The GLUCOLAB™ Diabetes Monitoring System is used for the quantitative measurement of glucose level in whole blood as an aid in monitoring the effectiveness of diabetes management in the home and in clinical settings, including physician's office laboratories and point of care sites. The GLUCOLAB™ System provides plasma-equivalent results. The GLUCOLAB™ System is not intended to be used with neonatal blood samples. The GLUCOLAB™ 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 forearm, upper arm, palm, calf and thigh. GlucoLab™ control is used with GlucoLab™ Brand System to check that the meter and test strips are working together as a system and that you are performing the test correctly. It is very important that you do control solution tests routinely to make sure you are getting accurate results. Control Solutions are sold separately.
Device Story
GLUCOLAB™ is an in vitro diagnostic blood glucose monitoring system; utilizes electrochemical test strips; measures glucose concentration in whole blood samples; intended for home and clinical use; operated by patients or healthcare professionals; provides plasma-equivalent glucose results; aids in diabetes management; supports fingertip and alternate site testing (forearm, upper arm, palm, calf, thigh); includes control solution for system verification.
Clinical Evidence
No clinical data provided; substantial equivalence based on bench testing and comparison to predicate devices.
Technological Characteristics
Electrochemical glucose monitoring system; utilizes dehydrogenase glucose enzyme reaction on test strips; generates electrical current proportional to glucose concentration; supports fingertip and alternate site testing; standalone meter device.
Indications for Use
Indicated for quantitative blood glucose monitoring in patients with diabetes in home, clinical, and point-of-care settings. Contraindicated for use with neonatal blood samples.
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:
k051285
B. Purpose For Submission:
Premarket Notification 510(k) of intention to manufacture and market the GLUCOLAB™ Diabetes Monitoring System.
C. Analyte:
Whole Blood Glucose
D. Type of Test:
Quantitative, utilizing Glucose Oxidase technology.
E. Applicant:
Infopia, Co., Ltd.
F. Proprietary and Established Names:
GLUCOLAB™ Blood Glucose Monitoring System.
G. Regulatory Information:
1. Regulation section:
21 CFR §862.1345, Glucose test system.
21 CFR §862.1660, Single (Specified) Analyte Controls (Assayed and Unassayed)
2. Classification:
Class II (analyte)
Class I (controls)
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3. Product Code:
NBW, CGA (glucose)
JJX (control)
4. Panel:
75 (Clinical Chemistry)
H. Intended Use:
1. Intended use(s):
See Indications for use below.
2. Indication(s) for use:
The GLUCOLAB™ Diabetes Monitoring System is used for the quantitative measurement of glucose level in whole blood as an aid in monitoring the effectiveness of diabetes management in the home and in clinical settings, including physician's office laboratories and point of care sites. The GLUCOLAB™ System provides plasma-equivalent results. The GLUCOLAB™ System is not intended to be used with neonatal blood samples. The GLUCOLAB™ 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 forearm, upper arm, palm, calf and thigh.
GlucoLab™ control is used with GlucoLab™ Brand System to check that the meter and test strips are working together as a system and that you are performing the test correctly. It is very important that you do control solution tests routinely to make sure you are getting accurate results. Control Solutions are sold separately.
3. Special condition for use statement(s):
Provides plasma equivalent results.
For over-the-counter or professional use
Not for use in neonates
Patients should not test on the forearm, upper arm, palm, calf or thigh when they think their blood glucose is rapidly falling, such as within two hours of exercise or a rapid-acting insulin injection or insulin pump bolus. Testing with
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a fingertip sample may identify a hypoglycemic (low blood sugar) level sooner than a test with a forearm or palm sample.
Patients should not test on the forearm, upper arm, palm, calf or thigh when it has been less than two hours after a meal, a rapid-acting insulin injection or insulin pump bolus, physical exercise, or they think their glucose level is changing rapidly.
Patients should not test on the forearm, upper arm, palm, calf or thigh when they are concerned about the possibility of hypoglycemia.
