Devices and methods for measuring a concentration of a target analyte in a biological fluid in vivo are provided herein. In some examples, a device includes an indwelling sensor and sensor electronics. The sensor may include a substrate; a first electrode disposed on the substrate; an ionophore disposed on the substrate to selectively transport the target ion to or within the first electrode; and a second electrode disposed on the substrate. The sensor electronics is configured to generate a signal corresponding to an electromotive force which is at least partially based on a potential difference that is generated between the first electrode and the second electrode responsive to the ionophore transporting the target ion to the first electrode.
A61B 5/0245 - Measuring pulse rate or heart rate using sensing means generating electric signals
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
A61B 5/287 - Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS]
A61B 5/316 - Modalities, i.e. specific diagnostic methods
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G01N 27/333 - Ion-selective electrodes or membranes
A continuous (multi-)analyte sensor device is provided, comprising a (multi-)analyte sensor operably coupled to a signal transducer, the (multi-)analyte sensor comprising at least one membrane, the least one membrane comprising a least one first transducing element, and at least one of a mediator and/or a regenerative cofactor.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
3.
AUTOMATIC SUSPENSION AND RESUMPTION OF MEDICAMENT DELIVERY
Automatic suspension and resumption of medicament delivery is described. In one or more implementations, a request to suspend delivery of a medicament to a user is received. A medicament delivery system is controlled to suspend delivery of the medicament to the user, and to automatically resume medicament delivery to the user after a suspension time period. In one or more implementations, a medicament delivery system is controlled to suspend delivery of a medicament to a user during performance of an activity, and to automatically resume delivery of the medicament to the user after performance of the activity is completed. In one or more implementations, a medicament delivery system is controlled to suspend medicament delivery to a user at a first time based on a location of the user, and to resume medicament delivery to the user at a second time based on a subsequent location of the user.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
Location-aided glycemic control is described. A glycemic control system obtains sensor data is from one or more sensors, and detects a location of a user based on the sensor data. The glycemic control system predicts an activity that the user will perform at the location based on the location of the user, and generates a recommendation to control a glycemic response of the user to the predicted activity. In one or more implementations the glycemic control system causes display of the recommendation to control the glycemic response of the user. In one or more implementations the recommendation corresponds to administering an amount of a medicament to the user to control the glycemic response to the predicted activity, and the glycemic control system communicates instructions to a medicament delivery system which causes the medicament delivery system to administer the amount of the medicament to the user.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
A61M 5/172 - Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters electrical or electronic
5.
SYSTEMS AND METHODS FOR ENHANCED WIRELESS COMMUNICATIONS BETWEEN ANALYTE SENSOR SYSTEMS AND DISPLAY DEVICES
Techniques and protocols for enhancing wireless communications between an analyte sensor system and one or more other devices are described. Such techniques may include, for example, dynamic adjustment of transmission power when transmitting invitations, transmitting invitations with alternate (or different) payloads during different invitation periods, a reduction of handshake messages (c.g., client characteristic configuration descriptor (CCCD) messaging), etc. The various enhancements described herein may relate to various aspects of wireless communication protocols, including, for example, authentication, connection protocols, invitation message structure and content, device pairing, data transmission, etc.
H04L 67/12 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
The present examples relate generally to apparatuses, systems, and methods for deploying a medical device to skin of a host. The medical device may comprise a transcutaneous analyte sensor applied to the skin of a host. The apparatuses, systems, and methods may be for reducing friction between a sensor and an insertion element and/or for controlling sensor deployment.
Certain aspects provide a monitoring system comprising a continuous analyte sensor configured to generate analyte measurements associated with analyte levels of a patient, and a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/1477 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means non-invasive
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
Certain aspects of the present disclosure provide a monitoring system comprising a continuous analyte sensor configured to generate analyte measurements associated with analyte levels of a patient, and a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements.
G16H 40/60 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A61B 5/1477 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means non-invasive
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
9.
SYSTEMS AND METHODS FOR OPTIMIZING TREATMENT USING PHYSIOLOGICAL PROFILES
Certain aspects of the present disclosure provide a monitoring system comprising a continuous analyte sensor configured to generate analyte measurements associated with analyte levels of a patient, and a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/1477 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means non-invasive
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
Various examples are directed to systems and methods for measuring a parameter related to patient health. An analyte sensor system may detect that the analyte sensor system has been applied to a host and may store analyte data describing the host. The analyte sensor system may determine that sensor use at the analyte sensor system has terminated and upload stored analyte data to an upload computing device.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
11.
SYSTEM AND METHOD FOR TITRATING BASAL INSULIN DOSES
A continuous glucose monitor (CGM)-driven basal insulin titration system and method for patients with Type 2 Diabetes can be adapted to the needs and concerns of subjects just starting on basal insulin therapy. The method uses as inputs historical CGM data, basal insulin dose information, reports of hypoglycemia, and past recommendations and generates an adjusted insulin dose along with a report advising whether to continue the titration process, or to stop. The method can generate a new recommendation on a regular basis (e.g., each day) until it determines an adequate, consistent dose size.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
12.
PROXIMITY PAIRING AND SECURITY OF A CONTINUOUS ANALYTE SENSOR SYSTEM
Techniques and protocols for facilitating wireless secure communications between a sensor system and one or more other devices are disclosed. In certain embodiments, the techniques and protocols include secure proximity pairing techniques with reduced power. A method for pairing an analyte sensor system and one or more display devices includes broadcasting, from the analyte sensor system, for an initial connection, a low power general advertisement including an indication indicating the low power general advertisement is for proximity pairing. The method includes receiving, from a first display device, a connection request message in response to the low power general advertisement; performing an authentication procedure with the first display device; and pairing and bonding with the first display device based on successful authentication with the first display device.
Disclosed herein are systems and methods for a continuous analyte monitoring system for measuring a concentration of a first analyte and concentration of a second analyte in a host. One such system utilizes a working electrode and a reference electrode to measure glucose concentration and oxygen concentration in bodily fluid of a host. The system includes a sensor control circuit that applies a first bias condition to the sensor to measure a first signal generated by the sensor indicative of a concentration of the first analyte. The sensor control circuit also applies a second bias condition to the sensor to measure a second signal generated by the sensor indicative of a concentration of a second analyte at the host.
Various embodiments disclosed relate to analyte sensor configurations. The present disclosure can include planar analyte sensors or coaxial analyte sensors. The planar analyte sensors can include one or more insulating and conductive layers and a substrate layered on each other. The coaxial analyte sensors can include one or more co-extruded wire electrodes with a substrate. The continuous analyte monitoring systems discussed herein can be configured to monitor one or more analytes to provide predictive and real-time health data and benefits.
