaced controls covering the whole assay range are generated. For data analysis a linear regression was calculated from the result of the -2, -1 and 0 Pools of each sample series. The expected results and the deviation from the measured values were calculated from the equation. The result is shown in the table below.
| Pool (Sample) | measured | calculated | dev (%) |
| --- | --- | --- | --- |
| -2 (A) | 18.7 | 19.5 | -3.9 |
| -1 (A) | 656.7 | 655.2 | 0.2 |
| 0 (A) | 1290.2 | 1291 | -0.1 |
| 1 (A) | 1855.7 | 1926.7 | -3.7 |
| 2 (A) | 2481.8 | 2562.5 | -3.1 |
| -2 (B) | 19.1 | 24.2 | -21 |
| -1 (B) | 659.9 | 649.7 | 1.6 |
| 0 (B) | 1270.2 | 1275.3 | -0.4 |
| 1 (B) | 1862.7 | 1900.8 | -2 |
| 2 (B) | 2439.2 | 2526.4 | -3.5 |
| -2 (C) | 19.1 | 20.4 | -6.5 |
| -1 (C) | 664.9 | 662.2 | 0.4 |
| 0 (C) | 1302.7 | 1304 | -0.1 |
| 1 (C) | 1891.6 | 1945.8 | -2.8 |
| 2 (C) | 2510.9 | 2587.6 | -3 |
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# Sample Dilution
For testing Sample Dilution of clinical serum and plasma samples a dilution series with Immuno 1 Sample Diluent B and Immuno 1 Myoglobin Calibrator Level 1 was run. The recovery of the undiluted sample was set to 100%
| Dilution of Clinical Serum Samples with Immuno 1 Sample Diluent B | | | | | |
| --- | --- | --- | --- | --- | --- |
| sample content % | Identification | AU/min | conc.(meas.) | conc.(calc) | recovery (%) |
| 100 | Serum Sample A | 1.1004 | 880.5 | 880.5 | 100 |
| 75 | | 0.7783 | 623.3 | 660.4 | 106.0 |
| 50 | | 0.4671 | 426.6* | 440.3 | 103.2 |
| 25 | | 0.2536 | 205.1 | 220.1 | 107.3 |
| 10 | | 0.1013 | 81.6 | 88.1 | 108 |
| 0 | | 0.0027 | 0.6 | 0.0 | - |
| 100 | Serum Sample B | 2.7817 | 2420.1 | 2420.1 | 100 |
| 75 | | 2.2541 | 1881.2 | 1815.1 | 96.5 |
| 50 | | 1.5296 | 1233 | 1210.1 | 98.1 |
| 25 | | 0.7516 | 602.1 | 605.0 | 100.5 |
| 10 | | 0.2922 | 236.2 | 242.0 | 102.5 |
| 0 | | 0.002 | 0.1 | 0.0 | - |
| 100 | Serum Sample C | 3.1881 | 2887.9 | 2887.9 | 100 |
| 75 | | 2.5717 | 2197.4 | 2165.9 | 98.6 |
| 50 | | 1.7897 | 1456.1 | 1444.0 | 99.2 |
| 25 | | 0.9565 | 765.2 | 722.0 | 94.4 |
| 10 | | 0.3813 | 307.7 | 288.8 | 93.9 |
| 0 | | 0.0021 | 0.2 | 0.0 | - |
| 100 | Serum Sample D | 2.7268 | 2360.9 | 2360.9 | 100 |
| 75 | | 2.0877 | 1724.3 | 1770.7 | 102.7 |
| 50 | | 1.4969 | 1205.5 | 1180.5 | 97.9 |
| 25 | | 0.7662 | 613.7 | 590.2 | 96.2 |
| 10 | | 0.2915 | 235.6 | 236.1 | 100.2 |
| 0 | | 0.0023 | 0.3 | 0.0 | - |
| 100 | Serum Sample E | 2.6725 | 2302.7 | 2302.7 | 100 |
| 75 | | 2.0673 | 1705.4 | 1727.0 | 101.3 |
| 50 | | 1.456 | 1171.4 | 1151.4 | 98.3 |
| 25 | | 0.7215 | 578.2 | 575.7 | 99.6 |
| 10 | | 0.2689 | 217.5 | 230.3 | 105.9 |
| 0 | | 0.002 | 0.1 | 0.0 | - |
