K043512 · American Healthcare, Inc. · CGA · Apr 8, 2005 · Clinical Chemistry
Device Facts
Record ID
K043512
Device Name
EASYGLUCO IGM-0002 G2
Applicant
American Healthcare, Inc.
Product Code
CGA · Clinical Chemistry
Decision Date
Apr 8, 2005
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 862.1345
Device Class
Class 2
Indications for Use
The EASYGLUCO Blood Glucose Monitoring System is used for the quantitative measurement of glucose levels in whole blood as an aid in monitoring the effectiveness of diabetes management in the home and in clinical settings. EASYGLUCO Blood Glucose Monitoring System is for testing outside the body (in vitro diagnostic use only). Testing sites include the traditional fingertip testing along with alternate site testing on the arm, palm, thigh and calf.
Device Story
EasyGluco Blood Glucose Monitoring System measures glucose in whole blood via electrochemical amperometry using glucose oxidase enzyme. System includes meter, test strips, auto-lancet, and control solution. User applies blood sample to test strip; meter detects current generated by oxidation of reduced mediator; result displayed on screen. Used in home or clinical settings by patients or healthcare professionals. Provides quantitative glucose levels to assist in diabetes management. Includes diabetes management software for data tracking. Supports alternate site testing (arm, palm, thigh, calf) to supplement traditional fingertip testing.
Clinical Evidence
No clinical data provided; substantial equivalence established via bench testing and comparison to predicate devices.
Technological Characteristics
In vitro diagnostic system; electrochemical sensing principle using dehydrogenase glucose-reactive electrodes on test strips. Standalone meter device. No specific materials or software architecture details provided.
Indications for Use
Indicated for quantitative blood glucose measurement in patients with diabetes to monitor management effectiveness. Suitable for home and clinical use. Supports fingertip and alternate site testing (arm, palm, thigh, calf).
Regulatory Classification
Identification
A glucose test system is a device intended to measure glucose quantitatively in blood and other body fluids. Glucose measurements are used in the diagnosis and treatment of carbohydrate metabolism disorders including diabetes mellitus, neonatal hypoglycemia, and idiopathic hypoglycemia, and of pancreatic islet cell carcinoma.
Special Controls
*Classification.* Class II (special controls). The device, when it is solely intended for use as a drink to test glucose tolerance, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9.
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY DEVICE ONLY TEMPLATE
A. 510(k) Number: k043512
B. Purpose For Submission:
Premarket Notification 510(k) of intention to manufacture and market the EasyGluco Blood Glucose Monitoring System
C. Analyte: Whole Blood Glucose
D. Type of Test: Quantitative, utilizing Glucose Oxidase technology.
E. Applicant: American HealthCare, Inc.
F. Proprietary and Established Names: EasyGluco Blood Glucose Monitoring System
G. Regulatory Information:
1. Regulation section: 21 CFR §862.1345, Glucose test system.
2. Classification: Class II, I
3. Product Code: NBW, CGA, JJX
4. Panel: 75 Chemistry
H. Intended Use:
1. Intended use(s):
See indication for use below.
2. Indication(s) for use:
The EASYGLUCO Blood Glucose Monitoring System is used for the quantitative measurement of glucose levels in whole blood as an aid in monitoring the effectiveness of diabetes management in the home and in clinical settings. EASYGLUCO Blood Glucose Monitoring System is for testing outside the body (in vitro diagnostic use only). Testing sites include the traditional fingertip testing along with alternate site testing on the arm, palm, thigh and calf.
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3. Special condition for use statement(s):
Provides plasma equivalent results.
4. Special instrument Requirements:
Not Applicable
I. Device Description:
The EASYGLUCO System consists of the EASYGLUCO meter, EASYGLUCO Test Strips, Auto-Lancet Device, Infopia Check Strip and Greenlan Lancets, and Control Solution. Control Solution is sold separately from the kit. Controls previously cleared under K031501.