Certain aspects of the present disclosure relate to methods and systems for providing decision support around kidney disease. In certain aspects, a method includes monitoring one or more analytes of the patient during a plurality of time periods to obtain analyte data, the one or more analytes including at least potassium and the analyte data containing potassium data, processing the analyte data from the plurality of time periods to determine at least one rate of change of potassium for the patient based on the potassium data, and generating a disease prediction using the analyte data for the one or more analytes, including the potassium data and the at least one rate of change of potassium for the patient.
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
An apparatus includes an analyte sensor, a memory, and a processor. The processor monitors, using the analyte sensor, an analyte of a patient during a time period to obtain measured analyte data for the analyte and monitors other measured sensor data indicative of a physiological state of the patient during the time period. The processor also determines, based on the physiological state of the patient during the time period, expected analyte data for the analyte and determines a correction factor based on the expected analyte data and the measured analyte data. The correction factor is indicative of an error in calibration of the analyte sensor. The processor also determines whether recalibration of the analyte sensor is possible. If recalibration is possible, the processor recalibrates the analyte sensor based on the correction factor, and if recalibration is not possible, the processor recommends, to the patient, to replace the analyte sensor.
Certain aspects of the present disclosure relate to methods and systems for generating and utilizing analyte measurements. In certain aspects, a monitoring system comprises a continuous analyte sensor configured generate analyte measurements associated with analyte levels of a patient and a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/00 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
18.
SYSTEM AND METHOD FOR ACTIVATING AN ANALYTE MONITORING SYSTEM
Aspects of the present disclosure provide a power activation module for powering one or more wearable electronic components. The power activation module includes a switch configured to provide a path for current flow between a battery associated with the power activation module, the one or more wearable electronic components, and a ground terminal. The power activation module also includes a sensor configured to detect whether a signal is applied to the sensor and, based on the detection, output a first digital output signal for controlling, at least in part, the switch to control the current flow from the battery to the one or more wearable electronic components. The power activation module also includes a lock pin configured to receive a lock signal, wherein when the lock signal is received, the switch is locked to allow current flow from the battery to the one or more wearable electronic components.
Implementations relate generally to devices for measuring an analyte in a host. Implementations may provide reduced sizes for wearable devices including a transcutaneous analyte sensor for analyte measurement.
Systems, devices, and methods for data collection and development as well as providing user interaction policies are provided. In one embodiment, a method includes collecting contextual data for a first subset of a plurality of users. The method further includes generating a first set of contextual profiles for the first subset of the plurality of users based on the collected contextual data. Additionally, the method includes training one or more imputation models to develop the contextual data for the second subset of the plurality of users. The method also includes generating the contextual data for the second subset of the plurality of users using the one or more imputation models. Further, the method includes generating a second set of contextual profiles for the second subset of the plurality of users based on the generated contextual data for the second subset of the plurality of users.
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
Various examples are directed to systems and methods that may utilize an analyte sensor system comprising a sensor enclosure; an analyte sensor extending from the sensor enclosure; and sensor electronics positioned within the sensor enclosure. The sensor electronics may be configured to detect that a wireless signal has changed from a first state to a second state, where the wireless signal may be provided through the sensor enclosure. After detecting that the wireless signal has changed from the first state to the second state, the sensor electronics may monitor whether the wireless signal remains in the second state for at least a stability threshold time period. The sensor electronics may execute a responsive action in the sensor system based at least in part on whether the wireless signal remains in the second state for at least the stability threshold time period.
Techniques and protocols for facilitating wired or wireless secure communications between a sensor system and one or more other devices deployed in healthcare facilities are disclosed. In certain embodiments, the techniques and protocols include secure device pairing techniques and protocols for achieving heightened security, for example, recommended in healthcare facilities. In certain embodiments, a method comprises executing, at an application layer of a sensor system, a password authenticated key exchange (PAKE) protocol with a display device to derive an authentication key; executing, at the sensor system, an authenticated pairing protocol with the display device; after the authenticating is successful, establishing an encrypted connection between the sensor system and the display device; and transmitting, from the sensor system to the display device, analyte data indicative of measured analyte levels via the encrypted connection.
Certain aspects of the present disclosure relate to methods and systems for providing decision support around glucose management for patients with diabetes. Time-varying inputs including blood glucose, meal intake information, and amount of infused insulin are processed using a machine learning model to obtain predicted glucose levels for a plurality of prediction horizons and uncertainties for the predictions. A confidence interval is generated for each prediction and the confidence intervals are compared to hypo- and hyperglycemic thresholds. If a confidence interval is entirely below or entirely above the hypo- and hyperglycemic thresholds, respectively, then a decision support output is provided.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
25.
SENSING SYSTEMS AND METHODS FOR PROVIDING DIABETES DECISION SUPPORT USING CONTINUOUSLY MONITORED ANALYTE DATA
Certain aspects of the present disclosure relate to methods and systems for predicting glycemic events in a patient induced as a result of physical activity. In certain aspects, a method includes monitoring a plurality of analytes of the patient continuously during a time period to obtain analyte data, the plurality of analytes including at least glucose and lactate. The method further includes processing the analyte data from the time period to determine an intensity level of physical activity engaged by the patient during the time period. The method further includes generating a glycemic event prediction using at least the analyte data for the plurality of analytes and the determination of physical activity intensity. The method further includes generating one or more recommendations for treatment for the patient based, at least in part, on the glycemic event prediction.
Glucose monitoring over phases and corresponding phased information display is described. A multi-phase glucose monitoring program that includes at least a first phase and a second phase is initiated. First glucose data of a user is obtained during the first phase of the multi-phase glucose monitoring program. The output of the first glucose data in a glucose monitoring user interface is prevented during the first phase of the multi-phase glucose monitoring program. Second glucose data of the user is then obtained during a second phase of the multi-phase glucose monitoring program. The second glucose data is output, in real-time, in the glucose monitoring user interface during the second phase of the multi-phase glucose monitoring program.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
G06F 9/451 - Execution arrangements for user interfaces
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
Glucose level measurements of a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements can be produced substantially continuously, such that the device may be configured to produce the glucose level measurements at regular or irregular intervals of time, responsive to establishing a communicative coupling with a different device, and so forth. These glucose level measurements are analyzed to detect deviations from past glucose measurements, such as glucose measurements received earlier in the day or glucose measurements received at corresponding times of one or more preceding days. Indications of detected deviations are provided to the user or communicated elsewhere, such as to a healthcare professional.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
28.