4. Special instrument Requirements:
GLUCOLAB™ Blood Glucose Monitoring System
I. Device Description:
The GLUCOLAB™ Blood Glucose Monitoring System consists of the GLUCOLAB Meter, GLUCOLAB™ Test Strips, Control Solution, Lancing Device, Check Strip, Manual, Warranty registration card, Patient logbook, 1X3V Li-(CR2032) battery, Carrying Pouch. Control solutions are sold separately from the kit.
J. Substantial Equivalence Information:
1. Predicate device name(s):
LifeScan, Inc. OneTouch Ultra®
LifeScan, Inc. SureStep
Roche Diagnostics Corp. Accu-Chek
2. Predicate K number(s):
k024194
k984261
k021513
3. Comparison with Predicate:
The technological characteristics of the new device GlucoLab™ Blood Glucose Monitoring System are substantially equivalent to the LifeScan. Inc. ONE TOUCH Ultra® Blood Glucose Monitoring System previously cleared under (k024194). The GlucoLab™ Blood Glucose Monitoring System provides the same glucose monitoring capability as the predicate device, the ONE TOUCH® Ultra®. The primary differences are in the memory functions and battery lifetime. In addition, the ingredients of test strip, such as enzyme stabilizer, buffer and binder are different. The tables below list the similarities and differences between the Predicate and Proposed device.
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Similarities
| | GlucoLab (k051285) | ONE TOUCH® Ultra® (k024194) |
| --- | --- | --- |
| Detection Method | Amperometry: current is generated by oxidation of reduced mediator. | Amperometry |
| Enzyme | Glucose Oxidase (Aspergillus niger) | Glucose Oxidase (Aspergillus niger) |
| Mediator | Hexaammineruthenium chloride | Potassium ferricyanide |
| Electrode | Carbon electrode | Carbon electrode |
| Test range | 10 ~ 600 mg/dL | 20 ~ 600 mg/dL |
| Hematocrit Range | 30 ~55% | 30 ~ 55% |
| Test Time | 5 seconds | 5 seconds |
| Sample Volume | 1uL | 1uL |
| Temperature & Humidity range | 50 ~ 104°F
10 ~ 40°C
10 ~ 90% | 43 ~ 111°F
6 ~ 44°C
10 ~ 90% |
| Open use time | 3 months | 3 months |
| Coding | Button (C1 ~C45) | Button (C1 ~ C49) |
| Power | 3V Li battery (CR2032) | 3V Li battery (CR2032) |
| Warranty | 3 years | 3 years |
Differences
| | GlucoLab (k051285) | ONE TOUCH® Ultra® |
| --- | --- | --- |
| Memory capability | From 7 to 99-day average and 250 tests in the memory | 14-day average and last 150 tests in the memory |
| Battery life | Running 5,000 test | Running 1,000 test |
| Size: LxWxH (mm) | 74x53x20 | 79x57x21 |
| Weight | 40g (with battery) | 42g (with battery) |
| Software | GlucoLab™ 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
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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
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.
12) 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 an electrical current that will stimulate a chemical reaction. This reaction is measured by the GlucoLab™ 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 concentrations for the test. Each of the samples was measured 5 times for precision.
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# 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 | 43 | 1.6 | 3.7 |
| Level 2 | 5 | 81.6 | 2.1 | 2.5 |
| Level 3 | 5 | 132.6 | 1.9 | 1.5 |
| Level 4 | 5 | 211.6 | 5.9 | 2.8 |
| Level 5 | 5 | 318.2 | 12 | 3.8 |
| Control Samples | No. of Assay | Day-to-Day Precision Mean (mg/dL) | SD (mg/dL) | CV (%) |
| Low | 80 | 50.4 | 2.0 | 3.9 |
| Normal | 80 | 122.7 | 2.6 | 2.1 |
| High | 80 | 321.7 | 7.0 | 2.2 |
This study showed variability from strip to strip in blood tests of $3.8\%$ or less and from day to day in control tests of $3.9\%$ or less.
