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The MyGlucoHealth Wireless System is intended for self-testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of glucose control. The system is comprised of the MyGlucoHealth Wireless Meter, MyGlucoHealth Control Solutions and MyGlucoHealth Wireless Test Strip. The system is intended to be used for the quantitative measurement of glucose (sugar) in whole blood samples drawn from the fingertip, ventral palm, hand,upper arm, forearm, calf and/or thigh. The MyGlucoHealth Wireless Meter is intended to be used by a single patient and should not be shared. The MyGlucoHealth Wireless System is not to be used for the diagnosis of or screening for diabetes or for neonatal use. Alternate site testing should be done during steady-state times (when glucose is not changing rapidly).

The MyGlucoHealth Wireless System comprised of the MyGlucoHealth Wireless Meter, MyGlucoHealth Control Solutions and MyGlucoHealth Wireless Test Strip. The MyGlucoHealth Wireless Meter is different from the MyGlucoHealth Blood Glucose Meter (MGH-BT1) only by the way the glucose data is transferred to the data repository. An embedded cellular module within the meter enables Wireless communication between the meter and the Entra Health Systems remote database called MyHealthPoint TeleHealth Manager (K132930).

The test strip (proprietary to the MyGlucoHealth Wireless Meter) is used only with the MyGlucoHealth Wireless Meter. It is for the quantitative measurement of the concentration of glucose in whole blood that can be taken from the fingertip, ventral palm, dorsal hand, upper arm, forearm, calf and/or thigh by diabetic patients or health care professionals. The results obtained are plasma calibrated to allow for easy comparison to the laboratory method. The MyGlucoHealth Wireless System is not to be used for the diagnosis of diabetes or for neonatal use. Alternate site testing should be done during steady-state times when glucose is not changing rapidly.

Here's a breakdown of the acceptance criteria and the study information for the MyGlucoHealth Wireless System, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

The provided document refers to the ISO 15197:2003 standard, which outlines requirements for blood glucose monitoring systems. While the specific numerical acceptance criteria from this standard are not explicitly listed in detail within the summary, the summary states that "All testing demonstrated the safety and effectiveness of the MyGlucoHealth Wireless Meter and substantial equivalence to the predicate."

A key aspect of performance for blood glucose monitoring systems is accuracy. Although a detailed table of performance against specific accuracy thresholds (e.g., within X% of a reference method) is not present in this summary, the substantial equivalence claim implies that its performance is comparable to the predicate device, which would have met such criteria.

Note: For a complete understanding of the device's accuracy against ISO 15197:2003, one would typically need to refer to the full test report or the specific performance data submitted with the 510(k) application, which is not fully included here. However, the mention of the standard indicates the type of criteria the device aims to meet.

Study Information

The document describes non-clinical testing and clinical testing.

2. Sample size used for the test set and the data provenance:

• Non-Clinical Testing:
  • Cleaning Validation Test (Robustness): 3 different meters were tested.
  • Cleaning Validation Test (Efficacy): The sample size for materials tested for disinfection efficacy is not specified, but it was performed by an outside testing laboratory.
• Clinical Testing (Human Factor testing): The sample size is not specified in the provided summary.
• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective for the clinical (human factors) study, or the non-clinical testing. However, given it's for FDA submission, it's generally expected to be prospective for human factors and laboratory-controlled for non-clinical.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• This information is not provided in the summary. For a blood glucose meter, the "ground truth" for accuracy assessments is typically established by comparative measurements against a well-established laboratory reference method (e.g., YSI analyzer), rather than expert consensus on individual readings.
• For the human factors testing, the definition of ground truth (e.g., successful task completion) would be based on predefined criteria, not expert assessment of individual readings.

4. Adjudication method for the test set:

• Not applicable in the context of this summary. Blood glucose meter performance is determined by quantitative comparison to reference methods, not subjective interpretation requiring adjudication among experts. For human factors, task completion or error rates would be measured against predefined success/failure criteria.

5. 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:

• This is not applicable to a blood glucose monitoring system like the MyGlucoHealth Wireless System. MRMC studies are typically used for imaging diagnostics where human readers interpret medical images, often with AI assistance. This device is a standalone in vitro diagnostic (IVD) device for quantitative measurement.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

• Yes, the core performance evaluation of the MyGlucoHealth Wireless System is a standalone evaluation. The device itself measures glucose concentration and displays a numerical result. The "algorithm" here refers to the electrochemical principles and calibration within the test strip and meter to convert a biochemical reaction into a glucose reading. The system's accuracy is assessed directly against a reference method.

7. The type of ground truth used:

• For glucose measurement accuracy: Typically, a laboratory reference method (e.g., YSI glucose analyzer) is used as the ground truth. This is implied by the adherence to ISO 15197:2003, which requires comparison to a traceable reference measurement procedure.
• For cleaning/disinfection efficacy: Specific laboratory standards for viral inactivation are used.
• For human factors: Predefined criteria for task completion and user comprehension are typically used to establish ground truth.