GLYCEMIC IMPACT PREDICTION FOR IMPROVING DIABETES MANAGEMENT
Glucose level measurements and additional data regarding a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. This additional data identifies events or conditions that may affect glucose of the user, such as physical activity engaged in by the user. A glucose prediction system analyzes, for example, activity data of the user and determines when a bout of physical activity occurs. The glucose prediction system predicts what the glucose measurements of the user would have been had the physical activity not occurred, and takes various actions based on the predicted glucose measurements (e.g., provides feedback to the user indicating what their glucose would have been had they not engaged in the physical activity).
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
29.
RANKING FEEDBACK FOR IMPROVING DIABETES MANAGEMENT
Feedback regarding diabetes management by a user is generated, such as feedback identifying improvements in glucose measurements for a given time period over previous days, feedback identifying sustained positive patterns, feedback identifying deviations in glucose measurements between time periods, feedback identifying potential behavior modification that a user could take to engage in beneficial diabetes management behavior, feedback identifying what a user's glucose would have been had the particular events or conditions not occurred or not been present, and so forth. A feedback presentation system analyzes the identified feedback and selects feedback based on various rankings, rules and conditions for display to the user. The selected feedback is provided to the user at various times, such as regular reports (e.g., daily or weekly reports), in real time (e.g., notifying the user what his glucose level would have been had he not just taken a walk), and so forth.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
30.
DISEASE PREDICTION USING ANALYTE MEASUREMENT FEATURES AND MACHINE LEARNING
Disease prediction using analyte measurements and machine learning is described. In one or more implementations, a combination of features of analyte measurements may be selected from a plurality of features of the analyte measurements based on a robustness metric and a performance metric of the combination, and a machine learning model may be trained to predict a health condition classification using the combination. The performance metric may be associated with an accuracy of predicting the health condition classification, and the robustness metric may be associated with an insensitivity to analyte sensor manufacturing variabilities on the accuracy. Once trained, the machine learning model predicts the health condition classification for a user based on analyte measurements of the user collected by a wearable analyte monitoring device. The combination of features may be extracted from the analyte measurements of the user and input into the machine learning model to predict the classification.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
Glucose level measurements or other data regarding a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements or other data are analyzed based on various rules to determine time periods during a day of, for example, good diabetes management by the user and provide feedback indicating such to the user. Good diabetes management is identified in various manners, such as by identifying improvements in glucose measurements for a given time period over one or more previous days, identifying a time period of the day during which glucose measurements were the best, identifying sustained positive patterns (e.g., good diabetes management for a same time period in each of multiple days), and so forth.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
32.
BEHAVIOR MODIFICATION FEEDBACK FOR IMPROVING DIABETES MANAGEMENT
Glucose measurements are received and features for corresponding time periods over a time window are generated, the features being values indicating whether the user has been engaging in beneficial diabetes management behaviors. Using the aggregated features patterns indicating that beneficial diabetes management behaviors are not being engaged in are identified. Potential behavior modification feedback is generated by including in the potential behavior modification feedback at least one behavior modification feedback, for each of the identified patterns, that a user could take to engage in beneficial diabetes management behavior. At least one of the potential behavior modification feedback is selected and displayed or otherwise presented to the user.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
33.
PROXIMITY-BASED DATA ACCESS AUTHENTICATION AND AUTHORIZATION IN AN ANALYTE MONITORING SYSTEM
Methods and apparatus are provided for securely obtaining access to patient data associated with a patient using a sensor system configured for monitoring analyte levels of a patient. In one aspect, a method includes receiving, at a display device, one or more communications from the sensor system, wherein the one or more communications include identifiable information associated with the sensor system and are transmitted by the sensor system via an advertisement channel; inserting, at the display device, the identifiable information in a web request; providing, at the display device, the web request including the identifiable information to a data management system to request access to the patient data; and obtaining access to the patient data through a web browser upon the data management system verifying that the identifiable information matches a second identifiable information stored in the patient data.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
34.
APPARATUSES, SYSTEMS, AND METHODS OF IMPROVING PATCH PERFORMANCE FOR A MEDICAL DEVICE
The present embodiments relate generally to apparatuses, systems, and methods for deploying a medical device to skin of a host. The apparatuses, systems, and methods may be directed to removing a liner for a medical device so that the medical device may couple to the skin of the host. The medical device may comprise an on-skin wearable medical device.
The present disclosure relates generally to bioactive releasing membranes utilized with implantable devices, such as devices for the detection of analyte concentrations in a biological sample. More particularly, the disclosure relates to novel bioactive releasing membranes, to devices and implantable devices including these membranes, methods for forming the bioactive releasing membranes on or around the implantable devices, and to methods for monitoring analyte levels in a biological fluid sample using an implantable analyte detection device.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1459 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters invasive, e.g. introduced into the body by a catheter
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
Certain aspects of the present disclosure relate to methods and systems for optimized delivery of communications including content to users of a software application. The method also includes obtaining, by a customer engagement platform (CEP), a set of cohort selection criteria for identifying a user cohort to deliver the content; identifying, by a data analytics platform (DAP), the user cohort to communicate with in accordance with the set of cohort selection criteria; identifying, by the DAP, one or more communication configurations for communicating with one or more sub-groups within the user cohort; and to each user of the user cohort, transmitting one or more communications based on the content and a corresponding communication configuration for a sub-group that may include the corresponding user; and measuring engagement outcomes associated with usage of the corresponding one or more communication configurations in communication with each of the sub-groups.
An augmented analyte monitoring system is described. The augmented analyte monitoring system includes a wearable analyte monitoring device that includes a transmitter and an analyte sensor to obtain analyte data of a user, and an analyte augmentation wearable that includes one or more sensors (e.g., physical and/or biochemical sensors) to obtain additional physiological data for augmenting the analyte data of the user. The analyte augmentation wearable is communicably coupled to the wearable analyte monitoring device. The augmented analyte monitoring system further includes a sensor hub implemented at a computing device to obtain a data packet containing both the analyte data and the additional physiological data from at least one of the wearable analyte monitoring device or the analyte augmentation wearable, and augment the analyte data by storing the analyte data in association with the additional physiological data.
Certain aspects of the present disclosure relate to methods and systems for technical support of continuous analyte monitoring and sensor systems. In certain aspects, a method includes sensing, by an analyte sensor, analyte levels of a patient to generate one or more sensed signals. The method further includes generating, by a transmitter, a plurality of event indications based on the one or more sensed signals. The method further includes transmitting, by the transmitter, the plurality of event indications to a processor. The method also includes receiving the plurality of event indications indicating one or more errors associated with the analyte sensor. The method further includes determining one or more root causes associated with the plurality of event indications based on a pattern associated with the plurality of event indications. The method also includes taking one or more actions to resolve the one or more root causes.
