Automatic analyte sensor calibration and error detection

Abstract

To provide systems and methods that provide a pre-connected analyte sensor system that physically combines an analyte sensor to measurement electronics during a manufacturing phase of the sensor and in some cases in subsequent life phases of the sensor.SOLUTION: Systems and methods are provided that address the need to frequently calibrate analyte sensors, according to implementation. In more detail, the systems and methods provide a pre-connected analyte sensor system that physically combines an analyte sensor to measurement electronics during a manufacturing phase of the sensor and in some cases in subsequent life phases of the sensor, so as to allow improved recognition of sensor environment over time to improve subsequent calibration of the sensor.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] INCORPORATION BY REFERENCE TO RELATED APPLICATIONS Any and all priority claims identified in the Application Data Sheet, or any amendments thereto, are incorporated herein by reference under 37 CFR 1.57. This application claims the benefit of U.S. Provisional Application No. 62 / 666,606, filed May 3, 2018. The aforementioned applications are incorporated herein by reference in their entireties and expressly made a part hereof.

[0002] The embodiments described herein generally relate to systems and methods for processing and self-calibrating sensor data from continuous analyte sensors. [Background technology]

[0003] Diabetes mellitus is a disease in which the pancreas cannot make enough insulin (Type I or insulin-dependent) and / or insulin is ineffective (Type II or non-insulin-dependent). In the diabetic state, the patient or user suffers from hyperglycemia, which can cause many physiological disorders related to the deterioration of small blood vessels, such as kidney failure, skin ulcers, or bleeding into the vitreous humor of the eye. Hypoglycemic reactions (low blood sugar) can be induced by inadvertent overdosing of insulin or after normal administration of insulin or glucose-lowering drugs accompanied by abnormal exercise or inadequate food intake.

[0004] Traditionally, a person with diabetes carries a self-monitoring blood glucose (SMBG) monitor, which typically requires an uncomfortable finger-prick method. Due to lack of comfort and convenience, a person with diabetes typically only measures glucose levels two to four times per day. Unfortunately, such time intervals are spread so far apart that a person with diabetes may detect a hyperglycemic or hypoglycemic condition too late, sometimes resulting in dangerous side effects. Not only is a person with diabetes unlikely to notice a dangerous condition in time to prevent it, but the person may not know if his or her blood glucose concentration value is rising (higher) or falling (lower) based on traditional methods. Thus, a diabetic person may be inhibited from making an insulin therapy decision based on experience and knowledge.

[0005] Another device that some diabetics have used to monitor their blood glucose is a continuous analyte sensor, e.g., a continuous glucose monitor (CGM). A CGM typically includes a sensor that is placed invasively, minimally invasively, or non-invasively. The sensor measures the concentration of a given analyte, e.g., glucose, in the body and generates a raw signal using electronics associated with the sensor. The raw signal is converted into an output value that is depicted on a display. The output value resulting from the conversion of the raw signal is typically represented in a format that provides meaningful information to the user and that the user has become accustomed to analyzing, such as blood glucose expressed in mg / dL.

[0006] The above description assumes that the output values ​​are reliable and true, the same output values ​​that generally require a significant degree of user interaction to ensure proper calibration. Typically, a calibration check is performed before the analyte sensor leaves the factory, i.e., during the calibration check, the sensitivity values ​​are derived ex vivo. However, this calibration check only provides a snapshot at a point in time and does not take into account that sensor sensitivity changes over time. Furthermore, two sensors with the same results from the calibration check procedure may still perform differently when used on a given patient, since the sensitivity values ​​may change over time depending on the conditions before and after use.

[0007] One way to explain this is through the use of baseline checking during use, for example, with self-monitoring blood glucose meters. Many current CGMs rely heavily on such user interaction to confirm glucose concentration values ​​before administering insulin. However, the additional user action adds a significant source of error to the monitoring and reduces convenience by requiring more user action than necessary. Summary of the Invention [Problem to be solved by the invention]

[0008] Systems and methods according to the present principles, according to implementations, address many of the above-mentioned problems related to the need to frequently calibrate analyte sensors. More particularly, the systems and methods provide pre-connected analyte sensor systems that physically couple the analyte sensor to measurement electronics during the manufacturing phase of the sensor, and potentially within subsequent life phases of the sensor. [Means for solving the problem]

[0009] In one embodiment, the system includes at least an analyte sensor capable of measuring an analyte level in a host, and measurement electronics including a potentiostat circuit capable of placing a controlled voltage bias between two or more electrodes and measuring the amount of current that flows. The analyte sensor is pre-connected to the measurement electronics.

[0010] There are also several optional features: a sensor interconnect module that can secure the analyte sensor in place and / or provide a robust electrical coupling; and a measurement electronics module that can include one or more of the following: a temperature measurement circuit that can obtain a temperature reading from one or more temperature sensors; an impedance measurement circuit that can detect an impedance value from the analyte sensor or other electrical components; a capacitance measurement circuit that can detect a capacitance value from the analyte sensor or other electrical components; a motion detection circuit that uses one or more sensors such as an accelerometer or gyroscope to detect and quantify physical motion and / or orientation; a humidity measurement circuit having one or more sensors that can measure humidity; a clock that can keep a measurement of time, and / or a pressure measurement circuit having one or more pressure processors that can measure changes in the pressure of a gas (e.g., atmospheric pressure) or pressure applied to the device (e.g., force applied to a surface of the housing), and one or more processors that can process the data. Other features may include one or more radios capable of wirelessly transmitting data, one or more display / status indicators capable of communicating data to a user, one or more data storage units capable of storing pertinent information for future access, and one or more power sources (e.g., batteries) capable of providing reliable power for use by the measurement electronics.

[0011] Pre-connected analyte sensors can address various sources of error that may otherwise occur. These sources of error may include both precision and accuracy errors, which are important factors in determining the true value of a measurement performed by a measurement system. Precision can be described as the approximation of a measurement to a standard or known value. For example, take a width measurement of a known 1 cm cube, and if the value obtained is 1.1 cm, the precision of that measurement is 0.1 cm. Precision is the degree to which repeated measurements under unchanging conditions will show the same result. In the same cube example, if three measurements are made and the values ​​obtained are 1.1 cm, 1.2 cm, and 1.0 cm, the precision of that measurement is within 0.1 cm. However, precision and accuracy errors are compounded in determining the true measurement.

[0012] Accuracy and precision are not static factors that can have a large effect on errors in a measurement system. On the contrary, they are dynamic factors in which accuracy and precision can change over time. Typically, a model is used to quantify the sensor response to a signal (e.g., linear, nonlinear, etc.), and thus deviations in accuracy and precision of the model used to quantify the sensor response add additional error in converting the sensor signal to a reported value.

[0013] Thus, pre-connecting the analyte sensor system to the measurement electronics in the manufacturing phase and then using the same configuration during the sensor use phase has several advantages.

[0014] A pre-connected analyte sensor system can compensate for errors introduced by the precision and accuracy of manufacturing equipment. Variations in the manufacturing process can result in different values ​​of the various parameters being measured (e.g., analyte sensitivity, baseline, impedance, capacitance, interference sensitivity, etc.), and the errors resulting from these different parameter values ​​compound into the overall system error. The more variation there is in the manufacturing ramp, the more significant the consequences for the errors introduced into the system. These variations can include equipment aging, frequency of equipment calibration, number of different measurement points, multiple manufacturing lines, multiple manufacturing locations, equipment precision, calibration trueness, equipment cleanliness, etc.

[0015] A pre-connected analyte sensor system limits errors caused by physically connecting the analyte sensor to a portion of the sensor's electronics, where the portion of the electronics includes the measurement electronics, allowing measurements to be taken during and after the manufacturing process. Some possible types of measurements that can be taken by the measurement electronics are sensitive to factors such as contact resistance, leakage current, electrical path length, component volume, manufacturing tolerances, material properties, etc.

[0016] A pre-connected analyte sensor system limits the errors introduced by the measurement electronics. The measurement electronics are limited by their own manufacturing tolerances and their design constraints. Typically, calibration equipment is used to characterize the measurement electronics system. Precision and accuracy are measured and correction factors (e.g., gain, offset, linearity, temperature, resolution, etc.) are used by the circuit to compensate for the absolute errors. This adds cost and complexity to the manufacturing phase, as testing and programming time must be added to the process. Also, depending on the duration and equipment used to calibrate the system, changes in various attributes may result from the calibration time. Therefore, it is advantageous to calibrate the system as late as possible in the manufacturing process.

[0017] Fewer process steps in manufacturing have the advantage of efficiency and reduced opportunity for error. Calibration can be completed as a system by performing sensor calibration using the measurement electronics that will be used in the final product. For example, to calibrate the electronics and sensor as a single step with a known calibration solution, only the value of the calibration solution needs to be controlled. At a minimum, the measurement electronics applies a voltage bias to the sensor, measures the analog value of the current, and converts the analog value to a digital value. This digital value can be correlated to the actual value of the calibration solution. For this particular set of measurement electronics combined with this particular sensor, the relationship of the calibration solution to the analyte concentration is now related to a digital value that is corrected for individual measurement component variations (e.g., potentiostat variations, analog-to-digital converter errors, leakage errors, connection resistance variations, etc.). This system also eliminates manufacturing measurement electronics errors from the calibration equipment.

[0018] Such direct calibration solution-based system calibrations can be performed over a wide range of analyte values, interfering materials, and other factors that affect sensor performance (e.g., hypoxia). This correlation of digital values ​​to analyte concentrations in solution over a range can be used to build accurate compensation models for sensor performance in vivo.

[0019] In alternative embodiments, this calibration process can be extended to other types of possible measurements performed by the measurement electronics (e.g., impedance, capacitance, temperature, time, current, voltage, humidity, motion, etc.).

[0020] The value of a system that connects the measurement electronics to the analyte sensor during manufacturing can extend beyond the calibration portion of manufacturing. This allows the system to capture data during the following system phases: manufacturing, packaging, sterilization, shipping, storage, insertion, and in vivo. Useful measurements can be taken before, during, and after one or more of the following steps: sensor connection, membrane application, curing, environmental shift, sterilization, shipping, storage, insertion, in vivo, etc.

[0021] In transcutaneous analyte measurement systems currently available on the market, the sensor and measurement electronics are coupled just prior to or during sensor insertion. This configuration prevents measurements of the coupled system during any system phase prior to coupling of the measurement electronics with the analyte sensor. Additional measurements that can only be captured with a pre-connected system can be provided to the analyte processing algorithm. These measurements can be correlated with in vivo performance, fault detection, sensor life, sensitivity shift, calibration shift, sensor performance indicators, accuracy, etc. Measurement correlations can be used to identify or compensate for system experience over time, which is useful between in vivo system phases.

[0022] Multivariate models can be created for multiple measurements at different time points and system phases. This frequency and breadth of data collection allows system characteristics to be more accurately modeled. Part of this analysis can be accomplished using measurements taken by manufacturing or calibration equipment. These input measurements may optionally be incorporated in addition to measurements taken by the measurement electronics. In other embodiments, the model may simply include inputs from manufacturing and / or calibration equipment. Output measurements can be taken during the in vivo phase by manufacturing and / or calibration equipment or by reference measurements of blood analyte levels (e.g., YSI, finger stick glucometer, laboratory analysis, etc.).

[0023] For example, measurements such as impedance, temperature, current measurements, time, etc. may be acquired by the pre-connected measurement electronics during various phases of manufacturing such as pre-sensor attachment, post-sensor attachment, membrane application, curing, and calibration. The pre-connected system may collect spatial information such as location within the facility, location within the equipment, or equipment identifier. This data set may be combined with additional data sets from sensors located within the manufacturing equipment that collect variables such as humidity, temperature, material viscosity, time, equipment identifier, etc. Additional data sets may also be collected that track external variables such as time, date, room temperature, room humidity, manufacturing equipment, calibration equipment, operator, manufacturing line, manufacturing location, etc.

[0024] The collated measurements can be interpreted immediately or stored for further processing at a later time. This information can be used to adjust manufacturing parameters or build correction factors, determine lot classification, reject sensors, or be used by analyte processing algorithms. This vast amount of data can be input into tools such as machine learning algorithms to identify correlations.

[0025] Multivariate models can be used to identify and correct for relationships between input and output parameters. Some of these relationships are well known (e.g., the relationship of temperature with respect to analyte sensitivity measurements), and others need to be further identified. The tools used to identify and model these relationships may be linear regression additive models, generalized linear modeling, optionally incorporating one or more nonlinear functions, nonparametric data fitting empirical modeling, nonlinear regression modeling, neural network models, or other suitable models. This list is merely exemplary, and any suitable statistical or analytical tool can be used to model the system relationships. Other suitable data analysis methods are described in "Handbook of Chemometrics and Qualimetrics, Volume 20A" and "Handbook of Chemometrics and Qualimetrics, Volume 20B", published by Elsevier Science in 1998, and are incorporated by reference.

[0026] Many system measurements that can be obtained have correlations to additional system parameters. In this way, it is possible to draw correlations of parameters that are not directly measured but may be useful for input or processing with the analyte algorithm processing unit. This has several advantages, such as requiring less physical sensor components that add cost and complexity, gathering information that is not easily measured due to location or sensor size, and providing redundancy or improved accuracy for additional sensors (e.g., compensating for temperature with a current measurement circuit).

[0027] Exemplary applications utilizing predicted measurements may be some of the following: predicting humidity levels in vitro using temperature and sensor impedance measurements, calculating temperature gradients using one or more temperature sensors, estimating temperature at an unmeasured point such as the tip of an analyte sensor in vivo using temperature gradient data, estimating body movement using temperature and accelerometer data. This is not an exhaustive list, and any of the sensed measurements can be combined with one or more other sensed measurements to estimate one or more unsensed measurements.

[0028] By pre-connecting the sensor to some or all of the sensor electronics, the sensor can be monitored over all or a portion of its life, particularly during the portion of the sensor's life after it leaves the factory. Sensor monitoring can be advantageous for several reasons. In particular, sensor monitoring can address issues related to the manufacturing process that reduce variability (deviation from the sensor's factory-assigned calibration over time), precision (errors that add up to an entire analyte sensor system, resulting from variability in the individual components that make up the system), and consistency from sensor to sensor and from sensor lot to sensor lot. Additionally, pre-connected sensors can facilitate data transfer from the sensor to an external device, improving sensor safety by detecting when sensors deployed in the field are in a potentially dangerous condition.

[0029] In one aspect, the variability issue is addressed by performing various active measurements taken after manufacture. For example, in one embodiment, the environmental conditions (e.g., temperature, humidity) that the sensor and pre-connected electronics are maintained in while sealed when packaged during storage and prior to use can be monitored. For temperature, an on-board electronics temperature sensor such as a thermistor or thermocouple can be used to measure and store temperature data. Similarly, an on-board electronics humidity sensor can be provided to monitor humidity. Alternatively, an external temperature and / or humidity sensor physically coupled to the electronics (e.g., in the base, in the package) can be used to measure and store temperature and / or humidity data. In other cases, an independent temperature and / or humidity sensor that wirelessly communicates with the electronics can be used. In some cases, there may be an individual temperature and / or humidity sensor assigned to each analyte sensor. Alternatively, there may be a single temperature and / or humidity sensor assigned to each box / shipper / pallet of analyte sensors. In another implementation, the analyte sensor wire itself can be used to determine temperature and / or humidity by inference via impedance or current measurements, which can be stored in the pre-connected electronics.

[0030] In some embodiments, another environmental condition that may be monitored is the radiation dose given to the sensor for sterilization purposes after the sensor and any pre-connected electronics are sealed in the packaging. In one example, a sterilization detector is provided in the electronics so that the detector can quantify the dose using active electronics. In some cases, materials can be added to the packaging that are sensitive to the sterilization dose and can be electronically interrogated by the electronics after sterilization to determine sensor characteristics such as impedance, resistance, and / or capacitance. From this, it may be possible to infer the orientation of the device in the packaging during sterilization. A bulk detection of the sterilization dose may also be obtained for each box / shipper / pallet of analyte sensors. The measured dose can be used to assign a value to the analyte sensor via wireless communication with the pre-connected electronics, which is later used in deriving subsequent calibration parameters.

[0031] In an additional aspect, another environmental condition that may be monitored is the movement of the analyte sensor using an accelerometer, a triggering fuse burst, or other motion sensor. In this way, vibrations or shocks, such as from being dropped, can be detected, which may cause damage to the sensor membrane or applicator mechanism.

[0032] Still other environmental conditions that can be monitored include exposure to atmospheric gases and the duration that has elapsed since sensor manufacture.

[0033] Passive techniques can also be used in addition to or instead of the active monitoring techniques described above to address the variability issue. For example, in one implementation described in U.S. Application No. 62 / 521,969, entitled "Applicators for Applying Transcutaneous Analyte Sensors and Associated Methods of Manufacture," filed June 19, 2017, the packaging material used has a water vapor transmission rate below a certain threshold level, e.g., 10 grams / 100 inches. 2 / day or less than 1 gram / 100 inches 2 The packaging material can provide a humidity barrier that can be maintained at less than 1000 psi / day. Examples of packaging materials that can be used include metal foils (e.g., aluminum, titanium), metal substrates, aluminum oxide coated polymers, silicon dioxide coated polymers, polymer substrates coated with metals that are coated via metal vapor deposition, or low MVTR polymers (e.g., PET, HDPE, PVC, PP, PLA).

[0034] Yet another passive technique that can be used to monitor environmental conditions involves providing a visual indicator material in the packaging that changes color or visibility upon exposure to temperature and / or humidity over time. Alternatively, rather than being a visual indicator, the indicator can undergo a dimensional change in length or position in response to changes in temperature or humidity.

[0035] In some embodiments employing humidity and / or temperature monitoring in the packaging, if either or both of such monitor screens determine that the environmental conditions have exceeded acceptable limits at some point for some duration, the packaging may provide a mechanism to physically prevent the sensor in the packaging from being used. For example, materials that change dimensions with temperature and / or humidity, such as bimetals (similar to those used in thermostats), metals, or polymers, may be used in combination with an interlocking mechanism in the applicator to physically prevent the applicator from deploying (either permanently or temporarily), prevent the packaging from being opened, and / or prevent buttons and the like from being activated. The physical change in material dimensions will automatically enable this functionality when a predetermined environmental condition is exceeded.

[0036] In another aspect, system level compensation can be achieved that allows for larger parameter variations between individual system components while reducing the overall error. This can be achieved using data stored in the pre-connected electronics related to the monitored environmental conditions as input to an algorithm used to adjust the sensor calibration model. This adjustment can be made to initial and / or final sensor sensitivity, background signal, and / or equilibration rate. In some cases, the collected and stored data for individual sensors or sensor lots may be adapted to individual patients. Furthermore, the required adjustments can also be used as additional input information previously acquired over time for a large number of sensors and patients to calculate calibration compensation values ​​based on the performance of sensors experiencing similar conditions.

[0037] The algorithms used to adjust the sensor calibration model can also include a time component, which uses data obtained by examining the sensitivity and background signal profiles of the sensor over the time from insertion (when a factory calibrated initial sensitivity and background signal is used) to the transition to a stable final sensitivity and background signal. The sensor calibration model can be compensated based on the difference between the factory calibration values ​​and the rate of change during the sensitivity transition period. Typical break-in curves can be obtained for each sensor from this data as well as curves resulting from changes caused by sterilization, temperature, humidity, and / or storage time. These break-in curves can be used to compensate the sensor calibration model for deviations from the factory calibration.

[0038] In another aspect, adjustments to the sensor calibration values ​​can be made prior to inserting the analyte sensor in the patient using a sensor calibration model that is updated based on data stored in the pre-connected electronics related to the monitored environmental conditions. For example, the voltage bias applied to the analyte sensor may be adjusted based on the stored data. In some cases, the voltage bias is applied while the analyte sensor is in its packaging to modify the sensor characteristics, e.g., to allow the sensor to undergo break-in while in the packaging. Additionally, the packaging can include a calibration solution that can be embedded in a foam, gel, etc. to prevent leakage. The calibration solution can be released just before or while the package is opened to facilitate calibration of the sensor in the package. In yet another aspect, the estimated break-in time that the sensor requires before start-up can be adjusted based on stored data, including the age of the sensor and the measured impedance of the sensor. The break-in time thus estimated can be displayed on the display of the system.