b. Linearity/assay reportable range:
Test Procedure (Dilution Schemes)
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The claimed detection range for the GlucoLab Blood Glucose Monitoring System is 10-600 mg/dL. The linearity study presented in this submission that utilizes NCCLS EP6-A with the recommended dilution schemes did not challenge the lower (10-40 mg/dL) or upper (550-600 mg/dL) claimed detection range. So the sponsor performed two additional studies of 30 different paired samples that compared the GlucoLab at the 10-40 mg/dL range and the 550-600 mg/dL range to the YSI analyzer (reference). In the first study the glucose range tested was 10-39 mg/dL with the following correlation:
$$
\begin{array}{l}
y = 0.9779x + 0.4296 \\
R^2 = 0.9869 \\
n = 30 \\
\end{array}
$$
In the second study the glucose range tested was 550-598 mg/dL with the following correlation:
$$
\begin{array}{l}
y = 0.9791x + 15.162 \\
R^2 = 0.974 \\
n = 30 \\
\end{array}
$$
The NCCLS EP6-P2 recommends dilution schemes to estimate the linearity of the Quantitative Analytical Method.
According to the NCCLS EP6-P2 protocol, a blood sample of 25 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 2).
Table 2: Levels of Dilution Schemes
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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 $10\mathrm{mg / dL}$ and 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:
$1^{\mathrm{st}}$ order polynomial, $\mathrm{y} = \mathrm{ax} + \mathrm{b}$ $2^{\mathrm{nd}}$ order polynomial, $\mathrm{y} = \mathrm{aX}^2 +\mathrm{bX} + \mathrm{c}$
All dilution schemes start with a high and low concentration samples in which the concentrations meet or exceed the range of interest. For the test, the highest and lowest glucose concentration used was $661\mathrm{mg / dL}$ and $8\mathrm{mg / dL}$ . If a strip sensor has an ideal linearity $(r2 = 1)$ from Lowest to highest concentration, the ideal concentration of level 2 mixed with $0.875\mathrm{L}$ and $0.125\mathrm{H}$ volume ratio is a $99\mathrm{mg / dL}$ $[(0.875*37.4 + 0.125*530.2) / (0.875 + 0.125)]$ .
Table 3, below, shows a summary of the nine dilutions that were measured five times for precision.
Table 3: Test Result Summary
| Dilution | Rep1 | Rep2 | Rep3 | Rep4 | Rep5 | Mean |
| --- | --- | --- | --- | --- | --- | --- |
| 1* | 9 | 9 | 8 | 8 | 7 | 8.2 |
| 2 | 90 | 84 | 85 | 90 | 91 | 88 |
| 3 | 160 | 162 | 155 | 155 | 161 | 158.6 |
| 4 | 240 | 240 | 235 | 235 | 234 | 236.8 |
| 5 | 320 | 321 | 330 | 340 | 305 | 323.2 |
| 6 | 400 | 405 | 407 | 410 | 415 | 407.4 |
| 7 | 500 | 510 | 515 | 498 | 510 | 506.6 |
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The dilution number at Table 3 and Figure 1 corresponds to the Level number in Table 2. *meter would report HI or LO for these levels

Figure 1: Glucose Linearity Study (Dilution 1-9)
Table 4: The Polynomial Evaluation of Linearity
| Dilution | Actual Mean | Predicted 1st order | Predicted 2nd order | Difference |
| --- | --- | --- | --- | --- |
| 1 | 8.2 | 0.7 | 6.8 | -6.1 |
| 2 | 88 | 82.7 | 84.2 | -1.5 |
| 3 | 158.6 | 164.7 | 163.0 | 1.7 |
| 4 | 236.8 | 246.7 | 243.3 | 3.7 |
| 5 | 323.2 | 328.8 | 324.4 | 4.3 |
| 6 | 407.4 | 410.8 | 407.1 | 3.7 |
| 7 | 506.6 | 492.8 | 491.1 | 1.7 |
| Dilution | Actual Mean | Predicted 1st order | Predicted 2nd order | Difference |
| 8 | 569 | 574.8 | 576.3 | -1.5 |
| 9 | 661 | 656.8 | 662.9 | -6.1 |
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.
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The nonlinear $2^{\mathrm{nd}}$ fits the data better than a linear one, but the difference is lower than 4.3 mg/dL from 8.2 mg/dL to 661.8 mg/dL. The R2 of $1^{\mathrm{st}}$ order regression is a 0.9988
c. Traceability (controls, calibrators, or method):
Traceability referenced to NBS, NIST Standards
Users are directed to use the control solutions before the expiration date printed on the bottle. The controls are stable for three months after opened.
d. Detection limit:
Reportable range = 10 – 600 mg/dL
1.7 to 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 approximately $50 - 580~\mathrm{mg/dL}$ 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 $(30\sim 55\%)$, 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 (respectively).