J. Substantial Equivalence Information:
1. Predicate device name(s):
LifeScan, Inc. OneTouch® Ultra®
2. Predicate K number(s): K024194
3. Comparison with Predicate:
The US Diagnostics, Inc. EASYGLUCO Blood Glucose Monitoring System is substantially equivalent to the LifeScan, Inc. OneTouch Ultra Blood Glucose Monitoring System previously cleared under (k024194). The table below lists the similarities and differences between the Predicate and Proposed device.
This EASYGLUCO Blood Glucose Monitoring System is the exact same blood glucose meter previously cleared under k031501. The difference between the previously cleared EASYGLUCO Diabetes Monitoring System and this meter is a change in the meter case, the addition of the EasyGluco diabetes management software, and the addition of Alternate-Site testing of the arm, palm, thigh, and calf. A Software Validation Report, Users Guide, Alternate-Site raw data, and Human Factor Studies are included in this submission.
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# Substantial Equivalence Comparison
## Similarities
| | EasyGluco | One Touch® Ultra® |
| --- | --- | --- |
| Detection method | Amperometry: current is generated by oxidation of reduced mediator | Amperometry |
| Enzyme | Glucose Oxidase (Aspergillus niger) | Glucose Oxidase (Aspergillus niger) |
| Mediator | Potassium ferricyanide | Potassium ferricyanide |
| Electrode | Carbon electrode | Carbon electrode |
The other ingredients of the test strip, such as enzyme stabilizer, buffer and binder are different.
## Differences
| | EasyGluco | One Touch® Ultra® |
| --- | --- | --- |
| Test range | 10 – 600 mg/dL | 20 – 600 mg/dL |
| Hematocrit Range | 30 – 55% | 30 – 55% |
| Test Time | 9 seconds | 5 seconds |
| Sample Volume | 3 μL | 1 μL |
| Temperature & Humidity range | 50 - 95° F | 43 - 111° F |
| | 10 - 35° C | 6 – 44 ° C |
| | 10 – 90 % | 10 – 90% |
| Open use time | 3 months | 3 months |
| Coding | Button (C1 – C40) | Button (C1 – C49) |
| Memory capability | From 7 to 90-day average and 200 tests in the memory | 14-day average and last 150 tests in the memory |
| Power | 3V Li battery (CR2032) | 3V Li battery (CR2032) |
| Battery life | Running 5,000 test | Running 1,000 test |
| Size: LxWxH (mm) | 56x20x76 | 57x21x79 |
| Weight | 45g (with battery) | 42g (with battery) |
| Warranty | Lifetime | 3 years |
| Software | EasyGluco diabetes management software | IN TOUCH® diabetes management software |
## K. Standard/Guidance Document Referenced (if applicable):
1. National Committee for Clinical Laboratory Standards. Point-Care Blood Glucose Testing in Acute and Chronic care Facilities; Approved Guideline, 2nd Edition. NCCLS Document C30-A2 (ISBN1-56238-471-6).
2. National Committee for Clinical Laboratory Standards. Statistical Quality Control for Quantitative Measurements; Principle and Definitions; Approved Guideline, 2nd Edition. NCCLS Document C24-A2 (ISBN1-56238-371-X). 1999
3. National Committee for Clinical Laboratory Standards. Preliminary Evaluation of Quantitative Clinical Laboratory Methods; Approved Guideline. NCCLS Document EP10-A (ISBN1-56238-348-5). 1998
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4. National Committee for Clinical Laboratory Standards. Evaluation of Matrix Effects; Approved Guideline, NCCLS Document EP14-A (ISBN1-56238-434-1).
5. National Committee for Clinical Laboratory Standards. Estimation of Total analytical Error for Clinical Laboratory Methods; Proposed Guideline. NCCLS Document EP21-P (ISBN1-56238-456-2).
6. National Committee for Clinical Laboratory Standards. User Demonstration of performance for Precision and Accuracy; Approved Guideline. NCCLS Document EP15-A (ISBN1-56238-451-1).
7. National Committee for Clinical Laboratory Standards. Interference Testing in Clinical Chemistry; Proposed Guideline. NCCLS Document EP7-P (ISSN 0273-3099).
8. National Committee for Clinical Laboratory Standards. Evaluation of the Linearity of Quantitative Analytical Methods; Proposed Guideline, 2nd Edition. NCCLS Document EP6-P2 (ISBN1-56238-446-5).
