K183546 · Instrumentation Laboratory CO · CGA · Feb 16, 2019 · Clinical Chemistry
Device Facts
Record ID
K183546
Device Name
GEM Premier ChemSTAT
Applicant
Instrumentation Laboratory CO
Product Code
CGA · Clinical Chemistry
Decision Date
Feb 16, 2019
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 862.1345
Device Class
Class 2
Attributes
Pediatric
Indications for Use
The GEM Premier ChemSTAT is a portable critical care system for use by health care professionals to rapidly analyze lithium heparinized whole blood samples at the point of health care delivery in a clinical setting and in a central laboratory. The instrument provides quantitative measurements of Glucose (Glu), Lactate (Lac), Hematocrit (Hct), pH and partial pressure of carbon dioxide (pCO2) from arterial and venous heparinized whole blood. These parameters, along with derived parameters, aid in the diagnosis of a patient's acid/base status and metabolite balance. - Glucose (Glu) measurement is used in the diagnosis, monitoring and treatment of carbohydrate metabolism disturbances including diabetes mellitus, neonatal hypoglycemia, idiopathic hypoglycemia, and of pancreatic islet cell carcinoma. - Lactate (Lac) measurement is used to evaluate the acid-base status of patients suspected of having lactic acidosis, to monitor tissue hypoxia and strenuous physical exertion, and in the diagnosis of hyperlactatemia. - Hematocrit (Hct) measurements in whole blood of the packed red cell volume of a blood sample are used to distinguish normal from abnormal states, such as anemia and erythrocytosis (an increase in the number of red cells). - pH and pCO2 measurements in whole blood are used in the diagnosis and treatment of life-threatening acid-base disturbances.
Device Story
Portable critical care system; analyzes lithium heparinized whole blood (arterial/venous) at point-of-care or central lab. Input: whole blood sample via disposable GEM Premier ChemSTAT PAK (cartridge). Operation: cartridge contains sensors, process control solutions, sampler, and waste; analyzer uses internal logic/touchscreen to guide user. Principle: amperometry (Glu, Lac), conductivity (Hct), potentiometry (pH, pCO2). Output: quantitative measurements of Glu, Lac, Hct, pH, pCO2. iQM (Intelligent Quality Management) provides continuous monitoring, automatic error detection, correction, and documentation. Healthcare providers use output to diagnose/monitor acid-base status and metabolite balance; aids clinical decision-making for life-threatening disturbances.
Clinical Evidence
Method comparison study (CLSI EP09c) compared GEM Premier ChemSTAT to GEM Premier 4000 using lithium heparinized whole blood patient samples. Pooled data from 3 POC sites and internal CSL (N=431-559 per analyte) showed high correlation (R=0.995-0.999) across reportable ranges. Precision studies (CLSI EP05-A3) and reproducibility studies (multi-site POC) confirmed performance within specifications. Bench testing included LoB/LoD/LoQ, linearity, and analytical specificity (interference testing).
Technological Characteristics
Portable analyzer with touch-sensitive screen. Disposable multi-use PAK (cartridge) contains sensors, process control solutions, sampler, and waste. Measurement principles: Amperometry (Glu, Lac), Conductivity (Hct), Potentiometry (pH, pCO2). Calibration: 2-point. iQM (Intelligent Quality Management) system for continuous monitoring. Components traceable to NIST/CLSI standards. EEPROM chip in cartridge stores PC solution values.
Indications for Use
Indicated for health care professionals to analyze lithium heparinized whole blood (arterial/venous) for Glu, Lac, Hct, pH, and pCO2. Used for diagnosis, monitoring, and treatment of carbohydrate metabolism disturbances (e.g., diabetes, hypoglycemia), acid-base status (e.g., lactic acidosis, tissue hypoxia), and hematological states (e.g., anemia, erythrocytosis). For prescription use in clinical laboratories and point-of-care settings.
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.
Predicate Devices
GEM Premier 4000 (k133407)
Submission Summary (Full Text)
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1
# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY ASSAY ONLY TEMPLATE
A. 510(k) Number:
k183546
B. Purpose for Submission:
New Device
C. Measurand:
Glucose, Lactate, Hematocrit, pH, pCO2
D. Type of Test:
Quantitative, potentiometry for pH, and pCO2
Quantitative, amperometry for glucose and lactate
Quantitative, electrical conductivity for hematocrit
E. Applicant:
Instrumentation Laboratory Co.
