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The IWL2020 Blood Glucose Monitoring System is comprised of the IWL2020 meter and IWL2020 test strips. The IWL2020 Blood Glucose Monitoring System is intended to quantitatively measure glucose in fresh capillary whole blood from the fingertip as an aid in monitoring the effectiveness of glucose control.

The IWL2020 Blood Glucose Monitoring System is intended for in vitro diagnostic single patient use by people with diabetes at home.

The IWL2020 Blood Glucose Monitoring System is intended to be used by a single person and should not be shared. This system is not for use in diagnosis or screening of diabetes mellitus, nor for neonatal use.

The IWL2020 Blood Glucose Monitoring System consists of the following components:

• IWL2020 Meter
• IWL2020 Blood Glucose Test Strips
• IWL2020 Control Solutions

The IWL2020 Blood Glucose Monitoring System is a handheld device that incorporates features to aid in self-monitoring of blood glucose. The blood glucose results are displayed on the screen and stored in the meter's memory, and may also be transmitted via Bluetooth Low Energy (BLE) wireless communication. Our blood glucose monitoring system creates a glucose result from an amperometric reaction. Capillary whole blood from the user's fingertip reacts with the chemicals in the test strip to create a harmless electrical current in the test strip. The blood glucose meter reads the current and gives a blood glucose result.

The provided text describes the acceptance criteria and a study to prove that the device (IWL2020 Blood Glucose Monitoring System) meets these criteria. The study focuses on demonstrating the substantial equivalence of the IWL2020 system to a legally marketed predicate device (Accu-Chek Guide Blood Glucose Monitoring System).

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text outlines performance metrics rather than explicitly listing acceptance criteria as a separate table. However, the "Clinical Performance" section presents accuracy targets which can be interpreted as acceptance criteria based on standard glucose monitoring device expectations.

Additional Non-Clinical Performance Criteria and Results (from "Non-Clinical & Clinical Testing Summary and Conclusions"):

• Measurement Range: 20-600 mg/dL (Supported by Linearity Evaluation)
• Precision (Within-Run Evaluation):
  • Glucose Level 1 (20 mg/dL): 1.1 mg/dL SD
  • Glucose Level 2 (40 mg/dL): 1.2 mg/dL SD
  • Glucose Level 3 (80 mg/dL): 1.8 mg/dL SD
  • Glucose Level 4 (130 mg/dL): 2.1 %CV
  • Glucose Level 5 (200 mg/dL): 2.3 %CV
  • Glucose Level 6 (325 mg/dL): 2.7 %CV
  • Glucose Level 7 (450 mg/dL): 2.6 %CV
  • Glucose Level 8 (550 mg/dL): 2.6 %CV
• Precision (Intermediate Precision Evaluation):
  • Glucose Level 1 (27.8 mg/dL): 1.3 mg/dL SD
  • Glucose Level 2 (44.4 mg/dL): 1.3 mg/dL SD
  • Glucose Level 3 (113.6 mg/dL): 2.6 %CV
  • Glucose Level 4 (291.9 mg/dL): 2.3 %CV
  • Glucose Level 5 (495.1 mg/dL): 2.0 %CV
  • Glucose Level 6 (541.4 mg/dL): 2.2 %CV
• Linearity (R-squared for All Lots): 0.9985 (indicating strong linearity across the measurement range)
• Interference: All compounds met acceptance criteria except abnormally high concentrations of ascorbic acid (> 5 mg/dL) and xylose.
• Flex Studies: System operated within specified ranges and displayed errors correctly when outside operating ranges.
• Electromagnetic Interference and Electrical Safety: Passed according to national and international standards.

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

• Sample Size for Clinical Performance Test Set: 350 results (implicitly, as 346/350, 336/350, etc., are reported).
• Data Provenance:
  • Country of Origin: United States.
  • Retrospective or Prospective: Prospective. The text states: "A clinical (user evaluation) study was conducted with IWL2020 Blood Glucose Monitoring System in the intended user population, i.e. lay persons who perform self-testing using capillary whole blood, in the United States." This implies a specifically designed study for evaluation.

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

• The text states the clinical performance was "compared to the Roche/Hitachi cobas c 501 PCA-HK reference method." It also mentions "blood glucose readings obtained by trained technicians."
• This suggests the ground truth was established by a laboratory reference method, operated by "trained technicians." The number and specific qualifications (e.g., years of experience) of these technicians, or the experts overseeing the laboratory method, are not explicitly stated.