Certain aspects of the present disclosure relate to electronically sharing patient data. One aspect includes a method comprising capturing a computer readable code comprising an authentication code and clinic identifying information using an image capture component of a patient mobile device. The method also comprises authenticating the identified clinic with an information provider. The method further comprises displaying the clinic identifying information for confirmation and authenticating the patient. The method additionally comprises receiving a request to provide the clinic with patient data from the clinic. The method then comprises determining that the clinic is authorized to receive access to the patient data based on authentication of the clinic, authentication of the patient, and confirmation to share the patient data. The method also comprises transmitting the patient data to the clinic based on the determination that the clinic is authorized.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
Certain aspects of the present disclosure relate to methods of updating patient scheduling information. In one aspect, the method includes receiving patient data for a patient having a scheduled appointment on a future date, the patient data including a metric value for a biomarker and time and date information associated with the scheduled appointment. The method further includes comparing the metric value with one or more conditions established based at least in part on a patient history of the patient or population health data. The method also includes, after determining that the metric value satisfies at least one of the one or more conditions, rescheduling the scheduled appointment.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
41.
USING CONTINUOUS BIOMETRIC INFORMATION MONITORING FOR SECURITY
Measurements of biometric information of a user are obtained over time, such as blood glucose measurements. These biometric measurements are typically obtained by a wearable biometric information monitoring device being worn by the user. These biometric measurements are used by various different systems, such as a computing device of the user or a biometric information monitoring platform that receives biometric measurements from multiple different users. The biometric measurements are used for various security aspects, such as one or more of part of multi-factor authentication of the user, generating security keys (e.g., connection keys, encryption keys), identifying biometric measurements associated with different user identifiers but the same use, and protecting biometric measurements so as to be retrievable only by a recipient associated with an additional computing device, and so forth.
Data-stream bridging for sensor transitions is described. A first data stream of glucose measurements is received from a first glucose sensor worn by a user. A termination event for the first glucose sensor is detected when production and/or communication of the first glucose measurements via the first data stream ceases. Next, a second data stream of glucose measurements is received from a second glucose sensor worn by the user that replaces the first glucose sensor. During a warmup period for the second glucose sensor, estimated glucose values are output for the user based on both the first data stream of glucose measurements received from the first glucose sensor prior to the termination event and the second data stream of glucose measurements received from the second glucose sensor.
In implementations of systems for determining a similarity of sequences of glucose values, a computing device implements a similarity system to receive input data describing a sequence of user glucose values measured by a continuous glucose monitoring (CGM) system. The similarity system computes similarity scores for a plurality of sequences of glucose values by comparing each glucose values included in the sequence of user glucose values with ever glucose value included in each sequence of the plurality of sequences. A particular sequence of glucose values that is associated with a highest similarity score is identified. The similarity system determines an externality associated with the particular sequence. The similarity system generates an indication of the externality for display in a user interface.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
44.
ADAPTIVE SYSTEMS FOR CONTINUOUS GLUCOSE MONITORING
In implementations of adaptive systems for continuous glucose monitoring (CGM), a computing device implements an adaptive system to receive glucose data describing user glucose values measured by a sensor of a CGM system, the sensor is inserted at an insertion site. The adaptive system accesses orientation data describing forces measured by an accelerometer of the CGM system, and the adaptive system identifies a location of the insertion site based on the orientation data. Modified glucose data is generated by modifying the user glucose values based on the location of the insertion site. The adaptive system generates an indication of the modified glucose data for display in a user interface of a display device.
G16H 40/40 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G06F 3/0346 - Pointing devices displaced or positioned by the user; Accessories therefor with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
Certain aspects of the present disclosure relate generally to configuring applications used in conjunction with medical devices in order to help with monitoring and improving the patient's health. Certain aspects include a method including receiving a request for assets including access information associated with a user of a computing device, the access information including feature customization information for a health intervention application and being issued by an account management service for the health intervention application, and validating that the access information is valid. The method may also include responsive to the validating, generating configuration information identifying a set of assets with which the health intervention application is to be provisioned based, at least in part, on the feature customization information included in the access information, and transmitting the configuration information to the health intervention application to configure the health intervention application with the identified set of assets.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
46.
FILTERING OF CONTINUOUS GLUCOSE MONITOR (CGM) SIGNALS WITH A KALMAN FILTER
In accordance with a system and/or method for monitoring an analyte concentration, a sensor signal indicative of an analyte concentration in a host may be received from an analyte sensor. The sensor signal may be filtered using a Kalman filter having process noise with a process covariance and measurement noise with a measurement covariance. The filtering may include updating a value associated with at least one of the process covariance and the measurement covariance using a value associated with one or more parameters employed in a model of the Kalman filter. A filtered sensor signal representative of the analyte concentration in the host may be output from the Kalman filter.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
47.
PERSONALIZED MODELING OF BLOOD GLUCOSE CONCENTRATION IMPACTED BY INDIVIDUALIZED SENSOR CHARACTERISTICS AND INDIVIDUALIZED PHYSIOLOGICAL CHARACTERISTICS
A method for providing clinical data representative of a concentration of a blood analyte in a patient includes receiving a signal from a continuous analyte sensor located within interstitial fluid of the patient and independently modeling two or more factors that influence the signal, the factors arising from individualized characteristics of the sensor and/or individualized physiological characteristics of the patient.
The present disclosure relates generally to drug releasing membranes utilized with implantable devices, such as devices for the detection of analyte concentrations in a biological sample. More particularly, the disclosure relates to novel drug releasing membranes, to devices and implantable devices including these membranes, methods for forming the drug releasing membranes on or around the implantable devices, and to methods for monitoring analyte levels in a biological fluid sample using an implantable analyte detection device.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1459 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters invasive, e.g. introduced into the body by a catheter
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
Certain aspects of the present disclosure provide techniques for processing and presenting analyte data. Some example aspects may describe techniques for generating and providing a user interface view of a user's performance report for display. Some example aspects may describe techniques for providing one or more user interface views for display on one or more widgets.
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
50.
NETWORK PHYSICAL LAYER CONFIGURATIONS FOR AMBULATORY PHYSIOLOGICAL PARAMETER MONITORING AND THERAPEUTIC INTERVENTION SYSTEMS
Certain embodiments herein relate to a physiological parameter monitoring system. The system may include a sensor and sensor electronics connectable to the sensor. The system may also include a transmitter operably connected to the sensor electronics, the transmitter having or being configured to have at least a portion thereof positioned at a first location adjacent to and/or in contact with an external surface of a body of a host during a sensor session, the transmitter further configured to wirelessly transmit sensor information using human body communication. The system may further include a first display device comprising a display and a receiver, the receiver having or being configured to have at least a portion thereof positioned at a second location adjacent to and/or in contact with the external surface of the body during the sensor session, the receiver further configured to receive sensor information from the transmitter using human body communication.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
51.