[0039] In another aspect, the stored data can be used in conjunction with measurements taken in vivo to adjust for sensitivity shifts occurring in vivo. For example, impedance can be measured in vivo in response to a stimulation signal, which may be a pulse, single frequency, multiple frequencies, or spectroscopy (EIS) signal. The measured impedance shift can be correlated with changes in sensitivity, but the correlation can be more complicated with changes in temperature and ion concentration (such as sodium) of the surrounding fluid. To address this issue, impedance measurements can be taken at one or more temperatures in the factory, and changes in temperature can be mapped to shifts in the impedance measurements. This information can then be used in vivo by taking temperature measurements in vivo and making any adjustments to the relationship between the measured impedance shift and changes in sensitivity. Similarly, impedance measurements can be taken at one or more ion concentrations in the factory, and changes in concentration can be mapped to shifts in the impedance measurements. This information can then be used in vivo by taking ion concentration measurements in vivo and making any adjustments to the relationship between the measured impedance shift and changes in sensitivity. The ion concentration can be measured using a secondary electrode circuit, which can be located on the same body as the analyte measurement circuit or on a separate subcutaneous sensor body. In some cases, the ion concentration can be obtained by optical measurement via changes in the refractive index of the fluid. The light source for such optical measurement can be ambient light or a dedicated light source that exposes the fluid to light of known wavelengths.

[0040] The accuracy of pre-connected sensors depends in part on the errors added to the system in the factory by combining components with their individual variations. Such errors, which can have a significant impact on system-level calibration, can come from sensor sensitivity (e.g., slope, baseline, and O2), membrane imperfections (e.g., impedance detection), electronics (e.g., voltage bias accuracy, amperometric linearity, leakage current), calibration process (e.g., solution accuracy, measurement device accuracy), and interconnect coupling of analyte sensors and electronics (e.g., resistance values ​​and variations between the analyte sensor and measurement electronics, and between the analyte sensor and calibration electronics).

[0041] In another aspect, pre-connecting the analyte sensor and various components of the electronics can provide improved manufacturing. For example, such pre-connection can allow for improved sensor tracking and serialization by providing the components attached to the sensor with a surface on which a code (e.g., bar code, label, etc.) can be placed for use in identification. This code can serve as a unique identifier and can be applied during or prior to manufacturing. This code can also include sensor data such as calibration codes, sensitivity values, etc., which can be obtained during manufacturing. In some cases, wireless communication can be established with the pre-connected sensor during the manufacturing process. For example, the sensor can be identified and tracked via wireless interrogation using short-range wireless communication protocols such as RFID, NFC, and Bluetooth. Similarly, the analyte sensor can actively broadcast data, or an identifier of the analyte sensor, using a short-range wireless communication protocol. In this way, the efficiency of handling analyte sensors during manufacturing can be improved as the sensors are moved, connected, and disconnected multiple times. The pre-connected electronics body can also act as an anchor body for connection and alignment, which can improve manufacturing flow. Further improvements can be achieved by replacing the physical electrical connection with a contactless wireless method.

[0042] In another aspect, the calibration code affixed to the sensor, transmitter, packaging, or other component may be a dynamic calibration code that changes with changing environmental conditions. For example, a portion of the printed code (e.g., a barcode) may be obscured by an environmentally responsive dye, such as a thermochromic dye, which changes the value of the code. In the shipping industry, responsive dyes are used that turn black (or some other color) or change from clear to black when exposed to heat, cold, moisture, or impact (e.g., by being dropped). Thus, for example, if the calibration code was printed on the packaging, the calibration code may include a basic calibration code that adjusts the calibration curve of the sensor. By printing additional digits, the digits may either disappear or appear when exposed to environmental factors that significantly affect the calibration.

[0043] For example, in one example of a dynamic calibration code in bar code format, a predetermined number, such as "3," may indicate heat exposure. In this example, if the package is exposed to heat above a threshold, the number 3 disappears, as does its corresponding portion of the bar code. Another number, such as "7," may indicate that humidity exposure is present at a threshold. If humidity exceeds the threshold, the number 7 appears, as does its corresponding portion of the bar code. When software in the patient's mobile device or other receiver is scanned or otherwise entered, a calibration curve offset or adjustment may be generated. Additionally, this information may be sent back to the manufacturer to determine lot variations, as well as variations during shipment, thereby identifying poorly stored sensors. This information may also be fed back to further inventory write-down considerations. Additional responsive dyes may be placed on the periphery of the code and may include "cut-off" thresholds that will show or disappear if the sensor is exposed to something that renders it unusable. This same information may be used to generate end user credits and reshipments, as well as the aforementioned inventory write-down considerations.

[0044] In another aspect, pre-wiring the analyte sensor and various components of the electronics can enable manufacturing improvements by using closed-loop manufacturing feedback to monitor manufacturing variables in real time to modify the manufacturing process and improve the resulting sensor. The sensors can be in the form of bricks, fixtures, or individual sensors. Variables that can be monitored include, for illustrative purposes, temperature, humidity, the contents of the particular coating solution in which the sensor is immersed (e.g., PVP, ethanol, etc.) (which can be determined from the refractive index of the solution), the duration of immersion, the number of times the sensor is immersed in the solution, and the duration, temperature, and humidity of the curing process. The data collected during this monitoring process can allow a vast sensor data set related to the manufacturing process to be obtained, which can be used to create outcome-based predictors. For example, if as a result of this process, it is determined that at some point during the manufacturing process, the temperature is higher than its average value, the humidity is lower than its average value, and the sensor sensitivity is higher than its average value, then an update to the manufacturing process can be implemented based on this insight to reduce the deviation of the sensor sensitivity from its average value. Moreover, the processes can be continuously monitored to determine whether updates to the manufacturing process have actually improved its performance.

[0045] In addition to using the data collected for individual sensors as feedback during the manufacturing process, sensor lot information can be obtained and stored. In this way, additional information can be obtained that can be used as feedback during the manufacturing process. For example, long-term testing of sensor lots for, for example, sensitivity shifts can be stored in the cloud for use in suitable algorithms. Similarly, information related to the sensor shipping process (geographical information, means of transportation used, duration of the shipping process, etc.) can be obtained and stored, which can then be correlated with sensor data to determine the impact of environmental exposure.

[0046] In another aspect, in addition to using data collected during manufacturing as part of a closed loop feedback process, data relating to the sensor and the patient can also be used while the sensor is in vivo. For example, analytics from individual sensor performance in the patient may be used as input data to any number of algorithms used during the manufacturing process. Such data may be obtained from a device, such as a cell phone or other receiver, that is in communication with the sensor during use. The data obtained may be any available information, such as temperature, humidity, sensor movement (which may indicate, for example, that the patient is sleeping, exercising, etc.), compressive forces that may be determined from an accelerometer and that may act on the sensor while the user is in different positions (e.g., sitting, standing, lying down), and the patient near a known location (e.g., Wi-Fi beacons, cell phone towers, Internet of Things (IOT) devices), etc.

[0047] In another aspect, stored data obtained from the sensor during or after manufacture can be used to reduce the risk of potentially dangerous sensors lurking in the field. Such data can be used to investigate the effectiveness of various storage conditions (e.g., packaging barriers and packaging whistles) and sterilization conditions (e.g., by sampling sensor lots undergoing sterilization) and to better determine when the sensor is expected to expire based on available data related to the age of the sensor and the manufacturing, storage, and other environmental conditions the sensor has experienced. In this way, patients can be automatically notified (e.g., by app pop-up, email, automated phone call) when the sensor is expected to expire.

[0048] In a first aspect, a method is provided for self-calibration of an analyte sensor system including an analyte sensor operably coupled to sensor electronics, the method comprising: applying a bias voltage to the analyte sensor using the sensor electronics to generate sensor data, the analyte sensor system having an initial characteristic metric determined at a first time; using the sensor electronics at a second time following the first time to determine a change in the initial characteristic sensitivity metric of the analyte sensor system based at least in part on one or more manufacturing and / or environmental parameters; and using the sensor electronics to automatically calibrate the analyte sensor system without user involvement based at least in part on the determined change in the initial characteristic metric.

[0049] In an embodiment of the first aspect, or any other embodiment of that aspect, one or more environmental parameters are monitored between the first time and the second time.

[0050] In an embodiment of the first aspect, or any other embodiment, monitoring the one or more environmental parameters includes measuring an impedance of the analyte sensor by applying a stimulation signal to the analyte sensor, measuring a signal response to the stimulation signal, calculating the impedance based on the signal response, and determining a value for the environmental parameter based on an established relationship between the impedance and the environmental parameter.

[0051] In an embodiment of the first aspect, or any other embodiment of that aspect, the first time follows sensor fabrication and the second time is prior to in vivo use of the sensor.

[0052] In an embodiment of the first aspect, or any other embodiment of that aspect, the first time follows sensor fabrication and the second time follows initiation of sensor use in vivo.

[0053] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial characteristic metric is determined by initially calibrating the analyte sensor while the analyte sensor is operably coupled to a sensor interface configured to provide an electrical communication interface between the analyte sensor and each of the manufacturing station and the sensor electronics.

[0054] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial characteristic metric is further determined by measuring an in vitro sensitivity characteristic of the analyte sensor.

[0055] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial characteristic metric is determined by initially calibrating the analyte sensor while the analyte sensor is operably coupled to one or more components of the sensor electronics.

[0056] In an embodiment of the first aspect, or any other embodiment of that aspect, the one or more components comprises a potentiostat.

[0057] In an embodiment of the first aspect, or any other embodiment of that aspect, the analyte sensor is operably coupled to one or more components of the sensor electronics uninterruptedly and continuously between the first time and the second time.

[0058] In an embodiment of the first aspect, or any other embodiment of that aspect, the first time is during a first portion of a manufacturing life phase of the analyte sensor, and the second time is during a second portion of the manufacturing life phase following packaging of the analyte sensor and one or more components of the sensor electronics in a sterile package.

[0059] In an embodiment of the first aspect, or any other embodiment of that aspect, the first time is during a manufacturing life phase of the analyte sensor, and the second time is during in vivo use of the sensor.

[0060] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the one or more environmental parameters includes monitoring a temperature of the analyte sensor while in the sterile package.

[0061] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the temperature includes measuring an impedance of the analyte sensor by applying a stimulation signal to the analyte sensor, measuring a signal response to the stimulation signal, calculating the impedance based on the signal response, and determining a value of the temperature based on an established relationship between the impedance and the temperature.

[0062] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the temperature includes measuring the temperature using a temperature sensor included within the sterile package, where the temperature sensor is operably couplable to the sensor electronics.

[0063] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the one or more environmental parameters includes monitoring humidity of an environment of the analyte sensor while in the sterile package.

[0064] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the humidity includes measuring an impedance of the analyte sensor by applying a stimulation signal to the analyte sensor, measuring a signal response to the stimulation signal, calculating the impedance based on the signal response, and determining a value for the humidity based on an established relationship between the impedance and the humidity.

[0065] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the humidity includes measuring the humidity using a humidity sensor included within the sterile package, where the humidity sensor is operably couplable to the sensor electronics.

[0066] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the one or more environmental parameters includes monitoring a sterilization dose used to sterilize the analyte sensor.

[0067] In an embodiment of the first aspect, or any other embodiment of that aspect, determining a change in the initial characteristic metric includes determining the change through use of a mathematical function.

[0068] In an embodiment of the first aspect, or any other embodiment of that aspect, the production parameter is obtained from an identifier of the analyte sensor.

[0069] In an embodiment of the first aspect, or any other embodiment of that aspect, the identifier is affixed to the analyte sensor.

[0070] In an embodiment of the first aspect, or any other embodiment of that aspect, the identifier is obtained by wirelessly interrogating the analyte sensor.

[0071] In an embodiment of the first aspect, or any other embodiment of that aspect, the identifier is associated with a manufacturing lot from which the analyte sensor was obtained.

[0072] In an embodiment of the first aspect, or any other embodiment of that aspect, a user is selected to receive the analyte sensor system based at least in part on one or more analyte sensor characteristics.

[0073] In an embodiment of the first aspect, or any other embodiment of that aspect, the one or more sensor characteristics include an updated characteristic metric derived from the determined change in the initial characteristic metric.

[0074] In an embodiment of the first aspect, or any other embodiment of that aspect, the value of the monitored environmental parameter is stored for subsequent use in automatically calibrating the analyte sensor system.

[0075] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the temperature of the analyte sensor while in the sterile package includes determining whether the temperature is above or below a pre-established threshold.

[0076] In an embodiment of the first aspect, or any other embodiment of that aspect, monitoring the temperature of the analyte sensor while in the sterile package includes determining whether humidity is above or below a pre-established threshold.

[0077] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial characteristic metric reflects an initial sensor sensitivity.

[0078] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial characteristic metric reflects an initial sensor sensitivity and baseline value.

[0079] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial characteristic metric reflects an initial sensor sensitivity profile.

[0080] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial calibration coefficients are derived from a sensor characteristic metric.

[0081] In an embodiment of the first aspect, or any other embodiment of that aspect, a change in the initial sensor characteristic is indicative of a sensor failure.

[0082] In an embodiment of the first aspect, or any other embodiment of that aspect, the one or more production parameters are measured prior to the second time.

[0083] In an embodiment of the first aspect, or any other embodiment of that aspect, the one or more production parameters are measured prior to the first time.

[0084] In a second aspect, a method is provided for self-calibration of an analyte sensor system including an analyte sensor operably coupled to sensor electronics, the method including: applying, with the sensor electronics, a bias voltage to the analyte sensor to generate sensor data, the analyte sensor system having an initial characteristic metric determined at a first time when the analyte sensor is operably connected to one or more components of the sensor electronics; using the sensor electronics at a second time following the first time to determine a change in the initial characteristic metric of the analyte sensor system based at least in part on one or more manufacturing and / or environmental parameters, the second time being prior to or during in vivo use of the sensor; and using the sensor electronics to automatically calibrate the analyte sensor system without user involvement based at least in part on the change in the determined initial characteristic metric.

[0085] In a third aspect, a method for self-calibration of an analyte sensor system including an analyte sensor operably coupled to sensor electronics is provided, the method including: applying, with the sensor electronics, a bias voltage to the analyte sensor to generate sensor data, the analyte sensor system having an initial calibration coefficient used to convert the sensor data to an analyte concentration value; using the sensor electronics to update the calibration coefficient of the analyte sensor system multiple times during one or more life phases of the analyte sensor based at least in part on one or more manufacturing and / or environmental parameters monitored during the one or more life phases; and using the sensor electronics to automatically calibrate the analyte sensor system based at least in part on the updated calibration coefficient without user involvement.

[0086] In an embodiment of the third aspect, or any other embodiment of that aspect, the one or more life phases comprises a plurality of life phases.

[0087] In an embodiment of the third aspect, or any other embodiment of that aspect, the multiple life phases include manufacturing, shipping, storage, insertion, and use phases.

[0088] In an embodiment of the third aspect, or any other embodiment of that aspect, using the sensor electronics to update the calibration coefficients of the analyte sensor system includes determining complex adaptive calibration values ​​that are based at least in part on manufacturing and environmental conditions experienced by the analyte sensor during multiple life phases of the analyte sensor.

[0089] In an embodiment of the third aspect, or any other embodiment of that aspect, the manufacturing parameters include process parameters and / or design parameters.

[0090] In an embodiment of the third aspect, or any other embodiment of that aspect, the manufacturing parameters include process parameters, the process parameters including temperature, humidity, cure, time, and soak time.

[0091] In an embodiment of the third aspect, or any other embodiment of that aspect, the manufacturing parameters include design parameters, the design parameters including a thickness and raw material properties of the analyte sensor membrane.

[0092] In an embodiment of the third aspect, or any other embodiment of that aspect, the sensor electronics are used to receive remotely stored sensor performance data for updating the calibration coefficients.

[0093] In an embodiment of the third aspect, or any other embodiment of that aspect, the received remotely stored sensor performance data pertains to analyte sensors that have experienced or been exposed to manufacturing and / or environmental parameters that are most similar to one or more of the monitored manufacturing and / or environmental parameters.

[0094] In a fourth aspect, a method is provided in which a sensor experiences multiple life phases including manufacturing, shipping, storage, and insertion, as well as use by a user as part of a sensor session, the method including disposing measurement electronics in operative connection to the sensor, determining a first calibration factor during the manufacturing life phase in a factory that manufactures the sensor using multiple manufacturing parameters, determining a second calibration factor during the shipping or storage phase, and calibrating a signal from the sensor once inserted into a user using a combined calibration factor of the user monitoring device, the combined calibration factor being based on both the first calibration factor and the second calibration factor.

[0095] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration coefficient is stored in the sensor electronics or in measurement electronics associated with the sensor assembly.

[0096] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the measurement electronics forms part of the sensor electronics.

[0097] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the measurement electronics is separate from the sensor electronics.

[0098] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the measurement electronics are disposed in the same package as the sensor electronics.

[0099] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the measurement electronics are disposed in a different package than the sensor electronics.

[0100] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the sensor assembly and measurement electronics are disposed in a package for shipping.

[0101] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the user monitoring device is a dedicated receiver or a smartphone.

[0102] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the transmitting is from the sensor electronics or measurement electronics to a dedicated receiver or smartphone.

[0103] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the second calibration coefficient is stored in the measurement electronics or the sensor electronics.

[0104] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the combined calibration coefficients are transmitted to a cloud server.

[0105] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the combined calibration coefficient, or the second calibration coefficient, or both, are transmitted to a factory to cause a change in one of the plurality of manufacturing parameters.

[0106] In an embodiment of the fourth aspect, or any other embodiment of that aspect, measuring the second calibration factor is performed by measurement electronics.

[0107] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration factor is a system level calibration factor that pertains to the calibration of all components in the sensor assembly.

[0108] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the sending further includes sending the sensor tracking number or serial number along with the combined calibration coefficient to a cloud server or a factory, so that a lot associated with the sensor can be identified.

[0109] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the measurement electronics is configured to detect a fault in the sensor electronics or the sensor.

[0110] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the transmitting further includes transmitting data about a detected fault in the sensor electronics or the sensor.

[0111] In an embodiment of the fourth aspect, or any other embodiment of that aspect, calibration coefficients stored within the user monitoring device are modified to compensate for the detected fault.

[0112] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the measurement electronics is configured to detect an electrical signal from the sensor wire, the sensor electronics, the housing, or a combination.

[0113] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the combined calibration coefficient is configured to compensate for individual process and shipping / storage variations of the sensor in vivo.

[0114] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration factor or the second calibration factor, or both, are indicative of a measured impedance.

[0115] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the impedance measurement is performed by measuring a step response at a single frequency or at multiple frequencies.

[0116] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the third calibration factor is measured prior to shipping, and the third calibration factor is indicative of impedance.

[0117] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration factor or the second calibration factor, or both, are indicative of a measured temperature.

[0118] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration factor or the second calibration factor, or both, are indicative of a measured humidity.

[0119] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the combined calibration coefficients are used to calculate a corrected calibration value, to detect physical damage to the sensor, or to detect exposure of the sensor assembly to temperature and / or humidity.

[0120] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the combined calibration factor is a complex adaptive value that combines calibration values ​​collected across sensor manufacturers and the conditions experienced between the time from sensor manufacturer to sensor insertion.

[0121] In an embodiment of the fourth aspect, or any other embodiment of that aspect, a user is selected to receive the sensor based on the first calibration coefficient, whereby population data or individual user data determines that a sensor having the first calibration coefficient is optimized for the user.

[0122] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the user is known to have a high average glucose level, and the first calibration factor is of relatively low sensitivity.

[0123] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the manufacturing life phase includes a packaging phase in which the pre-connected sensor assembly is packaged in a sterile package, and the first calibration coefficient is determined after the pre-connected sensor assembly is packaged in the sterile package.

[0124] In a fifth aspect, there is provided an improved method of calibrating a sensor associated with a pre-connected sensor assembly, where the sensor experiences multiple life phases including manufacturing, shipping, storage, and insertion, as well as use in a user as part of a sensor session, the method including disposing measurement electronics in operative connection to the sensor electronics, determining a first calibration factor during a manufacturing life phase at a factory where the sensor is manufactured using multiple manufacturing parameters, determining a second calibration factor during a shipping or storage phase, and calculating a combined calibration factor once inserted in a user and storing the same combined calibration factor in the sensor electronics, the combined calibration factor being based on both the first calibration factor and the second calibration factor, and the combined calibration factor configured to provide a conversion between a detected signal from the sensor wire and an analyte concentration in the user.

[0125] In an embodiment of the fifth aspect, or any other embodiment of that aspect, an indication of the analyte concentration is displayed.