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| Dilution of Serum Samples with Myoglobin Calibrator Level 1 | | | | | |
| --- | --- | --- | --- | --- | --- |
| Sample content % | Identification | AU/min | conc. (meas.) | conc. (calc) | dev. (%) |
| 100 | Serum Sample A | 1.0525 | 859.7 | 859.7 | 100 |
| 75 | | 0.8022 | 659.3 | 644.8 | 97.8 |
| 50 | | 0.5132 | 426.3 | 429.9 | 100.8 |
| 25 | | 0.2648 | 222.1 | 214.9 | 96.8 |
| 10 | | 0.0976 | 81 | 86.0 | 106.2 |
| 0 | | 0.0019 | -1.3 | 0.0 | - |
| 100 | Serum Sample B | 2.7167 | 2344.3 | 2344.3 | 100 |
| 75 | | 2.1286 | 1767.2 | 1758.2 | 99.5 |
| 50 | | 1.4933 | 1217.3 | 1172.2 | 96.3 |
| 25 | | 0.7267 | 598.8 | 586.1 | 97.9 |
| 10 | | 0.2955 | 247.5 | 234.4 | 94.7 |
| 0 | | 0.003 | -0.3 | 0.0 | - |
| 100 | Serum Sample C | 3.0948 | 2765.1 | 2765.1 | 100 |
| 75 | | 2.4005 | 2023.7 | 2073.8 | 102.5 |
| 50 | | 1.7318 | 1417.3 | 1382.6 | 97.6 |
| 25 | | 0.9207 | 754.1 | 691.3 | 91.7 |
| 10 | | 0.3701 | 309.3 | 276.5 | 89.4 |
| 0 | | 0.0018 | -1.4 | 0.0 | - |
| 100 | Serum Sample D | 2.8607 | 2499.2 | 2499.2 | 100 |
| 75 | | 2.1077 | 1748.1 | 1874.4 | 107.2 |
| 50 | | 1.4938 | 1217.7 | 1249.6 | 102.6 |
| 25 | | 0.751 | 618.3 | 624.8 | 101.1 |
| 10 | | 0.2835 | 237.7 | 249.9 | 105.1 |
| 0 | | 0.0019 | -1.3 | 0.0 | - |
| 100 | Serum Sample E | 2.7182 | 2345.6 | 2345.6 | 100 |
| 75 | | 2.1273 | 1766 | 1759.2 | 99.6 |
| 50 | | 1.4916 | 1215.9 | 1172.8 | 96.5 |
| 25 | | 0.7647 | 629.3 | 586.4 | 93.2 |
| 10 | | 0.297 | 248.8 | 234.6 | 94.3 |
| 0 | | 0.0021 | -1.1 | 0.0 | - |
| Dilution of Plasma Samples with Immuno 1 Sample diluent B | | | | | |
| --- | --- | --- | --- | --- | --- |
| Sample content [%] | Identification | AU/min | conc. (meas) | conc (calc) | dev (%) |
| 100 | Plasma Sample AA | 1.44 | 1224.1 | 1224.1 | 100 |
| 75 | | 1.0586 | 898 | 918.075 | 102.2 |
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| Sample content [%] | Identification | AU/min | conc. (meas) | conc (calc) | dev (%) |
| --- | --- | --- | --- | --- | --- |
| 50 | | 0.6935 | 591.2 | 612.05 | 103.5 |
| 25 | | 0.3398 | 292.3 | 306.025 | 104.7 |
| 10 | | 0.1379 | 118.7 | 122.41 | 103.1 |
| 0 | | 0.0024 | 0.1 | 0 | - |
| 100 | Plasma Sample BB | 2.6022 | 2332.7 | 2332.7 | 100 |
| 75 | | 2.0189 | 1747.1 | 1749.525 | 100.1 |
| 50 | | 1.3546 | 1150.3 | 1166.35 | 101.4 |
| 25 | | 0.6843 | 583.5 | 583.175 | 99.9 |
| 10 | | 0.2568 | 221.3 | 233.27 | 105.4 |
| 0 | | 0.0023 | -0.1 | 0 | - |
| 100 | Plasma Sample CC | 2.5736 | 2302.1 | 2302.1 | 100 |
| 75 | | 1.9753 | 1705.9 | 1726.575 | 101.2 |
| 50 | | 1.361 | 1155.8 | 1151.05 | 99.6 |
| 25 | | 0.6662 | 568.3 | 575.525 | 101.3 |
| 10 | | 0.2714 | 233.9 | 230.21 | 98.4 |
| 0 | | 0.0022 | -0.2 | 0 | - |
| 100 | Plasma Sample DD | 1.2854 | 1090.7 | 1090.7 | 100 |