F. Proprietary and Established Names:
GEM Premier ChemSTAT
G. Regulatory Information:
| Product Code | Classification | Regulation Section | Panel |
| --- | --- | --- | --- |
| CGA | Class II | 862.1345 Glucose Test system | Chemistry (75) |
| KHP | Class I | 862.1450 Lactic acid test system | Chemistry (75) |
| GKF | Class II | 864.5660 Automated hematocrit instrument | Hematology (81) |
| CHL | Class II | 862.1120 Blood Gases (pCO2) and Blood pH system | Chemistry (75) |
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H. Intended Use:
1. Intended use(s):
See indications for use.
2. Indication(s) for use:
The GEM Premier ChemSTAT is a portable critical care system for use by health care professionals to rapidly analyze lithium heparinized whole blood samples at the point of health care delivery in a clinical setting and in a central laboratory. The instrument provides quantitative measurements of Glucose (Glu), Lactate (Lac), Hematocrit (Hct), pH and partial pressure of carbon dioxide (pCO2) from arterial and venous heparinized whole blood. These parameters, along with derived parameters, aid in the diagnosis of a patient's acid/base status and metabolite balance.
- Glucose (Glu) measurement is used in the diagnosis, monitoring and treatment of carbohydrate metabolism disturbances including diabetes mellitus, neonatal hypoglycemia, idiopathic hypoglycemia, and of pancreatic islet cell carcinoma.
- Lactate (Lac) measurement is used to evaluate the acid-base status of patients suspected of having lactic acidosis, to monitor tissue hypoxia and strenuous physical exertion, and in the diagnosis of hyperlactatemia.
- Hematocrit (Hct) measurements in whole blood of the packed red cell volume of a blood sample are used to distinguish normal from abnormal states, such as anemia and erythrocytosis (an increase in the number of red cells).
- pH and pCO2 measurements in whole blood are used in the diagnosis and treatment of life-threatening acid-base disturbances.
3. Special conditions for use statement(s):
For prescription use only at point-of-care and central laboratory settings.
4. Special instrument requirements:
GEM Premier ChemSTAT analyzer
I. Device Description:
The GEM Premier ChemSTAT system is a prescription-use-only, portable system used by health care professionals to analyze arterial and venous lithium heparinized whole blood samples at point-of-care or a central laboratory. The GEM Premier ChemSTAT system contains 2 key components: GEM Premier ChemSTAT analyzer and a disposable, multiuse GEM Premier ChemSTAT PAK Cartridge/PAK (GEM PAK).
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The GEM Premier ChemSTAT analyzer has the internal logic and processing power necessary to perform analysis. It employs a touch-sensitive color screen and a set of menus and buttons for user interaction.
The GEM Premier ChemSTAT PAK (or GEM PAK) is a disposable, multi-use PAK that houses all components necessary to operate the instrument. These components include the sensors, solutions, sampler, and waste bag. The GEM PAK enables analysis of 75 to 450 samples.
J. Substantial Equivalence Information:
1. Predicate device name(s):
GEM Premier 4000
2. Predicate 510(k) number(s):
k133407
3. Comparison with predicate:
| Similarities | | |
| --- | --- | --- |
| Item | Candidate Device
GEM Premier ChemSTAT
(k183546) | Predicate Device
GEM Premier 4000
(k133407) |
| Intended Use | Quantitative measurement of glucose, lactate, hematocrit, pH and pCO_{2} in arterial and venous heparinized whole blood. | Same |
| Intended User | Central Laboratory and Point-of-Care professionals. | Same |
| Measurement Principle | Amperometry (Glucose and Lactate)
Potentiometry (pH and pCO_{2})
Conductivity (Hematocrit) | Same |
| Measuring Range Glucose | 4-685 mg/dL | Same |
| Measuring Range lactate | 0.3 -17.0 mmol/L | Same |
| Measuring Range pH | 7 -8.00 | Same |
| Measuring Range Hematocrit | 15 – 72% | Same |
| Measuring Range pCO_{2} | 6 -125 mmHg | Same |
| Calibration | 2-point calibration | Same |
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| Differences | | |
| --- | --- | --- |
| Item | Candidate Device
GEM Premier ChemSTAT
(k183546) | Predicate Device
GEM Premier 4000
(k133407) |
| Sample Volume | 65 to 150 μL | 150 μL |
| Sample Type | Lithium heparinized whole blood (arterial and venous) | Lithium heparinized whole blood (arterial, venous and capillary) |
## K. Standard/Guidance Document Referenced (if applicable):
- CLSI EP05-A3. Evaluation of Precision of Quantitative Measurement Procedures; Approved Guideline, 3rd Edition.
- CLSI EP06-A. Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical Approach; Approved Guideline.
- CLSI EP07. Interference Testing in Clinical Chemistry; Approved Guideline, 3rd Edition.