SYSTEMS AND METHODS FOR RISK BASED INSULIN DELIVERY CONVERSION
Systems and methods are provided for managing hyperglycemia and hypoglycemia by reconciling incoming data to provide safe and reliable control to range using automatic bolus determination wherein the rate of insulin delivery is dependent on the level of hyperglycemic risk or hypoglycemic risk. Additionally, some implementations are directed to converting insulin delivery into a rate based on glycemic risk.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
52.
BAYESIAN FRAMEWORK FOR PERSONALIZED MODEL IDENTIFICATION AND PREDICTION OF FUTURE BLOOD GLUCOSE IN TYPE 1 DIABETES USING EASILY ACCESSIBLE PATIENT DATA
A method of predicting future blood glucose concentrations of an individual patient includes: selecting an individualized nonlinear physiological model of glucose-insulin dynamics, the selected model having a plurality of model parameters whose values are to be determined; estimating values for each of the model parameters in the plurality of model parameters, a first subset of the model parameters having values estimated from a priori population data and a second subset of the model parameters having values personalized for the individual patient by applying a parameter estimation technique to a priori information and data for the individual patient to obtain a posteriori information; and; applying a nonlinear prediction technique to the selected model using the estimated values for each of the model parameters to obtain a predicted blood glucose concentration of the individual patient at a future time.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61M 5/172 - Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters electrical or electronic
A61M 37/00 - Other apparatus for introducing media into the body; Percutany, i.e. introducing medicines into the body by diffusion through the skin
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
53.
REUSABLE APPLICATORS FOR TRANSCUTANEOUS ANALYTE SENSORS, AND ASSOCIATED METHODS
The present embodiments relate generally to systems and methods for measuring an analyte in a host. More particularly, the present embodiments provide sensor applicators and methods of use to insert the sensor into an individual's skin. Applicators are disclosed for inserting the sensor. Such applicators may be reusable applicators configured to implant multiple different sensors.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
User interfaces for glucose insight presentation are leveraged. A glucose monitoring application is configured to process glucose measurements to determine one or more glucose insights, e.g., about a user's glucose. The glucose measurements, for example, may be obtained from a glucose monitoring device that collects glucose measurements of the user at predetermined intervals, e.g., every five minutes. The glucose monitoring application configures a user interface, based on configuration data, to present one or more visual elements representative of the one or more glucose insights. For example, the glucose monitoring application may configure the user interface to include a visual element in the form of a color field which represents whether the user's current glucose measurement (e.g., the most recent glucose measurement obtained from the glucose monitoring device) is below, within, or above a glucose range.
Meal and activity logging with a glucose monitoring interface is described. A glucose monitoring application is configured to display a user interface that includes a glucose graph that plots glucose measurements of a user over time. The glucose measurements, for example, may be obtained from a glucose monitoring device that collects glucose measurements of the user at predetermined intervals, e.g., every five minutes. Unlike conventional event logging approaches, the glucose monitoring application displays representations of logged events in the user interface along with the glucose graph. The logged events, for example, may include meals consumed by the user, and/or various activities performed by the user, such as exercise, meditation, sleep, and so forth. Notably, the glucose monitoring application controls the display of the event representations to be presented at positions on the glucose graph that correspond to times associated with the respective events.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
A method of predicting future blood glucose concentrations of an individual patient includes: identifying an individualized linear black box model of glucose-insulin by estimating a plurality of impulse response functions each accounting for an input-output relation of a plurality of individualized patient data sets, the impulse response functions being functions in a Reproducing Kernel Hilbert Space (RKHS); and applying a linear predicting technique to the selected model using the identified impulse response functions to obtain a predicted blood glucose concentration of the individual patient at a future time.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
57.
BOLUS ADVISOR WITH CORRECTION BOLUSES BASED ON RISK, CARB-FREE BOLUS RECOMMENDER, AND MEAL ACKNOWLEDGEMENT
Basal insulin recommendations and bolus recommendations are provided by analyzing profiles of blood glucose risk to determine whether basal or bolus amounts should be increased or decreased in consideration of the ratio of basal insulin vs. bolus insulin as a portion of total daily insulin. In some embodiments, systems and methods seek to correct systematic imbalances between rapid acting bolus and daily basal utilizing physiological cloning, which models patient diabetes data resulting from patient physiology and behavior (lifestyle and diet). In some embodiments, the systems and methods use constraints on percentage of total daily insulin attributed to basal and/or bolus. In some embodiments, optimization is performed without using patient-provided carbohydrate information.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
58.
DETECTION OF ANOMALOUS COMPUTING ENVIRONMENT BEHAVIOR USING GLUCOSE
Detection of anomalous computing environment behavior using glucose is described. An anomaly detection system receives glucose measurements and event records during a first time period. Missing events that are missing from the event records during the first time period are identified by processing the glucose measurements using an event engine simulator. An anomaly detection model is generated based on the missing events during the first time period. Subsequently, the anomaly detection system receives additional glucose measurements and additional event records during a second time period. Missing events that are missing from the additional event records during the second time period are identified by processing the additional glucose measurements using the event engine simulator. Anomalous behavior is detected if the identified missing events that are missing from the event records during the second time period are outside a predicted range of missing events of the anomaly detection model.
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
59.
MEDICAMENT INJECTION PEN FOR DISTINGUISHING BETWEEN PRIMING PEN EVENTS AND THERAPEUTIC PEN EVENTS
This application relates to a medicament delivery device such as medicament injection pen that can distinguish between a priming dosage and the injection of therapeutic dosage into a patient. In one aspect, the medicament injection device includes a housing having a chamber configured to contain a cartridge of medicament, and a dose setting and dispensing mechanism configured to set and dispense a dose of the medicament from the cartridge. The device may also include a logging module configured to detect and record as a pen event a dispensed volume of a medicament dose and a time when the medicament dose is dispensed. The device may further include a dose distinguisher configured to distinguish between pen events associated with priming doses and pen events associated with therapeutic doses based at least in part on historical user data identifying pen events as a therapeutic pen event or a priming pen event.