9. National Committee for Clinical Laboratory Standards. Evaluation of Performance of Clinical Chemistry Devices; Approved Guideline. NCCLS Document EP5-A (ISBN1-56238-368-X).
10. Clinical Chemistry, 2nd Edition
11. MERCK INDEX, 11th Edition.
Korea Pharmacopeia, 5th Edition.
## L. Test Principle:
The Test Principle used by this device is electrochemical biosensor technology using glucose Oxidase. The strip uses the enzyme glucose Oxidase to produce a current that will stimulate a chemical reaction. This reaction is measured by the Meter and displayed as a blood glucose result.
## M. Performance Characteristics (if/when applicable):
### 1. Analytical performance:
#### a. Precision/Reproducibility:
The sponsor indicated precision studies were assessed by taking 4 mL of blood that was treated with EDTA drawn in a vacuum tube. Glucose was added to the 4 mL of blood to generate 5 different levels of glucose concentration for the test. Each of the samples was measured 5 times for precision. Below are the Glucose Concentration Ranges for each level that were measured.
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| Level | Glucose Conc. range |
| --- | --- |
| 1 | 30 ~ 50 mg/dL |
| 2 | 51 ~ 110 mg/dL |
| 3 | 111 ~ 150 mg/dL |
| 4 | 151 ~ 250 mg/dL |
| 5 | 251 ~ 400 mg/dL |
# Day-to-Day precision also known as Between Day Precision
The sponsor prepared three control solutions of Low, Normal and High. Each of the controls was measured twice a day, once in the morning and once in the afternoon for a month.
Table 1 (below) shows a summary of the Within-Run Precision and the Day-to-Day Precision Tests.
Table 1: Summary of Test Results
| Control Samples | No. of Assay | Within-Run Precision | | |
| --- | --- | --- | --- | --- |
| | | Mean (mg/dL) | SD (mg/dL) | CV (%) |
| Level 1 | 5 | 47.2 | 1.6 | 3.5 |
| Level 2 | 5 | 94.2 | 2.3 | 2.4 |
| Level 3 | 5 | 131 | 4.5 | 3.4 |
| Level 4 | 5 | 221 | 6.0 | 2.7 |
| Level 5 | 5 | 339.8 | 7.4 | 2.2 |
| Control Samples | No. of Assay | Day-to-Day Precision | | |
| --- | --- | --- | --- | --- |
| | | Mean (mg/dL) | SD (mg/dL) | CV (%) |
| Low | 80 | 74.2 | 2.8 | 3.7 |
| Normal | 80 | 128.8 | 6.2 | 4.8 |
| High | 80 | 252.9 | 9.9 | 3.9 |
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The study showed variability from strip to strip in blood tests of $3.5\%$ or less and from day to day-in control tests of $4.8\%$ or less.
# b. Linearity/assay reportable range:
# Test Procedure (Dilution Schemes)
The NCCLS recommends dilution schemes to estimate the linearity of the Quantitative Analytical Method.
According to the NCCLS EP6-P2 protocol, a blood sample of $25~\mathrm{mL}$ was taken, treated with the EDTA in a vacuum tube, and let set for a day. Two glucose concentrations of 10 mL (high and low concentrations) were prepared. As a measuring tool, nine glucose concentrations were prepared using the following dilution schemes (see Table 5).
Table 5: Levels of Dilution Schemes
S=9 Samples
| Level 1(Low, L) | L |
| --- | --- |
| Level 2 | 0.875L + 0.125H |
| Level 3 | 0.750L + 0.250H |
| Level 4 | 0.625L + 0.375H |
| Level 5 | 0.500L + 0.500H |
| Level 6 | 0.375L + 0.625H |
| Level 7 | 0.250L + 0.750H |
| Level 8 | 0.125L + 0.875H |
| Level 9(High, H) | H |
The meter used in this test can display below $10\mathrm{mg / dL}$ over $600\mathrm{mg / dL}$ for checking linear range.
Each of the glucose levels was measured 5 times to test for precision.
