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510(k) Data Aggregation
(87 days)
Control-IQ technology is intended for use with compatible integrated continuous glucose monitors (iCGM) and alternate controller enabled (ACE) pumps to automatically increase, decrease, and suspend delivery of basal insulin based on iCGM readings and predicted glucose values. It can also deliver correction boluses when the glucose value is predicted to exceed a predefined threshold.
Control-IQ technology is intended for the management of Type 1 diabetes mellitus in persons 2 years of age and greater.
Control IQ technology is intended for single patient use and requires a prescription.
Control-IQ technology (Control-IQ, the device) is a software-only device intended for the management of type 1 diabetes mellitus. The device controls insulin delivery from a compatible alternate controller enabled insulin pump (ACE pump) based on inputs provided by a compatible integrated continuous glucose monitor (iCGM) and inputs provided the user (e.g., carbohydrate intake, exercise, and sleep schedule). Control-IQ technology is meant to be installed on a compatible ACE pump.
Control-IQ Technology has three different modes: Normal, Sleep, and Exercise. The glucose targets are not customizable in these modes but can change based on the mode selected. During normal mode, Control-IQ Technology aims to control glucose within a target range of 112.5 – 160 mg/dL. During sleep mode, this range is changed to 112.5-120 mg/dL, and it is changed to 140-160 mg/dL during exercise mode.
Control-IQ technology includes an integrated feature whereby iCGM values are automatically populated into the glucose field of the integrated bolus calculator when the Control-IQ technology is active (i.e., the device is operating in closed-loop mode). This feature is disabled when Control-IQ is turned off.
Control-IQ technology requires users to input their total daily insulin requirement, which should be established with the help of a health care provider before using the device.
The provided text describes the acceptance criteria and the study conducted for the Tandem Diabetes Care Control-IQ Technology (K232382).
Here's the breakdown of the information requested:
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of specific numerical acceptance criteria for the Control-IQ technology. However, it states the primary outcome of the pivotal clinical study. The device's performance is reported in relation to this outcome.
| Acceptance Criteria (Implicit) | Reported Device Performance (Primary Outcome) |
|---|---|
| Percent time in range 70-180 mg/dL | This was the primary outcome of the study, and the study was successful enough to support the expanded age indication. Specific numerical values for the performance are not provided in this document, but the study conclusion supports the device's efficacy for the expanded age range. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 102 subjects were enrolled in the pivotal clinical study.
- Data Provenance: The document does not specify the country of origin. It indicates the study was a prospective, randomized controlled trial.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
This information is not provided in the document. The study's "ground truth" for glucose values would inherently come from the continuous glucose monitors (CGM) used by the subjects in the trial, but the involvement of independent experts to establish a "ground truth" for the test set is not mentioned.
4. Adjudication Method for the Test Set
This information is not provided in the document.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of Human Readers Improve With AI vs. Without AI Assistance
A MRMC comparative effectiveness study involving human readers and AI assistance was not done. This device is an automated glycemic controller, where the AI (Control-IQ technology) directly controls insulin delivery, rather than assisting human interpretation of data for medical decision making.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, the pivotal clinical study directly evaluated the performance of the Control-IQ technology (algorithm only without human-in-the-loop for basal insulin adjustments and correction boluses based on predictions). Subjects were randomized to either Control-IQ or Standard Care, directly comparing the automated system's performance.
7. The Type of Ground Truth Used
The ground truth for the primary outcome (percent time in range 70-180 mg/dL) was based on CGM measured glucose values.
8. The Sample Size for the Training Set
The document does not provide information on the sample size used for the training set of the Control-IQ algorithm. The clinical study described is a pivotal trial for evaluation of the device, not necessarily for its training.
9. How the Ground Truth for the Training Set Was Established
The document does not provide information on how the ground truth for the training set was established.
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(111 days)
Control-IQ technology is intended for use with compatible integrated continuous glucose monitors (iCGM) and alternate controller enabled (ACE) pumps to automatically increase, decrease, and suspend delivery of basal insulin based on iCGM readings and predicted glucose values. It can also deliver correction boluses when the glucose value is predicted to exceed a predefined threshold.
Control-IQ technology is intended for the management of Type 1 diabetes mellitus in persons 6 years of age and greater.
Control-IQ technology is intended for single patient use and requires a prescription.