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Figure 2: Blood glucose conc. Vs Hematocrit %
The % bias of the assay value of the GlucoLab™ system is relative to the YSI and does not have a negative or positive correlation to hematocrit level in this experiment. 98% of the data is within +/- 20% bias and 86% lies within +/- 10% in the overall range of glucose and hematocrit.
The potential interference of various exogenous and endogenous substances was tested by spiking the levels listed below in Table 5 into non-spiked control samples. The results were compared and the % error was calculated for the highest level tested.
Table 5: Summary of Tested Interferences:
| | Levels Tested | | Mean of Test Results | | |
| --- | --- | --- | --- | --- | --- |
| Interferences | Non-spiked Control (mg/dL) | Highest Level Tested(mg/dL) | Control (mg/dL) | High (mg/dL) | Error % |
| Acetaminophen | 0 | 20 | 98.7 | 100.7 | 2.0 |
| Bilirubin | 0 | 40 | 97.3 | 96.0 | -1.4 |
| Gentistic acid | 0 | 50 | 114.3 | 140.3 | 22.7 |
| Uric acid | 0 | 20 | 92.3 | 86.0 | -6.9 |
| Levo-Dopa | 0 | 4 | 98.0 | 109.3 | 11.6 |
| Creatinine | 0 | 30 | 119.3 | 126.0 | 5.6 |
| Methyl-Dopa | 0 | 2.5 | 105.3 | 113.3 | 7.6 |
| Tolazamide | 0 | 5 | 94.0 | 114.3 | 21.6 |
| Dopamine | 0 | 13 | 108.3 | 121.3 | 12.0 |
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| | Levels Tested | | Mean of Test Results | | |
| --- | --- | --- | --- | --- | --- |
| Interferences | Non-spiked Control (mg/dL) | Highest Level Tested(mg/dL) | Control (mg/dL) | High (mg/dL) | Error % |
| Ascorbate | 0 | 3 | 112.3 | 115.3 | 2.7 |
| EDTA | 0 | 640 | 100.0 | 104.3 | 4.3 |
| Glutathione | 0 | 1 | 119.7 | 129.0 | 7.8 |
| Heparin | 0 | 1,000 | 126.3 | 127.7 | 1.1 |
| Ibuprofen | 0 | 40 | 121.3 | 130.7 | 7.7 |
| Salicylic acid | 0 | 50 | 135.7 | 137.3 | 1.2 |
| Tetracycline | 0 | 0.4 | 126.3 | 129.1 | 2.2 |
| Tolbutamide | 0 | 100 | 101.0 | 103.3 | 2.3 |
| Urea | 33 | 500 | 112.3 | 113.0 | 0.6 |
| Cholesterol | 209 | 500 | 123.0 | 138.7 | 12.7 |
| Triglyceride | 210 | 2,890 | 110.7 | 123.3 | 11.4 |
The sponsor states that high test concentrations were referenced to NCCLS Document EP7-P.
The sponsor states that:
- Acetaminophen, uric acid, ascorbic acid (vitamin C), and other reducing substances (when occurring in normal blood or normal therapeutic concentrations) do not significantly affect results. However, abnormally high concentrations in blood may cause inaccurately high results.
- Cholesterol up to 500 mg/dL or triglycerides up to 2890 mg/dL do not significantly affect the results. Glucose values, however, in specimens beyond these levels should be interpreted with caution.
- Blood samples that contain a high concentration of dissolved oxygen may lower the test result.
- Tolazamide or Gentistic acid treatment may increase the test result.
- Antiglycolytic agents and anticoagulants in blood samples may affect the test results.
f. Assay cut-off:
Not Applicable
2. Comparison studies:
a. Method comparison with predicate device:
The method comparison to the predicate device was assessed with One hundred sixty subjects with Type 1 or Type 2 diabetes during a normally scheduled clinic visits. In the study protocol, both the lay user and a trained technician obtained fingerstick
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glucose readings on the GlucoLab™ and ONETOUCH ULTRA, as well as alternate site glucose testing on the forearm, palm, upper arm, thigh and calf using both the GlucoLab™, and ONETOUCH ULTRA meters. No effort was made to determine whether patients were in the steady state or had rapidly changing blood glucose concentrations.