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
Data describing glucose measurements is received from a continuous glucose monitoring (CGM) system worn by a user and predicted glucose values during a future time period are generated for the user based on the data. A determination is made that at least one of the predicted glucose values satisfies a threshold value for an alert, which is associated with a prediction horizon that defines an amount of time prior to satisfaction of the threshold value for communicating the alert to the user. Output of the alert is caused responsive to determining that the at least one predicted glucose value satisfies the threshold value for the alert within the prediction horizon, relative to a current time. The prediction horizon is modified based on a user response to the alert. Output of a subsequent instance of the alert is caused based on the modified prediction horizon.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
61.
POPULATION MALADY IDENTIFICATION WITH A WEARABLE GLUCOSE MONITORING DEVICE
Population malady identification with a wearable glucose monitoring device is described. A malady identification system obtains temperature measurements that are produced by wearable glucose monitoring devices worn by users of a user population. The malady identification system further obtains location data describing locations of the users and associates each of the temperature measurements with a respective location. The malady identification system utilizes identification logic (e.g., one or more machine learning models) to identify presence of a malady in the users at one or more of the locations based on the temperature measurements and the location data. The malady identification system generates a communication for notifying at least one of the users about the presence of the malady.
Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.
Glucose measurement and glucose-impacting event prediction using a stack of machine learning models is described. A CGM platform includes stacked machine learning models, such that an output generated by one of the machine learning models can be provided as input to another one of the machine learning models. The multiple machine learning models include at least one model trained to generate a glucose measurement prediction and another model trained to generate an event prediction, for an upcoming time interval. Each of the stacked machine learning models is configured to generate its respective output when provided as input at least one of glucose measurements provided by a CGM system worn by the user or additional data describing user behavior or other aspects that impact a person's glucose in the future. Predictions may then be output, such as via communication and/or display of a notification about the corresponding prediction.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
64.
GLUCOSE PREDICTION USING MACHINE LEARNING AND TIME SERIES GLUCOSE MEASUREMENTS
Glucose prediction using machine learning (ML) and time series glucose measurements is described. Given the number of people that wear glucose monitoring devices and because some wearable glucose monitoring devices can produce measurements continuously, a platform providing such devices may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process and covers a robust number of state spaces unlikely to be covered without the enormous amount of data. In implementations, a glucose monitoring platform includes an ML model trained using historical time series glucose measurements of a user population. The ML model predicts upcoming glucose measurements for a particular user by receiving a time series of glucose measurements up to a time and determining the upcoming glucose measurements of the particular user for an interval subsequent to the time based on patterns learned from the historical time series glucose measurements.
Techniques and protocols for establishing secure communications between a display device, a sensor system, and a server system are disclosed. In certain embodiments, the techniques and protocols include secure diabetes device identification techniques and protocols, user-centric mutual authentication techniques and protocols, and device-centric mutual authentication techniques and protocols.
Hypoglycemic event prediction using machine learning is described. A CGM platform includes a machine learning model trained using historical time series glucose measurements of a user population. Once trained, the machine learning model predicts hypoglycemic events for users. When predicting hypoglycemic events, a time series of glucose measurements for a day time interval is received. The glucose measurements of this time series for the day time interval are provided by a CGM system worn by the user. The machine learning model predicts whether a hypoglycemic event will occur during a night time interval that is subsequent to the day time interval by processing the time series of glucose measurements using the trained machine learning model. The hypoglycemic event prediction is then output, such as via communication and/or display of a notification about the hypoglycemic event prediction.
Certain aspects of the present disclosure relate to a method of configuring an application with one or more application features. The method comprises receiving a request to configure the application for use by a user. The method further comprises identifying an objective for the user and identifying classifying information associated with the user, the classifying information including at least one of the objective, interest, ability, demographic information, disease progression information, or medication regimen information of the user. The method further comprises selecting a group of users based on one or more similarities between the user and the group of users. The method further comprises identifying the one or more application features based on the objective of the user and a correlation of each of the plurality of application features with the objective. The method further comprises configuring the application with the one or more application features.
Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.
Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.
Techniques for data analysis and user guidance are provided for determining and providing one or more treatments to a user based on where the user is or will be in their menstrual cycle. In certain embodiments, a method of personalizing diabetes treatment based on information relating to a menstrual cycle of a user is provided. The method includes measuring, using a glucose monitoring system, blood glucose measurements of the user. The method further includes receiving information relating to the menstrual cycle of the user. The method further includes determining a treatment for the user to achieve a target blood glucose during a sub-phase or phase of the menstrual cycle of the user based on at least one of historical data associated with the user and historical data associated with a stratified group of users.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61M 5/172 - Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters electrical or electronic
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
Various examples are directed to systems and methods for generating a bolus dose for a host. A bolus application may display a first bolus configuration parameter question at a user interface and receive, through the user interface, a first answer to the first bolus configuration parameter question. The first answer may describe a previous bolus determination technique of the host. The bolus application may select a second bolus configuration parameter question using the first answer and provide the second bolus configuration parameter question at the user interface. The bolus application may determine a set of at least one bolus configuration parameter using the first answer and a second answer to the second bolus configuration parameter question.
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 20/17 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
G16H 20/60 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
72.
END OF LIFE DETECTION FOR ANALYTE SENSORS EXPERIENCING PROGRESSIVE SENSOR DECLINE
Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes receiving a sensor signal from an analyte sensor. A plurality of risk factors associated with end of life symptoms of analyte sensors is evaluated. The risk factors include a downward drift in sensor sensitivity over time, an amount of non-symmetrical, nonstationary noise and a duration of noise. An end of life status of the analyte sensor is determined based at least in part on the evaluating. An output related to the end of life status of the analyte sensor is provided.
Certain aspects of the present disclosure relate generally to a method for identifying a risk of sepsis in a body of a patient. The method includes measuring lactate concentrations associated with the body over one or more time periods. The method further includes identifying the risk of sepsis based on the lactate concentrations.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/1468 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means
A61B 5/155 - Devices for taking samples of blood specially adapted for continuous or multiple sampling, e.g. at predetermined intervals
G16H 10/00 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
74.
MULTI-STATE ENGAGEMENT WITH CONTINUOUS GLUCOSE MONITORING SYSTEMS
Multi-state engagement with continuous glucose monitoring (CGM) systems is described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains packages of glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform generates state information for the user by processing these CGM packages and the additional data, at least in part, by using one or more models. Based on this state information, the data analytics platform controls communication with the user, which may include generating intervention strategies to prevent users from transitioning to a negative state such as discontinuing use of the CGM system.
