Abstract: A method of operating an analyte device includes: receiving an analyte signal measured from an analyte sensor device having a sensor tail; generating adjusted analyte data based on the analyte signal, the generating the adjusted analyte data including reducing a background signal in the analyte signal in accordance with an offset signal; computing an analyte value based on the adjusted analyte data; and displaying the analyte value on a display device.

Abstract: A method of operating an analyte device includes: receiving an analyte signal measured from an analyte sensor device having a sensor tail; generating adjusted analyte data based on the analyte signal, the generating the adjusted analyte data including reducing a background signal in the analyte signal in accordance with an offset signal; computing an analyte value based on the adjusted analyte data; and displaying the analyte value on a display device.

Abstract: Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Abstract: Systems and methods for monitoring glucose variability in a subject are described. Data indicative of glucose levels of the subject is received from a sensor control device. A first glucose variability metric of the subject in a first time period is determined. The first glucose variability metric may be compared to a threshold. A first indicator is displayed if the first glucose variability metric does not exceed the threshold and a second indicator is displayed if the first glucose variability metric exceeds the threshold. Additional glucose variability metrics may be determined for subsequent time periods according to a rolling window, and the indicators may be displayed real time or in a report. The glucose variability metric may be a measure of variability compared to a baseline, a difference between a maximum and minimum glucose level, or time in or out of a target range.

Abstract: The present disclosure describes lactate-responsive sensors, sensing systems incorporating a lactate-responsive sensor, and methods of use thereof that would be beneficial for continuously monitoring lactate levels and determining lactate thresholds (both aerobic and anaerobic thresholds). The present disclosure also relates to an analyte sensor for continuously detecting glucose and lactate levels.

Abstract: The present disclosure describes lactate-responsive sensors having first and second lactate-responsive sensing areas, sensing systems incorporating the lactate-responsive sensor, and methods of using the same that for continuously monitoring lactate levels and determining variance between lactate concentrations derived from signals independently obtained from the first and second lactate-responsive areas.

Abstract: Disclosed herein are system, method, and computer program product embodiments for improving detection and treatment of patient conditions based on continuous analyte data. The disclosed techniques utilize analyte data, such as lactate, glucose, and creatinine, provided from a continuous analyte sensor to predict patient outcomes. The prediction may also take into account other medical information associated with the patient, such as patient vital signs and medical history. The disclosed system allows for early and non-invasive prediction of patient outcomes in various settings including a hospital setting, a home setting, disease (e.g., heart failure, sepsis) detection, and high risk surgery monitoring. The disclosed system also is configured to monitor patient conditions and generating alerts and/or notifications based on the predicted patient outcomes to provide preemptive treatment of patient conditions.

Abstract: Systems and methods for displaying metrics related to a user are described. Data indicative of glucose levels of the user is received. A first alert point for a potential glucose episode in a data set of time correlated glucose data, e.g., a glucose vs. time curve, is identified if the last received glucose data point satisfies at least one alert condition. A first potential local minimum in a first time period is identified. The first potential local minimum is confirmed as a first start point of a first glucose episode if the first potential local minimum satisfies at least one local minimum condition. An integrated area under the curve over time of a first portion of the graph beginning at the first start point of the first glucose episode to the first alert point is calculated. A first count value is assigned to the first portion.

Abstract: Embodiments described herein include a device and a non-transitory computer-readable medium. The device includes one or more processors, an analyte sensor, a communication module, and memories. The processors are configured to generate analyte data indicative of a monitored analyte level measured by the analyte sensor corresponding to a first time, generate analyte data indicative of the monitored analyte level measured by the analyte sensor corresponding to a second time, calculate a correction parameter based on the analyte data corresponding to the analyte data corresponding to the first time and analyte data corresponding to the second time, and perform a lag correction to obtain the monitored analyte level using at least the calculated correction parameter. The calculated correction parameter comprises a lag time determined from the analyte data. The performed lag correction comprises a linear correction model based on the calculated correction parameter.

Abstract: The present disclosure provides therapeutic compositions and methods for delivering a therapeutic agent in close proximity to an analyte sensor. In certain embodiments, the present disclosure provides analyte sensors including one or more therapeutic agents, e.g., covalently-bound therapeutic agents. In certain embodiments, the present disclosure further provides therapeutic releasing compositions and methods of delivering such therapeutic releasing compositions.

Abstract: A method of operating an analyte device includes: receiving an analyte signal measured from an analyte sensor device having a sensor tail; generating adjusted analyte data based on the analyte signal, the generating the adjusted analyte data including reducing a background signal in the analyte signal in accordance with an offset signal; computing an analyte value based on the adjusted analyte data; and displaying the analyte value on a display device.

Abstract: Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Abstract: Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Abstract: Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Abstract: Analyte sensor faults are detected. Datasets of glucose values sensor electronics are coupled to a glucose sensor in fluid contact with interstitial fluid under a skin surface. Baseline median glucose value and glucose variability values are computed, based on the first dataset. A baseline data point is stored. Evaluation median glucose value and variability are computed, based on the second dataset of glucose values. An evaluation data point is stored. A magnitude of a vector that extends between the baseline data point and the evaluation data point is computed. A component of the magnitude of the vector that is parallel to a hypoglycemia risk contour line is computed and compared to a predefined threshold value. An indication that a sensor fault has been detected if the component is greater than a threshold is displayed.

Abstract: Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Abstract: Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Abstract: Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Abstract: Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Abstract: Analyte sensor faults are detected. Datasets of glucose values sensor electronics are coupled to a glucose sensor in fluid contact with interstitial fluid under a skin surface. Baseline median glucose value and glucose variability values are computed, based on the first dataset. A baseline data point is stored. Evaluation median glucose value and variability are computed, based on the second dataset of glucose values. An evaluation data point is stored. A magnitude of a vector that extends between the baseline data point and the evaluation data point is computed. A component of the magnitude of the vector that is parallel to a hypoglycemia risk contour line is computed and compared to a predefined threshold value. An indication that a sensor fault has been detected if the component is greater than a threshold is displayed.