In order to evaluate the straight line for the Sensory Strip that was used, the following formula was used:
$$
1 ^ {\text {s t o r d e r}} \text {p o l y n o m i a l ,} y = a x + b, 2 ^ {\text {n d o r d e r}} \text {p o l y n o m i a l ,} y = a X ^ {2} + b X + c
$$
All dilution schemes start with a high and low concentration of samples in which the concentrations meet or exceed the range of interest. For the test, the highest and lowest glucose concentration used was $630\mathrm{mg / dL}$ and $7\mathrm{mg / dL}$ . If a strip sensor has an ideal linearity $(r2 = 1)$ from Lowest to highest concentration, the ideal concentration of level 2
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mixed with 0.875L and 0.125H volume ratio is a $99\mathrm{mg / dL}$ $[(0.875*37.4 + 0.125*530.2) / (0.875 + 0.125)]$
Table 6. below, shows a summary of the nine dilutions that were measured five times for precision.
Table 6: Test Result Summary
| Dilution | Rep1 | Rep2 | Rep3 | Rep4 | Rep5 | Mean |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | 7 | 7 | 8 | 7 | 9 | 7.6 |
| 2 | 90 | 90 | 84 | 88 | 88 | 88 |
| 3 | 156 | 158 | 158 | 158 | 156 | 157.2 |
| 4 | 240 | 240 | 238 | 238 | 234 | 238 |
| 5 | 307 | 306 | 300 | 298 | 305 | 303.2 |
| 6 | 405 | 402 | 397 | 392 | 398 | 398.8 |
| 7 | 467 | 468 | 468 | 475 | 470 | 469.6 |
| 8 | 542 | 540 | 550 | 550 | 552 | 546.8 |
| 9 | 630 | 625 | 622 | 620 | 630 | 625.4 |
The dilution number at Table 6 and Figure 1 represents the Level number at Table 5.

Figure 1: Glucose Linearity Study (Dilution 1-9)
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Table 7: The Polynomial Evaluation of Linearity
| Dilution | Actual Mean | Predicted 1st order | Predicted 2nd order | Difference |
| --- | --- | --- | --- | --- |
| 1 | 7.6 | 6.1 | 8.7 | -2.7 |
| 2 | 88 | 83.3 | 84.0 | -0.7 |
| 3 | 157.2 | 160.5 | 159.8 | 0.8 |
| 4 | 238 | 237.7 | 236.1 | 1.6 |
| 5 | 303.2 | 315.0 | 313.1 | 1.9 |
| 6 | 398.8 | 392.2 | 390.6 | 1.6 |
| 7 | 469.6 | 469.4 | 468.6 | 0.8 |
| 8 | 546.8 | 546.6 | 547.3 | -0.7 |
| 9 | 625.4 | 623.8 | 626.5 | -2.7 |
It has been determined that the polynomial evaluation of linearity assumes that the data set is not linear. This approach assumes that the data points fall perfectly on a line or curve in the absence of random error. The method consists of two parts. The first part examines whether a nonlinear polynomial fits the data better than a linear one. The second part assesses whether the difference between the best-fitting nonlinear and linear polynomial is less than the amount of allowable bias for the method, which should be predefined.
The nonlinear $2^{\mathrm{nd}}$ fits the data better than a linear one, but the difference is lower than $1.9\mathrm{mg / dL}$ from $7.6\mathrm{mg / dL}$ to $625.4\mathrm{mg / dL}$ . The $\mathbf{R}^2$ of $1^{\mathrm{st}}$ order regression is a 0.9994
c. Traceability (controls, calibrators, or method):
CAS# (Chemical Abstract Service)
MDL# (MDL, inc. formerly Molecular Design Laboratories)
Glucose # 492615 SigmaUltra MFCD00063989
Traceability referenced to NBS, NIST Standards
d. Detection limit:
$10 - 600\mathrm{mg / dL}$
0.6 - 33.3 mmol/L
See linearity study above.
e. Analytical specificity:
Interference testing was conducted to determine the effect of select endogenous and exogenous substances.
