The i-STAT BNP test is an in vitro diagnostic test for the quantitative measurement of B-type natriuretic peptide (BNP) in whole blood or plasma samples using EDTA as the anticoagulant. BNP measurements can be used as an aid in the diagnosis and assessment of severity of congestive heart failure. The i-STAT Controls are assayed liquid plasma used to verify the integrity of newly received i-STAT BNP cartridges. The i-STAT BNP Calibration Verification Controls are assayed liquid plasma used to verify the calibration of i-STAT BNP cartridges throughout the reportable range.
Device Story
i-STAT BNP test is a single-use cartridge-based assay for quantitative BNP measurement in whole blood or plasma. Operates on i-STAT 1 Analyzer; requires EDTA anticoagulant. Cartridge contains electrochemical sensors on silicon chip; utilizes two-site ELISA principle. Sample introduced to cartridge; BNP captured by antibody-enzyme conjugate; enzyme cleaves substrate to produce electrochemically detectable product proportional to BNP concentration. Used in clinical settings; provides rapid quantitative results to assist physicians in diagnosing and assessing congestive heart failure severity. System includes automated self-checks for sensor performance, fluid integrity, and thermal/pressure transducers; results suppressed if deviations occur. Calibration is factory-set.
Clinical Evidence
Clinical evidence derived from studies using Abbott AxSYM BNP assay to support reference range transfer. Study population included 890 non-heart failure individuals and 693 heart failure patients (NYHA classes I-IV). AUC for heart failure diagnosis is 0.90 (95% CI: 0.86-0.92) at 100 pg/mL threshold. Clinical sensitivity 74.2%, specificity 91.5%. Method comparison with ARCHITECT assay (n=433) showed correlation slope of 0.97 and r=0.961.
Technological Characteristics
Two-site ELISA; electrochemical (amperometric) detection. Cartridge contains silicon chip with electrochemical sensors, antibody/alkaline phosphatase conjugate, buffer, and preservatives. Sample type: EDTA whole blood or plasma. Factory-set calibration. Dimensions/form factor: single-use cartridge for i-STAT 1 Analyzer. No optical interferents due to electrochemical detection.
Indications for Use
Indicated for quantitative measurement of BNP in whole blood or plasma (EDTA) to aid in diagnosis and severity assessment of congestive heart failure in patients. For use by trained healthcare professionals.
Regulatory Classification
Identification
The B-type natriuretic peptide (BNP) test system is an in vitro diagnostic device intended to measure BNP in whole blood and plasma. Measurements of BNP are used as an aid in the diagnosis of patients with congestive heart failure.
Special Controls
*Classification.* Class II (special controls). The special control is “Class II Special Control Guidance Document for B-Type Natriuretic Peptide Premarket Notifications; Final Guidance for Industry and FDA Reviewers.”
Predicate Devices
Biosite Triage BNP Test (k021317)
Reference Devices
Abbott ARCHITECT BNP test
Submission Summary (Full Text)
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1
510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION
DECISION SUMMARY
ASSAY AND INSTRUMENT COMBINATION TEMPLATE
A. 510(k) Number:
k053597
B. Purpose for Submission:
New device
C. Measurand:
B-type natriuretic peptide
D. Type of Test:
Quantitative
E. Applicant:
i-STAT Corporation
F. Proprietary and Established Names:
i-STAT BNP test
i-STAT Control Level 1
i-STAT Control Level 2
i-STAT Control Level 3
i-STAT BNP Calibration Verification Control Set
G. Regulatory Information:
1. Regulation section:
862.1117, B-type natriuretic peptide test system
862.1660, Single (specified) analyte controls (assayed and unassayed)
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2. Classification:
Class II, Class I
3. Product code:
NBC, JJX
4. Panel:
75 Chemistry
H. Intended Use:
1. Intended use(s):
The i-STAT BNP test is an in vitro diagnostic test for the quantitative measurement of B-type natriuretic peptide (BNP) in whole blood or plasma samples using EDTA as the anticoagulant. BNP measurements can be used as an aid in the diagnosis and assessment of severity of congestive heart failure.
The i-STAT Controls are assayed liquid plasma used to verify the integrity of newly received i-STAT BNP cartridges.
The i-STAT BNP Calibration Verification Controls are assayed liquid plasma used to verify the calibration of i-STAT BNP cartridges throughout the reportable range.