The readings were taken as close in time as possible. Within 5 minutes, a venous whole blood sample was drawn and centrifuged for making serum. The serum sample was tested by Hitachi 747. The sponsor indicated 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.
Table 6. Summary of test results with finger capillary blood and palm blood
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (Palm) vs Hithchi747 | Slope: | 0.9789 | 0.9877 | 0.9520 |
| | Y-intercept: | -4.2446 | -0.1052 | 9.8344 |
| | R²: | 0.9849 | 0.9702 | 0.9821 |
| GlucoLab™ (Palm) vs Hithchi747 | Slope: | 0.9995 | 0.9892 | 0.9339 |
| | Y-intercept: | -4.8527 | 1.4874 | 6.7677 |
| | R²: | 0.9829 | 0.9757 | 0.9707 |
| GlucoLab™ (Capillary) vs Hithchi747 | Slope: | 0.9864 | 0.9791 | 0.9784 |
| | Y-intercept: | -0.6224 | 1.1418 | 0.5512 |
| | R²: | 0.9921 | 0.9740 | 0.9848 |
| GlucoLab™ (Palm) vs GlucoLab™ (Capillary) | Slope: | 1.0048 | 0.9872 | 0.9418 |
| | Y-intercept: | -3.097 | 4.5952 | 8.5922 |
| | R²: | 0.9781 | 0.9565 | 0.9596 |
Clarke Error Grids
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (Palm) vs Hithchi747 | A-region | 100% | 98% | 98% |
| | B-region | 0% | 2% | 2% |
| GlucoLab™ (Palm) vs Hithchi747 | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
| GlucoLab™ (Capillary) vs Hithchi747 | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
| GlucoLab™ (Palm) vs GlucoLab™ (Capillary) | A-region | 100% | 96 % | 96 % |
| | B-region | 0% | 4 % | 4 % |
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Table 7. Summary of test results with finger capillary blood and forearm, upper arm blood.
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (Arm)vs Hithchi747 | Slope: | 1.0291 | 0.9604 | 1.011 |
| | Y-intercept: | -2.4835 | 5.2987 | -0.6795 |
| | R²: | 0.9774 | 0.9828 | 0.9782 |
| GlucoLab™ (Arm) vs Hithchi747 | Slope: | 1.0014 | 1.0311 | 1.0018 |
| | Y-intercept: | -5.3191 | -6.0259 | -0.6571 |
| | R²: | 0.9872 | 0.9824 | 0.9887 |
| GlucoLab™ (Capillary) vs Hithchi747 | Slope: | 0.9828 | 1.008 | 0.9568 |
| | Y-intercept: | -1.4284 | -3.8832 | 3.6514 |
| | R²: | 0.9881 | 0.9783 | 0.9898 |
| GlucoLab™ (Palm) vs GlucoLab™ (Capillary) | Slope: | 1.0067 | 1.0076 | 1.0347 |
| | Y-intercept: | -1.5563 | 0.4899 | -2.1928 |
| | R²: | 0.9754 | 0.9744 | 0.9755 |
Clarke Error Grids
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (Arm) vs Hithchi747 | A-region | 98 % | 98 % | 100% |
| | B-region | 2 % | 2 % | 0% |
| GlucoLab™ (Arm) vs Hithchi747 | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
| GlucoLab™ (Capillary) vs Hithchi747 | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
| GlucoLab™ (Palm) vs GlucoLab™ (Capillary) | A-region | 100% | 98% | 98 % |
| | B-region | 0% | 2% | 2 % |
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Table 8. Summary of test results with finger capillary blood and calf, thigh blood.