# Hematocrit Study
In this study, approximately $2\mathrm{mL}$ of blood was taken from 64 random diabetic individuals. The blood samples were treated with the EDTA vacuum tube and the glucose concentration was adjusted to 75~590
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mg/dL by adding an adequate amount of the phosphate buffer (20mM with pH 7.4) that contains a different level of glucose. In order to adjust the Hematocrit value (30 ~ 55%), a proper volume of the centrifuged plasma (serum) was removed. The Hematocrit level and glucose concentration in the blood was assessed by using the Nova Stat Profile M and the YSI2300 STAT PLUS (respectively).
Test Result of Blood Glucose Range and Samples:
Result of Blood Glucose and Hematocrit Range

Figure 5: Blood glucose conc. Vs Hematocrit %)

Figure 6: Bias between meter result and the corresponding comparison result.
The % bias of the assay value of the EASYGLUCO™ system is relative to the YSI and does not have a negative or positive correlation to hematocrit level in this experiment. 97% of the data is within +/- 20% bias and 72% lies within +/- 10% in the overall range of glucose and hematocrit.
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Summary of Tested Interferences:
| | | Mean of Test Results | | |
| --- | --- | --- | --- | --- |
| Interferences | High Test Level(mg/dL) | Low (mg/dL) | High (mg/dL) | Error % |
| Acetaminophen | 20 | 92.7 | 102.7 | 9.7 |
| Bilirubin | 40 | 124.3 | 137.0 | 10.2 |
| Gentisic acid | 50 | 128.7 | 206.0 | 60.1 |
| Uric acid | 20 | 118.3 | 153.7 | 29.9 |
| Levo-Dopa | 4 | 120.0 | 132.3 | 10.3 |
| Creatinine | 30 | 108.0 | 117.7 | 3.4 |
| Methyl-Dopa | 2.5 | 107.7 | 120.0 | 11.4 |
| Tolazamide | 5 | 119.3 | 126.0 | 5.6 |
| Dopamine | 13 | 132.7 | 205.3 | 54.7 |
| Ascorbate | 3 | 121.7 | 125.3 | 3.0 |
| EDTA | 640 | 114.0 | 117.0 | 2.6 |
| Glutathione | 1 | 132.3 | 134.0 | 1.3 |
| Heparin | 1,000 | 123.0 | 133.3 | 8.4 |
| Ibuprofen | 40 | 103.0 | 107.0 | 3.9 |
| Salicylic acid | 50 | 121.3 | 124.7 | 2.7 |
| Tetracycline | 0.4 | 135.0 | 137.3 | 1.7 |
| Tolbutamide | 100 | 98.3 | 101.7 | 3.4 |
| Urea | 500 | 114.0 | 112.3 | -1.5 |
| Cholesterol | 500 | 135.3 | 153.7 | 13.5 |
| Triglyceride | 2,890 | 122.3 | 153.7 | 25.6 |
According to the sponsor, the list of interfering substances and their high test level in clinical chemistry were referenced to NCCLS Document EP7-P.
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All low levels $= 0$ except: Urea $= 3\mathrm{mg / dL}$ Cholesterol $= 209\mathrm{mg / dL}$ Triglyceride $= 210\mathrm{mg / dL}$
It has been determined that reducing substances such as uric acid affect the testing result by falsely increasing values and may activate or deactivate the activity of Glucose Oxidase (GOX), activating GOX makes the test result falsely high.
f. Assay cut-off: Not Applicable
# 2. Comparison studies:
a. Method comparison with predicate device:
Method comparison to the predicate device was assessed with One hundred sixty subjects with Type 1 or Type 2 diabetes during normally scheduled clinic visits. In the study protocol, both the lay user and a trained technician obtained fingerstick glucose readings on the EASYGLUCO and ONETOUCH ULTRA, as well as alternate site glucose testing on the forearm, part of hand, upper arm, thigh and calf using both the EASYGLUCO and ONETOUCH ULTRA meters.
The readings were taken as close in time as possible. Within 5 minutes, a venous whole blood sample was drawn from alternate sites and centrifuged for making serum. The serum sample was tested on the Hitachi 747. The sponsor indicates that during the comparison studies, alternate sites were vigorously rubbed by the lay user and trained technician before testing, and in some cases a warming pad was used. It has been suggested that the alternate site -to-finger difference may be minimized by rubbing the site before blood collection.