2. Indication(s) for use:
See Intended use(s) above.
3. Special conditions for use statement(s):
Prescription use only
4. Special instrument requirements:
i-STAT 1 Analyzer
I. Device Description:
Each i-STAT BNP cartridge provides a sample inlet, sensors to detect the BNP, and all the necessary reagents needed to perform the test. The cartridge contains a buffer and preservatives. A list of reactive ingredients is indicated below:
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| Reactive Ingredient | Biological Source |
| --- | --- |
| Antibody/Alkaline Phosphatase Conjugate | Murine IgG:Bovine Intestine |
| IgG | Caprine IgG: Murine IgG |
| Sodium Aminophenyl Phosphate | N/A |
| Heparin | Porcine Intestine |
The i-STAT BNP Controls are supplied as assayed frozen liquid plasma at 3 levels, Control Level 1, Control Level 2 and Control Level 3. The human sera used in the preparation of this product has been tested by FDA approved test methods and found negative/non-reactive for HIV-1, HIV-2, HBsAg, HCV, HTLV-1 and HTLV-2.
The i-STAT Verification Control Set is supplied as 3 levels of assayed frozen liquid plasma at 3 levels, Level 1, Level 2 and Level 3. The human sera used in the preparation of this product has been tested by FDA approved test methods and found negative/non-reactive for HIV-1, HIV-2, HBsAg, HCV, HTLV-1 and HTLV-2.
# J. Substantial Equivalence Information:
1. Predicate device name(s):
Biosite Triage BNP Test
2. Predicate $510(\mathrm{k})$ number(s):
k021317
3. Comparison with predicate:
| Similarities | | |
| --- | --- | --- |
| Item | Device | Predicate |
| Assay methodology | Two-site ELISA | Two-site ELISA |
| Capture site | Heterogeneous | Heterogeneous |
| Capture antibodies | Monoclonal | Monoclonal |
| Enzyme label antibody | Monoclonal | Monoclonal |
| Sample type | Whole blood or plasma | Whole blood or plasma |
| Acceptable samples | EDTA anti-coagulated blood or plasma | EDTA anti-coagulated blood or plasma |
| Differences | | |
| --- | --- | --- |
| Item | Device | Predicate |
| Enzyme label | Fluorescent dye | Alkaline phosphatase |
| Enzyme detection | Fluorescent | Electrochemical |
| Sample volume | 250 μL | 20 μL |
| Reportable range | 15-5000 pg/mL | 5-5000 pg/mL |
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# K. Standard/Guidance Document Referenced (if applicable):
CLSI Guideline EP7-A; CLSI Guideline EP9-A2; CLSI Guideline C-28-A2
# L. Test Principle:
The i-STAT BNP test cartridge uses a two-site enzyme-linked immunosorbant assay (ELISA) method. Antibodies specific for BNP are located on an electrochemical sensor fabricated on a silicon chip. Also deposited in another location on the sensor silicon chip is an antibody/alkaline phosphatase enzyme conjugate specific to a separate portion of the BNP molecule. The whole blood or plasma sample is brought into contact with the sensors allowing the enzyme conjugate to dissolve into the sample. The BNP within the sample becomes labeled with alkaline phosphatase and is captured onto the surface of the electrochemical sensor during an incubation period of approximately seven minutes. The sample is washed off the sensors, as well as excess enzyme conjugate. Within the wash fluid is a substrate for the alkaline phosphatase enzyme. The enzyme bound to the antibody/antigen/antibody sandwich cleaves the substrate releasing an electrochemically detectable product. The electrochemical (amperometric) sensor measures this enzyme product which is proportional to the concentration of BNP within the sample.
# M. Performance Characteristics (if/when applicable):
# 1. Analytical performance:
# a. Precision/Reproducibility:
Precision data were collected as follows: duplicates of each control were tested daily for a period of 20 days for each of 3 lots of cartridges, resulting in a total of 434 replicates. The average statistics are presented below.