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (calf and thigh) vs Hithchi747 | Slope: | 0.9942 | 0.9913 | 0.9960 |
| | Y-intercept: | 0.5819 | 1.8588 | -0.7162 |
| | R²: | 0.9839 | 0.9893 | 0.9784 |
| GlucoLab™ (calf and thigh) vs Hithchi747 | Slope: | 0.9863 | 0.9756 | 0.9502 |
| | Y-intercept: | 2.2320 | 2.8426 | 5.2581 |
| | R²: | 0.9846 | 0.9901 | 0.9804 |
| GlucoLab™ (Capillary) vs Hithchi747 | Slope: | 1.017 | 0.9729 | 0.9963 |
| | Y-intercept: | -3.791 | 0.7884 | -0.2352 |
| | R²: | 0.9899 | 0.9890 | 0.9812 |
| GlucoLab™ (Palm) vs GlucoLab™ (Capillary) | Slope: | 0.9608 | 0.9935 | 0.9348 |
| | Y-intercept: | 7.5222 | 3.6716 | 3.9854 |
| | R²: | 0.9763 | 0.9825 | 0.9800 |
Clarke Error Grids
| | | Site 1 | Site 2 | Site 3 |
| --- | --- | --- | --- | --- |
| OneTouch (calf and thigh) vs Hithchi747 | A-region | 100% | 98 % | 100% |
| | B-region | 0% | 2 % | 0% |
| GlucoLab™ (calf and thigh) vs Hithchi747 | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
| GlucoLab™ (Capillary) vs Hithchi747 | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
| GlucoLab™ (Palm) vs GlucoLab™ (Capillary) | A-region | 100% | 100% | 100% |
| | B-region | 0% | 0% | 0% |
The comparison test results demonstrated similar results from both meters, with OneTouch at alternate site, GlucoLab™ at alternate site, and GlucoLab™ at fingerstick capillary according to the slope, Y-intercept, linearity and error % in Clark Error Grid region. Test results with GlucoLab™ at the alternative site of hand versus fingerstick capillary blood, correlation coefficient are $0.9339 \sim 1.0048$ . Test results with GlucoLab at alternative site of arm (forearm $n = 84$ and upper arm $n = 72$ ) versus fingerstick capillary blood, correlation coefficient are $0.9568 \sim 1.0347$ . Test results with GlucoLab at alternative site of leg (calf $n = 85$ and thigh $n = 74$ ) versus fingerstick capillary blood, correlation coefficient are $0.9704 \sim 0.9988$ . The GlucoLab Blood Glucose Monitoring System demonstrated equivalence to the OneTouch Ultra predicate device.
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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 GlucoLab™ System was assessed by comparing blood glucose results obtained by patients with those obtained using the Hitachi 747, a laboratory instrument. Glucose levels were measured on 410 fresh capillary blood specimens by 104 diabetic patients and three healthcare professionals at three different clinical centers.
The correlation between Hitachi 747 and GlucoLab™ were confirmed in the blood samples with correlation coefficients of $R = 0.9840$ (healthcare professionals) and $R = 0.9830$ (lay users) (Fig. 1 and Fig. 2 respectively). Results indicate that the use of the GlucoLab™ generate results similar to the Hitachi 747.

Figure 3: Linear regression (healthcare professionals)
Slope 1.0068
y-intercept -1.6886
Correlation coefficient(r) 0.984
No. of samples 410
Range tested $66 - 435\mathrm{mg / dL}$
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Page 17 of 18
Figure 4: Linear regression (Lay users)

Slope 1.0071
y-intercept -0.8296
Correlation coefficient(r) 0.983
No. of samples 104
Range tested 85-430 mg/dL
Readability of lay-user labeling was assessed by Flesch Kincaid testing. The sponsor reports that the labeling reads at a 7.71 grade reading level.
4. Clinical cut-off:
Not Applicable
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. Instrument Name:
GLUCOLAB blood glucose meter
O. System Descriptions:
1. Modes of Operation:
Automatic once sample is applied
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Page 18 of 18
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:
manual
4. Specimen Sampling and Handling:
Fingerstick (capillary blood samples)
5. Calibration:
Check strip, meter coding
6. Quality Control:
Two levels of control materials are available
P. Other Supportive Instrument Performance Characteristics Data Not Covered In The “Performance Characteristics” Section above:
Altitude – testing can be conducted at elevations up to 10,000 feet above sea level
Acceptable temperature range = 10 – 40 °C
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.
18
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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.