Control-IO technology is indicated for use with NovoLog or Humalog U-100 insulin.
The Control-IQ technology, is an interoperable automated glycemic controller. This is a device intended to automatically calculate drug doses based on inputs such as glucose and other relevant physiological parameters, and to command the delivery of such drug doses from a connected infusion pump. Interoperable automated divcemic controllers are designed to reliably and securely communicate with digitally connected devices to allow drug delivery commands to be sent, received, executed, and confirmed. Interoperable automated glycemic controllers are intended to be used in conjunction with digitally connected devices for the purpose of maintaining qlycemic control.
The provided text is a 510(k) summary for Tandem Diabetes Care's Control-IQ technology. While it states that a clinical study was performed, it does not provide any specific acceptance criteria or detailed results of that study (e.g., specific metrics like mean glucose, time in range, or hypoglycemia rates). It only generically states that it "demonstrates that the device is safe and effective in the population evaluated (ages ≥ 6 and ≤ 13 years old)."
Therefore, I cannot fulfill all parts of your request with the given information. However, I can extract what is provided.
Here's a summary based on the provided text, highlighting the missing information:
Description of Acceptance Criteria and Proving Study
The provided 510(k) summary for the Control-IQ technology indicates that a clinical study was performed to assess the efficacy and safety of the device. However, the document does not explicitly state the specific acceptance criteria used for this study, nor does it provide detailed performance metrics or results from the study. It only makes a general statement about the device being safe and effective.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Not Explicitly Stated) | Reported Device Performance (Not Explicitly Stated) |
|---|---|
| Specific quantitative thresholds for glucose control, hypoglycemia, or other clinical outcomes were not provided in the document. | Specific quantitative results from the clinical study (e.g., mean HbA1c, time in range, standard deviation of glucose) were not provided in the document. |
| Safety endpoints (e.g., severe hypoglycemia, DKA rates) were not explicitly defined with thresholds. | General statement: "The study demonstrates that the device is safe and effective in the population evaluated (ages ≥ 6 and ≤ 13 years old)." |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: The document mentions a "randomized controlled trial" but does not specify the sample size of participants in this trial.
- Data Provenance: The document does not specify the country of origin of the data. It implies the study was prospective ("randomized controlled trial").
3. Number of Experts Used to Establish Ground Truth and Their Qualifications
- Not applicable for this type of device. The "ground truth" for glycemic control in a clinical study is typically established by objective physiological measurements (e.g., CGM readings, lab glucose values) rather than expert review of images or medical records.
4. Adjudication Method for the Test Set
- Not applicable/Not mentioned. Adjudication methods are typically used when there's subjective interpretation involved, such as in image-based diagnostics. For a glycemic control device, the primary "data" are objective glucose measurements.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- No. An MRMC study is not relevant for this type of device (automated glycemic controller). These studies are typically used for diagnostic devices that involve human interpretation of medical images or other data. This device is an automated system designed to manage glucose levels directly.
6. Standalone Performance Study
- Yes, implicitly. The clinical study performed on the Control-IQ technology assessed the device's performance (efficacy and safety) as an automated system. While it interacts with an iCGM and an ACE pump, the "Control-IQ technology" itself is the algorithm and software component being evaluated for its standalone ability to manage glucose. The study evaluated its performance, not how it assists a human to perform a task.
7. Type of Ground Truth Used
- The ground truth for evaluating an automated glycemic controller in a clinical study would be based on physiological measurements such as:
- Continuous Glucose Monitoring (CGM) data
- Laboratory-confirmed blood glucose values
- Clinical outcomes data (e.g., episodes of hypoglycemia/hyperglycemia, hospitalizations, DKA events).
- The document does not explicitly state the specific ground truth metrics used, but these are standard for such studies.
8. Sample Size for the Training Set
- Not provided. The document does not mention the sample size or details of any training set used for developing or training the Control-IQ algorithm. It only refers to a "Clinical study" for validation.
9. How the Ground Truth for the Training Set Was Established
- Not provided. Since details about a training set are not mentioned, how its ground truth was established is also not available in this document.
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(151 days)
Control-IQ technology is intended for use with compatible integrated continuous glucose monitors (iCGM) and alternate controller enabled (ACE) pumps to automatically increase, decrease, and suspend delivery of basal insulin based on iCGM readings and predicted glucose values. It can also deliver correction boluses when the glucose value is predicted to exceed a predefined threshold.