| Aqueous Control | Mean | % CV (within-run) | % CV (total) |
| --- | --- | --- | --- |
| Level 1 | 126 | 9.0 | 11.1 |
| Level 2 | 1551 | 6.6 | 8.1 |
| Level 3 | 3337 | 8.0 | 9.8 |
Whole blood imprecision data were collected as follows: whole blood samples from 5 healthy donors were spiked to low, intermediate and high BNP concentrations affording 15 samples, each of which was measured in 10 i-STAT BNP cartridges from a single cartridge lot; three lots of cartridges were employed. The mean within-sample BNP concentration ranged from $84 - 3925\mathrm{pg / mL}$ and the within-sample imprecision $(\% \mathrm{CV})$ ranged from 3.4 to $9.4\%$ ; the average BNP concentration and imprecision were $1464~\mathrm{pg / mL}$ and $6.5\%$ respectively. The individual results are presented in the table below:
| Donor | Mean BNP in pg/mL | % CV |
| --- | --- | --- |
| 1 | 99 | 6.3 |
| 1 | 765 | 3.4 |
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| Donor | Mean BNP in pg/mL | % CV |
| --- | --- | --- |
| 1 | 3803 | 4.1 |
| 2 | 107 | 7.8 |
| 2 | 1049 | 7.8 |
| 2 | 2638 | 5.7 |
| 3 | 108 | 9.2 |
| 3 | 1036 | 6.5 |
| 3 | 3805 | 5.4 |
| 4 | 84 | 9.1 |
| 4 | 783 | 9.4 |
| 4 | 3925 | 7.6 |
| 5 | 95 | 7.0 |
| 5 | 763 | 3.9 |
| 5 | 2900 | 4.8 |
# b. Linearity/assay reportable range:
The dilution linearity of the i-STAT BNP test was studied using EDTA whole blood and plasma samples derived from 3 separate donors. For each donor, the original BNP negative sample and a BNP spiked sample were prepared. This process yielded three BNP positive whole blood samples that were then assayed in duplicate for each of 3 separate i-STAT BNP cartridge lots. These whole blood samples were then diluted using an equal mass of the original unspiked whole blood and assayed in duplicate. From this whole blood data, the BNP recovery was calculated.
| Whole blood | Concentration | Diluted concentration | % recovery |
| --- | --- | --- | --- |
| A | 590 | 312 | 106% |
| B | 2765 | 1429 | 103% |
| C | 5123 | 2803 | 109% |
The plasma derived from these three donors was combined in all pair-wise combinations in equal volumes. These combinations were then assayed in duplicate for each of 3 separate i-STAT BNP cartridge lots. The BNP recovery for each pair was calculated using the average of the 6 results.
| Plasma Blood Sample | Concentration pg/mL) | Diluted Concentration (pg/mL) | % Recovery |
| --- | --- | --- | --- |
| A | 590 | — | — |
| B | 2764 | — | — |
| C | 5123 | — | — |
| A+B | — | 1570 | 94% |
| B+C | — | 3992 | 101% |
| A+C | — | 2734 | 96% |
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A plasma sample was spiked with BNP to a value of approximately 5000 pg/mL. This sample was subjected to a series of dilutions with fresh, un-spiked plasma in order to prepare a range of concentrations. The concentration of each sample/dilution was calculated based on the measured concentration of the initial solution and the dilutions performed. The diluted samples were then measured in i-STAT BNP test cartridges (N = 6-10). The procedure was repeated with a whole blood sample. The results of these experiments are summarized in the following table:
| Sample | Dilution | Calculated [BNP] (pg/mL) | Measured [BNP] (pg/mL) | %Recovery |
| --- | --- | --- | --- | --- |
| Plasma | 1 | 52 | 57 | 110% |
| Plasma | 2 | 104 | 114 | 110% |
| Plasma | 3 | 259 | 265 | 103% |
| Plasma | 4 | 518 | 560 | 108% |
| Plasma | 5 | 1036 | 1002 | 97% |
| Plasma | 6 | 2072 | 2277 | 110% |
| Plasma | 7 | 3107 | 3384 | 109% |
| Plasma | 8 | 4143 | 4222 | 102% |
| Whole Blood | 1 | 44 | 41 | 93% |
| Whole Blood | 2 | 88 | 88 | 100% |
| Whole Blood | 3 | 269 | 287 | 107% |
| Whole Blood | 4 | 537 | 554 | 103% |
| Whole Blood | 5 | 725 | 720 | 99% |
| Whole Blood | 6 | 1450 | 1367 | 94% |
| Whole Blood | 7 | 3042 | 2826 | 93% |
| Whole Blood | 8 | 4056 | 3856 | 95% |
c. Traceability, Stability, Expected values (controls, calibrators, or methods):
The i-STAT BNP calibrators are traceable to an internal reference standard that has been prepared gravimetrically with synthetic BNP. The internal reference standard underwent a one-time value assignment to align with the ARCHITECT BNP assay with a decision threshold of 100 pg/mL. Manufacturers working calibrators are prepared by gravimetric manipulation of the standard and incorporate a one-time value assignment for alignment of methods. The i-STAT, AxSYM and ARCHITECT assays have been designed, by virtue of their calibration, to report comparable values. The i-STAT vs. ARCHITECT method comparison data exhibits a correlation slope of 0.97 (see method comparison section). Similar data for the ARCHITECT vs. AxSYM exhibited a slope of 1.03.