Table1. Summary of test results with finger capillary blood and palm blood obtained by lay user.
| | | Site | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (Palm) vs Hithchi747 | Slope: Y- | 1.0134 | 0.9978 | 1.0119 |
| | intercept: | -0.7481 | 2.1388 | 2.5393 |
| | Linearity: | 0.9895 | 0.99 | 0.9823 |
| EasyGluco (Palm) vs Hithchi747 | Slope: Y- | 0.9862 | 1.0028 | 1.034 |
| | intercept: | 6.0758 | 2.0754 | -3.1266 |
| | Linearity: | 0.9864 | 0.9819 | 0.9792 |
| EasyGluco (Capillary) vs Hithchi747 | Slope: Y- | 0.9966 | 1.0102 | 0.9965 |
| | intercept: | 3.9397 | 2.57 | 3.427 |
| | Linearity: | 0.9914 | 0.9895 | 0.9904 |
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| | | Site | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| EasyGluco (Palm) vs EasyGluco (Capillary) | Slope: Y-intercept: Linearity: | 0.9876 2.557 0.991 | 0.9863 0.7271 0.9796 | 1.0329 -5.7836 0.9798 |
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (Palm)vs Hithchi747 | A-region B-region | 100% 0% | 100% 0% | 100% 0% |
| EasyGluco (Palm) vs Hithchi747 | A-region B-region | 100% 0% | 100% 0% | 98 % 2 % |
| EasyGluco (Capillary) vs Hithchi747 | A-region B-region | 100% 0% | 100% 0% | 99 % 2 % |
| EasyGluco (Palm) vs EasyGluco (Capillary) | A-region B-region | 100% 0% | 100% 0% | 96 % 4 % |
Table 2. Summary of test results with finger capillary blood and Arm blood obtained by lay user.
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (Arm)vs Hithchi747 | Slope: Y-intercept: Linearity: | 1.0074 -1.3599 0.9881 | 0.9744 3.5826 0.9803 | 1.0083 0.2512 0.9835 |
| EasyGluco (Arm) vs Hithchi747 | Slope: Y-intercept: Linearity: | 1.0068 -0.6325 0.9912 | 0.9805 5.3055 0.9824 | 0.9692 6.766 0.958 |
| EasyGluco (Capillary) vs Hithchi747 | Slope: Y-intercept: Linearity: | 1.008 -5.0107 0.9914 | 0.9557 5.8853 0.9822 | 0.9803 0.0449 0.9873 |
| EasyGluco (Arm) vs EasyGluco (Capillary) | Slope: Y-intercept: Linearity: | 0.9944 5.1729 0.991 | 1.0195 0.4686 0.9876 | 0.9833 7.7189 0.9895 |
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Table 3. Summary of test results with finger capillary blood and calf, thigh blood obtained by lay user.
| | | Site1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (Arm) vs Hithchi747 | A-region | 100% | 98 % | 94% |
| | B-region | 0% | 2 % | 6% |
| EasyGluco (Arm) vs Hithchi747 | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
| EasyGluco (Capillary) vs Hithchi747 | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
| EasyGluco (Arm) vs EasyGluco (Capillary) | A-region | 100% | 98% | 98 % |
| | B-region | 0% | 2% | 2 % |
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (calf and thigh) vs Hithchi747 | Slope: Y-intercept: Linearity: | 0.9886-1.4253 | 0.9952-3.1461 | 0.9839-0.5269 |
| | Slope: Y-intercept: Linearity: | 0.9927 | 0.9878 | 0.9899 |
| | Slope: Y-intercept: Linearity: | 0.9765-0.914 | 1.0147-6.7943 | 0.9957-1.5729 |
| EasyGluco (calf and thigh) vs Hithchi747 | Slope: Y-intercept: Linearity: | 0.9892 | 0.9899 | 0.9872 |
| | Slope: Y-intercept: Linearity: | 0.97033.2533 | 1.0076-4.0122 | 0.97623.6655 |
| | Slope: Y-intercept: Linearity: | 0.9870 | 0.9902 | 0.9859 |
| EasyGluco (calf and thigh) vs EasyGluco (Capillary) | Slope: Y-intercept: Linearity: | 1.0002-2.3942 | 1.0034-2.1346 | 1.013-4.0721 |
| | Slope: Y-intercept: | 0.9898 | 0.9926 | 0.9876 |
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| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (calf and thigh) vs Hithchi747 | A-region B-region | 100% 0% | 100% 0% | 98% 2% |