Control-IQ technology is intended for the management of Type 1 diabetes mellitus in persons 14 years of age and greater.
Control-IQ technology is intended for single patient use and requires a prescription.
Control-IQ technology is indicated for use with NovoLog or Humalog U-100 insulin.
Control-IQ technology (Control-IQ, the device) is a software-only device intended for use by people with diabetes. The device controls insulin delivery from a compatible alternate controller enabled insulin pump (ACE pump) based on inputs provided by a compatible integrated continuous glucose monitor (iCGM) and inputs provided the user (e.g., carbohydrate intake, exercise, and sleep schedule). Control-IQ technology is meant to be installed on a compatible ACE pump.
Control-IQ technology works to control glucose towards a glucose target range of 112.5-160 mg/dL during normal use. Glucose targets are not customizable but can be changed by a user if sleep or exercise modes are set or announced. During sleep mode, this range is changed to 112.5-120 mg/dL, and it is changed to 140-160 mg/dL during exercise mode.
Control-IQ technology includes an integrated feature whereby iCGM values are automatically populated into the glucose field of the integrated bolus calculator when the Control-IO technology is active (i.e., the device is operating in closed-loop mode). This feature is disabled when Control-IQ is turned off.
Using Control-IQ technology requires that users input their weight and their total daily insulin requirement, which should be established with the help of a health care provider before using the device.
Acceptance Criteria and Device Performance for Control-IQ Technology
1. Table of Acceptance Criteria and Reported Device Performance
The provided document does not explicitly state pre-defined acceptance criteria in terms of numerical thresholds for outcomes like HbA1c, Time in Range, or adverse event rates. Instead, the "Performance Characteristics" section reports the observed results of the pivotal study, which are then analyzed in the "Benefit/Risk Analysis" to generally conclude that the device's benefits outweigh its risks in light of the special controls.
However, based on the implicit goals of the study and the reported findings, we can infer some criteria that the device's performance needed to satisfy to be considered acceptable for De Novo authorization. These inferred criteria are focused on demonstrating improvements in glycemic control without an unacceptable increase in adverse events compared to the control arm (Sensor-augmented pump - SAP).
| Acceptance Criteria (Inferred) | Reported Device Performance (Control-IQ - CLC vs. SAP) |
|---|---|
| Efficacy: | |
| Improvement in Time in Range (70-180 mg/dL): Demonstrate a meaningful increase in the percentage of time users spend within the target glycemic range. | CLC: 71% ± 12% (post-randomization) |
| Reduction in HbA1c: Show a reduction in average HbA1c levels, indicating improved long-term glycemic control. | CLC: 7.06 ± 0.79 (post-randomization) |
| Reduction in Mean Glucose: Demonstrate a decrease in average glucose levels. | CLC: 156 ± 19 mg/dL (post-randomization) |
| Safety: | |
| Acceptably Low Rate of Severe Hypoglycemia: Ensure the device does not significantly increase the risk of severe hypoglycemic events. | CLC: 0 events (post-randomization) |
| Acceptably Low Rate of Diabetic Ketoacidosis (DKA): Ensure the device does not significantly increase the risk of DKA. | CLC: 1 DKA event (post-randomization) |
| Acceptable Rates of Hyperglycemia/Ketosis: Ensure that hyperglycemia leading to ketosis does not become unacceptably high with the device, especially distinguishing between device-induced events and reporting artifacts. | CLC: 12 Hyperglycemia with Ketosis events (post-randomization) |
| No Increase in Time Below Range (TBR <70 mg/dL and <54 mg/dL): Maintain or reduce the time spent in hypoglycemic ranges. | CLC: 1.58% ± 1.15% (<70 mg/dL), 0.29% ± 0.29% (<54 mg/dL) (post-randomization) |
| Safety of Auto-populating Bolus Calculator: Demonstrate that the auto-populated bolus calculator does not lead to an increased risk of hypoglycemia post-bolus, especially compared to manual adjustments. | For iCGM 70-180 mg/dL and 181-250 mg/dL, no significant difference in low CGM readings post-bolus between automatic and manual entry. For >250 mg/dL, a slightly higher rate of 5+ consecutive readings <70 mg/dL for automatic (13%) vs. manual (9%), mitigated by labeling changes. |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: The pivotal clinical study (randomization phase) included 168 participants, with 112 in the intervention arm (Control-IQ) and 56 in the control arm (Sensor-augmented pump - SAP). The study involved 6 months of follow-up for the primary study.