8. The sample size for the training set:

• This information is not explicitly provided. For a traditional electrochemical blood glucose meter, there isn't a "training set" in the machine learning sense. The device's calibration and algorithm are developed through engineering and chemistry principles, using a variety of samples during development and manufacturing to establish its operating characteristics.

9. How the ground truth for the training set was established:

• Not applicable in the machine learning context. For this type of device, ground truth for calibration and development would be established through precise laboratory measurements using reference methods and well-characterized samples over a range of glucose concentrations, hematocrit levels, and interfering substances.

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The MyHealthPoint Telehealth Manager is an accessory software that wirelessly collects, records and transmits biometric data (including glucose, blood pressure, weight, body composition, activity, body temperature, ECG and pulse oximeter readings) from a variety of supported home-monitoring devices as well as supporting manual uploading of data. The MyHealthPoint application uses the same data repository platform that is already cleared, MyGlucoHealth Software System (K081703).

MyHealthPoint TeleHealth Manager is intended to be used by patients in non-clinical settings (e.g. home) to collect, record and transmit their biometric data to a remote secure server. Stored data is accessible by healthcare professionals and caregivers for analysis, messaging and intervention using standard digital communication technologies and protocols. Patients may also view the data to assist in self-management of their specific health condition. The MyHealthPoint Telehealth Manager is intended to be used in combination with a variety of external vital sign devices.

The MyHealthPoint Telehealth Manager is intended to be used by patients identified by their healthcare organization that would benefit from remote monitoring. It is not intended as a replacement of the oversight of healthcare professionals nor does it provide "real-time" or emergency monitoring. It does not measure, interpret or make any decisions on the data that it conveys.

The MyHealthPoint Telehealth Manager is a software platform accessible via PC or mobile smart phone that collects member's biometric data such as activity, blood pressure, blood glucose, ECG, body temperature, body composition/weight and pulse oximetry, and has the ability to send medication reminders. The MyHealthPoint TeleHealth Manager can be used by patients during their daily lives to collect biometric readings from various personal health monitors from a variety of manufacturers to assist in maintaining wellness regimens and the clinical monitoring of patients with chronic disease. The system can be accessed by members, clinicians and caregivers for analysis and intervention using standard digital communication technologies and protocols. The MyHealthPoint Telehealth Manager is intended for use in remote monitoring of patient biometrics and supports messaging between member's, clinicians and caregivers. In addition to monitoring, MyHealthPoint TeleHealth Manager supports reminders, alerts, and online graphical reports to help patients and their healthcare professionals better understand and manage their conditions.

The provided document is a 510(k) premarket notification for the MyHealthPoint TeleHealth Manager, a software platform. The document primarily focuses on demonstrating substantial equivalence to predicate devices rather than presenting a detailed clinical study with specific acceptance criteria and performance metrics for an AI algorithm.

Here's a breakdown of the requested information based on the provided text, with explicit notes where information is not available or not applicable to this type of submission:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: Not specified. The submission describes "extensive bench testing" and "user performance testing" but does not provide any details on the number of cases, data points, or users involved in these tests.
• Data Provenance: Not specified. The document does not mention the country of origin of any data, nor whether it was retrospective or prospective. Given the nature of a 510(k) for a software platform emphasizing substantial equivalence and verification of data transfer/storage, the focus is on functional testing rather than clinical data from a patient population.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Not Applicable / Not Specified. The submission does not detail any expert-derived ground truth or clinical interpretations. The "performance" assessment focuses on the software's ability to capture, transmit, and store data correctly, and the usability of the manual. It's a technical verification, not a clinical validation requiring expert ground truth.

4. Adjudication Method for the Test Set

• Not Applicable / Not Specified. No adjudication method is mentioned as there's no indication of a diagnostic or interpretive task requiring expert consensus for ground truth establishment.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No. An MRMC study was not done. The device is a data management and communication system, not an AI algorithm performing diagnostic interpretations that would typically be evaluated in an MRMC study. The document states it "does not measure, interpret or make any decisions on the data that it conveys."

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Partially Applicable / Not Explicitly Described as "Standalone Study". The "Verification testing that product meets product performance and functional specifications" and "Verification that biometric data submitted by personal home-use devices are captured (wirelessly or manually), transmitted and stored properly" essentially describe standalone testing of the software's core functionalities (data handling, transmission, storage). However, it's not structured as a typical "standalone algorithm performance study" for an AI diagnostic device, as the MyHealthPoint TeleHealth Manager itself is not an AI diagnostic algorithm.

7. Type of Ground Truth Used

• Functional/Technical Verification. The "ground truth" for the tests described would be based on expected software behavior and data integrity rather than clinical outcomes, pathology, or expert consensus on a medical diagnosis. For example, for data integrity, the ground truth would be that the transmitted data perfectly matches the source data. For functional specifications, the ground truth would be the defined expected behavior of the software.

8. Sample Size for the Training Set

• Not Applicable / Not Specified. The MyHealthPoint TeleHealth Manager is described as a software platform for data collection and display, not a machine learning or AI algorithm that requires a training set. There is no mention of an algorithm being trained on data.