- CLSI EP17-A2. Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures; Approved Guideline, 2nd Edition.
- CLSI EP25-A. Evaluation of Stability of In Vitro Diagnostic Reagents; Approved Guideline.
- CLSI EP37 Supplemental Tables for Interference Testing in Clinical Chemistry, 1st Edition.
## L. Test Principle:
### Glucose and Lactate
The Glucose and Lactate sensors are amperometric biosensors consisting of a platinum electrode operated at a positive potential with respect to the card reference electrode. Glu or Lac determination is accomplished by enzymatic reaction of Glu or Lac with oxygen in the presence of glucose oxidase or lactate oxidase and the electrochemical oxidation of the resulting hydrogen peroxide (H₂O₂) at the platinum electrode. The current flow between the platinum electrode and the ground electrode is proportional to the rate at which H₂O₂ molecules diffuse to the platinum and are oxidized, which in turn is directly proportional to the metabolite (Glu or Lac) concentration.
### Hematocrit
Hct is measured by an electrical conductivity technique. The conductivity technique is based on the principle that because plasma is more conductive than blood cells due to the high resistance of the cell membranes, the resistivity of blood will increase as the concentration of cells increases.
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pH
The pH sensors are based on the principle of ion selective electrodes in which electrical potential can be established across a membrane resulting from chemical selectivity of the membrane to a specific ion. The potential can be described by this simplified form of the Nernst equation $\mathrm{E} = \mathrm{E}' + (\mathrm{S} \times \log \mathrm{C})$ , where $\mathrm{E}$ is the electrode potential, $\mathrm{E}'$ is the standard potential for that membrane, $\mathrm{S}$ is the sensitivity (slope), and $\mathrm{C}$ is the ion activity. $\mathrm{E}'$ and $\mathrm{S}$ can be determined by the sensor response to the Process Control (PC) Solutions, and the equation can be solved for the activity of the ion of interest.
pCO2
The pCO2 sensor is a patented design that relies on generated potential of the bicarbonate sensor versus the pH sensor. The potential difference between the two sensors is related to the logarithm of pCO2 content in the sample.
# M. Performance Characteristics (if/when applicable):
# 1. Analytical performance:
# a. Precision/Reproducibility:
i. Internal Precision Study - Whole Blood
An internal precision study was performed at an internal site using 5 whole blood samples with different concentrations of analytes for Glucose, Lactate, pH, $\mathrm{pCO_2}$ , and Hematocrit. Testing was performed on 3 different GEM Premier ChemSTAT analyzers with 3 cartridges for 5 days, with 1 run per day and 8 replicates measured per run per level for a total of 120 data sets per sample.
The results are summarized in the table below:
| Analyte | Level | Mean | Within Run | | Between Analyzer | | Total Imprecision | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | SD | %CV | SD | %CV | SD | %CV |
| Glucose (mg/dL) | 1 | 24 | 0.5 | 2.3 | 0.0 | 0.0 | 0.5 | 2.3 |
| | 2 | 48 | 0.9 | 1.9 | 0.3 | 0.7 | 0.9 | 2.0 |
| | 3 | 122 | 1.3 | 1.1 | 1.0 | 0.8 | 1.7 | 1.4 |
| | 4 | 356 | 2.7 | 0.8 | 1.6 | 0.4 | 3.2 | 0.9 |
| | 5 | 620 | 3.2 | 0.5 | 4.3 | 0.7 | 5.4 | 0.9 |
| Lactate (mmol/L) | 1 | 0.7 | 0.06 | 8.9 | 0.00 | 0.0 | 0.06 | 8.9 |
| | 2 | 2.0 | 0.06 | 2.8 | 0.04 | 1.9 | 0.07 | 3.3 |
| | 3 | 4.9 | 0.05 | 1.1 | 0.10 | 2.1 | 0.11 | 2.3 |
| | 4 | 7.8 | 0.13 | 1.7 | 0.13 | 1.6 | 0.18 | 2.3 |
| | 5 | 14.2 | 0.23 | 1.6 | 0.25 | 1.8 | 0.34 | 2.4 |