4. Adjudication Method for the Test Set

• The text does not mention any adjudication method (e.g., 2+1, 3+1) for the clinical performance data. The comparison is directly between the device readings and the laboratory reference method readings.

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

• No, an MRMC comparative effectiveness study was not performed as described.
• The study was a user evaluation (clinical performance) to assess how well "non-professional, inexperienced lay persons" obtained accurate readings with the IWL2020 system compared to a laboratory reference. It does not compare human readers with AI assistance versus without AI assistance. The IWL2020 is a blood glucose monitoring system, not an AI-powered image analysis tool for diagnostic imaging, which is where MRMC studies are typically applied.

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

• The primary clinical performance study described is the user evaluation, which involves "lay persons who perform self-testing." This inherently includes human-in-the-loop performance.
• However, the "Non-Clinical & Clinical Testing Summary and Conclusions" section also includes various non-clinical evaluations (e.g., Within-Run Precision, Intermediate Precision, Linearity, Interference, Flex Studies, Electromagnetic Interference, Electrical Safety). These tests assess the device's technical performance in a controlled setting, which can be seen as standalone testing of the algorithm/device's core functionality, without user variability being the primary focus. Specifically, the "System Accuracy" refers to the accuracy of the device's readings when compared to a reference method, which is an assessment of the device's inherent capability.

7. Type of Ground Truth Used

• Laboratory Reference Method: The ground truth for the clinical performance study was established using the "Roche/Hitachi cobas c 501 PCA-HK reference method." This is a highly accurate laboratory instrument.

8. Sample Size for the Training Set

• The document describes testing and validation, but it does not explicitly state the sample size used for a training set. Blood glucose monitoring systems typically derive their algorithms from fundamental electrochemical principles and calibrations, rather than machine learning models that require distinct training sets in the same way as, for example, an AI imaging diagnostic tool. The "linearity evaluation" used blood samples from eleven glucose concentration levels run on 36 IWL2020 meters using strips from three lots, which would contribute to the device's calibration or "training" in a broad sense, but not in the context of a 'training set' for a deep learning model.

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

• As a training set is not explicitly mentioned in the context of a machine learning model, the method for establishing its ground truth is not detailed. For the device's fundamental calibration and performance, the "linearity evaluation" involved preparing blood samples at "eleven blood glucose concentration levels." These concentrations would typically be precisely measured by a highly accurate laboratory reference method (like the Roche/Hitachi cobas c 501 PCA-HK or similar primary standards) to serve as the ground truth for establishing the device's internal calibration curve.

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The ACCU-CHEK Connect Diabetes Management App is indicated as an aid in the treatment of diabetes. The software provides for electronic download of blood glucose meters, manual data entry, storage, display, transfer, and self-managing of blood glucose and other related health indicators which can be shown in report and graphical format.

The ACCU-CHEK Bolus Advisor, as a component of the ACCU-CHEK Connect Diabetes Management App, is indicated for the management of diabetes by calculating an insulin dose or carbohydrate intake based on user-entered data. Before its use, a physician or healthcare professional must activate the bolus calculator and provide the patient-specific target blood glucose. insulin-to-carbohydrate ratio, and insulin sensitivity parameters to be programmed into the software.

The ACCU-CHEK Connect Diabetes Management App is designed to facilitate efficient collecting, transmitting, and analyzing of blood glucose results and other diabetes management data. The App helps:

Wireless transfer of data from ACCU-CHEK Aviva Connect Blood Glucose Meter. Assist in general diabetes management through logging of contextual data. ACCU-CHEK Bolus Advisor support of mealtime insulin dosing calculations. Perform structured testing. Wireless transfer of data from mobile devices to ACCU-CHEK Connect Online Diabetes Management System and optionally share this data with healthcare provider (HCP) or caregiver.

The insulin bolus calculations provided by the app are meant for patients undergoing multiple daily injection therapy. Bolus calculators, such as the ACCU-CHEK Bolus Advisor, have been demonstrated to facilitate the optimization of glycemic control in patients who are trained in multiple daily insulin injection therapy and under the supervision of healthcare professional experienced in managing insulin-treated patients. Such calculators have also been shown to reduce patient fear of hypoglycemia and improve patient confidence in diabetes management.