| EasyGluco (calf and thigh) vs Hithchi747 | A-region B-region | 100% 0% | 100% 0% | 100% 0% |
| EasyGluco (Capillary) vs Hithchi747 | A-region B-region | 100% 0% | 100% 0% | 100% 0% |
| EasyGluco (Palm) vs EasyGluco (Capillary) | A-region B-region | 98% 2% | 100% 0% | 100% 0% |
The comparison test results demonstrated similar results from both meters, with OneTouch at alternate site, EasyGluco at alternate site, and EasyGluco at fingerstick capillary according to the slope, Y-intercept, linearity and error $\%$ in Clarke Error Grid region. Test results with EasyGluco at alternative site of hand versus at fingerstick capillary blood, correlation coefficient are $0.9862 \sim 0.9876$ . Test results with EasyGluco at alternative site of arm versus at fingerstick capillary blood, correlation coefficient are $0.9944 \sim 1.0068$ . Test results with EasyGluco at alternative site of leg versus at fingerstick capillary blood, correlation coefficient are $0.97656 \sim 1.002$ . The EasyGluco Blood Monitoring System demonstrates equivalence to the OneTouch Ultra predicate device.
# Reference
John M. E: Rapid Changes in Postprandial Blood Glucose Produce Concentration Differences at Finger, Forearm, and Thigh Sampling Sites. Diabetes Care 25: 961-964, 2002
b. Matrix comparison: Not Applicable
# 3. Clinical studies:
a. Clinical sensitivity: Not Applicable
b. Clinical specificity: Not Applicable
c. Other clinical supportive data (when $a$ and $b$ are not applicable):
The accuracy of the EASYGLUCO Blood Monitoring System was assessed by comparing blood glucose results obtained by patients with those obtained using the
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Hitachi 747, a laboratory instrument. Glucose levels were measured on 416 and 104 fresh capillary blood specimens by 104 diabetic patients and three healthcare professionals at three different clinical centers.
The correlation between Hitachi 747 and EASYGLUCO™ were confirmed in the 416 blood samples with the correlation coefficient $R = 0.979$ and the 104 patients with the correlation coefficient $R = 0.978$ (Fig. 1 and Fig. 2 respectively). Results indicate that the use of the EASYGLUCO™ generate similar results as the Hitachi 747. The correlation between the EASYGLUCO and Hitachi 747 are within the accuracy standards of NCCLS.
Obtained by Healthcare Professionals in Clinical Centers
Figure 1: Linear regression of the 416 blood glucose samples with the Hitachi 747 Vs. EASYGLUCO Blood Glucose Monitoring System at the Clinical Centers.

ents - Hitachi 747 Vs.

4. Clinical cut-off: Not Applicable
| Slope | 0.957 |
| --- | --- |
| y-intercept | 5.4 |
| Correlation coefficient(r) | 0.979 |
| No. of samples | 416 |
| Range tested | 68-430mg/dL |
| Slope | 0.917 |
| --- | --- |
| y-intercept | 12.1 |
| Correlation coefficient(r) | 0.978 |
| No. of samples | 104 |
| Range tested | 81-425mg/dL |
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## 5. Expected values/Reference range:
The Range of Expected values was referenced from the Joslin Diabetes Manual.
Expected blood glucose levels for people without diabetes:
| Time | Range (mg/dL) | Range (mmol/L) |
| --- | --- | --- |
| Before Breakfast: | 70-105 | 3.9-5.8 |
| Before lunch or dinner: | 70-110 | 3.9-6.1 |
| 1 hour after meals: | Less than 160 | Less than 8.9 |
| 2 hours after meals: | Less than 120 | Less than 6.7 |
| Between 2 and 4 AM: | Greater than 70 | Greater than 3.9 |
## N. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10
## O. Conclusion:
The submitted material 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.