(212 days)
The PocketChem EZ Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips and palms. Testing is done outside the body (In Vitro diagnostic use). It is indicated for use at home (over the counter [OTC]) by persons with diabetes, or in clinical settings by healthcare professionals, as an aid to monitor the effectiveness of diabetes control.
The PocketChem EZ Blood Glucose Monitoring System is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.
The PocketChem EZ Blood Glucose Monitoring System consists of a meter, test strips, and two levels of control solutions for use as an aid to monitor the effectiveness of diabetes control.
Here's a breakdown of the acceptance criteria and study information for the PocketChem EZ blood glucose monitoring system, based on the provided FDA 510(k) summary:
Please note: The provided document is a 510(k) summary, which is a high-level overview. It typically does not contain the granular detail found in a full study report or detailed FDA review. Therefore, some specific details, particularly around statistical methods, exact sample sizes for each test type, and expert qualifications for ground truth determination, are not explicitly stated in this summary.
Acceptance Criteria and Reported Device Performance
The 510(k) summary indicates that "Bench testing included evaluation of interferences, linearity, linear range, hematocrit effects, altitude effects, control solution functionality, and analytical precision. Clinical testing included evaluation of accuracy for finger stick and palm test sites."
However, the provided text does not explicitly list the quantitative acceptance criteria for these tests (e.g., specific bias limits, precision CVs, or accuracy percentages within certain zones). It only states that the "bench testing results and clinical testing results support the Indications for Use and the claim of substantial equivalence to the predicate."
Without the specific numerical acceptance criteria, it's impossible to create a table with "Reported Device Performance" alongside defined "Acceptance Criteria." The summary implies that the device met internal and FDA-recommended criteria for blood glucose monitoring systems, but these criteria are not detailed here.
Study Details
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A table of acceptance criteria and the reported device performance
- Not explicitly provided in the document. The 510(k) summary states that "A full array of in-house and clinical testing was done consistent with relevant FDA guidance's for blood glucose monitoring systems." This implies that the device met the generally accepted performance standards for blood glucose meters at the time for accuracy (e.g., ISO 15197 or FDA guidance documents), precision, linearity, and interference. However, the specific numerical targets and results are not included in this summary.
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Sample sizes used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size: Not explicitly stated for specific tests. The summary mentions "clinical testing," which typically involves patient samples. FDA guidance for blood glucose meters usually requires a minimum number of subjects (e.g., 100-150 subjects) for accuracy studies.
- Data Provenance: Not explicitly stated. Given ARKRAY Factory USA, Inc. is based in Minneapolis, MN, it is likely that at least some, if not all, clinical data was collected in the USA, but this is an inference.
- Retrospective or Prospective: Not explicitly stated, but clinical accuracy studies for medical devices are generally prospective.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
- Not applicable in the same way as imaging studies. For blood glucose meters, the "ground truth" (reference method) is typically established by laboratory-based analyzers (e.g., YSI analyzer) operated by trained laboratory technicians, not by expert consensus or interpretations of images by clinicians. The summary does not specify the number or qualifications of the individuals performing the reference measurements, but it's assumed they were trained laboratory personnel.
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Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not applicable. Adjudication methods like 2+1 or 3+1 are typically used in studies where human readers are making subjective interpretations (e.g., radiologists reviewing images) and their disagreements need to be resolved. For objective measurements like blood glucose, a highly accurate laboratory reference method (the "ground truth") is used against which the device's readings are compared.
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If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
- No. This is not relevant for a blood glucose monitoring system. MRMC studies are used for diagnostic imaging devices where human interpretation plays a role and the AI might assist that interpretation. The PocketChem EZ is a direct measurement device.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, implicitly. The accuracy and precision studies performed for blood glucose meters are standalone evaluations of the device's performance against a reference method. The device provides a quantitative measurement directly, without requiring human interpretation of an output that needs to be "assisted." The clinical testing for "accuracy for finger stick and palm test sites" refers to this standalone performance.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Laboratory Reference Method: For blood glucose monitoring systems, the ground truth is established by a highly accurate laboratory-based reference method, such as a YSI glucose analyzer, which is considered the gold standard for glucose measurement in clinical laboratories. The summary doesn't explicitly name the reference method but "clinical testing included evaluation of accuracy" implies comparison to such a standard.
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The sample size for the training set
- Not applicable for this type of device. Blood glucose meters are not typically "trained" in the way AI algorithms are. Their underlying chemical/enzymatic reactions and electronic algorithms are designed and calibrated during development, not "trained" on large datasets of patient results in the sense of machine learning.
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How the ground truth for the training set was established
- Not applicable. As above, there isn't a "training set" with ground truth in the machine learning sense for a blood glucose meter. The device's calibration and performance are validated against standard glucose solutions and patient samples measured by a reference method.
§ 862.1345 Glucose test system.
(a)
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.(b)
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.