[0126] In an embodiment of the fifth aspect, or any other embodiment of that aspect, the displaying occurs on a user monitoring device in signal communication with the sensor electronics.

[0127] In an embodiment of the fifth aspect, or any other embodiment of that aspect, the user monitoring device is a dedicated receiver or a smartphone.

[0128] In a sixth aspect, an improved method of manufacturing a sensor assembly including a sensor wire, a housing, and sensor electronics is provided, the method including pre-connecting at least the sensor wire to at least a portion of the sensor electronics sufficient to monitor manufacturing parameters, monitoring the manufacturing parameters while completing manufacturing of the sensor assembly, and modifying one or more of the manufacturing parameters during a subsequent manufacturing process used to manufacture additional sensor assemblies, the modifying being based at least in part on the monitored manufacturing parameters.

[0129] In an embodiment of the sixth aspect, or any other embodiment of that aspect, the sensor electronics are pre-connected to the sensor wire, but the battery and radio remain disconnected.

[0130] In an embodiment of the sixth aspect, or any other embodiment of that aspect, the battery is pre-connected to the sensor wires and a portion of the sensor electronics sufficient to monitor the manufacturing parameters.

[0131] In an embodiment of the sixth aspect, or any other embodiment of that aspect, the radio is pre-connected to a battery and sensor wires, as well as a portion of the sensor electronics sufficient to monitor manufacturing parameters.

[0132] In an embodiment of the sixth aspect, or any other embodiment of that aspect, the combined error of the pre-connected sensor wire and the portion of sensor electronics sufficient to monitor the manufacturing parameter therein is less than the reflected or summed error of the sensor wire and the portion of sensor electronics sufficient to monitor the manufacturing parameter considered separately.

[0133] In a seventh aspect, an improved pre-connected sensor assembly is provided comprising a sensor wire, a housing, and sensor electronics, where the sensor is pre-connected to the housing and / or the sensor electronics, and the sensor wire is at least pre-connected to an interposer configured to enable measurement of a physical attribute of the sensor without requiring a direct connection to the sensor wire.

[0134] In an embodiment of the seventh aspect, or any other embodiment of that aspect, the battery is pre-connected to the sensor wires and housing, and / or the sensor electronics.

[0135] In an embodiment of the seventh aspect, or any other embodiment of that aspect, the radio is pre-connected to the battery and the sensor wires and housing, and / or the sensor electronics.

[0136] In an eighth aspect, a method is provided for self-calibration of an analyte sensor system including an analyte sensor operably coupled to sensor electronics, the method comprising: operably coupling the analyte sensor to one or more components of the sensor electronics at a first time to define a packagable analyte sensor array, the packagable sensor array having an initial sensitivity metric determined subsequent to the first time; applying an analyte interrogation signal to the analyte sensor using the one or more components of the sensor electronics at a second time subsequent to the first time, measuring a signal response to the stimulation signal, determining a second sensitivity metric based at least in part on the measured signal response; and automatically calibrating the packagable sensor array based at least in part on the initial sensitivity metric and the second sensitivity metric without user intervention.

[0137] In an embodiment of the eighth aspect, or any other embodiment of that aspect, the analyte sensor is operably coupled to one or more components of the sensor electronics uninterruptedly and continuously between the first time and the second time.

[0138] In an embodiment of the eighth aspect, or any other embodiment of that aspect, applying the analyte interrogation signal comprises applying a stimulation signal to the analyte sensor, and measuring the signal response comprises measuring an impedance of the packagable analyte sensor array.

[0139] In an embodiment of the eighth aspect, or any other embodiment of that aspect, automatically calibrating the packagable sensor array is based on an established relationship between impedance and analyte sensor sensitivity, and automatically calibrating the packagable sensor array includes automatically calibrating the packagable sensor array in vivo.

[0140] In a ninth aspect, a method is provided for performing operations using an analyte sensor system including an analyte sensor operably coupled to sensor electronics, the method including: applying, using the sensor electronics, a bias voltage to the analyte sensor to generate sensor data, the analyte sensor system having an initial characteristic metric determined at a first time when the analyte sensor is operably connected to one or more components of the sensor electronics; using the sensor electronics at a second time following the first time to determine a change in the initial characteristic metric of the analyte sensor system based at least in part on one or more manufacturing and / or environmental parameters, the second time being prior to or during in vivo use of the sensor; and performing an operation selected from the group including generating a message, initiating a recalibration process, using a default calibration value, and using a temperature and / or humidity compensated calibration value based at least in part on the determined change in the initial characteristic metric.

[0141] In an embodiment of the ninth aspect, or any other embodiment of that aspect, generating the message includes generating an error message.

[0142] In an embodiment of the ninth aspect, or any other embodiment of that aspect, generating the message includes generating a message requesting a manual recalibration.

[0143] These, as well as other features and advantages, will be appreciated as the same becomes better understood by reference to the following detailed description when considered in conjunction with the accompanying drawings. [Brief description of the drawings]

[0144] [Figure 1] FIG. 1 is a schematic diagram of an analyte sensor system attached to a host and in communication with multiple exemplary devices, according to some embodiments. [Diagram 2] 2 is a block diagram illustrating electronics associated with the sensor system of FIG. 1 in accordance with some embodiments. [Diagram 3] FIG. 1 shows a perspective view of a wearable device having an analyte sensor according to some embodiments. [Figure 4] FIG. 1 shows a schematic diagram of a pre-connected analyte sensor according to some embodiments. [Diagram 5] FIG. 1 illustrates a block diagram of a system having a manufacturing system and a wearable device for an analyte sensor, according to some embodiments. [Figure 6] 1 shows a schematic diagram of sensor sensitivity as a function of time during a sensor session, according to one embodiment. [Figure 7] 7 shows a schematic diagram of a conversion function at different periods of a sensor session according to the embodiment of FIG. 6. [Figure 8A] 1 illustrates examples of different phases in the life cycle of an analyte sensor system. [Figure 8B]1 illustrates examples of different phases in the life cycle of an analyte sensor system. [Figure 9] 1 shows a schematic block diagram of one particular example of a pre-connected analyte sensor system. [Figure 10] A 5000-sample Monte Carlo simulation using randomly selected numbers within the statistical distribution of the input variables comparing a non-prewired system with a prewired system. [Figure 11] 1 illustrates an example of an automatic calibration process that can be performed by sensor electronics in an analyte monitoring system without user involvement. [Figure 12] Included are time curves showing monitored temperature (a), humidity (b), and sensitivity (c), respectively, through various phases over the life of the analyte sensor. [Figure 13] 1 illustrates sensor output signals obtained from an analyte sensor during various steps in a manufacturing process. [Figure 14] 1 shows the NMR spectrum of PVP in DMSO-d6. [Figure 15] 1 shows the HNMR spectrum of Carbosil in DMSO. [Figure 16] 1 shows the HNMR spectrum of the RL membrane (Carbosil / PVP mixture from which the solvent has been removed). [Figure 17] 1 shows the composition of RL solutions prepared using different Carbosil / PVP ratios. [Figure 18] HNMR calibration curve is shown. [Figure 19] 1 is a graph showing initial sensor drift when ethylene oxide (ETO) sterilization is used. [Figure 20] 1 illustrates examples of different life phases that an analyte sensor can go through. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0145] definition To facilitate understanding of the embodiments described herein, several terms are defined below.

[0146] As used herein, the term "analyte" is a broad term given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, a substance or chemical component within a bodily fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymphatic fluid, or urine) that may be analyzed. Analytes may include naturally occurring substances, man-made substances, metabolic products, and / or reaction products. In some embodiments, the analyte that is subject to measurement by the sensor heads, devices, systems, and methods disclosed herein is glucose. However, other analytes are considered as well, including acarboxyprothrombin, acylcarnitines, adenine phosphoribosyltransferase, adenosine deaminase, albumin, α-fetoprotein, amino acid composition (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan), andrenostenedione, antipyrine, arabinitol enantiomers, arginase, benzoylecgonine (cocaine), biotinidase, biopterin, c-reactive protein, carnitine, carnosinase, CD4, ceruloplasmin, chenodeoxycholic acid, chloroquine, cholesterol, cholinesterase, conjugated 1-β-hydroxycholic acid, cortisol, creatine kinase, creatine kinase MM isoenzyme, and cyclosporine A. , d-penicillamine, de-ethylchloroquine, dehydroepiandrosterone sulfate, DNA (acetylation polymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobinopathies, A, S, C, E, D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber's hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sex differentiation, 21-deoxycortisol), desbutylhalofantrine, dihydropteridine reductase, diphtheria / tetanus antitoxin, erythrocyte arginase, erythrocyte protoporphyrin, esterase D, fatty acids / acylglycines, free beta-human chorionic gonadotropin, free erythrocyte porphyrin, free thyroxine (FT4),Free tri-iodothyronine (FT3), fumarylacetoacetase, galactose / gal-1-phosphate, galactose-1-phosphate uridyltransferase, gentamicin, analyte-6-phosphate dehydrogenase, glutathione, glutathione peroxidase, glycocholate, glycosylated hemoglobin, halofantrine, hemoglobin variants, hexosaminidase A, human erythrocyte carbonic anhydrase I, 17α-hydroxyprogesterone, hypoxanthine phosphoribosyltransferase, immunoreactive Reactive trypsin, lactate, lead, lipoproteins ((a), B / A-1, β), lysozyme, mefloquine, netilmicin, phenobarbitone, phenytoin, phytanic acid / pristanic acid, progesterone, prolactin, prolidase, purine nucleoside phosphorylase, kinin, inverted tri-iodothyronine (rT3), selenium, serum pancreatic lipase, sisomicin, somatomedin C, specific antibodies (adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's disease virus, dengue virus, Medina virus, Insects, Tapeworm, Entamoeba histolytica, Enterovirus, Giardia lamblia, Helicobacter pylori, Hepatitis B virus, Herpes virus, HIV-1, IgE (atopic disease), Influenza virus, Leishmania donovani, Leptospira, Measles / Mumps / Rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, Parainfluenza virus, Plasmodium falciparum, Poliovirus, Pseudomonas aeruginosa, Respiratory exanthematous virus, Rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Plum Analytes that may be present include, but are not limited to, venom treponema, Trypanosoma cruzi / Langer, Vesicular stomatitis virus, Wuchereria bancrofti, Yellow fever virus), specific antigens (Hepatitis B virus, HIV-1), acetoacetate, sulfadoxine, theophylline, thyrotropin (TSH), thyroxine (T4), thyroxine-binding globulin, trace elements, transferrin, UDP-galactose-4-epimerase, urea, uroporphyrinogen I synthase, vitamin A, leukocytes, and zinc protoporphyrin. Salts, sugars, proteins, fats, vitamins, and hormones that occur naturally in blood or interstitial fluids may also constitute analytes in certain embodiments. Analytes,For example, metabolites, hormones, antigens, antibodies, etc. may naturally occur in biological fluids. Alternatively, analytes, such as contrast agents for diagnostic imaging, radioisotopes, chemical agents, fluorocarbon-based artificial blood, or drugs or pharmaceutical compositions may be introduced into the body, such as insulin, ethanol, cannabis (marijuana, tetrahydrocannabinol, hashish), inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons), cocaine (crack cocaine), stimulants (amphetamines, methamphetamines, Ritalin, Silate, Preludin, Didrex, Prestate, Boranil, Sandrex, Predine), depressants (barbiturates, methaqualone, psychotropic drugs, etc.), and the like. Fixed drugs such as Valium, Librium, Miltown, Serax, Equinyl, Tranxine), hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin), narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tasionex, fentanyl, Darvon, Talwin, Lowmortil), designer drugs (fentanyl, meperidine, amphetamine, methamphetamine, and analogs of phencyclidine such as ecstasy), anabolic steroids, and nicotine can be introduced into the body. Metabolites of drugs and pharmaceutical compositions are also contemplated as analytes. Analytes such as neurochemical drugs and other chemicals produced in the body may also be analyzed, for example, ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), and 5-hydroxyindoleacetic acid (FHIAA).

[0147] As used herein, the terms "continuous analyte sensor" and "continuous glucose sensor" are broad terms that are to be given their ordinary and customary meanings to those of skill in the art (and are not to be limited to any special or customized meanings) and refer to, but are not limited to, a device that measures analyte / glucose concentration continuously or continuously and / or calibrates the device (e.g., by continuously or continuously adjusting or determining the sensitivity and background of the sensor) at time intervals ranging, for example, from a fraction of a second up to, for example, 1, 2, or 5 minutes or more.

[0148] As used herein, the term "biological sample" is a broad term that is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning) and refers to a sample derived from a host's body or tissue, such as, but not limited to, blood, interstitial fluid, spinal fluid, saliva, urine, tears, sweat, or other similar fluids.

[0149] As used herein, the term "host" is a broad term that is given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers to mammals, including, but not limited to, humans.

[0150] As used herein, the term "membrane system" is a broad term given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, a permeable or semi-permeable membrane that may consist of two or more domains and may typically be constructed of a material several microns or more thick that is permeable to oxygen and possibly glucose. In one example, the membrane system includes an immobilized glucose oxidase enzyme that allows an electrochemical reaction to occur to measure the concentration of glucose.

[0151] As used herein, the term "domain" is a broad term that is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning) and refers to, but is not limited to, a region of a film that may be a layer, a uniform or non-uniform gradient (e.g., anisotropy), a functional aspect of a material, or provided as part of a film.

[0152] As used herein, the term "sensing region" is a broad term that is to be given its ordinary and customary meaning to those skilled in the art (and is not to be limited to any special or customized meaning) and refers to, but is not limited to, a region of a monitoring device that is responsible for the detection of a particular analyte. In one embodiment, the sensing region generally includes a non-conductive body, at least one electrode, a reference electrode, and optionally a counter electrode fixed within the body through the body that forms an electronic connection with an electroactive surface at one location on the body and another location on the body, and a membrane system affixed to the body and covering the electroactive surface.

[0153] As used herein, the term "electroactive surface" is a broad term that is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning) and refers to, but is not limited to, the surface of an electrode where an electrochemical reaction occurs. In one embodiment, the working electrode measures hydrogen peroxide (H2O2) to produce a measurable electronic current.

[0154] As used herein, the term "baseline" is a broad term that is given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, a component of an analyte sensor signal that is unrelated to analyte concentration. In one example of a glucose sensor, the baseline is substantially composed of signal contributions due to factors other than glucose (such as interfering species, non-reactive associated hydrogen peroxide, or other electroactive species with an oxidation potential that overlaps with hydrogen peroxide). In some embodiments where the calibration is defined by solving the equation y=mx+b, the value of b represents the baseline of the signal. In certain embodiments, the value of b (i.e., the baseline) may be zero or near zero. This may be the result of, for example, a baseline subtraction electrode or a low bias potential setting. As a result, for these embodiments, the calibration may be defined by solving the equation y=mx.

[0155] As used herein, the term "inactive enzyme" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, an enzyme (e.g., glucose oxidase, GOx) that is incorporated in an inactive state (e.g., by denaturation of the enzyme) and has substantially no enzymatic activity. Enzymes can be inactivated using a variety of techniques known in the art, such as, for example, heating, freeze-thawing, denaturation in organic solvents, acid or base, cross-linking, genetically altering enzymatically important amino acids, etc. In some embodiments, a solution containing an active enzyme can be applied to the sensor, and the applied enzyme is then inactivated by heating or treatment with an inactivating solvent.

[0156] As used herein, the term "non-enzymatic" is a broad term given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, a lack of enzyme activity. In some embodiments, the "non-enzymatic" membrane portion does not contain enzyme, whereas in other embodiments, the "non-enzymatic" membrane portion contains an inactive enzyme. In some embodiments, an enzyme solution is applied that contains an inactive enzyme or no enzyme at all.

[0157] As used herein, the term "substantially" is a broad term that is to be given its ordinary and accustomed meaning to those of ordinary skill in the art (and is not to be limited to any special or customized meaning) and refers to most, but not necessarily all, of what is specified.

[0158] As used herein, the term "about" is a broad term that is to be given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and when associated with any numerical value or range, refers to, but is not limited to, the understanding that the amount or condition that the term modifies can be varied somewhat beyond the stated amount so long as the disclosed functionality is achieved.

[0159] As used herein, the term "ROM" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, a read-only memory, which is a type of data storage device manufactured with fixed contents. ROM is broad enough to include, for example, EEPROM, which is an electrically erasable programmable read-only memory (ROM).

[0160] As used herein, the term "RAM" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, a data storage device in which the order of access to different locations does not affect the speed of access. RAM is broad enough to include, for example, SRAM, which is a static random access memory that retains data bits in memory as long as power is applied.

[0161] As used herein, the term "A / D converter" is a broad term that should be given its ordinary and accustomed meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, hardware and / or software that converts an analog electrical signal into a corresponding digital signal.

[0162] As used herein, the terms "raw data stream" and "data stream" are broad terms given their ordinary and customary meanings to those of skill in the art (and are not limited to any special or customized meanings) and refer to, but are not limited to, an analog or digital signal directly related to an analyte concentration measured by an analyte sensor. In one example, the raw data stream is digital data in counts (e.g., voltage or amperage) that represents the analyte concentration, converted by an A / D converter from an analog signal. These terms broadly encompass multiple time-spaced data points from a substantially continuous analyte sensor that includes individual measurements taken at time intervals ranging from a tenth of a second up to, for example, 1, 2, or 5 minutes or longer.

[0163] As used herein, the term "counts" is a broad term given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, a unit of measurement for a digital signal. In one embodiment, the raw data stream measured in counts is directly related to a voltage (e.g., converted by an A / D converter), which is directly related to a current from the working electrode.

[0164] As used herein, the term "sensor electronics" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, the components of a device (e.g., hardware and / or software) configured to process data. In the case of an analyte sensor, the data includes biological information obtained by the sensor regarding the concentration of an analyte in a biological fluid. U.S. Patent Nos. 4,757,022, 5,497,772, and 4,787,398 describe suitable electronic circuitry that may be utilized with certain embodiment devices.

[0165] As used herein, the term "potentiostat" is a broad term given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers, without limitation, to an electrical system that applies a potential at a preset value between a working electrode and a reference electrode of a two-electrode or three-electrode cell and measures the current through the working electrode. The potentiostat forces the necessary current to flow between the working and counter electrodes to maintain the required potential, as long as the required cell voltage and current do not exceed the compliance limits of the potentiostat.

[0166] As used herein, the term "operably connected" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, one or more components being linked to another component(s) in a manner that allows for the transmission of a signal between the components. For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, which can then be transmitted to an electronic circuit. In this case, the electrodes are "operably linked" to the electronic circuit. These terms are broad enough to include wired and wireless connections.

[0167] As used herein, the term "filtering" is a broad term that is to be given its ordinary and customary meaning to those skilled in the art (and is not to be limited to any special or customized meaning) and refers to, but is not limited to, the modification of a set of data to make it smoother or more continuous, or to remove or reduce outliers, for example, by performing a moving average of a raw data stream.

[0168] As used herein, the term "algorithm" is a broad term that is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning) and refers to, but is not limited to, a computational process (e.g., a program) involved in transforming information from one state to another, e.g., using computer processing.

[0169] As used herein, the term "calibration" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, the process of determining the calibration of a sensor to provide a quantitative measurement (e.g., analyte concentration). As an example, calibration may be updated or recalibrated over time to account for changes associated with the sensor, such as changes in sensor sensitivity and sensor background. Additionally, calibration of a sensor may include automatic, self-calibration, e.g., without the use of a reference analyte value after the point of use.

[0170] As used herein, the term "sensor data" is a broad term given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, data received from a continuous analyte sensor that includes one or more temporally and spatially located sensor data points.

[0171] As used herein, the terms "reference analyte value" and "reference data" are broad terms given their ordinary and customary meanings to those of skill in the art (and are not limited to any special or customized meanings) and refer to, but are not limited to, reference data from a reference analyte monitoring device, such as a blood glucose meter, that includes one or more reference data points. In some embodiments, the reference glucose value is obtained, for example, from a self-monitoring blood glucose (SMBG) test (e.g., from a finger or forearm blood test) or from a YSI (Yellow Springs Instruments) test.

[0172] As used herein, the terms "interferent" and "interfering species" are broad terms that are to be given their ordinary and customary meanings to those of skill in the art (and are not to be limited to any special or customized meanings) and refer to, but are not limited to, influences and / or species that interfere with the measurement of an analyte of interest in a sensor to produce a signal that does not accurately represent the analyte measurement. In one example of an electrochemical sensor, an interfering species is a compound that has an oxidation potential that overlaps with the analyte being measured, producing a positive spurious signal.