| Hct (%) | 1 | 18 | 0.3 | 1.6 | 0.1 | 0.8 | 0.3 | 1.8 |
| | 2 | 33 | 0.3 | 0.9 | 0.2 | 0.7 | 0.4 | 1.1 |
| | 3 | 44 | 0.3 | 0.7 | 0.2 | 0.5 | 0.4 | 0.9 |
| | 4 | 57 | 0.3 | 0.5 | 0.2 | 0.4 | 0.4 | 0.7 |
| | 5 | 65 | 0.4 | 0.7 | 0.3 | 0.5 | 0.5 | 0.8 |
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| Analyte | Level | Mean | Within Run | | Between Analyzer | | Total Imprecision | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | SD | %CV | SD | %CV | SD | %CV |
| pH | 1 | 7.07 | 0.008 | 0.1 | 0.000 | 0.0 | 0.008 | 0.1 |
| | 2 | 7.25 | 0.007 | 0.1 | 0.000 | 0.0 | 0.007 | 0.1 |
| | 3 | 7.34 | 0.008 | 0.1 | 0.002 | 0.0 | 0.008 | 0.1 |
| | 4 | 7.49 | 0.009 | 0.1 | 0.003 | 0.0 | 0.010 | 0.1 |
| | 5 | 7.69 | 0.010 | 0.1 | 0.008 | 0.1 | 0.013 | 0.2 |
| pCO2(mmHg) | 1 | 110 | 1.4 | 1.3 | 0.0 | 0.0 | 1.4 | 1.3 |
| | 2 | 71 | 0.9 | 1.2 | 0.0 | 0.0 | 0.9 | 1.2 |
| | 3 | 51 | 0.7 | 1.4 | 0.0 | 0.0 | 0.7 | 1.4 |
| | 4 | 29 | 0.4 | 1.5 | 0.1 | 0.2 | 0.5 | 1.6 |
| | 5 | 12 | 0.6 | 4.8 | 0.0 | 0.0 | 0.6 | 4.8 |
ii. External Reproducibility Study with Aqueous Controls -Point-of-Care Setting:
An external reproducibility study was performed in 3 clinical point of care sites using aqueous control solutions. Testing was performed by 9 different operators on six different GEM Premier ChemSTAT analyzers, using a single lot of GEM Premier CHEMSTAT PAK's cartridges. Each site tested quality control materials for each analyte with two levels of GEM ChemSTAT CVP and 4 to 5 levels of GEM ChemSTAT PVP. Each control was tested in triplicate, twice a day for five days for a total of 30 replicates per level with $n = 90$ data sets across all three sites per analyte per control level. Individual POC site statistics were analyzed by two-way nested ANOVA with factors day and run nested within day. Multisite statistics were determined via three-way nested ANOVA with the factors being site, day and nested within site and run nested within site and day.
The results are summarized in the table below for all 3 sites POC sites.
| Glucose (mg/dL) | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Control Level | Mean | Repeatability | | Between Day | | Between Site | | Reproducibility | |
| | | SD | %CV | SD | %CV | SD | %CV | SD | %CV |
| CVP 1 | 392 | 1.0 | 0.2 | 0.7 | 0.2 | 4.3 | 1,1 | 5.9 | 1.5 |
| CVP 2 | 78 | 1.5 | 1.9 | 0.6 | 0.8 | 0.0 | 0.0 | 1.6 | 2.1 |
| PVP 1 | 642 | 1.3 | 0.2 | 1.1 | 0.2 | 6.5 | 1.0 | 6.8 | 1.1 |
| PVP 2 | 393 | 1.6 | 0.4 | 1.3 | 0.3 | 3.3 | 0.8 | 4.2 | 1.1 |
| PVP 3 | 115 | 1.4 | 1.2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.5 | 1.3 |
| PVP 4 | 80 | 0.6 | 0.7 | 0.5 | 0.6 | 0.3 | 0.3 | 0.9 | 1.1 |
| PVP 5 | 14 | 0.5 | 3.6 | 0.2 | 1.6 | 1.4 | 9.7 | 1.5 | 10.5 |
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| Lactate (mmol/L) | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Control Level | Mean | Repeatability | | Between Day | | Between Site | | Reproducibility | |
| CVP 1 | 8.2 | 0.04 | 0.5 | 0.02 | 0.3 | 0.03 | 0.3 | 0.07 | 0.8 |
| CVP 2 | 1.7 | 0.03 | 1.8 | 0.01 | 0.7 | 0.04 | 2.4 | 0.05 | 3.1 |
| PVP 1 | 15.7 | 0.07 | 0.4 | 0.07 | 0.5 | 0.09 | 0.6 | 0.15 | 0.9 |
| PVP 2 | 8.1 | 0.06 | 0.7 | 0.04 | 0.4 | 0.07 | 0.9 | 0.11 | 1.3 |
| PVP 3 | 5.0 | 0.03 | 0.6 | 0.04 | 0.8 | 0.03 | 0.6 | 0.6 | 1.2 |
| PVP 4 | 1.7 | 0.03 | 1.5 | 0.01 | 0.6 | 0.01 | 0.9 | 0.03 | 1.9 |
| PVP 5 | 0.5 | 0.02 | 4.1 | 0.03 | 5.1 | 0.03 | 5.6 | 0.04 | 8.6 |
| Hematocrit (%) | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Control Level | Mean | Repeatability | | Between Day | | Between Site | | Reproducibility | |