Systems and methods are provided for identifying therapeutic zones where there is glycemic dysfunction of a specific type that can be addressed by making strategic changes to behavior and/or therapy parameters. Systems and methods described herein evaluate large historical data sets to: identify a therapeutic zone or zones with glycemic dysfunction that are most readily addressable; quantify the glycemic impact of a plurality of different therapeutic adjustments in terms of either adjustments to historical doses or the parameters of a prospective dosing strategy to determine the highest possible improvement; and/or identify patient dosing strategies to provide therapy recommendations adapted for the patient's preferred behavioral dosing strategy.
Recommendations based on continuous glucose monitoring (CGM) are described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform processes these measurements and the additional data to predict a health indicator by using models. This prediction serves as a basis for generating a recommendation, such as a message recommending the user take action or adopt a behavior to mitigate a predicted negative health condition.
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
77.
JOINT STATE ESTIMATION PREDICTION THAT EVALUATES DIFFERENCES IN PREDICTED VS. CORRESPONDING RECEIVED DATA
Systems and methods are provided for reconciling untrusted data of a subject using trusted data pertaining to the subject. Systems and methods are directed to evaluating differences in predicted data with respect to corresponding received data. Systems and methods estimate metabolic states from a combination of trusted and untrusted metabolic inputs, along with optionally using a personalized mathematical model with parameter optimization. Systems and methods provide for reconciled untrusted inputs with their measured impact of the glycemic signals that is consistent with a metabolic model. Estimation of future metabolic states for decision support and automated insulin dosing is enabled. Replay of scenarios with estimated or reconciled data is also provided.
Various examples are directed to a glucose sensor comprising a working electrode to support an oxidation reaction and a reference electrode to support a redox reaction. The reference electrode may comprise silver and silver chloride. The Glucose sensor may also comprise an anti-mineralization agent positioned at the reference electrode to reduce formation of calcium carbonate at the reference electrode.
C12Q 1/00 - Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
G01N 27/07 - Construction of measuring vessels; Electrodes therefor
Adaptive on board estimation of exogenous pharmacon responsive to transient (i.e., impermanent) physiological effects is provided. Dynamically estimating an equivalent amount of an exogenous pharmacon on board (XOB), such as insulin and/or carbohydrates, left in the subject, is based on predictions of glucose time-series data. These estimated values, such as insulin on board (IOB), are useful for diabetes management software, including decision support and/or artificial pancreas (AP) algorithms, for example.
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/60 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
80.
SYSTEM AND METHOD FOR WIRELESS COMMUNICATION OF ANALYTE DATA
Systems, methods, apparatuses, and devices, for the wireless communication of analyte data are provided. In some embodiments, a method and calibration station for calibrating a continuous analyte sensor system is provided. Methods and testing systems for testing a continuous analyte sensor system is provided. Continuous analyte sensor systems, display devices and peripheral devices configured for wireless communication of analyte, connection, alarm and/or alert data and associated methods are provided.
A61B 5/1495 - Calibrating or testing in vivo probes
A61B 5/05 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
G01D 18/00 - Testing or calibrating apparatus or arrangements provided for in groups
Various analyte sensing apparatuses and associated housings are provided. Some apparatuses comprise one or more caps. Some apparatuses comprise a two-part adhesive patch. Some apparatuses comprise one or more sensor bends configured to locate and/or hold a sensor in place during mounting. Some apparatuses utilize one or more dams and/or wells to retain epoxy for securing a sensor. Some apparatuses utilize a pocket and one or more adjacent areas and various transitions for preventing epoxy from wicking to undesired areas of the apparatus. Some apparatuses include heat-sealable thermoplastic elastomers for welding a cap to the apparatus. Related methods of fabricating such apparatuses and/or housings are also provided.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/05 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.
A61M 5/168 - Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters
A61M 5/172 - Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters electrical or electronic
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
83.
SAFETY TOOLS FOR DECISION SUPPORT RECOMMENDATIONS MADE TO USERS OF CONTINUOUS GLUCOSE MONITORING SYSTEMS
Systems and method are described for determining if a decision support recommendation is to be presented to a user for treatment of a diabetic state, including receiving a plurality of input data items impacting a diabetic state of a user of continuous glucose monitor, the input data items serving as input data to a process for determining a decision support recommendation; assigning a reliability level to each of the input data items; calculating a reliability metric based on the reliability levels assigned to each of the input data items; determining a decision support recommendation based on the process and the input data and presenting the decision support recommendation to the user on a user interface only if the reliability metric exceeds a threshold.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/1477 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means non-invasive
A61B 5/1486 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using enzyme electrodes, e.g. with immobilised oxidase
A61B 5/1495 - Calibrating or testing in vivo probes
Various examples described herein are directed to systems, apparatuses, and methods for mitigating break-in in an analyte sensor. An example analyte sensor system comprises an analyte sensor applicator comprising a needle; an analyte sensor comprising at least a working electrode and a reference electrode, the analyte sensor positioned at least partially within a lumen of the needle; and a hydrating agent positioned within the lumen of the needle to at least partially hydrate the needle.
A61B 5/153 - Devices for taking samples of blood specially adapted for taking samples of venous or arterial blood, e.g. by syringes
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
Various examples are directed to systems and methods of and using analyte sensors. An example analyte sensor system comprises an analyte sensor and a hardware device in communication with the analyte sensor. The hardware device may be configured to perform operations comprising applying a first bias voltage to the analyte sensor, the first bias voltage less than an operational bias voltage of the analyte sensor, measuring a first current at the analyte sensor when the first bias voltage is applied, and applying a second bias voltage to the analyte sensor. The operations may further comprise measuring a second current at the analyte sensor when the second bias voltage is applied, detecting a plateau bias voltage using the first current and the second current, determining that the plateau bias voltage is less than a plateau bias voltage threshold, and executing a responsive action at the analyte sensor.
Various examples are directed to systems and methods for patient monitoring. An example method comprises receiving an estimated glucose concentration level of the patient from a continuous glucose monitoring (CGM) system for a first time period. The method may also include receiving non-glucose information relating to the patient for the first time period and determining a relationship between the estimated glucose concentration level and the non-glucose information. The method may also include receiving non-glucose information relating to the patient for a second time period and determining diabetic information about the patient for the second time period based upon the determined relationship and the non-glucose information. The method may include electronically delivering a notification about the diabetic information.
Systems and methods for wireless communication of analyte information are disclosed. Example embodiments include an analyte sensor system. The analyte sensor system may include an analyte sensor to gather information related to a level of an analyte in a host. The analyte sensor system may include circuitry to transmit and receive wireless signals using a first antenna. The analyte sensor may be coupled to the circuitry before the analyte sensor system is transitioned into an operational state. The analyte sensor system may include a second antenna to receive an input signal from a source external to the analyte sensor system and to transmit a modified signal. The circuitry may use the first antenna to receive the modified signal The analyte sensor system may transition into the operational state in response to the modified signal received using the first antenna.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/05 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1495 - Calibrating or testing in vivo probes
88.