| 75 | | 0.9627 | 817.3 | 818.025 | 100.1 |
| 50 | | 0.635 | 542.1 | 545.35 | 100.6 |
| 25 | | 0.3104 | 267.3 | 272.675 | 102 |
| 10 | | 0.1269 | 109.1 | 109.07 | 100 |
| 0 | | 0.002 | -0.4 | 0 | - |
| 100 | Plasma Sample EE | 1.62 | 1382.2 | 1382.2 | 100 |
| 75 | | 1.2144 | 1030.1 | 1036.65 | 100.6 |
| 50 | | 0.8063 | 685.9 | 691.1 | 100.8 |
| 25 | | 0.3932 | 337.8 | 345.55 | 102.3 |
| 10 | | 0.1604 | 138.2 | 138.22 | 100 |
| 0 | | 0.0022 | -0.1 | 0 | - |
| Dilution of Plasma Samples with Immuno 1 Calibrator Level 1 | | | | | |
| --- | --- | --- | --- | --- | --- |
| Sample content % | Identification | AU/min | conc (meas) | conc (calc) | dev (%) |
| 100 | Plasma Sample AA | 1.4613 | 1216.1 | 1216.1 | 100 |
| 75 | | 1.1144 | 929.1 | 912.075 | 98.2 |
| 50 | | 0.814 | 683.6 | 608.05 | 88.9 |
| 25 | | 0.3588 | 307 | 304.025 | 99 |
| 10 | | 0.1268 | 108.8 | 121.61 | 111.8 |
| 0 | | 0.0025 | -0.1 | 0 | - |
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| Dilution of Plasma Samples with Immuno 1 Calibrator Level 1 | | | | | |
| --- | --- | --- | --- | --- | --- |
| Sample content % | Identification | AU/min | conc (meas) | conc (calc) | dev (%) |
| 100 | Plasma Sample BB | 2.724 | 2392.9 | 2392.9 | 100 |
| 75 | | 2.1735 | 1843.6 | 1794.675 | 97.3 |
| 50 | | 1.4542 | 1210.1 | 1196.45 | 98.9 |
| 25 | | 0.7596 | 639.2 | 598.225 | 93.6 |
| 10 | | 0.283 | 242.9 | 239.29 | 98.5 |
| 0 | | 0.0023 | -0.2 | 0 | - |
| 100 | Plasma Sample CC | 2.7637 | 2435.5 | 2435.5 | 100 |
| 75 | | 2.1392 | 1811.5 | 1826.625 | 100.8 |
| 50 | | 1.4943 | 1243.9 | 1217.75 | 97.9 |
| 25 | | 0.7273 | 612.7 | 608.875 | 99.4 |
| 10 | | 0.2993 | 256.7 | 243.55 | 94.9 |
| 0 | | 0.0023 | -0.3 | 0 | - |
| 100 | Plasma Sample DD | 1.3432 | 1117.6 | 1117.6 | 100 |
| 75 | | 1.0524 | 878.4 | 838.2 | 95.4 |
| 50 | | 0.6883 | 580.7 | 558.8 | 96.2 |
| 25 | | 0.3372 | 288.8 | 279.4 | 96.7 |
| 10 | | 0.1365 | 117.3 | 111.76 | 95.3 |
| 0 | | 0.0021 | -0.4 | 0 | - |
| 100 | Plasma Sample EE | 1.6146 | 1345.7 | 1345.7 | 100 |
| 75 | | 1.2409 | 1033 | 1009.275 | 97.7 |
| 50 | | 0.8323 | 698.6 | 672.85 | 96.3 |
| 25 | | 0.3987 | 340.5 | 336.425 | 98.8 |
| 10 | | 0.1619 | 139.1 | 134.57 | 96.7 |
| 0 | | 0.0021 | -0.4 | 0 | - |
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# Hook Effect
Samples with Myoglobin concentrations up to 1 Million ng/mL were assayed with the Technicon Immuno 1 Myoglobin Assay. The assay will not erroneously compute raw data to concentrations within the calibration range of the assay as long as the Myoglobin content in the sample is less than 150,000 ng/mL. In the following figure there is a graphical representation of the expected concentrations against the reported for antigen levels between 500 ng/mL and 1 Million ng/mL.