| CVP 1 | 42 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| CVP 2 | 22 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| PVP 1 | 18 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| PVP 2 | 23 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| PVP 3 | 43 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.2 |
| PVP 4 | 68 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| pH | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Control Level | Mean | Repeatability | | Between Day | | Between Site | | Reproducibility | |
| CVP 1 | 7.11 | 0.005 | 0.1 | 0.002 | 0.0 | 0.000 | 0.0 | 0.006 | 0.1 |
| CVP 2 | 7.54 | 0.003 | 0.0 | 0.001 | 0.0 | 0.001 | 0.0 | 0.003 | 0.0 |
| PVP 1 | 7.59 | 0.004 | 0.1 | 0.002 | 0.0 | 0.002 | 0.0 | 0.005 | 0.1 |
| PVP 2 | 7.11 | 0.007 | 0.1 | 0.003 | 0.0 | 0.000 | 0.0 | 0.008 | 0.1 |
| PVP 3 | 7.36 | 0.003 | 0.0 | 0.000 | 0.0 | 0.001 | 0.0 | 0.005 | 0.1 |
| PVP 4 | 7.55 | 0.004 | 0.1 | 0.002 | 0.0 | 0.002 | 0.0 | 0.005 | 0.1 |
| pCO2 (mmHg) | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Control Level | Mean | Repeatability | | Between Day | | Between Site | | Reproducibility | |
| CVP 1 | 92 | 1.2 | 1.4 | 0.0 | 0.0 | 0.2 | 0.2 | 2.0 | 2.2 |
| CVP 2 | 16 | 0.3 | 2.0 | 0.1 | 0.6 | 0.3 | 0.3 | 0.4 | 2.8 |
| PVP 1 | 60 | 1.1 | 1.9 | 0.2 | 0.3 | 0.5 | 0.5 | 1.3 | 2.1 |
| PVP 2 | 92 | 2.1 | 2.3 | 0.8 | 0.9 | 0.8 | 0.8 | 2.5 | 2.7 |
| PVP 3 | 38 | 0.6 | 1.6 | 0.0 | 0.0 | 0.3 | 0.3 | 0.7 | 1.9 |
| PVP 4 | 16 | 0.4 | 2.4 | 0.1 | 0.5 | 0.2 | 0.2 | 0.4 | 2.8 |
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# iii. External Precision - Whole Blood
A precision study was performed at 3 external clinical point-of care (POC) sites, using 5 whole blood patient samples for over 5 days, by 6 different operators on 3 GEM Premier ChemSTAT instruments (one analyzer per site), using a single lot of GEM Premier ChemSTAT PAKs (cartridges). Reproducibility was not assessed for whole blood samples because samples at each clinical site are unique. Each whole blood patient sample was run in triplicate on a single GEM Premier ChemSTAT instrument.
The results are summarized in the table below:
| Analyte | Site | N | Mean | Within Sample SD or CV% |
| --- | --- | --- | --- | --- |
| Glucose (mg/dL) | POC1 | 12 | 38 | 1.7 |
| | POC2 | 3 | 23 | 0.6 |
| | POC3 | 15 | 49 | 1.7 |
| | Pooled | 30 | 42 | 1.6 |
| | POC1 | 54 | 122 | 1.0% |
| | POC2 | 63 | 112 | 0.8% |
| | POC3 | 51 | 115 | 1.0% |
| | Pooled | 168 | 116 | 0.9% |
| Lactate (mmol/L) | POC1 | 9 | 1.9 | 0.07 |
| | POC2 | 27 | 1.8 | 0.08 |
| | POC3 | 9 | 2.2 | 0.07 |
| | Pooled | 45 | 1.9 | 0.08 |
| | POC1 | 57 | 5.7 | 1.7% |
| | POC2 | 39 | 3.7 | 2.5% |
| | POC3 | 54 | 3.8 | 1.8% |
| | Pooled | 150 | 4.5 | 1.9% |
| Hematocrit (%) | POC1 | 69 | 32 | 0.5 |
| | POC2 | 66 | 40 | 0.4 |
| | POC3 | 63 | 31 | 0.6 |
| | Pooled | 198 | 35 | 0.5 |
| pH | POC1 | 63 | 7.26 | 0.008 |
| | POC2 | 66 | 7.36 | 0.009 |
| | POC3 | 66 | 7.31 | 0.007 |
| | Pooled | 195 | 7.31 | 0.008 |
| pCO2 (mmHg) | POC1 | 54 | 49 | 1.2 |
| | POC2 | 60 | 40 | 0.7 |
| | POC3 | 60 | 50 | 0.9 |
| | Pooled | 174 | 46 | 0.9 |
| | POC1 | 18 | 74 | 1.4% |
| | POC2 | 6 | 66 | 1.6% |
| | POC3 | 3 | 78 | 1.5% |
| | Pooled | 27 | 73 | 1.5% |
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# b. Linearity/assay reportable range:
A linearity study was performed following CLSI EP06-A guidelines. Nine to ten levels per analyte were prepared by tonometry, spiking or diluting whole blood samples to challenge the claimed measuring ranges. Each level was analyzed in triplicate on six GEM Premier ChemSTAT analyzers, with three different cartridges for all analytes except for pH and pCO2 which were tested on three GEM Premier ChemSTAT analyzers and results compared to reference analyzers.