[0173] As used herein, the term "sensor session" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, the period during which a sensor is applied to (e.g., implanted within) a host or sensor values ​​are obtained using the sensor. For example, in some embodiments, a sensor session extends from the time of implantation of the sensor (e.g., including inserting the sensor into subcutaneous tissue and placing the sensor in fluid communication with the host's circulatory system) to the time when the sensor is removed.

[0174] As used herein, the term "sensitivity" or "sensor sensitivity" is a broad term given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, the amount of signal generated by a given concentration of the analyte being measured or the sample being measured (e.g., H2O2) associated with the analyte being measured (e.g., glucose). For example, in one embodiment, the sensor has a sensitivity of about 1 to about 300 picoamps of current for every mg / dL of glucose analyte.

[0175] As used herein, the term "sensitivity profile" or "sensitivity curve" is a broad term that is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning) and refers to, but is not limited to, a display of change in sensitivity over time.

[0176] As used herein, the term "process set point" is a broad term that is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning) and refers to, but is not limited to, a desired or target value for a certain variable or process of a system.

[0177] As used herein, the term "process variation" is a broad term that is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning) and refers to the degree of deviation from a set point, usually expressed, but not limited to, as a standard deviation.

[0178] As used herein, the term "Monte Carlo simulation" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), including, but not limited to, defining a universe of possible inputs, randomly generating inputs from probability distributions across that universe, performing deterministic computations on the inputs, and aggregating the results. Monte Carlo simulation samples from the probability distributions for each variable to generate hundreds or thousands of possible outcomes. The results are analyzed to obtain the probability of different outcomes occurring.

[0179] As used herein, the term "accuracy" is a broad term that is given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, the closeness of a measurement to a standard or known value.

[0180] As used herein, the term "accuracy" is a broad term that is to be given its ordinary and accustomed meaning to those of skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, the degree to which repeated measurements under unchanging conditions give the same result.

[0181] overview Commercially available transcutaneous analyte measurement systems consist of separate modules that are physically interconnected just prior to or at the time of final sensor deployment. Analyte sensor modules are typically characterized by various measurement factors (e.g., analyte sensitivity, baseline, impedance, capacitance, temperature, time, humidity, interference sensitivity, etc.). Historically, these characteristics have been quantified once the analyte sensor manufacturing process is complete, using manufacturing test equipment. These measurements are performed on the sensor subsystem using a test configuration, such as placing the analyte sensor in one or more solutions of known analyte concentration.

[0182] Measurements derived from the manufacturing process of an analyte sensor may sometimes be used to create one or more metrics of sensor performance. These metrics may be transferred to the analyte algorithm processing unit using a variety of methods (e.g., calibration codes, wireless transfer, lot matching). In other embodiments, the measurements are used to determine whether an individual sensor, or a lot of sensors, meets acceptable quality standards. Analyte sensor metrics, in vivo calibration, sensor environmental conditions, and a priori information are typical inputs to the analyte algorithm processing unit.

[0183] Conventional in vivo continuous analyte sensing techniques typically rely on reference measurements performed during a sensor session to calibrate a continuous analyte sensor. The reference measurements are matched with substantially time-corresponding sensor data to create matched data pairs. A regression is then performed on the matched data pairs (e.g., by using least squares regression) to generate a transformation function that defines the relationship between the sensor signal and the estimated glucose concentration.

[0184] In an important note setting, calibration of continuous analyte sensors may often be performed by using a calibration solution with a known concentration of analyte as a reference. This calibration procedure can be cumbersome as the calibration bag is separate from (and in addition to) the IV (intravenous) bag typically used. In an outpatient setting, calibration of continuous analyte sensors has traditionally been performed by capillary blood glucose measurements (e.g., finger stick glucose tests) through which reference data is obtained and entered into the continuous analyte sensor system. This calibration procedure typically involves the use of finger stick measurements, which are often inconvenient and can be painful.

[0185] Traditionally, systems and methods for in vitro calibration (e.g., factory calibration) of continuous analyte sensors by manufacturers have often been inaccurate with respect to high levels of sensor accuracy without relying on periodic recalibration. Part of this may be due to changes in sensor characteristics (e.g., sensor sensitivity) that may occur during sensor use. Thus, calibration of continuous analyte sensors typically requires periodic input of reference data, whether or not those inputs are associated with calibration solutions or fingerstick measurements. As previously mentioned, such inputs can be very burdensome for patients, regardless of the setting.

[0186] Described herein are continuous analyte sensors that are either factory calibrated or allow for continuous automatic self-calibration during a sensor session, and can achieve high levels of accuracy without (or with reduced reliance on) reference data from a reference analyte monitor (e.g., from a blood glucose meter). Factory calibration typically refers to an initial calibration that is performed before the sensor leaves the factory and is not altered over time. In contrast, automatic self-calibration refers to a process in which the calibration is updated without user involvement at one or more time intervals following the factory calibration, where the updates are based on information obtained during the manufacture and / or subsequent life phases of the analyte sensor. This calibration update is typically accomplished by sending a signal, for example from the cloud, to the sensor or sensor electronics.

[0187] FIG. 20 shows examples of various life phases that an analyte sensor may undergo, including a sensor manufacturing phase, a sensor packaging phase, a sensor storage phase, a pre-in vivo phase, and a sensor session phase. As will be discussed in more detail below, in some cases, the sensor may undergo additional or fewer life phases. At various times t during these phases, the complex adaptive calibration coefficient C(t,p i ) can be generated, whose coefficients vary with time t since the sensor was manufactured and with various parameters p i where i>1. These parameters p i represents the environmental conditions experienced by the analyte sensor (and any pre-connected electronics, if present), for example, from sensor manufacture to sensor use during the sensor session phase, possibly combined with additional information such as patient-specific data. This complex adaptive calibration factor may be calculated using one or more initial calibration factors C m The initial calibration factor C can be calculated by multiplying the initial calibration coefficient C by 1000. Mis obtained by a “calibration check” procedure in the factory where the sensor undergoes in vitro calibration. At each of the following times, such as t1 (during the shipping phase) and t2 (during the sensor session phase), a complex adaptive calibration coefficient C t1 and C t2 is C M1 , and parameter measurements, respectively. Additional complex adaptive calibration coefficients can be obtained during the shipping phase (e.g., C S1 , C S2 , ..., C Sn ), storage phase (C ST1 and C ST2 ), pre-in vivo phase (e.g., C P ), and the sensor session phase (e.g., C SS1 , C SS2 , ..., C SSn ) in order to provide a measurement of the sensitivity of the analyte sensor over a period of time. In this manner, the experience of the analyte sensor over its lifetime is encoded in a format that allows it to be used by a suitable calibration algorithm to determine, for example, the sensitivity of the sensor and / or a baseline value for the sensor.

[0188] In some embodiments, the continuous analyte sensor is an invasive, minimally invasive, or non-invasive device. The continuous analyte sensor can be a subcutaneous, transcutaneous, or intravascular device. In certain embodiments, one or more of these devices can form a continuous analyte sensor system. For example, the continuous analyte sensor system can consist of a combination of subcutaneous and transcutaneous devices, a combination of subcutaneous and intravascular devices, a combination of transcutaneous and intravascular devices, or a combination of subcutaneous, transcutaneous, and intravascular devices. In some embodiments, the continuous analyte sensor can analyze multiple intermittent biological samples (e.g., blood samples). The continuous analyte sensor can use any glucose measurement method, including those that use mechanisms such as enzymatic, chemical, physical, electrochemical, spectroscopic, polarimetric, calorimetric, iontophoretic, and radiometric.

[0189] In certain embodiments, a continuous analyte sensor includes one or more working electrodes and one or more reference electrodes that operate together to measure a signal associated with the concentration of an analyte in a host. The output signal from the working electrode is typically a raw data stream that is calibrated, processed, and used to generate an estimated analyte (e.g., glucose) concentration. In certain embodiments, the continuous analyte sensor can measure an additional signal associated with the baseline and / or sensitivity of the sensor, thereby allowing monitoring of the baseline and / or additional monitoring of sensitivity changes or drift that may occur in the continuous analyte sensor over time.

[0190] In some embodiments, the sensor extends through a housing, which maintains the sensor on the skin and provides an electrical connection of the sensor to the sensor electronics. In one embodiment, the sensor is formed from a wire. For example, the sensor can include an elongated conductive body, such as a bare elongated conductive core (e.g., a metal wire), or an elongated conductive core coated with one, two, three, four, five or more layers of material, each of which may or may not be conductive. The elongated sensor can be long and thin, yet flexible and strong. For example, in some embodiments, the smallest dimension of the elongated conductive body is less than about 0.1 inches, less than about 0.075 inches, less than about 0.05 inches, less than about 0.025 inches, less than about 0.01 inches, less than about 0.004 inches, or less than about 0.002 inches. Other embodiments of elongated conductive bodies are disclosed in U.S. Patent Application Publication No. 20110027127A1, which is incorporated herein by reference in its entirety. A membrane system is desirably deposited over at least a portion of the electroactive surfaces of the sensor 102 (including the working electrode and, optionally, the reference electrode) to provide protection of exposed electrode surfaces from the biological environment, diffusion resistance (limitation) of analytes if necessary, catalysts to enable enzymatic reactions, limiting or blocking interferents, and / or hydrophilicity at the electrochemically reactive surfaces of the sensor interface. Disclosures regarding different membrane systems that may be used with the embodiments described herein are described in U.S. Patent Publication No. US20090247856A1, which is incorporated herein by reference in its entirety.

[0191] In the prior art, calibrating sensor data from a continuous analyte sensor typically required defining a relationship between measurements generated by the sensor (e.g., in units of nA, or digital counts after A / D conversion) and one or more reference measurements (e.g., in units of mg / dL or mmol / L). In certain embodiments, one or more reference measurements taken shortly after manufacture of the analyte sensor and prior to use of the sensor are used for calibration. The reference measurements could have been taken in many forms. For example, in certain cases the reference measurements may be determined from in vivo analyte concentration measurements.

[0192] With factory calibration or automatic self-calibration, the need for recalibration may be omitted or reduced by using reference data during a sensor session, such that recalibration may be invoked only in certain limited circumstances, such as upon detecting sensor failure. Additionally or alternatively, in some embodiments, a continuous analyte sensor may be configured to request and accept one or more reference measurements (e.g., from a finger prick glucose measurement or a calibration solution) at the start of a sensor session. In some embodiments, the use of a reference measurement at the start of a sensor session in conjunction with a predefined sensor sensitivity profile may eliminate or substantially reduce the need for further reference measurements.

[0193] Turning to a basic description of glucose sensor functionality, with certain implantable enzyme-based electrochemical glucose sensors, the sensing mechanism relies on certain phenomena that have an approximately linear relationship with glucose concentration, such as (1) the diffusion of the analyte through a membrane system located between the implantation site (e.g., the subcutaneous space) and the electroactive surface, (2) the rate of an enzyme-catalyzed reaction of the analyte to produce a measurand within the membrane system (e.g., the rate of the glucose oxidase reaction of glucose with O2 to produce gluconic acid and H2O2), and (3) the diffusion of the measurand (e.g., H2O2) to the electroactive surface. Because of this approximately linear relationship, calibration of the sensor is obtained by solving the following equation:

[0194] y=mx+b

[0195] where y represents the sensor signal (counts), x represents the estimated glucose concentration (mg / dL), m represents the sensor sensitivity to analyte concentration (counts / mg / dL), and b represents the baseline signal (counts). As described elsewhere herein, in certain embodiments, the value b (i.e., the baseline) can be zero or near zero. As a result, for these embodiments, the calibration can be defined by solving the mathematical equation y=mx.

[0196] In some embodiments, the continuous analyte sensor system is configured to evaluate the change or drift in sensor sensitivity during an entire sensor session as a function of time (e.g., time elapsed since the start of the sensor session). As described elsewhere herein, this sensitivity function plotted against time may resemble a curve. Additionally or alternatively, the system may also be configured to determine the change or drift in sensor sensitivity as a function of time and one or more other parameters that may also affect the sensor sensitivity or provide additional information regarding the sensor sensitivity. These parameters may affect the sensor sensitivity or provide additional information regarding the sensor sensitivity prior to the sensor session, such as parameters associated with sensor fabrication (e.g., the material used to fabricate the sensor membrane, the thickness of the sensor membrane, the temperature at which the sensor membrane was cured, the length of time the sensor was immersed in a particular coating solution, etc.). In certain embodiments, some of the parameters include information that may be obtained prior to the sensor session and accounted for in a calibration code associated with a particular sensor lot. Other parameters can be related to the conditions surrounding the sensor after manufacture of the sensor but prior to a sensor session, such as the level of exposure of the sensor to a particular level of humidity or temperature while the sensor is in the package in transit from the manufacturing facility to the patient. Still other parameters (e.g., permeability of the sensor membrane, temperature at the sample site, pH at the sample site, oxygen level at the sample site, etc.) can affect sensor sensitivity or provide additional information regarding sensor sensitivity during a sensor session.

[0197] Determining the sensor sensitivity at different times of the sensor session based on a predefined sensor sensitivity profile can be performed prior to the sensor session or at the beginning of the sensor session. Additionally, in certain embodiments, the sensor sensitivity determination can be continuously adjusted based on the sensor sensitivity profile to account for parameters that affect the sensor sensitivity or provide additional information regarding the sensor sensitivity during the sensor session. These determinations of sensor sensitivity change or drift can be used to provide self-calibration, update the calibration, supplement the calibration based on known measurements (e.g., from a reference analyte monitor), and / or pass / fail a reference analyte measurement from a reference analyte monitor. In some embodiments, the pass / fail of a reference analyte measurement can be based on whether the reference analyte measurement is within a range of values ​​associated with a predefined sensor sensitivity profile.

[0198] Some of the continuous analyte sensors described herein may be configured to measure a signal associated with a non-analyte constant signal in the host. This non-analyte constant signal is preferably measured directly below the membrane system on the sensor. In one example of a continuous glucose sensor, the non-glucose constant signal that can be measured is oxygen. In some embodiments, changes in oxygen transport can indicate a change or drift in the sensitivity of the glucose signal and can be measured by switching the bias potential of the working electrode, auxiliary oxygen measuring electrode, or oxygen sensor, etc.

[0199] Additionally, some of the continuous analyte sensors described herein may be configured to measure changes in the amount of background noise in the signal. Detection of a change above a certain threshold may provide a basis for triggering a calibration, updating the calibration, and / or passing or failing an inaccurate reference analyte value from the reference analyte monitoring. In one example of a continuous glucose sensor, the background noise is substantially comprised of signal contributions from factors other than glucose (e.g., interfering species, non-reaction related hydrogen peroxide, or other electroactive species with an oxidation potential that overlaps with hydrogen peroxide). That is, the continuous glucose sensor is configured to measure a signal associated with the baseline (including substantially all non-glucose related currents generated) as measured by the sensor in the host. In some embodiments, an auxiliary electrode located directly under the non-enzyme portion of the membrane system is used to measure the baseline signal. This baseline signal can be subtracted from the glucose+baseline signal to obtain a signal that is fully or substantially fully associated with the glucose concentration. The subtraction can be accomplished electronically in the sensor using a differential amplifier, digitally in the receiver, and / or otherwise in the hardware or software of the sensor or receiver as described in more detail elsewhere herein.

[0200] At the same time, by determining sensor sensitivity based on the sensitivity profile and by measuring a baseline signal, the continuous analyte sensor can continuously self-calibrate during a sensor session without relying on (or reducing) a reference measurement from a reference analyte monitor or calibration solution.

[0201] Sensor Systems FIG. 1 illustrates an exemplary system 100 according to some exemplary implementations. The system 100 includes an analyte sensor system 101 including a sensor electronics 112 and an analyte sensor 138. The system 100 may include other devices and / or sensors, such as a drug delivery pump 102 and a glucose meter 104. The analyte sensor 138 may be physically connected to the sensor electronics 112, may be integral with the sensor electronics (e.g., non-removably attached), or may be removably attached. For example, the continuous analyte sensor 138 may be connected to the sensor electronics 112 via a sensor interposer that mechanically and electrically interfaces the analyte sensor 138 to the sensor electronics. The sensor electronics 112, the drug delivery pump 102, and / or the glucose meter 104 may be coupled to one or more devices, such as display devices 114, 116, 118, and / or 120.

[0202] In some exemplary implementations, the system 100 may include a cloud-based analyte processor 490 configured to analyze analyte data (and / or other patient-related data) provided over the network 409 (e.g., via wired, wireless, or combinations thereof) from the sensor system 101 and other devices, such as display devices 114, 116, 118, and / or 120, associated with the host (also referred to as the patient), to generate reports that provide high-level information, such as statistics, regarding analytes measured over a particular time frame. A full discussion of using a cloud-based analyte processing system may be found in U.S. Patent Application No. 13 / 788,375, filed March 7, 2013, and published as US20130325352A1, entitled "Cloud-Based Processing of Analyte Data," which is incorporated herein by reference in its entirety. In some implementations, one or more steps of the factory calibration algorithm or the automatic self-calibration algorithm may be performed in the cloud.

[0203] In some exemplary implementations, the sensor electronics 112 may include electronic circuitry associated with measuring and processing data generated by the analyte sensor 138. This generated analyte sensor data may include algorithms that can be used to process and calibrate the analyte sensor data, although these algorithms may be provided in other manners. The sensor electronics 112 may include hardware, firmware, software, or a combination thereof that provides a measurement of the level of an analyte via an analyte sensor, such as a glucose sensor. Exemplary implementations of the sensor electronics 112 are further described below with respect to FIG. 2.

[0204] In one implementation, the factory calibration or self-calibration algorithms described herein can be performed by the sensor electronics.

[0205] As mentioned above, the sensor electronics 112 may be coupled (e.g., wirelessly, etc.) with one or more devices, such as the display devices 114, 116, 118, and / or 120. The display devices 114, 116, 118, and / or 120 may be configured to present (and / or alert) information, such as sensor information, transmitted by the sensor electronics 112 for display on the display devices 114, 116, 118, and / or 120.

[0206] In one implementation, the factory calibration or self-calibration algorithms described herein may be performed, at least in part, by the display device.

[0207] In some example implementations, the relatively small key fob-like display device 114 may comprise a watch, a belt, a necklace, a pendant, jewelry, an adhesive patch, a pager, a key fob, a plastic card (e.g., a credit card), an identification (ID) card, and / or the like. This small display device 114 may include a relatively small display (e.g., smaller than the large display device 116) and may be configured to display a particular type of displayable sensor information, such as a numerical value, an arrow, or a color code.

[0208] In some example implementations, the relatively large portable display device 116 may include a smartphone, a portable receiver device, a palmtop computer, etc. This large display device may include a relatively large display (e.g., larger than the small display device 114) and may be configured to display information such as a graphical representation of sensor data, including current and historical sensor data, output by the sensor system 100.

[0209] In some example implementations, the analyte sensor 138 may comprise a glucose sensor configured to measure glucose in blood or interstitial fluid using one or more measurement techniques, such as enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, colorimetric, iontophoretic, radiometric, immunochemical, etc. In implementations in which the analyte sensor 138 includes a glucose sensor, the glucose sensor may comprise any device capable of measuring the concentration of glucose and may measure glucose using a variety of techniques, including invasive, minimally invasive, and non-invasive sensing techniques (e.g., fluorescence monitoring) to provide data, such as a data stream indicative of the glucose concentration in the host. The data stream may be sensor data (raw and / or filtered) that may be converted into a calibrated data stream used to provide glucose values ​​to a host, such as a user, patient, or caregiver (e.g., a parent, relative, guardian, teacher, doctor, nurse, or any other individual interested in the health of the host). Additionally, the analyte sensor 138 may be implanted as at least one of the following types of analyte sensors: an implantable glucose sensor, a transcutaneous glucose sensor implanted within a host blood vessel or externally to the body, a subcutaneous sensor, a refillable subcutaneous sensor, and an intravascular sensor.

[0210] Although the disclosure herein refers to some implementations including an analyte sensor 138 comprising a glucose sensor, the analyte sensor 138 may include other types of analyte sensors as well. Also, while some implementations refer to the glucose sensor as an implantable glucose sensor, other types of devices capable of detecting the concentration of glucose and providing an output signal representative of the glucose concentration may be used as well. These may include, for example, fully implantable, subcutaneous, transcutaneous sensors. Additionally, while the description herein refers to glucose as the analyte to be measured, processed, etc., other analytes may be used as well, including, for example, ketone bodies (e.g., acetone, acetoacetate and beta-hydroxybutyrate, lactate, etc.), glucagon, acetyl-CoA, triglycerides, fatty acids, citric acid cycle intermediates, choline, insulin, cortisol, testosterone, etc.