Stability studies were performed to evaluate the intended storage (open and closed vial) for the i-STAT controls and calibration verification materials. The real-time frozen stability of BNP control/calibration verification materials was established for 3 lots of material, each comprised of 3 levels. Stability was judged to be acceptable provided that the mean BNP concentration measured at each test event be within ± 20 % of the original mean concentration. The stability was acceptable over 5 months frozen storage. The stability studies are ongoing.
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The labeling for the i-STAT controls and calibration verification materials states that, after thawing, the opened or unopened vial is stable for 4 hours when capped and stored at 2 – 8°. Stability studies performed support the 4 hour time limit.
d. Detection limit:
The limit of the blank for the BNP method is 15 pg/mL, which is the lowest BNP level that can be distinguished from zero. The value was estimated using a control material with < 5 pg/mL BNP during a 20 day precision study in which 3 separate lots of BNP test cartridges were tested in duplicate using a pool of 6 i-STAT 1 analyzers for a total of 147 test results.
e. Analytical specificity:
The following muscle proteins were tested at both 1000 pg/mL and 20,000 pg/mL concentrations and found to have no detectable cross-reactivity for BNP: ANP, CNP, and N-terminal pro-BNP.
The i-STAT BNP assay employs electrochemical rather than optical detection. An electrogenic substrate is cleaved by an enzyme label giving rise to an electroactive product that can be oxidized at a sensor electrode generating a signal comprised of electrical current, therefore optical interferents, including hemoglobin, bilirubin, and chylomicrons, do not interfere with this mode of detection.
The following substances were found to have no significant effect (less than 10%) on the BNP method, when added to a plasma pool containing approximately 1000 pg/mL of B-type natriuretic peptide at the concentrations indicated:
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| Compound | Test Level (μmol/L unless otherwise indicated) |
| --- | --- |
| Acetaminophen | 1660 |
| Allopurinol | 294 |
| Ampicillin | 152 |
| Ascorbic Acid | 227 |
| Acetyl Salicylic Acid | 3333 |
| Atenolol | 37.6 |
| Caffeine | 308 |
| Captopril | 23 |
| Chloramphenicol | 155 |
| Diclofenac | 169 |
| Digoxin | 6.15 |
| Dopamine | 5.87 |
| Enalaprilat | 0.86 |
| Erythromycin | 81.6 |
| Furosemide | 181 |
| Sodium Heparin | 90 U/mL |
| Ibuprofen | 2425 |
| Isosorbide dinitrate | 636 |
| Methyldopa | 71 |
| Nicotine | 6.2 |
| Nifedipine | 1.156 |
| Phenytoin | 198 |
| Propanolol | 7.71 |
| Salicylic Acid | 4.34 |
| Theophylline | 222 |
| Verapamil | 4.4 |
| Warfarin | 64.9 |
f. Assay cut-off:
BNP results less than or equal to 100 pg/mL are representative of normal values in patients without CHF. See Clinical cut-off section below.
2. Comparison studies:
a. Method comparison with predicate device:
Method comparison data were collected using CLSI guideline EP9-A2. Venous blood samples were collected in EDTA evacuated tubes and analyzed in duplicate on the i-STAT System. A portion of the specimen was centrifuged and the separated plasma was analyzed in duplicate on the i-STAT 1 System and on the comparative method, the Abbott ARCHITECT BNP assay, within 1 hour of collection. Deming regression analysis was performed on the first replicate of each sample. In the method comparison table, n is the number of specimens in the first data set, Sxx and Syy refer to estimates of imprecision based on the duplicates of the comparative and the i-STAT methods respectively. Sy.x is the standard error of the estimate, and r is the correlation coefficient. The samples had BNP values ranging from 5-5000 mg/dL.