The results are summarized in the table below:
| Analyte | Linear regression Equation | R2 | Sample range tested | Claimed Measuring Range |
| --- | --- | --- | --- | --- |
| Glucose | y = 1.023x - 0.502 | 1.00 | 3-749 mg/dL | 4-685 mg/dL |
| Lactate | y = 1.004x + 0.000 | 0.9998 | 0.2-17.8 mmol/L | 0.3-17.0 mmol/L |
| Hct | y = 0.984x + 1.909 | 0.9975 | 13-74 % | 15-72% |
| pH | y = 1.006x - 0.042 | 0.9996 | 6.76-8.10 | 7.00-8.00 |
| pCO2 | y = 1.030x - 0.843 | 0.9994 | 2-137 mmHg | 6-125 mmHg |
The results of the linearity study support the claimed measuring range as described in the table above.
c. Traceability, Stability, Expected values (controls, calibrators, or methods):
Traceability:
Glucose is traceable by automated spectrophotometry using hexokinase method per CDC no. 77-8660 using secondary standard prepared from NIST #917.
Lactate traceability is established by automated spectrophotometry using lactate oxidase with secondary standard prepared from USP #1614308.
$\mathrm{pCO}_2$ traceability is established through tonometry at $37^{\circ}\mathrm{C}$ using NIST traceable gas mixtures.
pH traceability is established through a direct potentiometry method which uses secondary standards prepared from NIST SRM 186I & 186II phosphate salts.
Hematocrit traceability is established by centrifugation using whole blood per CLSI H7-A3 for establishing true correlation. Maintained from lot to lot by controlling conductivity through controlling sodium level.
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# d. Detection limit:
The limit of blank (LoB), limit of detection (LoD) and limit of quantitation (LoQ) were evaluated in accordance to CLSI EP17-A2 guideline, for Glu, Lac, Hct, pH, and $p\mathrm{CO}_2$ .
The LoB was assessed by testing blank samples on 3 days with 3 different cartridge lots $(N = 60/$ analyte/lot). After recording 60 blank sample measurements, the results were ranked from lowest to highest. LoB was independently calculated for each lot using the non-parametric method.
The LoD was determined by testing low-level samples over three days using three cartridge lots on three analyzers. The LoB used for LoD calculation is the maximum value across the three cartridge lots. The is calculated using the formula:
$$
\mathrm {L o D} = \mathrm {L o B} + \frac {1 . 6 4 5}{1 - \left(\frac {1}{4 (L - \bar {J})}\right)} * \mathrm {S D} _ {\mathrm {L}}
$$
Where:
$\mathrm{L} =$ total number of all low-level sample results across all cartridge lots $\mathrm{J} =$ number of low level samples (number of days)
The LoQ was assessed by testing low-level whole blood samples. Sixty replicates of the low-level samples were measured per day on 3 analyzers (N=60/analyte/day) using 3 reagent lots. The LoQ is defined as the lowest concentration at which measured total error is less than the pre-defined total error of $\pm 6\mathrm{mg / dL}$ for glucose, $\pm 0.4\mathrm{mmol / L}$ for lactate, $\pm 4\%$ for hematocrit and $\pm 5\mathrm{mmHg}$ for $\mathrm{pCO_2}$ . Total error (TE) for low level sample was calculated using the following formula:
$\mathrm{TE} = \left| \left( \text{meanGEM Premier ChemSTAT} - \text{meanPredicate Device} \right) \right| + 1.96 * \mathrm{SDLowLevel}$
The results are summarized in the table below
| Analyte | LoB | LoD | LoQ | Claimed Measuring Range |
| --- | --- | --- | --- | --- |
| Glucose mg/dL | 0 | 1 | 1 | 4-685 mg/dL |
| Lactate(mmol/L) | 0.0 | 0.0 | 0.1 | 0.3-17.0 mmol/L |
| Hematocrit (%) | 2 | 3 | 10 | 15-72% |
| pCO2(mmHg) | 1 | 3 | 3 | 6-125 mmHg |
Linearity studies were used to support the lower end of the measuring range for pH (see section M.1.b above).
