Dexcom, Inc.

Systems and methods of use for continuous analyte measurement of a host's vascular system are provided. In some embodiments, a continuous glucose measurement system includes a vascular access device, a sensor and sensor electronics, the system being configured for insertion into communication with a host's circulatory system.

A continuous analyte monitoring system includes first and second analyte sensors configured to sense analytes such as lactate and glucose in the tissue of a user. A controller Is coupled to the analyte sensors and configured evaluate first samples of outputs of the first analyte sensor and second samples of outputs of the second analyte sensor with respect to one another to determine whether the first samples and the second samples indicate compression of the tissue. If the first samples and the second samples indicate compression of the tissue, compensate for the compression of the tissue with respect to the first samples. The controller may evaluate the machine learning models using a machine learning model or a filter.

Continuous Glucose Monitoring (CGM) devices provide glucose concentration measurements in the subcutaneous tissue with limited accuracy and precision. Therefore, CGM readings cannot be incorporated in a straightforward manner in outcome metrics of clinical trials e.g. aimed to assess new glycemic-regulation therapies. To define those outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting” algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the high accuracy of available BG references (possibly very sparsely collected) and the high temporal resolution of CGM data (usually noisy and affected by significant bias). The inputs of the algorithm are: a CGM time series; some reference BG measurements; a model of blood to interstitial glucose kinetics; and a model of the deterioration in time of sensor accuracy, together with (if available) a priori information (e.g. probabilistic distribution) on the parameters of the model. The algorithm first checks for the presence of possible artifacts or outliers on both CGM datastream and BG references, and then rescales the CGM time series by exploiting a retrospective calibration approach based on a regularized deconvolution method subject to the constraint of returning a profile laying within the confidence interval of the reference BG measurements. As output, the retrofitting algorithm produces an improved “retrofitted” quasi-continuous glucose concentration signal that is better (in terms of both accuracy and precision) than the CGM trace originally measured by the sensor. In clinical trials, the so-obtained retrofitted traces can be used to calculate solid outcome measures, avoiding the need of increasing the data collection burden at the patient level.

Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

The present invention relates generally to systems and methods for processing, transmitting, and displaying data received from continuous analyte sensor, such as a glucose sensor. In some embodiments, the continuous analyte sensor system comprises a sensor electronics module that includes power saving features. One feature includes a low power measurement circuit that can be switched between a measurement mode and a low power mode, in which charging circuitry continues to apply power to electrodes of a sensor during the low power mode. In addition, the sensor electronics module can be switched between in a low power storage mode higher power operational mode via a switch. The switch can include a reed switch or optical switch, for example. A validation routine can also be implemented to ensure an interrupt signal sent from the switch is valid. The continuous analyte sensor can be physically connected to a sensor electronics module, which is in direct wireless communication with a plurality of different display devices.

“Zero-click” viewing of sensor data without any user input is provided. A display with sensor data may be “always on,” and may enable discrete viewing of sensor data without significant user hassle. Also, a system may be configured to display only current data, and/or to display the most current data for only a set interval. Also, one device in a continuous analyte monitoring system may be designated as a primary device, or hub, for receiving sensor data, and may control the flow of information and/or alerts to other devices in the system. Sensor data and/or alerts may be sent to a hierarchy of devices and/or persons in a designated order.

Systems and methods are disclosed that employ several or numerous factors in the determination of a glycemic urgency index (GUI), which may be based on a measured blood glucose level as well as other factors. The other factors may include time derivatives of the glucose level and/or other factors, e.g., user-entered data, data measured by other sensors or received from a network source, or historical data. The GUI is then presented to the user in an interesting way, e.g., via a background color or other inconspicuous notifier, e.g., on a mobile device such as a smart phone. The GUI may also be employed in the triggering of actionable alerts and alarms on an electronic device for the user. The GUI, or another index calculated from combinations of the variables and parameters described, may further be employed to drive a medicament delivery device such as a pump.

An integrated system for the monitoring and treating diabetes is provided, including an integrated receiver/hand-held medicament injection pen, including electronics, for use with a continuous glucose sensor. In some embodiments, the receiver is configured to receive continuous glucose sensor data, to calculate a medicament therapy (e.g., via the integrated system electronics) and to automatically set a bolus dose of the integrated hand-held medicament injection pen, whereby the user can manually inject the bolus dose of medicament into the host. In some embodiments, the integrated receiver and hand-held medicament injection pen are integrally formed, while in other embodiments they are detachably connected and communicated via mutually engaging electrical contacts and/or via wireless communication.

Systems and methods for integrating a continuous glucose sensor, including a receiver, a medicament delivery device, a controller module, and optionally a single point glucose monitor are provided. Integration may be manual, semi-automated and/or fully automated.