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Method Comparison
| Abbott ARCHITECT | |
| --- | --- |
| N | 433 |
| Mean (pg/mL) | 482.1 |
| Sxx (pg/mL) | 38.1 |
| Syy (pg/mL) | 97.6 |
| Slope | 0.971 |
| Intercept | -14.4 |
| Sy.x | 198.0 |
| Xmin | 5 |
| Xmax | 4797.7 |
| Correlation, r | 0.961 |
b. Matrix comparison:
EDTA plasma is the only sample type indicated. The labeling states that performance characteristics have not been established for samples taken from capillary tubes and direct skin punctures (e.g. fingersticks) so these sample types should not be used with the BNP cartridge.
3. Clinical studies:
Clinical studies performed with the Abbott AxSYM BNP assay are included in the labeling for the i-STAT BNP assay. The applicant provided the following to support the transfer of reference ranges:
- The AxSYM, ARCHITECT and i-STAT BNP assays employ an identical antibody set. The average imprecision is similar for the 3 assays as follows: AxSYM average %CV = 7.9 %; ARCHITECT average %CV = 5.2 %; i-STAT average %CV = 9.7 %.
- The i-STAT, AxSYM and ARCHITECT assays have been designed, by virtue of their calibration, to report comparable values.
- The CLSI document C28-A2, How to Define and Determine Reference Intervals in the Clinical Laboratory, provides guidance concerning the transferability of reference ranges from one measurement system to another. The i-STAT vs. ARCHITECT method comparison data exhibits a correlation slope of 0.97 (see method comparison section above). Also, similar data for the ARCHITECT vs. AxSYM exhibited a slope of 1.03 (see k060964).
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# a. Clinical Sensitivity:
In studies performed with the AxSYM BNP Assay, age-matched analysis of the heart failure and non-heart failure populations was performed based on the data published by the American Heart Association in the 2000 Heart and Stroke Statistical Update and according to the age structure of the United States population. The age distributions in the intended use population are approximately as follows: individuals less than 45 years old comprise $9\%$ , individuals 45-54 years old comprise $11\%$ , individuals 55-64 years old comprise $22\%$ , individuals 65-74 years old comprise $26\%$ , and individuals 75 years and older comprise $32\%$ . The resulting combined AUC is 0.87 (0.85 to 0.90, $95\%$ CI). The clinical sensitivity and specificity using a decision threshold of $100~\mathrm{pg / mL}$ is presented in the table below.
| | Males (Age Group) | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | All | <45 Years | 45-54 Years | 55-64 Years | 65-74 Years | 75+ Years |
| Sensitivity | 71.0% | 47.1% | 57.1% | 57.3% | 70.6% | 86.1% |
| | (328/462) | (8/17) | (24/42) | (51/89) | (115/163) | (130/151) |
| 95% Confidence Interval | 66.6 to 75.1% | 23.0 to 72.2% | 41.0 to 72.3% | 46.4 to 67.7% | 62.9 to 77.4% | 79.5 to 91.2% |
| Specificity | 94.8% | 97.2% | 100.0% | 97.9% | 88.7% | 89.5% |
| | (403/425) | (104/107) | (71/71) | (92/94) | (102/115) | (34/38) |
| 95% Confidence Interval | 92.3 to 96.7% | 92.0 to 99.4% | 94.9 to 100.0% | 92.5 to 99.7% | 81.5 to 93.8% | 75.2 to 97.1% |
| | Females (Age Group) | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | All | <45 Years | 45-54 Years | 55-64 Years | 65-74 Years | 75+ Years |
| Sensitivity | 80.5% | 44.4% | 73.3% | 50.0% | 80.6% | 91.7% |
| | (186/231) | (4/9) | (11/15) | (13/26) | (58/72) | (100/109) |
| 95% Confidence Interval | 74.8 to 85.4% | 13.7 to 78.8% | 44.9 to 92.2% | 29.9 to 70.1% | 69.5 to 88.9% | 84.9 to 96.2% |
| Specificity | 88.4% | 95.9% | 90.7% | 89.6% | 85.7% | 80.5% |
| | (411/465) | (94/98) | (68/75) | (69/77) | (114/133) | (66/82) |
| 95% Confidence Interval | 85.1 to 91.2% | 89.9 to 98.9% | 81.7 to 96.2% | 80.6 to 95.4% | 78.6 to 91.2% | 70.3 to 88.4% |
# b. Clinical specificity:
See Clinical Sensitivity section above.