The ACCU-CHEK Connect Diabetes Management App is not intended to serve as an accessory to an insulin pump.

The provided document describes a 510(k) premarket notification for the "ACCU-CHEK Connect Diabetes Management App" for iOS platform. The submission aims to demonstrate substantial equivalence to an existing predicate device, the Android OS version of the same app (K141929). The core of the argument for substantial equivalence relies on the fact that the bolus calculator algorithm and intended use have not changed, and the modifications are primarily related to adapting the app to the iOS operating system.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with numerical targets and corresponding performance metrics for the modified device. Instead, the acceptance is based on demonstrating that the iOS version performs equivalently to the already cleared Android version. The performance is assessed by confirming that the changes do not introduce new hazards or alter the core functionality.

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

• Test Set Sample Size: The document does not specify a numerical sample size for a "test set" in the traditional sense of a performance study with patient data.
  • For functional and performance requirements: "Design verification bench testing" was conducted, but no sample size for this testing is provided. The testing aimed to confirm that the device meets performance requirements, likely through technical validation.
  • For Human Factors: The human factors evaluation was an "expert evaluation" involving a comparison of screenshots and review of changes. It does not appear to be a study with user participants from a specific test set.
• Data Provenance: Not applicable in the context of this submission, as it largely focuses on the technical equivalence between two versions of the same software for different operating systems. The core bolus algorithm, if it was validated with patient data, would have been done for the predicate device.

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

• Human Factors: An "Human Factors expert" performed the evaluation. The exact number of experts (singular or plural often used generically) is not explicitly stated if it was more than one, nor are their specific qualifications (e.g., years of experience, specific certifications) detailed, beyond them being "Human Factors expert".
• Other Testing: The document does not describe the establishment of a "ground truth" for a test set in the context of clinical outcomes or diagnostic accuracy, as the device is a diabetes management app with an unchanged bolus calculator algorithm. The "ground truth" for the bolus calculation would have been established during the development and validation of the predicate device's algorithm, adhering to medical and physiological principles of insulin dosing.

4. Adjudication Method for the Test Set

Not applicable. There is no mention of an adjudication method, as the studies described (bench testing, expert human factors evaluation) do not involve subjective interpretation or a need for external consensus on a "ground truth" for clinical cases in this specific 510(k) submission.

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

No. An MRMC comparative effectiveness study was not done. This type of study is typically used for diagnostic imaging devices where human readers interpret medical images with and without AI assistance. The ACCU-CHEK Connect Diabetes Management App is a diabetes management software, not an imaging diagnostic device, and thus this methodology is not relevant to its validation as described here.

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

Yes, in essence, the predicate device's bolus calculator algorithm would have undergone standalone validation. The current submission explicitly states: "The insulin bolus calculator algorithm is unchanged as compared to the predicate device." This implies that the standalone performance of the algorithm itself was established during the predicate's clearance. The "Design verification bench testing" for the modified app would primarily verify that the implementation of this unchanged algorithm on the new platform correctly computes the same results.

7. The Type of Ground Truth Used

For the bolus calculator functionality (which is based on the unchanged algorithm from the predicate):

• The ground truth would be based on established medical and physiological principles for insulin dosing, as programmed into the algorithm's parameters (e.g., target blood glucose, insulin-to-carbohydrate ratio, insulin sensitivity, insulin action profiles). The accuracy of its calculations would be verified against these predefined parameters and typically, in a predicate device, against known physiological models or expert-derived reference calculations.

For the human factors evaluation:

• The "ground truth" was implicitly the established safety and usability of the predicate (Android) version, and the goal was to ensure the iOS version maintained this without introducing new hazards. The "expert evaluation" served as the primary method to assess this.

8. The Sample Size for the Training Set

The document does not mention a training set or its sample size. This submission is for a software modification (porting to a new OS) rather than the development of a new AI/ML algorithm that typically requires a large training set. The bolus calculator algorithm is based on predefined physiological equations and parameters, not on machine learning models trained on vast datasets.

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

Not applicable, as there is no mention of a training set for an AI/ML model in this submission. The bolus calculator algorithm's "ground truth" (i.e., its correctness) would have been established at the time of the predicate device's development through clinical and algorithmic validation against medical standards and physiological models, as it is a rule-based system, not a data-driven learning system.

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