9. How the Ground Truth for the Training Set Was Established

• Not Applicable / Not Specified. As there is no mention of a training set or an AI algorithm requiring training, the establishment of ground truth for such a set is not discussed.

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The Myglucohealth glucose monitoring system provides a quick and easy way for diabetic patients to measure and self-monitor blood glucose levels. The system is comprised of the MGH-BT1 (w/Bluetooth wireless download capability) or the MGH-1 (w/o Bluetooth) blood glucose meter, control solution and test strips that carry a biosensor used for the quantitative measurement of the concentration of glucose in capillary whole blood that can be taken from the fingertip, ventral palm, hand, upper arm, forearm, calf and/or thigh by diabetic patients or health care professionals. The results obtained are plasma calibrated to allow for easy comparison to the laboratory method. Further, results from either meter may be uploaded to a memory device through a standard RS32 connection, or, with the-BT1 model, wirelessly transmitted to a bluetooth capable PC or Cell phone. The Myglucohealth glucose monitoring systems are not to be used for the diagnosis or screening of diabetes or for neonatal use. Alternate site testing should be done during steady-state times when glucose is not changing rapidly.

Systems are intended for the quantitative measurement of the concentration of glucose in whole blood that can be taken from the fingertip, ventral palm, dorsal hand, upper arm, forearm, calf and/or thigh by diabetic patients or health care professionals. Results are plasma calibrated to allow for easy comparison to lab method. The Myglucohealth glucose monitoring systems are not to be used for the diagnosis of diabetes or for neonatal use. Alternate site testing should be done during steady-state times when glucose is not changing rapidly.

The provided document is a 510(k) premarket notification for the Myglucohealth Glucose Monitoring System. It focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed clinical study report with specific acceptance criteria and performance data in the format requested.

Therefore, the requested tables and specific details about acceptance criteria and study data (like sample sizes, ground truth establishment, expert qualifications, adjudication methods, or MRMC studies) are not fully contained within the provided text.

The document primarily states that the device is equivalent to its predicate by virtue of similar materials, manufacturing methods, and intended use, and validates its wireless data transfer.

However, I can extract information related to what is available and format it to the best of my ability, highlighting where information is missing.

1. Table of Acceptance Criteria and Reported Device Performance

The document states compliance with ISO 15197 for the device in general, and then specifies a "100% correlation to actual monitor data" for the wireless transfer function. ISO 15197 sets accuracy requirements for blood glucose monitoring systems. Without the full ISO 15197 report or a specific excerpt, the precise accuracy criteria and detailed performance (e.g., bias, precision, percentage within specific error margins) cannot be fully reported from this document.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Wireless Transfer Validation: "A significant number of users of varying demographic ages, gender, education and background were studied."
  • Specific number: Not specified in the document.
• Data Provenance: The document does not specify country of origin for the data, nor whether it was retrospective or prospective. It only mentions the "users" were studied, suggesting a prospective study for the wireless transfer validation. For the general device performance and ISO 15197 compliance, the document does not provide details on the study design or data provenance.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided in the document. The document focuses on the technical and regulatory aspects of device equivalence and wireless function validation, not on clinical study details involving experts for ground truth establishment.

4. Adjudication Method for the Test Set

This information is not provided in the document. Since there is no mention of independent expert review or a traditional "test set" in a clinical study sense (beyond the wireless transfer validation), an adjudication method is not described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not performed or mentioned. This type of study is typically for evaluating diagnostic imaging systems or decision support tools where human interpretation is a key component, often comparing human performance with and without AI assistance. The Myglucohealth Glucose Monitoring System is a handheld device for quantitative measurement, which does not involve human interpretation in the same manner.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The wireless data transfer validation could be considered a form of standalone performance assessment for that specific function (algorithm in this case refers to the transmission protocol). It evaluated the system's ability to transfer data accurately without human intervention in the data's content.

• Wireless Data Transfer: A standalone assessment was performed for the wireless uploading of data, demonstrating "100% correlation to actual monitor data."
• Glucose Measurement Performance: The document states compliance with ISO 15197. This standard inherently requires a standalone (device-only) performance evaluation against a reference method. However, the specific study details for this compliance are not provided in the document.

7. The Type of Ground Truth Used

• For Wireless Data Transfer Validation: The "actual monitor data" served as the ground truth. This means the data directly displayed on the device's screen was compared to the wirelessly transmitted data.
• For Glucose Measurement Performance: For ISO 15197 compliance, the ground truth would typically be established by a laboratory reference method (e.g., a highly accurate spectrophotometric method). This is not explicitly stated but is standard practice for such device validations. The document mentions "Results are plasma calibrated to allow for easy comparison to lab method," which supports this inference.

8. The Sample Size for the Training Set

This information is not provided in the document. Blood glucose monitoring systems typically do not involve "training sets" in the machine learning sense for their core measurement function. If firmware or algorithms were optimized, this process is not detailed. The "significant number of users" were likely for validation or verification, not a training set.

9. How the Ground Truth for the Training Set Was Established

This information is not provided in the document as a training set is not explicitly mentioned for the core glucose measurement function.

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