Fig. 4 Concentrations reported by the Immuno 1 Myoglobin assay against expected concentrations. There are no results reported if they above the dashed line in Fig. 4.
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# Recovery
Known amounts of Myoglobin Solution were spiked into four clinical samples, two serum samples, two plasma samples. For serum the recoveries were ranging from 96.5 to 105.2%; for plasma the recoveries are between 97.7 and 103%. For the recoveries of spiked antigen in plasma it is important to also use Myoglobin containing plasma as the spiking material. Serum- of buffer-based material may lead to deviations from the expected.
| Serum (PEY 2795) | | | |
| --- | --- | --- | --- |
| Sample | Expected | found | recovery |
| Serum
Sample α | 39.1 | 39.1 | 100.0 |
| | 77.2 | 78.1 | 101.2 |
| | 651 | 672.6 | 103.3 |
| | 1224 | 1250.1 | 102.1 |
| | 1798 | 1757.7 | 97.8 |
| | 2371 | 2377.2 | 100.3 |
| Serum
Sample β | 38.5 | 38.5 | 100.0 |
| | 77.2 | 77.4 | 100.3 |
| | 651 | 685 | 105.2 |
| | 1224 | 1223.6 | 100.0 |
| | 1798 | 1735.1 | 96.5 |
| | 2371 | 2331.8 | 98.3 |
| Plasma (PEY 2795) | | | |
| --- | --- | --- | --- |
| Sample | expected | found | recovery |
| Plasma
Sample γ | 24.8 | 24.8 | 100.0 |
| | 77 | 78.1 | 101.4 |
| | 843.45 | 860.6 | 102.0 |
| | 1609.9 | 1624.3 | 100.9 |
| | 2376.35 | 2326.6 | 97.9 |
| | 3142.8 | 3180.4 | 101.2 |
| Plasma
Sample δ | 31.3 | 31.3 | 100.0 |
| | 83.5 | 81.6 | 97.7 |
| | 758.45 | 780.9 | 103.0 |
| | 1433.4 | 1459.2 | 101.8 |
| | 2108.35 | 2078 | 98.6 |
| | 2783.3 | 2782 | 100.0 |
{15}
# Expected Values
Samples from 77 non AMI individuals were assayed and gave the distribution of results shown in Fig. 4.
It was found that 98% of the values were 88 ng/mL or less.

Immuno 1 Myoglobin: Normal Distribution
Fig. 5 Technicon Immuno 1 Myoglobin Assay: Normal Distribution
# Minimum Detectable Concentration
The minimum detectable concentration was measured in 32 different runs on four different days using two different lots of reagents, calibrators and magnetic particles. The L1 calibrator containing no Myoglobin was measured 576 times all together. Calculated from the mean zero absorption plus two standard deviations the minimum detectable dose was determined as 1.8 ng/mL.
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.