SYSTEMS AND METHOD FOR ACTIVATING ANALYTE SENSOR ELECTRONICS
Various analyte sensor systems for controlling activation of analyte sensor electronics circuitry are provided. Related methods for controlling analyte sensor electronics circuitry are also provided. Various analyte sensor systems for monitoring an analyte in a host are also provided. Various circuits for controlling activation of an analyte sensor system are also provided. Analyte sensor systems utilizing a state machine having a plurality of states for collecting a plurality of digital counts and waking a controller responsive to a wake up signal are also provided. Related methods for such analyte sensor systems are also provided. Systems for controlling activation of analyte sensor electronics circuitry utilizing a magnetic sensor are further provided. One or more display device configured to display one or more analyte concentration values are also provided.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/05 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1495 - Calibrating or testing in vivo probes
89.
SYSTEMS AND METHODS FOR POWER MANAGEMENT IN ANALYTE SENSOR SYSTEM
An analyte sensor system may include a first communication circuit configured to transmit a wireless signal in a first communication mode and a second communication mode, and a processor, wherein the processor determines whether a first condition is satisfied, the first condition relating to the sensor signal or to communication by the first communication circuit, and shifts the system to a second communication mode responsive to the first condition being satisfied.
Systems and methods are provided that address the need to frequently calibrate analyte sensors, according to implementation. In more detail, systems and methods provide a preconnected analyte sensor system that physically combines an analyte sensor to measurement electronics during the manufacturing phase of the sensor and in some cases in subsequent life phases of the sensor, so as to allow an improved recognition of sensor environment over time to improve subsequent calibration of the sensor.
An analyte sensor system is provided. The system includes a base configured to attach to a skin of a host. The base includes an analyte sensor configured to generate a sensor signal indicative of an analyte concentration level of the host, a battery, and a first plurality of contacts. The system includes a sensor electronics module configured to releasably couple to the base. The sensor electronics module includes a second plurality of contacts, each configured to make electrical contact with a respective one of the first plurality of contacts, and a wireless transceiver configured to transmit a wireless signal based at least in part on the sensor signal. The system includes a first sealing member configured to provide a seal around the first and second plurality of contacts within a first cavity. Related analyte sensor systems, analyte sensor base assemblies and methods are also provided.
Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration level. The glucose concentration level may be provided to a stored model to determine a state. The guidance may be determined based at least in part on the determined state.
This document discusses, among other things, systems and methods to compensate for the effects of temperature on sensors, such as analyte sensor. An example method may include determining a temperature-compensated glucose concentration level by receiving a temperature signal indicative of a temperature parameter of an external component, receiving a glucose signal indicative of an in vivo glucose concentration level, and determining a compensated glucose concentration level based on the glucose signal, the temperature signal, and a delay parameter.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
C12Q 1/54 - Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving glucose or galactose
G01N 33/48 - Biological material, e.g. blood, urine; Haemocytometers
G01N 33/50 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
G05B 17/02 - Systems involving the use of models or simulators of said systems electric
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
94.
DIABETES MANAGEMENT PARTNER INTERFACE FOR WIRELESS COMMUNICATION OF ANALYTE DATA
Systems, devices, and methods are disclosed for wireless communication of analyte data. In embodiments, a method of using a diabetes management partner interface to configure an analyte sensor system for wireless communication with a plurality of partner devices is provided. The method includes the analyte sensor system receiving authorization to provide one of the partner devices with access to a set of configuration parameters via the diabetes management partner interface. The set of configuration parameters is stored in a memory of the analyte sensor system. The method also includes, responsive to input received from the one partner device via the diabetes management partner interface, the analyte sensor system setting or causing a modification to the set of configuration parameters, according to a system requirement of the one partner device.
Pre-connected analyte sensors are provided. A pre-connected analyte sensor includes a sensor carrier attached to an analyte sensor. The sensor carrier includes a substrate configured for mechanical coupling of the sensor to testing, calibration, or wearable equipment. The sensor carrier also includes conductive contacts for electrically coupling sensor electrodes to the testing, calibration, or wearable equipment.
A61B 5/05 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1468 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means
C12Q 1/54 - Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving glucose or galactose
A method for monitoring blood glucose levels includes receiving a time-varying electrical signal from an analyte sensor during a temporal phase of a monitoring session and selecting a calibration model from a plurality of calibration models. The selected calibration model includes one or more calibration model parameters. The method further includes estimating at least one of the one or more calibration model parameters of the selected calibration model based on at least the time-varying electrical signal during the temporal phase and estimating the blood glucose level of the user based on the selected calibration model and the at least one estimated parameter. An apparatus and non-transitory computer readable medium can carry out similar functionality.
The present embodiments relate generally to applicators of on-skin sensor assemblies for measuring an analyte in a host, as well as their method of use and manufacture. In some aspects, an applicator for applying an on-skin sensor assembly to a skin of a host is provided. The applicator includes an applicator housing, a needle carrier assembly comprising an insertion element configured to insert a sensor of the on-skin sensor assembly into the skin of the host, a holder releasably coupled to the needle carrier assembly and configured to guide the on-skin sensor assembly while coupled to the needle carrier assembly, and a drive assembly configured to drive the insertion element from a proximal starting position to a distal insertion position, and from the distal insertion position to a proximal retraction position.
Applicators for applying an on-skin assembly to skin of a host and methods of their use and/or manufacture are provided. An applicator includes an insertion assembly configured to insert at least a portion of the on-skin assembly into the skin of the host, a housing configured to house the insertion assembly, the housing comprising an aperture through which the on-skin assembly can pass, an actuation member configured to, upon activation, cause the insertion assembly to insert at least the portion of the on-skin assembly into the skin of the host, and a sealing element configured to provide a sterile barrier and a vapor barrier between an internal environment of the housing and an external environment of the housing.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/1473 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using chemical or electrochemical methods, e.g. by polarographic means invasive, e.g. introduced into the body by a catheter
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/1459 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters invasive, e.g. introduced into the body by a catheter
Flexible analyte sensors are provided. Flexible analyte sensors may be flexible continuous analyte sensors that facilitate continuous monitoring of an analyte such as blood glucose. The flexible analyte sensor may have a relatively flexible conductive or non-conductive core, may be formed from a plurality of substantially planar layers, or may be configured to transform from a freestanding sensor ex vivo to a non-freestanding sensor in vivo.
patents