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c. Other clinical supportive data (when a. and b. are not applicable):
# 4. Clinical cut-off:
Data from the clinical studies performed with the AxSYM BNP assay were used to generate The Receiver Operating Characteristic (ROC) curve of BNP decision thresholds versus clinical sensitivity and clinical specificity. At a decision threshold of $100\mathrm{pg / mL}$ , the BNP assay demonstrated a clinical sensitivity and specificity of $74.2\%$ and $91.5\%$ respectively. The area under the curve is 0.90 (0.86 to 0.92, $95\%$ CI).
# 5. Expected values/Reference range:
Plasma samples from 890 individuals (465 females, 425 males) who had not been diagnosed with heart failure were tested with the AxSYM BNP assay. This population included non-hospitalized patients with renal disease (not on dialysis), diabetes, hypertension and chronic obstructive pulmonary disease. BNP levels for these patients were not statistically different from the population of apparently healthy individuals. The data are summarized below.
Non-Heart Failure Population - All (Age Group)
| | All | <45 Years | 45-54 Years | 55-64 Years | 65-74 Years | 75+ Years |
| --- | --- | --- | --- | --- | --- | --- |
| Sample Size (N=) | 890 | 205 | 146 | 171 | 248 | 120 |
| Median (pg/mL) | 21 | 17 | 9 | 24 | 23 | 31 |
| Mean (pg/mL) | 39 | 28 | 21 | 37 | 47 | 63 |
| SD (pg/mL) | 66 | 36 | 30 | 48 | 80 | 109 |
| 95th Percentile | 135 | 85 | 87 | 119 | 160 | 254 |
| Percentage < 100 pg/mL | 91.5% | 96.6% | 95.2% | 94.2% | 87.1% | 83.3% |
| Minimum (pg/mL) | 0 | 0 | 0 | 0 | 0 | 0 |
| Maximum (pg/mL) | 907 | 263 | 142 | 380 | 907 | 837 |
Non-Heart Failure Population - Males (Age Group)
| | All | <45 Years | 45-54 Years | 55-64 Years | 65-74 Years | 75+ Years |
| --- | --- | --- | --- | --- | --- | --- |
| Sample Size (N=) | 425 | 107 | 71 | 94 | 115 | 38 |
| Median (pg/mL) | 14 | 12 | 1 | 17 | 21 | 37 |
| Mean (pg/mL) | 30 | 23 | 9 | 26 | 47 | 49 |
| SD (pg/mL) | 61 | 34 | 14 | 45 | 96 | 51 |
| 95th Percentile | 104 | 73 | 40 | 80 | 150 | 121 |
| Percentage < 100 pg/mL | 94.8% | 97.2% | 100.0% | 97.9% | 88.7% | 89.5% |
| Minimum (pg/mL) | 0 | 0 | 0 | 0 | 0 | 0 |
| Maximum (pg/mL) | 907 | 200 | 57 | 380 | 907 | 254 |
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Non-Heart Failure Population - Females (Age Group)
| | All | <45 Years | 45-54 Years | 55-64 Years | 65-74 Years | 75+ Years |
| --- | --- | --- | --- | --- | --- | --- |
| Sample Size (N=) | 465 | 98 | 75 | 77 | 133 | 82 |
| Median (pg/mL) | 26 | 23 | 23 | 37 | 23 | 25 |
| Mean (pg/mL) | 46 | 34 | 34 | 51 | 46 | 69 |
| SD (pg/mL) | 70 | 37 | 36 | 48 | 63 | 126 |
| 95^{th} Percentile | 150 | 89 | 111 | 155 | 159 | 266 |
| Percentage < 100 pg/mL | 88.4% | 95.9% | 90.7% | 89.6% | 85.7% | 80.5% |
| Minimum (pg/mL) | 0 | 0 | 0 | 0 | 0 | 0 |
| Maximum (pg/mL) | 837 | 263 | 142 | 230 | 374 | 837 |
Plasma samples from 693 patients with diagnosed heart failure (231 females, 462 males) were tested with the AxSYM BNP assay. All patients in this population were categorized according to the functional classification system published by the New York Heart Association (NYHA). This system divides heart failure patients into one of four categories of increasing disease progression (classes I to IV) based upon a subjective assessment of the patient's clinical signs and symptoms. The data from this study are summarized below.