# e. Analytical specificity:
An interference study was performed based on CLSI EP17-A guideline. The interference testing was conducted using whole blood samples at two different analyte concentrations. Substances were considered not interfering if the difference between the test and control samples was less than or equal to the following clinically significant interference limits set for each analyte:
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Substances identified as interfering substances were further characterized to determine the concentration that produces a clinically significant interference
The results are summarized in the table below for substances that showed non-significant interference when tested at the concentrations listed.
| Test substances | Tested concentrations | Tested analytes where interference was not observed |
| --- | --- | --- |
| Acetaminophen | 1030 μmol/L | Glucose, Lactate |
| Acetoacetate | 2 mmol/L | Glucose, Lactate |
| Albumin (Human) | 60 g/L | Hct |
| Ascorbic acid | 298 μmol/L | Glucose, Lactate |
| Atracurium | 50 mg/dL | Glucose, Lactate, Hct, pH, pCO2 |
| Bilirubin | 40 mg/dL | Glucose, Lactate, Hct, pH, pCO2 |
| Ceftriaxone | 1510 μmol/L | Glucose, Lactate, Hct, pH, pCO2 |
| Chlorpromazine | 10.3 μmol/L | Glucose, Lactate |
| Dobutamine | 0.121 mg/dL | Glucose, Lactate |
| Dopamine | 4.06 μmol/L | Glucose, Lactate |
| Epinephrine | 0.5 μmol/L | Glucose, Lactate, Hct, pH, pCO2 |
| Ethanol | 130 mmol/L | Glucose, Lactate |
| Ethylene glycol | 8.8 mmol/L | Glucose, Lactate |
| Etomidate | 50 mg/L | Glucose, Lactate, Hct, pH, pCO2 |
| Fentanyl | 0.03 μg/mL | Glucose, Lactate, Hct, pH, pCO2 |
| Fructose | 1 mmol/L | Glucose |
| Furosemide | 48.1 μmol/L | Glucose, Lactate, Hct, pH, pCO2 |
| Gadodiamide | 1.4 mmol/L | Glucose, Lactate, Hct, pH, pCO2 |
| Glycolic acid | 1.0 mmol/L | Glucose |
| Hematocrit | 25% | pH, pCO2, Glucose, Lactate |
| Hematocrit | 60% | pH, pCO2, Glucose, Lactate |
| Hemoglobin (Hemolysis) | 1000 mg/dL | Glucose, Lactate, Hct, pH, pCO2 |
| Heparin | 100,000 U/L | Glucose, Lactate |
| β-hydroxybutyrate | 2 mmol/L | Glucose, Lactate, pH |
| Ibuprofen | 1060 μmol/L | Glucose, Lactate, Hct, pH, pCO2 |
| Icodextrin | 20 mg/dL | Glucose, Lactate |
| Isoniazid | 438 μmol/L | Glucose, Lactate |
| Leukocytes / Platelets | 24.81 / 452 (x103/μl) | Hct 30% |
| | 27.60 / 564 (x103/μl) | Hct 60% |
| Maltose | 360 mg/dL | Glucose, Lactate |
| Maltose | 1000 mg/dL | Glucose, Lactate |
| Maltose | 10000 mg/dL | Glucose, Lactate |
| Maltose | 100000 mg/dL | Glucose, Lactate |
{11}
The table below lists substances that demonstrated interference with Glu, Lac, Hct, pH and/or $p\mathrm{CO}_2$ and the concentration of the interfering substance, as well as the bias observed and its direction (positive / negative):
| Interfering Substance | Affected Analytes | Analyte Conc. | Interfering Conc. Tested | Bias Observed (Mean) | Lowest Interfering Conc. with Analyte Impact | Bias Observed at the Lowest Concentration |
| --- | --- | --- | --- | --- | --- | --- |
| Galactose | Glucose | 40 mg/dL | 3.33 mmol/L | +13 % | 2.77 mmol/L | +10 % |
| | | 220 mg/dL | | No interference Observed | | |
| Glycolic acid | Lactate | 1.0 mmol/L | 1.0 mmol/L | +1.5 mmol/L | 0.3 mmol/L | +0.4 mmol/L |
| | | 1.7 mmol/L | | +1.6 mmol/L | 0.3 mmol/L | +0.4 mmol/L |