[0211] 2 illustrates example electronics 12 that may be used in sensor electronics 112 or implemented in a manufacturing station, such as a test station, calibration station, smart carrier, or other equipment used during manufacturing of device 101, according to some example implementations. Sensor electronics 112 may include electronic components configured to process sensor information, such as sensor data, and generate converted sensor data and displayable sensor information, for example, via a processor module. For example, the processor module may convert the sensor data into one or more of the following, filtered sensor data (e.g., one or more filtered analyte concentration values), raw sensor data, calibrated sensor data (e.g., one or more calibrated analyte concentration values), rate of change information, trend information, acceleration / deceleration information, sensor diagnostic information, location information, alarm / alert information, calibration information, etc., as may be determined by factory calibration or self-calibration algorithms, sensor data smoothing algorithms and / or filtering algorithms as disclosed herein.

[0212] In some embodiments, the processor module 214 is configured to accomplish a significant portion, if not all, of the data processing, including data processing pertaining to factory calibration or self-calibration. The processor module 214 may be integral to the sensor electronics 12 and / or may be located remotely, such as in one or more of the devices 114, 116, 118, and / or 120 and / or in the cloud 490. For example, in some embodiments, the processor module 214 may be located at least partially within the cloud-based analyte processor 490 or elsewhere in the network 406.

[0213] In some exemplary implementations, the processor module 214 may be configured to calibrate the sensor data, and the data storage memory 220 may store the calibrated sensor data points as transformed sensor data. The processor module 214 may also be configured to wirelessly receive calibration information from a display device, such as devices 114, 116, 118, and / or 120, in some exemplary implementations to enable calibration of the sensor data from the sensor 138. Furthermore, the processor module 214 may be configured to perform additional algorithmic processing on the sensor data (e.g., the calibrated and / or filtered data and / or other sensor information), and the data storage memory 220 may be configured to store the transformed sensor data and / or sensor diagnostic information associated with the algorithm. The processor module 214 may further be configured to store and use calibration information determined from a factory calibration or a self-calibration, as described below.

[0214] In some example implementations, the sensor electronics 112 may include an application specific integrated circuit (ASIC) 205 coupled to a user interface 222. The ASIC 205 may further include a potentiostat 210, a telemetry module 232 for transmitting data from the sensor electronics 112 to one or more devices, such as devices 114, 116, 118, and / or 120, and / or other components for signal processing and data storage (e.g., a processor module 214 and a data storage memory 220). Although FIG. 2 depicts an ASIC 205, other types of circuitry may also be used, including a field programmable gate array (FPGA), one or more microprocessors configured to provide some, if not all, of the processing performed by the sensor electronics 12, analog circuitry, digital circuitry, or a combination thereof.

[0215] 2, through a first input port 211 for sensor data, the potentiostat 210 is coupled to an analyte sensor 138, such as a glucose sensor, to generate sensor data from the analyte. The potentiostat 210 can also apply a voltage to the analyte sensor 138 via data line 212 to bias the sensor to measure a value (e.g., a current) indicative of the analyte concentration in the host (also referred to as the analog portion of the sensor). The potentiostat 210 may have one or more channels, depending on the number of working electrodes at the analyte sensor 138.

[0216] In some exemplary implementations, the potentiostat 210 may include a resistor that converts the current value from the sensor 138 into a voltage value, and in some exemplary implementations, a current-to-frequency converter (not shown) may be configured to continuously integrate the measured current value from the sensor 138 using, for example, a charge counting device. In some exemplary implementations, an analog-to-digital converter (not shown) may digitize the analog signal from the sensor 138 into so-called "counts" to allow processing by the processor module 214. The resulting counts may be directly related to the current measured by the potentiostat 210, which may be directly related to an analyte level, such as a glucose level, in the host.

[0217] The telemetry module 232 may be operatively connected to the processor module 214 and may provide hardware, firmware, and / or software to enable wireless communication between the sensor electronics 112 and one or more other devices, such as a display device, a processor, a network access device, etc. Various wireless technologies that may be implemented in the telemetry module 232 include Bluetooth, Bluetooth Low-Energy, ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16, cellular radio access technology, radio frequency (RF), infrared (IR), paging network communications, magnetic induction, satellite data communications, spread spectrum communications, frequency hopping communications, short range wireless communications, and / or the like. In some exemplary implementations, the telemetry module 232 includes a Bluetooth chip, although Bluetooth technology may be implemented in combination with the telemetry module 232 and the processor module 214.

[0218] The processor module 214 may control the processing performed by the sensor electronics 112. For example, the processor module 214 may be configured to process data (e.g., counts) from the sensors, filter the data, calibrate the data, perform fail-safe checks, and / or the like.

[0219] The potentiostat 210 can measure an analyte (eg, glucose, etc.) at discrete time intervals or continuously.

[0220] The processor module 214 may further include a data generator (not shown) configured to generate data packages for transmission to devices such as the display devices 114, 116, 118, and / or 120. Additionally, the processor module 214 may generate data packets for transmission to these external sources via the telemetry module 232. In some example implementations, the data packages may include identifier codes for the sensors and / or sensor electronics 112, raw data, filtered data, calibrated data, rate of change information, trend information, error detection or correction, and / or the like.

[0221] The processor module 214 may also include program memory 216 and other memory 218. The processor module 214 may be coupled to a communications interface, such as a communications port 238, and a power source, such as a battery 234. In addition, the battery 234 may be further coupled to a battery charger and / or regulator 236 to provide power to the sensor electronics 12 and / or charge the battery 234.

[0222] The program memory 216 may be implemented as a semi-static memory for storing data such as identifiers (e.g., sensor identifiers (IDs)) of the coupled sensors 138, and for storing code (also referred to as program code) for configuring the ASIC 205 to perform one or more operations / functions described herein. For example, the program code may configure the processor module 214 to process and filter data streams or counts, perform calibration methods described below, perform fail-safe checks, etc.

[0223] The memory 218 may also be used to store information. For example, the processor module 214 including the memory 218 may be used as a cache memory for the system, where temporary storage is provided for recent sensor data received from the sensors. In some example implementations, the memory may comprise memory storage components such as read-only memory (ROM), random access memory (RAM), dynamic RAM, static RAM, non-static RAM, electrically erasable programmable read-only memory (EEPROM), rewriteable ROM, flash memory, and the like.

[0224] The data storage memory 220 may be coupled to the processor module 214 and may be configured to store various sensor information. In some example implementations, the data storage memory 220 stores one or more days' worth of analyte sensor data. The stored sensor information may include one or more of the following: timestamps, raw sensor data (one or more raw analyte concentration values), calibrated data, filtered data, converted sensor data, and / or other displayable sensor information, calibration information (e.g., reference BG values ​​and / or previous calibration information such as from a factory calibration), sensor diagnostic information, etc.

[0225] The user interface 222 may include one or more buttons 224, a liquid crystal display (LCD) 226, a vibrator 228, an audio transducer (e.g., a speaker) 230, a backlight (not shown), and / or the like. The components that make up the user interface 222 may provide controls for interacting with a user (e.g., a host).

[0226] A battery 234 may be operatively connected to the processor module 214 (and possibly other components of the sensor electronics 12) and provide the necessary power to the sensor electronics 112. In other implementations, the receiver may be powered transcutaneously, for example, via inductive coupling.

[0227] The battery charger and / or regulator 236 may be configured to receive energy from an internal and / or external charger. In some example implementations, the battery 234 (or batteries) is configured to be charged via an inductive and / or wireless charging pad, although other charging and / or power mechanisms may also be used.

[0228] One or more communication ports 238, also referred to as external connector(s), may be provided to enable communication with other devices. For example, a PC communications (com) port may be provided to enable communication with systems separate or integrated with the sensor electronics 112. The communication ports may comprise, for example, serial (e.g., Universal Serial Bus or "USB") communication ports to enable communication with other computer systems (e.g., PCs, personal digital assistants or "PDAs," servers, etc.). In some example implementations, factory information or other data may be sent to or received from sensors, algorithms, or cloud data sources.

[0229] The one or more communication ports 238 may further include a second input port 237 that can receive calibration data, and an output port 239 that can be used to transmit calibrated data or data to be calibrated to a receiver or mobile device. Figure 2 illustrates these aspects diagrammatically. It will be appreciated that the ports may be physically separated, although in alternative implementations a single communication port may provide the functionality of both the second input port and the output port.

[0230] In some analyte sensor systems, the on-skin portion of the sensor electronics may be simplified to minimize the complexity and size of the on-skin electronics, for example, to provide raw, calibrated, and / or filtered data to a display device configured to perform calibration and other algorithms required for displaying the sensor data. However, the sensor electronics 112 (e.g., via the processor module 214) may be implemented to execute predictive algorithms used to generate the converted sensor data and / or displayable sensor information, including, for example, algorithms to evaluate the clinical appropriateness of optional criteria and / or sensor data, evaluate calibration data for best calibration based on capture criteria, evaluate the quality of the calibration, compare estimated analyte values ​​to measured analyte values ​​over time, analyze variability in estimated analyte values, evaluate stability of the sensor and / or sensor data, detect signal artifacts (noise), replace signal artifacts, determine rate of change and / or trend in sensor data, perform dynamic and intelligent analyte value estimation, perform diagnostics on the sensor and / or sensor data, set operating modes, evaluate anomalous data, etc. A connected receiver or a smart or wearable device may perform one or more of such calculations.

[0231] 3 shows a perspective view of an example implementation of the analyte sensor system 101 implemented as a wearable device, such as on-skin sensor assembly 600. As shown in FIG. 3, the on-skin sensor assembly includes a base 128. An adhesive 126 can couple the base 128 to the skin of a host. The adhesive 126 can be any adhesive suitable for skin adhesion, but is generally not, for example, a foam adhesive.

[0232] In some embodiments, the electronics unit 500 (e.g., a transmitter) may be coupled to the base 128 (e.g., via a mechanical interlocking mechanism such as a snap-fit ​​and / or interference fit feature). The electronics unit 500 may include sensor electronics 112 operable to measure and / or analyze glucose indicators sensed by the glucose sensor 138. The sensor electronics 112 in the electronics unit 500 may transmit information (e.g., measurements, analyte data, and glucose data) to a remotely located device (e.g., 114-120 shown in FIG. 1).

[0233] The sensor 138 may be provided as part of a pre-connected sensor that includes a sensor interposer (not visible in FIG. 3 ) that may be secured between the base 128 and the electronics unit 500 and electrically coupled to the electronics unit 500 to couple the sensor 138 to the sensor electronics (e.g., the sensor electronics 112 of FIG. 1 ).

[0234] FIG. 4 shows a schematic illustration of a pre-connected sensor 400. As shown in FIG. 4, the pre-connected sensor 400 includes a sensor interposer 402 permanently attached to the sensor 138. In the example of FIG. 4, the sensor interposer 402 includes a substrate 404, a first contact 406, and a second contact 408. The contact 406 is electrically coupled to a first contact at a proximal end of the sensor 138, and the contact 408 is electrically coupled to a second contact at a proximal end of the sensor 138. The distal end of the sensor 138 is a free end configured for insertion into the skin of a host.

[0235] As shown in FIG. 4, contact 406 is coupled to external contact 410 and contact 408 is coupled to external contact 412. As described in more detail hereinafter, external contacts 410 and 412 are sized, shaped, and positioned to electrically interface with sensor electronics 112 in electronics unit 500 in addition to electrically interfacing such one or more test stations and / or one or more calibration stations to processing circuitry of a manufacturing device. Various examples are described herein in which two contacts 410 and 412 on an interposer are coupled to two corresponding contacts 406 and 408 on a sensor 138, but this is merely illustrative. In other implementations, interposer 402 and sensor 138 may each be provided with a single contact or each be provided with three or more contacts. In some implementations, interposer 402 and sensor 138 may have the same number of contacts. In some implementations, interposer 402 and sensor 138 may have different numbers of contacts. For example, in some implementations, the interposer 402 may have additional contacts for coupling to or between various components of a manufacturing station.

[0236] The substrate 404 may be sized and shaped to mechanically interface with the base 128 and / or electronics unit 500 in addition to mechanically interfacing such one or more assembly equipment, test stations, and / or one or more calibration stations to manufacturing equipment. The interposer 402 may be attached and / or electrically coupled to the sensor 138. The interposer 402 may be attached to the sensor 138 using, by way of example, adhesives, spring contacts, packaged flexible circuits, conductive elastomers, cylindrical connectors, molded interconnect device structures, magnets, anisotropic conductive films, or other suitable structures or materials for mechanically and electrically attaching the interposer 402 to the sensor 138 prior to or during assembly, manufacturing, testing, and / or calibration operations. The interposer 402 may be attached to the sensor 138 using, by way of example, spot welding, swaging, crimping, clipping, soldering or brazing, plastic welding, overmolding, or other suitable methods for mechanically and electrically attaching the interposer 402 to the sensor 138 prior to or during assembly, manufacturing, testing, and / or calibration operations. The substrate 404 may include reference features (sometimes referred to as reference structures), such as recesses, openings, surfaces, or protrusions, for aligning, positioning, and orienting the sensor 138 relative to the interposer 402. The substrate 404 may also include one or more anchor features for securing and aligning the analyte sensor during manufacturing (e.g., relative to a manufacturing station), or the substrate itself may form the anchors.

[0237] FIG. 5 shows a block diagram of an exemplary system 5000 having one or more manufacturing stations 5091, one or more positioning or testing stations 5002, and / or another calibration station 5004, each having an on-skin sensor assembly 600 configured to receive a sensor interposer 402 and communicatively couple to a sensor 138 via the sensor interposer 402.

[0238] The system 5000 may include one or more positioning or testing stations 5002 having processing circuitry 5012 configured to perform testing operations with the sensor 138 to determine parameters and / or verify the operational integrity of the sensor 138. The testing operations may include verifying electrical characteristics of the sensor 138, verifying communication between the working electrode and the contacts 408, verifying communication between the reference or additional electrode and the contacts 406, and / or other electronic verification operations of the sensor 138. The processing circuitry 5012 may be communicatively connected to the sensor 138 for testing operations by inserting the substrate 404 into a receptacle 5006 (e.g., a recess in a housing of the test station 5002) until the contacts 410 are coupled to the contacts 5010 of the test station 5002 and the contacts 412 are coupled to the contacts 5008 of the test station 5002.

[0239] The system 5000 may include one or more calibration stations 5004 having a processing circuit 5020 configured to perform calibration operations on the sensor 138 to obtain calibration data for in vivo operation of the sensor 138. The calibration data obtained by the calibration equipment 5004 may be provided to the on-skin sensor assembly 600 for use during in vivo operation of the sensor 138. The processing circuit 5020 may be communicatively coupled to the sensor 138 for a calibration operation by inserting the substrate 404 into a receptacle 5014 (e.g., a recess in a housing of the calibration station 5004) until the contacts 410 are coupled to contacts 5018 of the test station 5002 and the contacts 412 are coupled to contacts 5016 of the test station 5002.

[0240] The system 5000 can include one or more manufacturing stations 5091. The manufacturing station 5091 can also serve to provide the functionality of a testing station as described herein, a calibration station as described herein, or another manufacturing station. The manufacturing station 5091 can include processing circuitry 5092 and / or mechanical components 5094 operable to perform testing, calibration, and / or other manufacturing operations, such as sensor linearization, membrane application, baking, calibration check, glucose sensitivity (e.g., sensitivity slope, baseline, and / or noise calibration), and / or visual inspection operations. Manufacturing parameters that may be measured during these various operations can include, by way of example, temperature, humidity, the contents (e.g., PVP, ethanol, etc.) of the particular coating solution in which the sensor is immersed (which can be determined from the refractive index of the solution), the duration of immersion, the number of times the sensor is immersed in the solution, and the duration, temperature, and humidity of the curing process.

[0241] 5, the test station 5002 and the calibration station 5004 include receptacles 5006 and 5014. However, this is merely exemplary and the interposer 402 can be attached to the test station 5002 and the calibration station 5004 and / or the manufacturing station 5091 using other attachment mechanisms, such as a gripping, clipping, or clamping style. For example, the manufacturing station 5091 can include gripping structures 5093 and 5095, at least one of which is movable to grip the interposer 402 (or a carrier having multiple interposers and sensors). The structure 5093 can be a stationary mechanism having one or more electrical contacts, such as contact 5008. The structure 5095 can be a movable mechanism that moves (e.g., slides in direction 5097) to grip and secure the interposer 402 in an electrically coupled position relative to the manufacturing station 5091. In other implementations, both functions 5093 and 5095 are movable.

[0242] The sensor interposer 402 may also include an identifier 450 (see, e.g., FIG. 4). The identifier 450 may be formed on or embedded within the substrate 404. The identifier 450 may be implemented as a visual or optical identifier (e.g., a barcode pre-printed or printed in situ on the substrate 404 or etched into the substrate 404), a radio frequency (RF) identifier, or an electrical identifier (e.g., a laser trimmed resistor, a capacitive identifier, an inductive identifier, or a microstorage circuit (e.g., an integrated circuit or other circuit with an identifier encoded within its memory) that is programmable with identifiers and / or other data before, during, or after testing and calibration). The identifier 450 may be used to track each sensor through its manufacturing process (e.g., by storing a history of test and / or calibration data for each sensor). For example, the identifier 450 may be used for binning test and calibration performance data. The identifier 450 may be a distinct raw value or may encode information in addition to an identification number. The identifier 450 may be used to digitally store data in a non-volatile memory of the substrate 404 or to store data external to the interposer 402 .

[0243] The test station 5002 may include a reader 5011 (e.g., an optical sensor, an RF sensor, or an electrical interface such as an integrated circuit interface) that reads the identifier 450 to obtain a unique identifier for the sensor 138. Test data obtained by the test station 5002 may be stored and / or transmitted along with the identifier for the sensor 138.

[0244] The calibration station 5004 may include a reader 5011 (e.g., an optical sensor, an RF sensor, or an electrical interface) that reads the identifier 450 to obtain a unique identifier for the sensor 138. The calibration data obtained by the calibration station 5004 may be stored and / or transmitted along with the identifier for the sensor 138. In some implementations, the calibration data obtained by the calibration station 5004 may be added to the identifier 450 by the calibration station 5004 (e.g., by programming the calibration data into the identifier). In some implementations, the calibration data obtained by the calibration station 5004 may be transmitted by the calibration station along with the identifier 450 to a remote system or device.

[0245] 5, the on-skin sensor assembly 600 can include one or more contacts, such as contacts 5022, configured to couple the electronics unit 500 to the contacts 410 and 412 of the interposer 402, and thus to the sensor 138. The interposer 402 can be sized and shaped to be secured within a cavity 5024 between the base 128 and the electronics unit 500, such that the sensor 138 is coupled to the electronics unit 500 via the interposer 402, the identifier 450 is accessible by a reader 5013, and the sensor 138 is secured in place and extends through the opening 180 for insertion in vivo.

[0246] While one calibration station and one test station are shown in Figure 5, it should be understood that one or more test stations and / or one or more calibration stations may be included within the system 5000. Although the calibration station 5004 and the test station 5002 are shown in Figure 5 as separate stations, it should be understood that in some implementations the calibration station and the test station may be combined into one or more calibration / test stations (e.g., a station in which processing circuitry for performing the test and calibration operations is provided in a common housing and coupled to a single interface 5006). Additionally, data from one or more manufacturing stations may be compiled and stored internally and / or stored and associated with the sensors and interposers.

[0247] The on-skin sensor assembly 600 may also include a reader 5013 (e.g., an optical sensor, an RF sensor, or an electrical interface) that reads the identifier 450 to obtain a unique identifier for the sensor 138. The sensor electronics in the electronics unit 500 can obtain calibration data for in vivo operation of the sensor 138 based on the read identifier 450. The calibration data may be stored in and obtained from the identifier 450 itself, or the identifier 450 may be used to obtain calibration data for the installed sensor 138 from a remote system, such as a cloud-based system.

[0248] Additional details relating to the example sensor systems shown in Figures 1-5 can be found in U.S. patent application Ser. No. 62 / 576,560, entitled "Preconnected Analyte Sensors," filed Oct. 24, 2017, which is hereby incorporated by reference in its entirety.

