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The InPen dose calculator, a component of the InPen app, is indicated for the management of diabetes by people with diabetes age 12 and older by calculating an insulin dose or carbohydrate intake based on user entered data. The device is indicated for use with NovoLog® or Humalog® U-100 insulin.

For an insulin dose based on amount of carbohydrates, a healthcare provide patient-specific target blood glucose, insulin-to-carbohydrate ratio, and insulin sensitivity parameters to be programmed into the software prior to use.

For an insulin dose based on fixed/variable meal sizes, a healthcare professional must provide patient-specific fixed doses/ meal sizes to be programmed into the software prior to use.

The InPen app is designed to manage the wireless transfer of insulin dose data from the InPen, log insulin dose data, and provide a dose calculator to aid mealtime insulin dose calculations. The insulin dose calculations provided by the app are meant for patients undergoing multiple daily injection (MDI) therapy. The InPen app is not intended to serve as an accessory to an insulin pump.

The provided text describes the InPen Dose Calculator, outlining its indications for use and
comparing it to a predicate device. However, it does not contain specific acceptance criteria, a
detailed study that proves the device meets those criteria, or the requested specific performance
metrics like sensitivity, specificity, or accuracy.

Here's an attempt to answer the questions based only on the provided text, highlighting
where information is missing:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state numerical acceptance criteria or present a table of device
performance against such criteria. It generally states that the device "satisfies
all functional performance and safety requirements, meets its intended use, and is safe for the
intended user population."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data,
retrospective or prospective)

The text mentions a "summative evaluation" where "patients with sufficient diabetes knowledge
completed self-training and then completed a series of critical tasks." However, it does not
specify the sample size of this test set, the country of origin of the data, or whether it was
retrospective or prospective.

3. 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)

This information is not provided in the document. The "ground truth" for the dose calculations
would theoretically be the correct insulin dose based on provided parameters, but the process of
establishing this for the test set is not detailed.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not describe any adjudication method for the test set.

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

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study.
The InPen Dose Calculator is an algorithm to calculate insulin doses, not designed for human
"readers" to interpret medical images or data. Therefore, the concept of human readers improving
with AI assistance in this context does not apply in the manner typically associated with MRMC
studies.

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

The "summative evaluation" involved patients using the device, but the core function is an algorithm
calculating doses based on user input. The document states that the "dose calculator uses the
standard approach using healthcare provider specified insulin-to-carbohydrate ratio and insulin
sensitivity factors for making calculations." This implies a standalone algorithmic function based
on pre-programmed parameters. The "Clinical Evidence" section focuses on usability and safety with
human users, rather than solely on the algorithm's performance in isolation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

For the dose calculations, the "ground truth" is implied to be the standard approach using
healthcare provider specified insulin-to-carbohydrate ratio and insulin sensitivity factors for
making calculations. The text also mentions: "the calculator includes a consideration for insulin
on-board based on the published study by Mudaliar et al (1999) for the duration of insulin action."
This suggests calculations are validated against established medical formulas and literature.

8. The sample size for the training set

The document does not provide any information about a training set or its sample size. The device
appears to be a rule-based calculator rather than a machine learning model that would typically
require a training set.

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

As no training set is mentioned or implied for this rule-based dose calculator, this question is not
applicable based on the provided text.

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The InPen dose calculator, a component of the InPen app, is indicated for the management of diabetes by people with diabetes age 12 and older by calculating an insulin dose or carbohydrate intake based on user entered data. Prior to use, a healthcare professional must provide the patient-specific target blood glucose, insulin-to-carbohydrate ratio, and insulin sensitivity parameters to be programmed into the software.

The InPen app is designed to manage the wireless transfer of insulin dose data from the InPen, log insulin dose data, and provide a dose calculator to aid mealtime insulin dose calculations. The insulin dose calculations provided by the app are meant for patients undergoing multiple daily injection (MDI) therapy. The InPen app is not intended to serve as an accessory to an insulin pump.

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

This document primarily focuses on demonstrating substantial equivalence to a predicate device (K160629) rather than presenting a novel clinical study with quantitative performance metrics against specific acceptance criteria. Therefore, the "acceptance criteria" discussed below are inferred from the demonstrated equivalence and risk management, not explicit numerical thresholds.

Acceptance Criteria and Reported Device Performance

Given that this 510(k) is for demonstrating substantial equivalence to a previously cleared device (K160629) and not presenting new clinical performance data with explicit numerical acceptance criteria, the "acceptance criteria" are primarily established by the equivalence of the product's attributes and the successful completion of verification and validation activities. The reported "device performance" is therefore that it functions identically to the predicate device in its calculations and features, and that risks are mitigated.

Study Information

The submission details primarily focus on demonstrating substantial equivalence to a predicate device (InPen System K160629) and robust software verification and validation (V&V), rather than a traditional clinical study with a test set of patient cases.

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

   • No explicit "test set" of patient cases with clinical outcomes is mentioned in this summary for demonstrating diagnostic or predictive accuracy. The performance data section refers to "Software Verification and Validation Testing" and "Risk Analysis."
   • It is likely that comprehensive software testing (unit testing, integration testing, system testing) was performed on a variety of input scenarios, but the specific "sample size" of test cases for these software tests is not quantified in this summary.
   • Data provenance is not applicable in the context of a clinical test set from patient data, as no such study is described. The V&V activities would involve internally generated test data.

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

   • Not applicable as there is no mention of a clinical test set requiring expert ground truth establishment for diagnostic or predictive accuracy. The "ground truth" for the software's calculations would be the mathematically correct output based on the predefined algorithm, parameters, and input data.

3. Adjudication method for the test set:

   • Not applicable, as no clinical test set with expert adjudication is described.

4. 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 MRMC comparative effectiveness study is mentioned. The device is a "dose calculator" with specific instructions for healthcare professionals to program patient-specific parameters. It assists the patient in calculating doses based on these established parameters and user input, rather than augmenting human interpretation of complex medical images or data that would typically feature in an MRMC study.

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

   • The "Software Verification and Validation Testing" would essentially be a standalone evaluation of the algorithm's performance against its specifications, assuming various inputs. The summary states: "Companion Medical has demonstrated the InPen dose calculator is appropriate for its intended use through the use of hazard analysis according ISO 14971. The dose calculator uses the standard approach using healthcare provider specified insulin-to-carbohydrate ratio and insulin sensitivity factors for making calculations. In addition, the calculator includes a consideration for insulin on-board based on the published study by Mudaliar, et.al. (1999) for the duration of insulin action." This indicates the algorithm's core functionality was evaluated.

6. The type of ground truth used:

   • For the software's calculation accuracy: The ground truth would be the mathematically correct insulin dose or carbohydrate intake as determined by the predefined algorithms and formulas using the input parameters (target blood glucose, insulin-to-carbohydrate ratio, insulin sensitivity, insulin on-board, and user-entered data). This would be established by independent calculation or a trusted reference implementation of the algorithm.
   • For risk management and safety: Ground truth is implicitly established by adherence to standards like ISO 14971 and relevant FDA guidance documents.

7. The sample size for the training set:

   • Not applicable. This device is a rule-based dose calculator, not a machine learning model that typically requires a "training set" of data to learn from. Its "knowledge" is encoded within the algorithms and patient-specific parameters provided by a healthcare professional.

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

   • Not applicable, as there is no training set for this type of device. The algorithm's logic is based on established medical formulas and and a published study for insulin on-board (Mudaliar, et al., 1999).

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