Heart Failure Population - All
| | NYHA Functional Class | | | | |
| --- | --- | --- | --- | --- | --- |
| | All | I | II | III | IV |
| Sample Size (N=) | 693 | 124 | 319 | 190 | 60 |
| Median (pg/mL) | 298 | 133 | 266 | 335 | 1531 |
| Mean (pg/mL) | 578 | 320 | 432 | 656 | 1635 |
| SD (pg/mL) | 771 | 388 | 574 | 841 | 1097 |
| 5th Percentile | 14 | 9 | 15 | 12 | 188 |
| 95th Percentile | 2154 | 1257 | 1534 | 2516 | >4000 |
| Percentage ≥ 100 pg/mL | 74.2% | 58.1% | 73.0% | 79.0% | 98.3% |
| Minimum (pg/mL) | 0 | 3 | 0 | 0 | 14 |
| Maximum (pg/mL) | >4000 | 1651 | >4000 | >4000 | >4000 |
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Heart Failure Population – Males
| | NYHA Functional Class | | | | |
| --- | --- | --- | --- | --- | --- |
| | All | I | II | III | IV |
| Sample Size (N=) | 462 | 94 | 215 | 121 | 32 |
| Median (pg/mL) | 268 | 122 | 258 | 293 | 1645 |
| Mean (pg/mL) | 524 | 314 | 409 | 597 | 1646 |
| SD (pg/mL) | 719 | 390 | 539 | 821 | 1032 |
| 5th Percentile | 12 | 9 | 14 | 22 | 265 |
| 95th Percentile | 1976 | 1281 | 1356 | 2288 | 3654 |
| Percentage ≥ 100 pg/mL | 71.0% | 56.4% | 70.7% | 76.0% | 96.9% |
| Minimum (pg/mL) | 0 | 3 | 0 | 0 | 14 |
| Maximum (pg/mL) | >4000 | 1408 | 3782 | >4000 | >4000 |
Heart Failure Population - Females
| | NYHA Functional Class | | | | |
| --- | --- | --- | --- | --- | --- |
| | All | I | II | III | IV |
| Sample Size (N=) | 231 | 30 | 104 | 69 | 28 |
| Median (pg/mL) | 385 | 174 | 298 | 466 | 1408 |
| Mean (pg/mL) | 685 | 341 | 481 | 760 | 1623 |
| SD (pg/mL) | 858 | 388 | 641 | 870 | 1186 |
| 5th Percentile | 16 | 14 | 21 | 12 | 244 |
| 95th Percentile | 2593 | 1022 | 2031 | 2718 | >4000 |
| Percentage ≥ 100 pg/mL | 80.5% | 63.3% | 77.9% | 84.1% | 100.0% |
| Minimum (pg/mL) | 0 | 10 | 0 | 0 | 173 |
| Maximum (pg/mL) | >4000 | 1651 | >4000 | >4000 | >4000 |
N. Instrument Name:
i-STAT 1 Analyzer
O. System Descriptions:
1. Modes of Operation:
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Single use cartridge
2. Software:
FDA has reviewed applicant’s Hazard Analysis and software development processes for this line of product types:
Yes ☐ X ☐ or No ☐
3. Specimen Identification:
Bar code reader is incorporated into the system
4. Specimen Sampling and Handling:
Whole blood samples are applied directly into the sample well of the cartridge
5. Calibration:
Factory set
6. Quality Control:
The reliability of the results is maintained through a combination of user testing and instrument self-checks. The self checks occur with every cartridge run and verify performance of the analyzer and cartridge sub-systems. This includes checks on the individual sensor’s performance, the integrity of the calibrant fluid, the response of the pressure and thermal transducers, and the flow of calibrant and sample within the cartridge. Any values that are statistically deviant from the factory established expectation values would cause the test results to be suppressed. Daily monitoring is through the use of internal and external electronic simulators. Liquid controls are provided for the verification of cartridge lot performance for all newly received cartridge lots.
P. Other Supportive Instrument Performance Characteristics Data Not Covered In The "Performance Characteristics" Section above:
Q. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10.
R. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
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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.