[0249] Determining Sensor Sensitivity As described elsewhere herein, in certain embodiments, self-calibration of an analyte sensor system may be performed by determining the sensor sensitivity based on the sensitivity profile (and a measured or estimated baseline), such that the following equation can be solved:

[0250] y=mx+b

[0251] where y represents the sensor signal (counts), x represents the estimated glucose concentration (mg / dL), m represents the sensor sensitivity to the analyte (counts / mg / dL), and b represents the baseline signal (counts). From this equation, a conversion function can be formed that converts the sensor signal to an estimated glucose concentration.

[0252] It has been found that the sensitivity of a sensor to an analyte concentration during a sensor session often changes or drifts as a function of time. FIG. 6 illustrates this phenomenon, providing a plot of sensor sensitivity 110 for a group of continuous glucose sensors as a function of time during a sensor session. FIG. 7 provides three plots of the conversion function at three different time periods of the sensor session. As shown in FIG. 7, the three conversion functions have different slopes, each of which corresponds to a different sensor sensitivity. Thus, a difference in the slopes over time indicates that a change or drift in sensor sensitivity is occurring over the sensor session.

[0253] Returning to the discussion associated with FIG. 6, the sensors were fabricated in substantially the same manner under substantially the same conditions. The sensor sensitivities associated with the plot on the y-axis are expressed as a percentage of the substantially steady-state sensitivity reached approximately 3 days after the start of the sensor session. Moreover, these sensor sensitivities correspond to measurements obtained from YSI testing. As shown in the plot, the sensitivity of each sensor (expressed as a percentage of the steady-state sensitivity), as measured, is very close to the sensitivity of the other sensors in the group at any given time of the sensor session. Without wishing to be bound by theory, it is believed that the observed upward trend in sensitivity (over time), especially noticeable early in the sensor session, may be due to conditioning and hydration of the sensing area of ​​the working electrode. It is also believed that the glucose concentration of the fluid surrounding the continuous glucose sensor during sensor start-up may also affect the sensitivity drift.

[0254] For the sensors tested in this discussion, the change in sensor sensitivity (expressed as a percentage of the substantially steady-state sensitivity) resembled a logarithmic growth curve over a time period defined by a sensor session. It should be understood that other continuous analyte sensors fabricated with different technologies, with different specifications (e.g., different membrane thicknesses or compositions), or under different manufacturing conditions may exhibit different sensor sensitivity profiles (e.g., those associated with a linear function). Nevertheless, with improved control over the operating conditions of the sensor fabrication process, a high level of reproducibility can be achieved such that the sensitivity profiles exhibited by individual sensors of a sensor population (e.g., a sensor lot) are substantially similar, and sometimes nearly identical.

[0255] It has been discovered that not only is the change or drift in sensitivity over a sensor session substantially consistent between sensors manufactured under substantially the same conditions and in substantially the same manner, but that modeling can be performed through a mathematical function that can accurately estimate this change or drift. As shown in FIG. 6, an estimable algorithmic function 120 can be used to define the relationship between time during a sensor session and sensor sensitivity. The estimable algorithmic function can be generated by testing a sample set (including one or more sensors) from a sensor lot under in vivo and / or ex vivo conditions. Alternatively, the estimable algorithmic function can be generated by testing each sensor under in vivo and / or ex vivo conditions.

[0256] In some embodiments, a sensor can be subjected to an in vitro sensor sensitivity drift test, where the sensor is exposed to changing conditions (e.g., step changes in glucose concentration in solution) and an in vitro sensitivity profile of the sensor is generated over a particular period of time. The duration of the test can substantially correspond to the entire sensor session of the corresponding in vivo sensor, or the duration can include a portion of the sensor session (e.g., the first day, first two days, or first three days of the sensor session, etc.). It is contemplated that the above test can be performed on each individual sensor, or alternatively, on one or more sample sensors of a sensor lot. From this test, an in vitro sensitivity profile can be created, from which an in vivo sensitivity profile can be modeled and / or formed.

[0257] From in vivo or in vitro testing, one or more data sets can be generated and plotted, each including data points relating sensitivity to time. A sensitivity profile or curve can then be fitted to the data points. If the curve fit is determined to be satisfactory (e.g., the standard deviation of the generated data points is below a certain threshold), the sensor sensitivity profile or curve can be determined to have passed quality control and be suitable for release. From there, the sensor sensitivity profile can be converted into an estimable algorithmic function, or alternatively, into a look-up table. The algorithmic function or look-up table can be stored, for example, in a computer-readable memory and accessed by a computer processor.

[0258] The estimable algorithmic function may be formed by applying curve fitting techniques, which recursively fit a curve to the data points by adjusting the function (e.g., by adjusting the constants of the function) until an optimal fit to the available data points is obtained. Simply put, a "curve" (i.e., a function, sometimes called a "model") is fitted and generated to relate one data value to one or more other data values, with the parameters of the curve being selected so that the curve estimates the relationship between the data values. As an example, selecting the parameters of the curve may mean selecting the coefficients of a polynomial function. In some embodiments, the curve fitting process may mean evaluating how closely the curve determined in the curve fitting process estimates the relationship between the data values ​​to determine the optimal fit. As used herein, the term "curve" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to a function, or a graph of a function, which may mean a rounded curve or a straight curve, i.e., a line.

[0259] The curve may be formed by any of a variety of curve fitting techniques, such as, for example, linear least squares fitting, nonlinear least squares fitting, Nelder-Mead simplex, Levenberg-Marquardt, and variations thereof. Additionally, the curve may be fitted using any of a variety of functions, including, but not limited to, linear functions (including constant functions), logarithmic functions, quadratic functions, cubic functions, square root functions, power functions, polynomial functions, rational functions, exponential functions, sine functions, and variations thereof, and combinations thereof. For example, in some embodiments, the estimable algorithm includes a linear function component that is given a first weight w1, a logarithmic function component that is given a second weight w2, and an exponential function component that is given a third weight w3. In further embodiments, the weights associated with each component may vary as a function of time and / or other parameters, while in alternative embodiments, one or more of these weights are constant as a function of time.

[0260] In certain embodiments, the correlation (e.g., R value) of the estimable algorithm function, which is a measure of the quality of the curve's fit to the data points for the data obtained from the sample sensor, may be one metric used to determine whether the function is optimal. In certain embodiments, the estimable algorithm function formed from the curve fitting analysis may be adjusted to account for other parameters, such as other parameters that may affect the sensor sensitivity or provide additional information regarding the sensor sensitivity. For example, the estimable algorithm function may be adjusted to account for the sensitivity of the sensor to hydrogen peroxide or other chemical species.

[0261] At any point during a sensor session, the estimable algorithm formed and used to accurately estimate the sensitivity of an individual sensor can be based on a factory calibration and / or based on a single initial reference measurement (e.g., using a single-point blood glucose monitor). In some embodiments, sensors across a population of serial analyte sensors manufactured in substantially the same manner under substantially the same conditions exhibit a substantially fixed in vivo to in vitro sensitivity relationship. For example, in one embodiment, the in vivo sensor sensitivity at a particular time after the start of sensor use (e.g., at about 5, 10, 15, 30, 60, 120, or 180 minutes after sensor use) is consistently equal to the in vitro measured sensitivity of the sensor or a comparable sensor. From this relationship, an initial value of the in vivo sensitivity can be generated, from which an algorithmic function corresponding to the sensor sensitivity profile can be formed. In other words, from this initial value (which represents one point in the sensor sensitivity profile), the remainder of the overall sensor sensitivity profile can be determined and plotted. The initial value of the in vivo sensitivity can be associated with any portion of the sensor sensitivity profile. In certain embodiments, multiple initial values ​​of in vivo sensitivity, spaced apart in time and corresponding to multiple in vitro sensitivities, can be calculated and combined together to generate a sensor sensitivity profile.

[0262] For some embodiments, it has been found that not only does the sensitivity of the sensor tend to drift over time, but the baseline of the sensor also drifts over time. Thus, in certain embodiments, the concepts behind the methods and systems used to predict sensitivity drift can also be applied to create a model that predicts baseline drift over time. Without wishing to be bound by theory, it is believed that the entire signal received by the sensor electrode is composed of a glucose signal component, an interference signal component, and an electrode-associated baseline signal component that is a function of the electrode and substantially dependent on the environment surrounding the electrode (e.g., extracellular matrix). As mentioned above, as used herein, the term "baseline" refers to, but is not limited to, the component of the analyte sensor signal that is not related to the analyte concentration. Thus, the baseline is composed of an interference signal component and an electrode-associated baseline signal component, as that term is defined herein. Again, without wishing to be bound by theory, it is believed that an increase in membrane permeability typically increases not only the glucose diffusion rate across the sensor membrane, but also the interference diffusion rate across the sensor membrane. Thus, changes in the permeability of the sensor membrane over time will cause the sensor sensitivity to drift, but will likely also cause the baseline interference signal components to drift. Simply put, the baseline interference signal components are not static, but typically change as a function of time, which in turn causes the baseline to drift over time. By analyzing how each of the aforementioned baseline components responds to changing conditions and time (e.g., as a function of time, temperature), a predictive model can be developed to predict how the sensor baseline will drift during a sensor session. It is believed that being able to proactively predict both the sensor sensitivity and baseline can achieve factory calibrated or automatically self-calibrated continuous analyte sensors, i.e., sensors that do not require the use of a reference measurement (e.g., a finger stick measurement) for calibration.

[0263] Calibration Code The process of manufacturing continuous analyte sensors may be subject to some degree of variation, sometimes between sensor lots, as described in more detail below. To compensate for this variation, one or more calibration codes may be assigned to each sensor or set of sensors to define parameters that may affect the sensor sensitivity or provide additional information regarding the sensitivity profile. The calibration codes may reduce the variation of different sensors and ensure that results obtained from using sensors from different sensor lots are generally equal and consistent by applying algorithms that adjust for those differences. In one embodiment, the analyte sensor system may be configured such that one or more calibration codes may be manually entered into the system by a user. In other embodiments, the calibration codes may be part of a calibration-encoded label that is adhered to (or inserted into) a package of multiple sensors. The calibration-encoded label may itself be read or interrogated by any of a variety of techniques, including, but not limited to, optical techniques, RFID (radio frequency identification), and the like, and combinations thereof. These techniques for transferring the codes to the sensor system are more automated, more accurate, and more convenient for the patient, and less prone to error, as compared to manual input devices. Manual input devices have an inherent risk of error being introduced, for example by the patient or hospital staff entering an incorrect code, which can result in an incorrect calibration and therefore an inaccurate glucose concentration reading, which in turn can result in the patient or hospital staff taking inappropriate action (e.g., administering insulin while in a hypoglycemic state).

[0264] In some embodiments, the calibration code assigned to the sensor may include a first calibration code associated with a predetermined logarithmic function corresponding to the sensitivity profile, a second calibration code associated with an initial in vivo sensitivity value, and other calibration codes, each code defining a parameter that affects or provides information about the sensor sensitivity. The other calibration codes may be associated with any inferred information or parameters described elsewhere herein and / or any parameters that help define the mathematical relationship between the measured signal and the analyte concentration. The calibration codes may be developed from these measurements or may be developed based on manufacturing parameters known, determined, or measured, for example, during the production of the lot, or by a combination thereof.

[0265] In some embodiments, packaging used to store and transport a continuous analyte sensor (or sensor set) can include a detector configured to measure a particular parameter that can affect sensor sensitivity or provide additional information regarding sensor sensitivity or other sensor characteristics. For example, in one embodiment, the sensor package can include a temperature detector configured to provide calibration information related to whether the sensor has been exposed to a temperature condition above (and / or below) one or more predetermined temperature values. In some embodiments, the one or more predetermined temperature values ​​can be greater than about 75°F, greater than about 80°F, greater than about 85°F, greater than about 90°F, greater than about 95°F, greater than about 100°F, greater than about 105°F, and / or greater than about 110°F. Additionally or alternatively, the one or more predetermined temperature values ​​may be below about 75°F, below about 70°F, below about 60°F, below about 55°F, below about 40°F, below about 32°F, below about 10°F, and / or below about 0°F. In certain embodiments, the sensor package may include a humidity exposure indicator configured to provide calibration information related to whether the sensor has been exposed to a humidity level above or below the one or more predetermined humidity values. In some embodiments, the one or more predetermined humidity values ​​may be above about 60% relative humidity, above about 70% relative humidity, above about 80% relative humidity, and / or above about 90% relative humidity. Alternatively or additionally, the one or more predetermined humidity values ​​may be below about 30% relative humidity, below about 20% relative humidity, and / or below about 10% relative humidity.

[0266] Upon detecting that a sensor is exposed to a particular level of temperature and / or humidity, the corresponding calibration code may be altered to compensate for the possible effects of this exposure on sensor sensitivity or other sensor characteristics. This alteration of the calibration code may be performed automatically by a control system associated with the sensor package. Alternatively, other embodiments may use an indicator (e.g., a color change indicator) that is adapted to undergo a change (e.g., a color change) upon exposure to a particular environment. By way of example, but not by way of limitation, the sensor package may include an indicator that irreversibly changes color from blue to red when the package is exposed to a temperature above about 85° F., and may include instructions to a user to enter a particular calibration code when the indicator has a red color. Although exposure to temperature and humidity are described herein as examples of conditions that may be detected by the sensor package and used to actuate an alteration of the calibration code information, it should be understood that other conditions may also be detected and used to actuate an alteration of the calibration code information.

[0267] In certain embodiments, the continuous analyte system can include a library of stored sensor sensitivity or calibration functions associated with one or more calibration codes. Each sensitivity or calibration function can consequently calibrate the system for a different set of conditions. Different conditions during sensor use can be associated with temperature, obesity index, and any of a variety of conditions or parameters that can affect sensor sensitivity or provide additional information regarding sensor sensitivity. The library can also include sensitivity profiles or calibrations for different types of sensors or different sensor lots. For example, a single sensitivity profile library can include sub-libraries of sensitivity profiles for different sensors made from different sensor lots and / or made with different design configurations (e.g., different design configurations customized for patients with different obesity indices).

[0268] Advanced and / or multivariate calibration As previously mentioned, the sensitivity of an analyte sensor may change over time as a result of a variety of different manufacturing and environmental parameters. In some embodiments, some of these parameters include information that can be obtained during each distinct phase of the life cycle of an analyte sensor. As shown in FIG. 8A and FIG. 8B, in certain embodiments, these different phases may include one or more of the following: a sensor manufacturing phase 5702, a sensor packaging phase 5704, a sensor package sterilization phase 5706 in which the sensor is sterilized while in the package (e.g., using any suitable sterilization gas, which may include conventional sterilization gases or, alternatively, nitrogen dioxide, chlorine dioxide, or ethylene oxide, or, alternatively, using an e-beam to sterilize at least the transmitter, the interior of which is shielded to deflect the e-beam), a sensor shipping phase 5708, a sensor storage phase 5710 (e.g., in a warehouse, retail environment, user facility), a sensor activation / insertion phase 5712 in which the sensor is placed in vivo, and a sensor in vivo phase 5714 in which the sensor is operable in vivo. Of course, in other embodiments, the life cycle of the analyte sensor may be divided into different life cycles. Figures 8A-8B further show examples of environmental and other factors that may be monitored during each life cycle phase and that may be taken into account when calibrating the sensor.

[0269] Further, in some cases, the various life cycle phases listed above can also be further divided into identifiable sub-stages. For example, in some embodiments, the sensor manufacturing phase can include a sub-phase in which the analyte sensor may be pre-connected to one or more components of the sensor electronics, or even all of the sensor electronics. If the analyte sensor system uses a sensor interface, such as the sensor interposer 402 shown in the embodiment of FIG. 4, the sensor can be pre-connected to the sensor interface and possibly also to one or more components of the sensor electronics. Alternatively, rather than treating the pre-connection process as part of the sensor manufacturing phase, that sub-phase may be identified as a separate phase that occurs before or after the sensor manufacturing phase.

[0270] The sensor manufacturing phase can be further divided into a wire cutting phase, a wire coating phase, a wire baking phase, and a wire skiving finishing phase, etc.

[0271] In addition to or instead of the sensor interposer, sensor electronics components that may be pre-connected to the analyte sensor may include a processor (e.g., in the embodiment of FIG. 2, processor module 214), a memory (e.g., in the embodiment of FIG. 2, data storage memory 220), a potentiostat (e.g., in the embodiment of FIG. 2, potentiostat 210), analog measurement circuitry, digital measurement circuitry, and / or a transmitter (e.g., in the embodiment of FIG. 2, telemetry module 232).

[0272] 9 shows a schematic block diagram of one particular example of a pre-connected analyte sensor system comprising an analyte sensor 5602, a sensor interconnect module 5604 (e.g., a sensor interposer), and measurement electronics 5608. The measurement electronics 5608 includes a potentiostat 5610, and several optional components. The optional components may include any one or more of the following: a temperature measurement circuit 5612, an impedance measurement circuit 5614, a processor 5616, a radio 5618, a humidity measurement circuit 5620, a pressure measurement circuit 5622, a motion detector circuit 5624, a capacitance measurement circuit 5626, a display / status indicator 5628, data storage 5630, a power supply 5632, and a clock 5634.

[0273] FIG. 9 also illustrates sources of potential errors 5640 and 5650 that may be reduced or eliminated by using a pre-connected analyte sensor system. These sources of errors may include, for example, errors that may occur if a user needs to connect a sensor to a transmitter or other electronics such as contact resistance, connection stability, electronic noise, and environmental factors. Additionally, FIG. 9 illustrates various errors in the measurement electronics that may be reduced or eliminated by using a pre-connected analyte sensor system. These errors may include, for example, tolerance of electronic components, leakage currents, measurement errors, resolution errors, electronic noise, and environmental factors that significantly affect the electronics.

[0274] FIG. 10 is a Monte Carlo simulation of 5000 samples using randomly selected values ​​within the statistical distribution of the input variables comparing a non-preconnected system with a preconnected system. It shows the number of samples that fall within the error target of 10 mg / dL or 10% glucose concentration. It shows the reduction in the statistical distribution of error that can be achieved with a preconnected system versus a non-preconnected system where various individual components with distributions in the component properties (e.g., gain, offset, contacts, etc.) are combined into a system.

[0275] The comparison uses unitless current measurements (counts) and is calibrated against known glucose calibration solutions. This is an exemplary model and does not necessarily take into account all variables that affect the system. Component induced variations and measurement variability are eliminated. In particular, gain and offset values ​​are not measured and calibrated to unit values, so their induced errors are eliminated. The system is calibrated with precise components that affect the values ​​of contact resistance, leakage current, and bias voltage. Thus, those variations are eliminated and they can be modeled as fixed values.

[0276] In some embodiments, some of the electronics may be embedded in an interposer or other interconnect component that is connected to the sensor. In this way, by pre-connecting the interposer to the sensor, some or all of the electronics will also be pre-connected to the sensor. This may allow calibration and other data to be conveniently stored during the manufacturing process. In some embodiments, the interposer (or other component connected to the sensor) may also be used for other purposes. For example, the interposer may be used to store a code that can track the sensor during the manufacturing phase and / or other life phases of the sensor. This code may be embodied, for example, in a series of resistors that are printed on the interposer or the like. This code may be programmed by a laser cut selection pattern to partially impart a final resistance to or on the printed resistor. This resistor may be read by a transmitter when the transmitter is placed on the interposer via a spring contact or the like.

[0277] In some embodiments, any of these or other components that may be pre-connected to the analyte sensor can be configured such that the connection is maintained over multiple periods during the lifecycle of the sensor. In this manner, the pre-connection can be maintained throughout the entire or multiple turns of the lifecycle of the analyte sensor. Thus, system-level calibrations (i.e., the analyte sensor and pre-connected components of the sensor electronics) performed over the lifecycle of the analyte sensor and / or the lifecycle of the sensor electronics need to correlate with changes in the system during one or more phases.

[0278] The components to which the analyte sensor is pre-connected can be packaged together with the analyte sensor in the sterile packaging used to ship and store the analyte sensor. Thus, in these embodiments, it may be advantageous if the pre-connected components are single-use, disposable components.

[0279] Parameters that may have a uniquely large effect on analyte sensor sensitivity during the manufacturing phase may include, but are not limited to, parameters such as the material used to fabricate the sensor membrane, the thickness of the sensor membrane, the temperature at which the sensor membrane was cured, the length of time the sensor was exposed to a particular coating solution, enzyme activity level, amount of coating applied, etc. Parameters that may have a uniquely large effect on analyte sensor sensitivity during the packaging phase may include, but are not limited to, sterilization dosage, sterilization method, enzyme activity, packaging material, etc. Additional parameters that may have a large effect on analyte sensor sensitivity during any and all phases may include various environmental parameters such as temperature and humidity and the duration the sensor was exposed to, for example, temperature and humidity measurements.