- Data Provenance: The study was a prospective, multicenter clinical trial conducted at Seven US clinical sites. The participants were diagnosed with Type 1 Diabetes and were aged 14 years and older.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
The document does not describe the use of experts to establish a "ground truth" for the test set in the traditional sense of diagnostic accuracy studies (e.g., radiologists interpreting images). Instead, the clinical study directly measured physiological outcomes (CGM readings, HbA1c, adverse events) from the participants. These are considered the objective "ground truth" data for assessing the device's performance in managing diabetes.
Healthcare providers were involved in training participants and optimizing pump settings, and monitoring participants for adverse events, but not for establishing a separate "ground truth" label for individual data points that the algorithm would then predict against.
4. Adjudication Method for the Test Set
As the test set involved direct physiological measurements and reported adverse events, a formal adjudication method like "2+1" or "3+1" (common in image-based diagnostic studies) is not applicable in the same way.
Adverse events were reported and summarized. The document mentions an evaluation of hyperglycemia/ketosis events:
- "Hyperglycemia / ketosis events not meeting the definition of DKA were reportable if they met one of the following criteria: evaluation or treatment was obtained at a health care provider facility... blood ketone level ≥1.0 mmol/L and communication occurred with a health care provider... blood ketone level ≥3.0 mmol/L, even if there was no communication with a health care provider."
- This indicates predefined criteria for event reporting rather than an external expert adjudication process of subjective assessments. The DKA event was noted as having an "infusion set failure" as its cause, implying a clinical assessment of the event's origin.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, an MRMC comparative effectiveness study was not explicitly conducted or mentioned. This type of study is more relevant for diagnostic devices where human readers interpret patient cases (e.g., images) with and without AI assistance. The Control-IQ device is an automated insulin delivery system, directly affecting physiological outcomes rather than assisting a human in making a diagnostic interpretation.
6. Standalone (Algorithm Only) Performance
Yes, the pivotal clinical study assessed the standalone performance of the Control-IQ technology. The intervention arm used "t:slim X2 with Control-IQ Technology and Dexcom G6 iCGM" (closed-loop control), representing the algorithm's performance without direct continuous human intervention in real-time insulin dosing decisions (though users still manually entered meal boluses and could adjust settings). The control arm (Sensor-augmented pump - SAP, with no automated insulin delivery) provided a baseline for comparison for human-managed delivery using similar sensor data.
The "Safety of CGM Auto-populating Bolus Calculator Feature" section also analyzes the performance when the iCGM values are "Automatic" (meaning auto-populated by the device) versus "Manual" (meaning patients manually adjusted the bolus calculation), providing insights into a more granular standalone feature compared to human override.
7. Type of Ground Truth Used
The ground truth used in the pivotal study was outcomes data directly measured from participants, including:
- Continuous Glucose Monitoring (CGM) readings (e.g., Time in Range, Time Below Range, Mean Glucose).
- HbA1c levels (a measure of average blood glucose over 2-3 months).
- Reported adverse events (severe hypoglycemia, DKA, hyperglycemia/ketosis).
- Surveys on quality of life aspects (diabetes-related distress and fear of hypoglycemia).
8. Sample Size for the Training Set
The document does not provide information on a separate "training set" sample size for the Control-IQ algorithm itself. Control-IQ is a software-only device containing a proprietary algorithm. Generally, such algorithms are developed and refined using a combination of preclinical modeling, simulations, and smaller-scale human studies, which would constitute the "training" or development sets. However, the exact size and nature of this development data are not detailed in this regulatory document, which focuses on the pivotal clinical validation study.
9. How the Ground Truth for the Training Set was Established
As with the training set sample size, the document does not describe how ground truth was established for any internal algorithm training set. For an automated insulin delivery algorithm, the ground truth for training would typically involve physiological responses to insulin in people with diabetes, potentially derived from historical patient data, controlled meal challenges, exercise protocols, and various other scenarios, often simulated or collected under tightly controlled observational studies. The goal is to build a model that accurately predicts glucose responses to insulin and other physiological factors.
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