| Hydroxyurea | Glucose | 40 mg/dL | 3.08 mg/dL | +207 % | 0.15 mg/dL | +10 % |
| | | 220 mg/dL | | +34 % | 0.90 mg/dL | +10 % |
| Hydroxyurea | Lactate | 1.0 mmol/L | 3.08 mg/dL | +3.8 mmol/L | 0.30 mg/dL | +0.4 mmol/L |
| | | 1.7 mmol/L | | +3.5 mmol/L | 0.33 mg/dL | +0.4 mmol/L |
| Mannose | Glucose | 40 mg/dL | 20 mg/dL | +12 % | 19 mg/dL | +10 % |
| | | 220 mg/dL | | No Interference Observed | | |
| Thiopental | pH | 7.40 | 1660 μmol/L | +0.04 | 789 μmol/L | +0.02 |
| | | 7.25 | | +0.03 | 1175 μmol/L | +0.02 |
| Mannitol | pH | 7.40 | 1660 μmol/L | +0.04 | 789 μmol/L | +0.02 |
| Mannose | Glucose | 40 mg/dL | 3.08 mg/dL | +207 % | 0.15 mg/dL | +10 % |
| Mannose | Lactate | 1.0 mmol/L | 3.08 mg/dL | +3.8 mmol/L | 0.30 mg/dL | +0.4 mmol/L |
{12}
f. Assay cut-off:
Not applicable
2. Comparison studies:
a. Method comparison with predicate device:
A method comparison study was performed at 3 external POC sites using lithium heparinized arterial and venous whole blood samples. Each sample was analyzed in singlicate on the GEM Premier ChemSTAT and on the GEM Premier 4000. To span the reportable range for each analyte, $< 10\%$ of contrived samples were spiked for each analyte.
The results are summarized in the table below:
| Analyte | N | Slope | Intercept | R | Sample Range |
| --- | --- | --- | --- | --- | --- |
| Glucose (mg/dL) | 432 | 1.019 | -0.558 | 0.999 | 35 to 684 |
| Lactate (mmol/L) | 432 | 1.000 | -0.100 | 0.997 | 0.6 to 16.0 |
| Hematocrit (%) | 431 | 1.032 | -0.626 | 0.997 | 16 to 71 |
| pH | 552 | 1.006 | -0.038 | 0.995 | 7.03 to 7.87 |
| pCO_{2} (mmHg) | 559 | 1.000 | 0.000 | 0.996 | 7 to 120 |
b. Matrix comparison:
Not applicable. The glucose, lactate, hematocrit, pH and $\mathrm{pCO_2}$ assays are for use with lithium heparinized whole blood only.
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):
Not applicable
4. Clinical cut-off:
Not applicable
{13}
# 5. Expected values/Reference range:
The following are the reference ranges from published literature:
| Analyte | Reference Range | Unit |
| --- | --- | --- |
| Glu* | 65 to 95 | mg/dL |
| | 3.6 to 5.3 | mmol/L |
| Lac* | 0.36 to 0.75 (arterial at rest) | mmol/L |
| | 2.24 to 6.76 (arterial at rest) | mg/dL |
| | 0.56 to 1.39 (venous at rest) | mmol/L |
| | 5.0 to 12.5 (venous at rest) | mg/dL |
| Hct* | 39-51 (male) and 35-47 (female) | % |
| pH* | 7.35 to 7.45 | pH |
| cH* | 44.7 to 35.5 | nmol/L |
| cH* | 44.7 to 35.5 | nEq/L |
| pH* | 7.32 to 7.43 (venous) | pH |
| cH* | 47.9 to 37.2 (venous) | nmol/L |
| cH* | 47.9 to 37.2 (venous) | nEq/L |
| pCO2** | 35 to 48 (male) and 32 to 45 (female) | mmHg |
| | 4.6 to 6.4 (male) and 4.3 to 6.0 (female) | kPa |
| | 6 to 7 mmHg (0.80 to 0.93 kPa) higher than arterial pCO2 (venous blood, right atrium) | |
The sponsor recommends that each laboratory establish their own reference ranges applicable to their patient population.
# References:
* Burtis, Carl and David Bruns, Tietz Textbook of Clinical Chemistry and Molecular Diagnostics, Elsevier Saunders, 7th Edition, 2015, pages 952-982.
** Wu, A., Tietz Clinical Guide to Laboratory Tests, W.B. Saunders Co., St. Louis MO, 4th Edition, 2006, pages 216.
# N. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Parts 801 and 809, as applicable.
# O. 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.