[0280] In some embodiments, the analyte sensor may be calibrated based on measurements of one or more of various parameters that significantly affect the analyte sensor sensitivity during two or more phases of the analyte sensor's life cycle. An exemplary calibration process 2400 according to some embodiments is now described with reference to FIG. 11. The calibration process may be performed by the sensor electronics in the analyte monitoring system without user involvement, thereby avoiding the need for external user calibration when the device is in use. In block 2402, the process begins when the analyte sensor is pre-connected to one or more components of the sensor electronics, for example during the manufacturing phase. Then, in block 2404, the analyte sensor, along with the pre-connected sensor electronics(s), undergoes an initial calibration process. This initial calibration process may use any available prior information, including sensor sensitivity information, to obtain calibration coefficients that may be used to convert sensor data (e.g., in units of current or counts) to estimated analyte values ​​(e.g., in analyte concentration units).

[0281] Several advantages may result from performing an initial calibration process after the analyte sensor is pre-connected to one or more components of the sensor electronics. Measurements may be taken during the manufacturing process phase to establish baseline values ​​for comparison at a later time period. These baseline values ​​may be used by processing algorithms to quantify scale and offset values ​​from known conditions. In some cases, the baseline measurements depend on sensor characteristics that are affected by the connection characteristics. This may allow measurements to be made that would not be possible with a separable system. For example, errors that may occur separately in the analyte sensor and sensor electronics may be reduced or eliminated by calibrating them as a single unit. Additionally, errors that may result from the act of connecting (and disconnecting) the analyte sensor to the sensor electronics may also be reduced or eliminated. For example, impedance measurements of a sensor may be more stable if the sensor remains continuously connected to the sensor electronics.

[0282] After pre-connecting the analyte sensor to one or more components of the sensor electronics, the calibration process 2400 proceeds to block 2406, where one or more environmental parameters affecting sensor sensitivity are monitored during one or more phases of the analyte sensor life cycle following the manufacturing phase. For example, the environmental parameters may be monitored during a sensor packaging phase, a sensor package sterilization phase, a sensor shipping phase, a sensor storage phase, a sensor insertion phase, and / or a sensor use phase.

[0283] Monitoring of the environmental parameter can be accomplished in any of several different ways. For example, if the analyte sensor is pre-connected to at least a component of the sensor electronics, which applies a stimulus signal to the analyte sensor and measures a signal response to the stimulus signal, the signal response can be used to determine an impedance value of the analyte sensor. Various techniques for calculating an impedance value of the analyte sensor based on a signal response are described elsewhere herein, such as one or more of the techniques described in U.S. Patent Application No. 14 / 144,343, issued as US20140114156A1 and entitled "Advanced Analyte Sensor Calibration and Error Detection," which is hereby incorporated by reference in its entirety. The determined impedance can then be compared to a pre-established impedance vs. environmental parameter relationship, such as a pre-established impedance vs. temperature relationship, a pre-established impedance vs. humidity relationship, or a pre-established impedance vs. membrane damage relationship, as also described in the aforementioned patent documents. In this manner, the environmental parameter(s) can be monitored.

[0284] In alternative embodiments, the environmental parameters can be monitored using environmental sensors, such as temperature or humidity monitors. For example, such monitors can be incorporated into the sterile packaging in which the analyte sensors are stored when they leave the factory. Alternatively, monitors, such as temperature monitors, can be incorporated directly into the sensor electronics themselves. In some cases, the monitor need not provide a numerical value for the environmental parameter, but can simply indicate whether the environmental parameter is outside a specified range within which the sensitivity of the analyte sensor is known to remain relatively stable. In this manner, relatively simple environmental monitors can be used.

[0285] Once the manufacturing and environmental parameter(s) are obtained, the calibration process 2400 proceeds to block 2408, where updated calibration coefficients are determined based on pre-established relationships between environmental and / or manufacturing parameters and analyte sensor sensitivity. In determining the coefficients to be updated and calibrated, information in addition to the measured parameter(s) can be taken into account. For example, the initial calibration coefficients can also be used. Using the updated calibration coefficients, the analyte sensor is properly calibrated such that the sensor data (e.g., in units of current or counts) can be converted to analyte values ​​(in units of analyte concentration).

[0286] The updated calibration coefficients can be determined at any suitable time after the environmental parameters are acquired. In part, this will depend on the particular components of the sensor electronics pre-connected to the analyte sensor. For example, if the pre-connected components include a suitable processor and associated memory, as well as a power source (e.g., a battery), the updated calibration coefficients can be determined as soon as the environmental parameters are acquired, for example, while in a sterile package or stored. Alternatively, if such a processor is not available, the environmental parameters can be stored in one of the pre-connected components and communicated when the remainder of the sensor electronics is connected, such as when the sensor insertion phase begins. Alternatively, if the pre-connected components also include a transmitter, the environmental parameters can be transmitted to the remainder of the sensor electronics or to another connected device.

[0287] In some embodiments, the updated calibration parameters may be determined by a processor and associated algorithms that are not built into the sensor electronics. Conversely, the updated calibration parameters may be stored in the pre-connected electronics and uploaded at a suitable time to a device in communication with the sensor electronics (e.g., display devices 114, 116, 118, and / or 120 of FIG. 1) or to a cloud-based processor (e.g., cloud-based analyte processor 490 of FIG. 1). The cloud-based processor or other device that calculates the updated calibration parameters then downloads the updated calibration parameters to the sensor electronics for use in calibrating the analyte sensor.

[0288] As previously mentioned, environmental parameters can be acquired multiple times during various phases of the analyte sensor's life cycle, or even multiple times during a single phase (e.g., storage). The environmental parameters acquired at each of these different times can then be used in combination with other factors (e.g., lot factors, in vivo measurements, cloud data, time since sensor manufacture, individual patient factors) to determine the final updated calibration coefficients.

[0289] In some embodiments, instead of or in addition to periodically updating the calibration coefficients at multiple times during various life cycle phases, a single complex adaptive calibration coefficient can be generated during the sensor usage phase. The complex adaptive calibration coefficient can incorporate the initial calibration coefficients obtained during sensor manufacturing along with the environmental conditions experienced by the analyte sensor (and any pre-connected electronics, if present) from sensor manufacturing to sensor insertion. In this way, the experience of the analyte sensor during its lifetime is encoded in a format that can be used by the calibration algorithm. Thus, rather than considering each individual environmental parameter, such as temperature and humidity, and sensor characteristics, such as impedance, individually, a single encoded value or profile can be provided to the calibration algorithm that encapsulates all manufacturing and / or environmental parameters, as well as sensor characteristics.

[0290] 12(a)-12(c) are timelines showing various phases over the life of an analyte sensor. The example shows a manufacturing phase, a sterilization phase, a shipping / storage phase, and an in vivo phase. Throughout these phases, the temperature and humidity experienced by the sensor as monitored are shown in FIG. 12(a) and FIG. 12(b), respectively, and FIG. 12(c) shows the change in sensitivity of the analyte sensor as determined based on these environmental parameters. The spikes in temperature shown during the manufacturing phase occur during the curing of the analyte sensor. Note that the sensitivity does not change significantly when the spikes are relatively small and / or short in duration, and therefore not all such spikes necessarily require recalibration of the analyte sensor. Other spikes that are larger in magnitude and / or duration result in a change in sensitivity, and therefore indicate that recalibration may be required if the spikes exceed these thresholds. Therefore, environmental parameters such as temperature and humidity may only need to be monitored to determine whether they are above or below certain thresholds that have been shown to have a significant effect on sensitivity.

[0291] It should be noted that all of the above-mentioned parameters that significantly affect the sensitivity of the analyte sensor and that are monitored at various times can also significantly affect the baseline signal of the sensor. Thus, in addition to monitoring these parameters to calibrate or adjust the sensitivity of the analyte sensor, these parameters can also be monitored to calibrate or adjust the baseline of the analyte sensor. More generally, the monitored parameters can be used to adjust any characteristic metric of the analyte sensor, as well as the sensitivity and / or baseline. Examples of such characteristic metrics include, but are not limited to, long term drift, analyte sensor current, exponential drift rate, ratio between fast and slow components in a double exponential sensitivity model, non-glucose baseline, binned bias between glucose concentration in the local tissue surrounding the sensor and blood glucose, constant baseline, asymptotic rise in magnitude of baseline due to membrane degradation, asymptotic rise in magnitude of baseline due to membrane degradation, onset / transition time of baseline rise due to membrane degradation, drift rate of baseline rise due to membrane degradation, initial magnitude of fast electrochemical run-in, drift rate of fast electrochemical run-in, initial magnitude of slow electrochemical run-in, drift rate of slow electrochemical run-in, initial magnitude of binned bias, final magnitude of binned bias, and drift rate of unseen binned bias.

[0292] The complex adaptive calibration coefficients described above can be determined in part using predetermined statistical phase relationships identified between the in-use sensor behavior and the sensor behavior measured during various phases of the analyte sensor life cycle for a large number of previously deployed sensors. That is, rather than simply using pre-established relationships between environmental and / or manufacturing parameters and analyte sensor sensitivity to calibrate a particular sensor, the complex adaptive calibration coefficients can be developed using relationships between one or more of the characteristic metrics of many sampled sensors measured at various locations and the resulting sensitivity or other characteristic metric of the sensor samples during the use phase.

[0293] For example, FIG. 13 shows a sensor output signal obtained from a sensor during each step of a manufacturing process, including at least one curing step, a membrane application step in which the sensor is coated in a particular membrane, and a manufacturing calibration measurement step to determine initial or in vitro values ​​of analyte sensitivity, baseline, interference sensitivity, impedance values, etc. As shown in the figure, the sensor output signal changes during each step. The shape of this signal over all or some of these steps defines a sensor signature that may be obtained for a large number of sensors during the manufacturing phase. By investigating the behavior of these sensors during each subsequent phase, and especially during the use phase, statistically useful correlations may be found between the sensor signature and the behavior of the sensor. In this way, by measuring the sensor signature of a particular sensor during various steps of the manufacturing phase, it may be possible to infer the behavior of the sensor (e.g., one or more of the characteristic metrics) at a later time. For example, it may be possible to obtain a predicted sensitivity profile of sensitivity change over time for a particular sensor.

[0294] While the immediate discussion has focused on the use of manufacturing and / or environmental parameters monitored during various life cycle phases of the analyte sensor to facilitate calibration of the sensor, these monitored parameters can also be used for other purposes as well. For example, various actions can be taken by the analyte monitoring system based on the monitored parameters. For example, if one or more of the environmental parameters exceed a specified threshold for a particular period of time, a message can be generated on the receiver (e.g., the user's mobile communication device) that indicates to the user, for example, that the expected end of the sensor's life has been prematurely reached, or that the quality of the calibration is below a recommended value, or that the confidence level of the sensor's reliability is below a recommended value, or that the sensor is suitable only for a particular mode of operation (e.g., the sensor's life, accuracy, outlook, trends, analyte values, alarms are not known for monitoring type 1 diabetic patients but not type 2 diabetic patients or vice versa), or that the sensor is suitable only for implantation in a particular site, such as the abdomen or arm. Alternatively, or in addition, other actions that may be performed as a result of monitoring environmental parameters include adjusting various start-up parameters of the analyte monitoring system (e.g., requiring a longer than normal break-in period for the sensor), switching to an operating mode in which glucose concentrations are only reported as being in a range that is not reported (e.g., low, medium, or high), initiating an in vivo calibration process, using default calibration values, and using temperature, humidity, and / or complex compensated calibration values.

[0295] The error reduction that can be achieved by calibrating a pre-connected system compared to a conventional (non-pre-connected) system is modeled by considering a subset of variables that affect the system. The model uses only unitless measurements that are current (counts) and calibrated against known glucose calibration solutions. Component induced variations and measurement variability are eliminated. In particular, gain and offset values ​​are not measured but are calibrated against unit values, so their induced errors are eliminated. The system is calibrated with precision components that affect the values ​​of contact resistance, leakage current, and bias voltage. Thus, the variations of those components are eliminated and can be modeled as fixed values.

[0296] Hierarchical models for sensor manufacturing process, sensor bench characterization, and in vivo performance In one variation of the subject matter described herein, the statistical process may be used as part of a closed-loop feedback manufacturing process.

[0297] It has been found that the sensor manufacturing process, sensor bench characterization, and in vivo sensor characteristics are all loosely coupled to each other. That is, the sensor process parameters are monitored to ensure they are within limits, and then each sensor is evaluated to determine if it meets predefined criteria, but the process parameters and in vitro characteristics do not predict in vivo sensor characteristics (e.g., sensitivity) to a high degree. Typically, in vivo characteristics are inferred less from manufacturing process variables and more from calibration, because in conventional systems, each sensor is calibrated using a blood glucose meter, typically every 12 hours.

[0298] If automated calibration techniques are employed, it may be necessary to rely more and more on sensor characteristics and less on calibration via instruments etc. Thus, mathematical relationships between manufacturing process variables, in vitro (or bench) characteristics of the sensor, and in vivo attributes become important. Furthermore, as the number of sensors manufactured increases, the necessary resources and time may not be available to exhaustively test each sensor for pass / fail criteria or estimate their in vivo attributes. Thus, a mathematical / statistical framework can provide an alternative way to relate the sensor manufacturing process to the in vitro sensitivity of the sensor and the predicted in vivo sensitivity. Ideally, the process variables can be set to produce a sensor with a particular sensitivity.

[0299] There are several sensor process and design parameters that can be adjusted to build a sensor with specific attributes. These include relative humidity, temperature, curing time, soak time, thickness of each layer, and raw material attributes / percentages. The in vitro and in vivo behavior of the sensor depends on these process variables and can be modeled using mathematical and statistical models. A hierarchical model is a type of multi-hierarchical statistical model in which different random effects that significantly affect the process and measurement are quantified at multiple levels as conditional probabilities. For example, the process variation at a particular set point can be modeled at level 1 (the highest level), the variation of the sensor behavior in vitro at level 2, and the variation of the sensor behavior in vivo at level 3. The model can finally relate these levels and use variables from one level (e.g., second or third level) to estimate variables at a different level (e.g., level 1).

[0300] An example of a hierarchical model framework for sensor manufacturing and in vivo characterization is described below, where: Xp represents process and design parameters such as relative humidity, temperature, cure time, soak time, thickness of each layer, raw material properties, etc. Mp is a vector of target sensor characteristics defined by the process parameters (Xp). Mp = N(f(Xp),Σ 2 )

[0301] That is, the sensor quality is a function of all sensor design parameters and process parameters. The overall distribution of the sensor quality has a mean of the process set point or target with variance Σ2. Note that non-normal distributions are also possible.

[0302] The vector of sensor characteristics verified on the bench is: Mb = N(Mp,Γ 2 )

[0303] For example, if a lot of 10,000 sensors is manufactured, 100 of them can be sampled to estimate the process quality. The distribution of bench verified quality is then determined by the mean target lot Mp and the variance Γ 2 is normalized using Mi:N(g(Mb),V 2 );

[0304] These are the actual in vivo properties of the sensor. The distribution of the sensor is described by a function "g" that transforms the in vitro properties to the in vivo properties. This is also called in vivo to in vivo correlation. A simple example of the function "g" is the in vitro to in vivo proportionality constant. In the general case, the function "g" is an in vitro to in vivo transformation with multiple factors. In matrix form, this can be written as:

[0305] Mi = G*Mb,

[0306] Here, G may have factors for sensitivity, drift, and baseline, as well as interdependencies.

[0307]

number

[0308] where the diagonal terms s, d, and b are the sensitivity, drift, and baseline relating in vitro factors to in vivo factors, while the off-diagonal terms are the cross-correlations between sensitivity and drift s_d and sensitivity and baseline s_b. The elements of the matrix can change over time.

[0309] Once this model is developed, there are a variety of applications in which it can be employed. In one application, process information can be incorporated into a continuous glucose monitor (CGM) algorithm (i.e., a joint probability algorithm), thus enabling scaled-down factory calibration, as described in US20140278189A1, entitled "Advanced Calibration for Analyte Sensors," which is incorporated by reference in its entirety. In another application, given that extensive sampling of the manufacturing process is cumbersome and expensive, this hierarchical model can be used to estimate process parameters and target sensor attributes through sampling of multiple lots from in vitro and in vivo. A third application involves tracking field performance and directly correlating that performance with production. This model can help proactively track process parameters based on field data, allowing corrective actions to be taken more quickly.

[0310] Estimation of sensor characteristics of in-situ data along the time axis In another variation of the subject matter described herein, sensor characteristics such as sensitivity can be estimated from field data. For example, predictive models can be created by mapping manufacturing parameters to in vivo sensor behavior from a very large data set (assuming the sensor has a unique sensor ID to map field data to manufacturing data).

[0311] The sensitivity of a sensor is typically estimated by comparing the sensor current to a reference glucose measurement. However, this becomes difficult or impossible when the field data does not have a reference glucose measurement for comparison (i.e., in the case of factory calibrated products) or when the reference glucose measurement is unreliable (e.g., when the meter quality is poor or unknown, making the reference measurement unreliable). This problem can be addressed as follows.

[0312] It is assumed that an individual user may have stable glucose excursion patterns over weeks or months, that these patterns are consistent with the patient's treatment approach, and that the patient's underlying physiology does not change dramatically. As a result, observed differences in raw sensor signal statistics (e.g., mean, standard deviation, median, percentiles, skewness, etc.) from sensor to sensor may reveal differences in sensor attributes such as sensitivity. Although sensitivity estimated in this way cannot be as reliable as sensitivity measured through comparison to accurate reference glucose measurements, with a sufficiently large data set, the information may be useful in detecting patterns in sensor behavior and may be used to build predictive models of in situ sensor behavior or to detect unexpected shifts in in situ sensor behavior.

[0313] For example, when wire obtained from a new wire vendor is introduced into production, it is generally expected not to have any effect on sensor sensitivity. However, field data shows that across thousands of users, the standard deviation of raw sensor readings is, for example, about 2% higher for sensors from lots using the new vendor than the historical standard deviation for each user. This pattern could trigger further investigation into the large effect of the wire vendor, or the data could be incorporated into factory calibration models. In this way, the sensitivity predicted by the factory calibration algorithm could be adjusted to account for the large effect of the wire vendor, improving accuracy.

[0314] NMR method for characterizing Carbosil / PVP ratios in diffusion resistance layer solutions. In yet another variation of the subject matter described herein, which can be used to improve the accuracy of sensors during manufacture, a method can be employed to characterize the Carbosil / PVP ratio in the diffusion resistant layer of the sensor.

[0315] This diffusion resistance layer is one of the most important layers in the CGM membrane of the sensor, which provides stable and predictable glucose and oxygen permeation and blocks some interfering agents. Currently, certain sensors use Carbosil2090A and PVP (K90) mixed system. Carbosil dissolves in THF but cannot dissolve in ethanol. However, PVP can dissolve in ethanol but cannot dissolve in THF. Therefore, the current diffusion resistance layer solution is prepared by using THF / ethanol mixed solvent to dissolve both Carbosil and PVP.

[0316] The performance of the sensor is related to the Carbosil / PVP ratio (e.g., higher PVP will give higher sensitivity). In particular, the uniformity of the dip coating will be affected by the Carbosil / PVP ratio. Also, the viscosity of the diffusion resistance layer dip solution will be affected by changes in the Carbosil / PVP ratio. Overall, the stability of the sensor will be affected by the Carbosil / PVP ratio.

[0317] To produce reproducible sensors, a consistent and accurate Carbosil / PVP ratio in the RL immersion solution is a key parameter to control. However, to date, no method has been developed to estimate the Carbosil / PVP ratio in the RL solution. Therefore, to improve the accuracy of the sensor and thus enhance the ability of auto-calibration, it is important to track the quality of each RL immersion solution before the sensor is immersed.

[0318] In one embodiment, nuclear magnetic resonance (NMR) spectroscopy is used to determine the Carbosil / PVP ratio in the diffusion resistant layer solution. In particular, proton NMR techniques can be employed.

[0319] One particular example of a process that can be employed to determine the Carbosil / PVP ratio is illustrated by the following steps.

[0320] 1. Prepare the sample. 1.1 C2090A / PVPTHF / EtOH solution with 22 wt% EtOH, 13.6 wt% PVP. Form a film using 1.2 RL solution and dry overnight at 50°C until a constant weight is reached. Remove the solvent. 1.3 Cut out one thin film and dissolve in DMSO-d6 at a concentration of 10 mg / mL. (20 mg / mL, 50 mg / mL) 2. Run proton NMR to obtain the FID signal, then perform baseline correction, adjust the phase, and obtain the spectrum. 3. Integration of the MDI peak in Carbosil; calculate the integral number of each proton. 4. Integration of the H2 peak in PVP; calculate the integral number of each proton. 5. Calculate the molar ratio of Carbosil and PVP. 6. Obtain a calibration curve for Carbosil / PVP mixture. 7. Calculate the wt% / wt% ratio of Carbosil / PVP based on the calibration curve.

[0321] Figure 14 shows the NMR spectrum of PVP in DMSO-d6. Figure 15 shows the HNMR spectrum of Carbosil in DMSO. Figure 16 shows the HNMR spectrum of RL membrane (Carbosil / PVP mixture with solvent removed). The MDI peak of Carbosil and the H2 peak of PVP were selected to calculate the Carbosil / PVP ratio.

[0322] A HNMR calibration process was carried out to validate this method. First, RL solutions with different Carbosil / PVP ratios were prepared with a given Carbosil / PVP ratio as shown in the table in Figure 17. Then, HNMR was performed using DMSO-d6 as the solvent. Figure 18 shows the resulting HNMR calibration curve.

[0323] Temperature and humidity detection during storage As mentioned above, impedance measurements of the analyte sensor can be taken during the shipping and storage phases to monitor the humidity of the sensor pre-connected to the electronics. Additionally, a temperature sensor in the transmitter can record the temperature and thus the temperature and humidity sensors can indicate if the analyte sensor was outside of the recommended humidity and temperature ranges for that analyte sensor during shipping and storage. Additionally, algorithms can be created to compensate for initial factory calibration parameters based on temperature and humidity conditions and duration of exposure. (Note that the initial factory calibration can be performed on a single sensor using a single bath, or on a lot or chunk of sensors, e.g., 30 sensors, that can be calibrated simultaneously using a single large bath).

[0324] In one variation, measuring the current alone may be sufficient to indicate humidity or extreme humidity. Some embodiments of the sensor system may periodically wake up and perform measurements to identify when the sensor has a signal to indicate system activation (by hydration after deployment). A fully pre-connected sensor may also measure current when only humidity is present. Thus, it may be a useful indicator that the analyte sensor has been exposed to humidity conditions during shipping and storage. If the system is not fully integrated with electronics, a removable adhesive tab (e.g., a "sticky tab") may be placed on the transmitter electrode, which can pass a current when humidity is present. This would allow the transmitter to measure humidity. The tab would be removed before using the transmitter.

[0325] In another variation, the storage condition of the sensor can be determined using resistors or other materials that have a known response to temperature, humidity, or a combination of both and generate an electrical signature (e.g., resistance, current). In addition to the circuit that activates the transmitter when the sensor is detected, that same circuit or another circuit can be placed to be activated whenever the temperature and / or humidity exceed a threshold value. Based on the duration of activation and the magnitude of the measurement (reflecting temperature and / or humidity), the system can adjust the calibration factor to better predict in vivo performance by inferring changes due to environmental conditions. In one particular implementation, strips of environmentally sensitive material are placed on both ends of the transmitter electrodes so that the strips can only conduct current under certain environmental conditions. In some cases, this acts as an irreversible circuit or material change that is activated only when a threshold value is exceeded, shifting the predicted sensor response to a new performance region or creating an on / off indicator to prevent use of the product if extreme conditions are reached.

[0326] In yet another variation, the packaging in which the sensor is stored can include a temperature and / or humidity sensitive material that changes color based on temperature and / or humidity, such that the color change can be indicative of the storage conditions experienced by the sensor. For example, in one example, the material may be disposed inside the package in the form of a small area (e.g., a dot). The color of the material can be detected directly by a camera or other detector of a mobile device in which a system app is placed, and the system app can determine the extent of the color change. Alternatively, the color change can be detected directly by a transmitter or other sensor electronics, and the color change can be used to better predict in vivo performance by inferring changes due to environmental conditions, as described above.

[0327] In yet another variation, the calibration parameters used by the calibration algorithm may vary from sensor to sensor, based on sensor manufacturing details and other factors. From the transmitter's perspective, the user inserts the sensor and enters a "sensor id" into the display, and based on this, the display will either send the actual set of parameters that needs to be used, or send a code that causes one of a predefined set of parameters to be used. To achieve this, the transmitter can store multiple sets of parameters. If this set of parameters is huge, storing multiple copies of the parameters may take up too much storage space.

[0328] To address this issue, in some cases, only one default set of calibration parameters can be stored in the transmitter, and to obtain an updated set, only the differences between the default and updated sets need to be transmitted. This can be more effective, since typically these differences will be small. This approach also provides the flexibility to change any individual parameter; that is, the set of parameters does not need to be fixed, and they can be changed during the factory calibration process. If the set of parameters is an ordered list, the changes can be specified as a list of paired values, such as (parameter number, new value).

[0329] Calibration Code Encoding In yet another aspect, the sensor calibration code or some other code assigned to the sensor in the factory can be associated with the customer's account in the following manner. In this example, the transmitters shipped with the sensors are assumed to be reusable and are shipped with enough sensors to cover the duration of the transmitter's life (generally determined by the battery in that transmitter). For example, a transmitter that is usable for three months would require six sensors that last for 14 days each. In such a system, the factory calibration code associated with the sensor can be communicated to the user's mobile device using the following method.

[0330] First, in step 1, a customer orders a package of sensors, preferably using a dedicated app on the customer's mobile device. In step 2, while in the factory, the package, and the sensor contained therein, are scanned to establish an association between the packaging and the sensor. At the time of shipment (step 3), a shipping label with the customer's account information is scanned along with the packaging, thereby creating an association between the customer's credentials and the packaging. This association is stored by the manufacturer, such as in a cloud server, for future reference.

[0331] In step 4, the package with the sensor is shipped to the customer. In step 5, the customer inserts the new sensor, installs the new transmitter, and the package starts the session. After the user starts the session, in step 6, the sensor code information stored, such as on a cloud server, can be retrieved since the sensor and transmitter package was previously associated with the customer's account.

[0332] Enhanced closed-loop manufacturing feedback process In another variation, additional information can be used to supplement the available information related to the manufacturing process, which is stored by sensors pre-connected to the sensor electronics. For example, a manufacturing process typically includes a series of steps performed at various stations in a factory. As a rule, the amount of time required by an operator to perform any given step should be approximately the same for each part or component being assembled or process being performed at that station. Significant variations in these times may indicate an incomplete station or process, where the operator needs to overly adjust parts and fixtures during assembly, which may highlight areas for process improvement. In some cases, a small device can be placed at each workstation to conduct a study of the time required to perform the activities required at that station. The device can include actuators (e.g., buttons, motion sensors, light sensors) that provide a simple non-invasive means by which an operator can quickly interact and have a microcontroller in the device record the time of each device interaction. The operator will be instructed to interact with the device each time the process or the like completes a task at their station. The device then stores the times and they can be output for later analysis.

[0333] Initial Calibration In another variation, when ethylene oxide (ETO) sterilization is used (instead of e-beam sterilization), the initial drift profiles for some conditions are found to be quite flat (see graph in FIG. 19, where group 4 (left) are ETO conditions and group 6 (right) are non-sterile conditions, using the same time scale with about 12 sensor drift profiles for each group). Thus, ETO treatment can be used to stabilize sensors against high humidity storage or shipping transport.

[0334] In another variation, the sensor can undergo ETO sterilization with a reusable desiccant in the packaging during the ETO process. This desiccant can then be "baked out" after ETO to restore its drying capabilities. After ETO sterilization, additional desiccant may be added to the final sensor packaging and / or the final packaging can employ a moisture barrier to minimize humidity. In this manner, several sensors can be sterilized using bulk packaging that includes the sensor and desiccant.

[0335] Communication of sensor parameters via NFC In yet another variation, sensor information of the type described herein that can be communicated (e.g., sensor parameters, calibration coefficients or codes, environmental characteristics) can be transmitted from the sensor to a transmitter via an NFC protocol. In one embodiment, this can be accomplished by providing an NFC tag on the sensor base or interposer and an NFC reader on the receiver (e.g., a user mobile device). The sensor information received by this receiver from the sensor base or interposer can then be communicated to the transmitter, for example, upon system start-up.

[0336] Electronic Hardware Correction Factory calibration correction techniques for continuous analyte monitoring systems typically employ digital techniques to store and adjust for sensor lot variations. In some embodiments, it is useful to use analog electronic circuitry to modify the sensor signal. Using a resistor of known value can help modify the analog signal to modify the amount of current or measured voltage. In one example, this resistor can be combined as part of a gain circuit with an op-amp to adjust the gain of the output signal. The resistor can be selected from a variety of known resistance values ​​or constructed via a process (e.g., laser trimmed resistors).

[0337] Coefficients affecting sensitivity and impedance A non-limiting set of coefficients that have been found to affect the impedance and / or sensitivity of pre-connected analyte sensors are shown in the table in Figure [IFD1675]. These coefficients are of interest for selecting manufacturing and storage conditions, but can be measured for individual sensors and / or sensor lots and correlated with the sensitivity and impedance measurements at various times during the sensor's life. In this manner, the values ​​of these coefficients can be used individually and / or in combination with each other to determine the relationship between measured impedance and sensitivity at any time during the sensor's life, thereby adjusting the calibration coefficients used to calibrate the sensor at any time during the sensor's operational life.

[0338] Periodic gradient parameter updates Currently, a "calibration check" procedure is performed in the factory where the sensor undergoes an in vitro calibration to obtain a slope value. This value is used to seed a joint probability algorithm with the initial and final sensitivity values ​​using a linear transformation, i.e. Average initial gradient = calcheck*mstart+bstart Average final gradient = calcheck*mfinal+bfinal

[0339] Deviations from this linear relationship can be accounted for by updating the average initial and terminal slopes using a linear combination of parameters measured at the factory (e.g., during a calibration check) and parameters measured in real time. For example, the formula for the terminal slope can be modified as follows:

[0340] Mean final gradient = a*calcheck+b*meanSensorCurrent+c*sigmaSensorCurrent+d*sensorCv+e*calcheck+...+Offset

[0341] Here, the real-time parameters include the mean sensor current (meanSensorCurrent), the standard deviation of the sensor current (signaSensorCurrent), and the coefficient of variation of the sensor current (sensorCv). Other real-time parameters that may be included in the mean terminal slope include the mean sensor current, the root mean square of the sensor current, and the sensor current taken at a specified percentile in the distribution of sensor current values. A similar approach may be taken to adjust the mean onset sensitivity. By using a combination of factory and real-time measurements in this manner, the performance of the system may be improved because a linear combination allows the factory information to be associated with the in vivo sensor measurements. The parameters described in the following equation may allow the estimate of the terminal slope to be updated periodically (e.g., daily) during sensor wear.

[0342] Retrospective calibration of CGM signals Retrospective calibration of CGM signals with and without SMBG use is important for the professional CGM market and other applications such as technical support, as well as for benchmarking the performance of factory calibrated CGMs. With retrospective calibration, there is an opportunity to remove certain artifacts that corrupt real-time CGM signals, such as time lags, intermittent glitches, compression, noise, and data gaps. As described below, in some embodiments, data gaps, noise, and artifacts in CGM signals can be removed using predictive algorithms. This approach generally works best after removal of time lags from the signal and smoothing.

[0343] It is generally believed that blood glucose levels can be predicted fairly reasonably for up to about 30 minutes in advance. The accuracy of the predicted signal decreases when the prediction horizon exceeds 30-40 minutes. Thus, any signal artifacts or data gaps shorter than 30-40 minutes can be replaced with a predicted signal without losing important information required for clinical use. Furthermore, assuming retrospective use, any errors in the predicted blood glucose levels can be removed by analysis of the data. Some ways this can be accomplished are:

[0344] 1. Identify the area(s) of artifact in the signal. 2. Replace the artifact signal with the predicted blood glucose value. 3. Evaluate the difference in predicted blood glucose levels at the end and beginning of the post-artifact signal. 4. Correct the predicted signal by feeding this error back into the prediction. For example, if there is a 30 mg / dL error between the end predicted CGM and the initial time point of the CGM after the artifact, this error can be distributed evenly (or with a weighted average) over the duration of the predicted signal. In this way, the predicted signal is corrected, resulting in a smooth correction of the artifact and no discontinuities.

[0345] In another embodiment, prediction can be used in both directions to increase the duration of artifacts that can be corrected. How artifacts of longer duration can be corrected is described below.

[0346] 1. Identify the start and end of the artifact that requires removal / replacement. 2. Create two CGM time series signals: the first time series which is the normal signal (time going forward from the start of the session to the end), and the second time series which is in reverse time (from the end of the session to the start). 3. Use the prediction to exchange both the forward and reverse time series artifacts, i.e., each artifact will have two possible exchanges: an exchange based on the forward time series signal, and an exchange based on the reverse time series signal. 4. Choose the midpoint between the two exchanged artifacts. These should correspond to the same point in time in the CGM signal. Depending on how the glucose signal fluctuates during this period, the two signals may match at the midpoint or differ at the midpoint. 5. Assuming that the prediction is reasonably accurate in the short term, the best estimate of the blood glucose level at the midpoint is the average of the values ​​from the two time series. 6. Here, the error between the mean value from the time series and the actual mean value can be fed back into the predicted artifact replacement to correct those values. 7. Corrections can be weighted depending on the quality of the signal before and after the artifact.

[0347] This approach to artifact correction makes the signal more reliable and increases the duration of artifacts that can be corrected / removed.

[0348] Replacement Sensor A sensor may sometimes fail before the sensor's sales period (e.g., 7 days). In some cases, the sensor electronics (e.g., transmitter) may be packaged with a three month supply of sensors (e.g., a six-pack) in a single box. In one variation, the transmitters may then be coded with a single, common sensor code. If one of the sensors in that sensor box fails and a replacement sensor needs to be sent to the customer, the transmitter may transmit the sensor code to a dedicated app on the customer's mobile device. The customer may then request a replacement sensor through the app. The app may then relay the sensor code to a manufacturer, etc., who may then send the appropriate sensor to the customer with the correct code to match the transmitter included in the original sensor box.

[0349] Configurable calibration frequency In one variation, the frequency at which the Transmitter issues calibration requests to the dedicated app on the customer's mobile device can be configurable. In one example, the Transmitter can have a default calibration frequency (e.g., one calibration per day, two calibrations per day, then one calibration per day, etc.) if the Transmitter has not been provided with existing calibration information. In another example, the Transmitter may or may not issue calibration requests to the dedicated app based on the availability of existing calibration information. Additionally, the calibration frequency may be based on the type of app running on the mobile device. The Transmitter can also store different default calibration frequencies based on the type of app being used.

[0350] Transferring calibration data to the transmitter In another variation, a method for transferring calibration coefficients from a disposable sensor to a transmitter or other sensor electronics without user involvement may operate as follows. The method uses memory embedded in the sensor that transfers the calibration coefficients and / or other information to the transmitter. Information that may be transferred may include, for example, lot numbers, expiration dates, and authentication information that may ensure that a manufacturer is using an authentic sensor. Such authentication information may operate according to cryptographic and other algorithms such as hashing (e.g., SHA-256) and / or may operate according to a standard such as the Federal Information Processing Standard.

[0351] This information can be transmitted from the sensor to a transmitter using any suitable connector, or wirelessly, for example using RFID, although these are not always suitable for low-cost, environmentally robust systems and may require significant development or retooling. Conversely, the following techniques can be employed to transfer calibration codes and / or other information:

[0352] Without loss of generality, the technique will be described as using a low bias voltage (e.g., less than 1 volt) and being applicable to sensors having at least two electrical connectors (e.g., a reference electrode and a working electrode). The sensor is assumed to be wired together and composed of a memory element in which information is stored. The memory element uses a single wire for power and signal and is connected to the working electrode of the sensor. A ground connection is connected to the reference electrode of the sensor.

[0353] To begin a session, the transmitter periodically checks for the presence of a new sensor by waking up from sleep mode, enabling a bias voltage, and looking for a predefined response from the sensor. If the response indicates the presence of a new sensor, the transmitter will transition to an operational mode, as described below. If the response is not as expected, indicating the absence of a sensor, the transmitter will return to a sleep state for a predefined period of time.

[0354] If the predetermined signal indicates the presence of a new sensor, the transmitter attempts to recover calibration coefficients and other information from the memory device. The memory device is configured to respond to the signal pulse only if the signal pulse is above a predetermined voltage level and above the sensor's nominal operating bias voltage. The memory device is both powered and communicates using the same pins. The memory device can operate in an active mode, where the memory device incorporates a short-term charge storage device (such as a capacitor) to power the memory chip while sending a signal back to the transmitter while the transmitter places its pin connected to the memory element at high impedance. Alternatively, the memory device can operate in a passive mode and place a high or low load on the transmitter, so that it can signal the appropriate information back to the transmitter using the transmitter as a master clock.

[0355] The time required to communicate the relevant calibration coefficients and any other information (expiration date, serial number, etc.) is generally short compared to the lifetime of the sensor, and the high voltages used during such short communication times will not damage any enzymes used in the sensor. Thus, short-term overvoltages will not affect the long-term operation of the sensor and may even aid in electrochemical break-in of the sensor. Once the memory device has passed the required information and the transmitter has ramped down to the nominal sensor bias voltage, the memory device can be designed to either enter a very high impedance state and not falsely raise the observed signal current from the sensor, or draw a known current that can be subtracted from the sensor signal, or a combination of both.

[0356] In some embodiments, the transmitter can signal the end of the sensor life to the memory element, which will place an indicator or the like in its internal memory indicating that the sensor has expired. This can prevent accidental reuse of the sensor, since even if the sensor is disconnected and reconnected, the memory element will communicate to the transmitter that the sensor has already been used. At this point, the duration of the communication session, and the value of the applied voltage, are less important, since it does not matter if the enzyme is damaged, since the sensor has reached the end of its life.

[0357] In an alternative embodiment, upon initiating a session, the transmitter may simply wake up and interrogate the connection for the presence of a memory device, and later check that the sensor is working properly upon successful transfer of the calibration data.

[0358] Calibration of EGV in a closed loop system In closed loop systems (e.g., artificial pancreas systems), updating the estimated glucose value (EGV) as a result of a calibration may lead to erroneous dosing because the EGV may be a larger change than the natural blood glucose change when the calibration occurs. When such EGV changes are input into the artificial pancreas algorithm, the algorithm may recognize that those changes lead to an inaccurate prediction of the EGV. Current artificial pancreas algorithms accept the calibration update and update the EGV after the update is complete.

[0359] This problem can be addressed in some embodiments by updating some EGV data points before and after calibration for use by the artificial pancreas algorithm, in this way the algorithm can capture the correct EGV changes.

[0360] Use of biometric data in preventing incorrect entry of calibration data Manual entry of calibration data or other reference information is prone to error. One method for detecting and preventing the use of incorrectly entered data may use the user's biometric data. Such information may be available to a dedicated app on the user's mobile device, either from sensors built into the mobile device or from a third-party device that can provide biometric data to the mobile device. If the entered calibration data or other data is found to be incompatible or inconsistent with the biometric data, the app may present an error message or take other appropriate action. As a simple example, if a 35-year-old male is found to have a heart rate of 170 bpm from a biometric sensor and the CGM shows a glucose reading of 40 mg / dL, this indicates that the glucose reading is incorrect.

[0361] Efficient storage of calibration coefficients and other parameters The transmitter needs to store relevant calibration coefficients and / or other parameters for various sensors. When a sensor is inserted, the user typically enters the sensor ID into a dedicated app, which then either transmits the parameters to the transmitter or transmits an identifier corresponding to a set of predefined parameters already stored or otherwise available in the transmitter. In either case, the transmitter may need to store multiple parameter sets. However, if one or more sets of parameters are huge, the memory available to the transmitter may not be sufficient to store all the required parameters.

[0362] In one variation, the transmitter can store a limited number of parameter sets (e.g., one) that can serve as a default set of parameters. Then, when a newer parameter set can be used, only the differences between the values ​​in the default parameter set and the values ​​in the new parameter set need to be stored in the transmitter. This can be a more efficient way to store data, since the differences will usually be small. This also provides flexibility to change any parameter, since parameters established during factory calibration do not need to remain fixed. In one embodiment, the default parameter sets can be provided as an ordered list, and their changes can be provided as a list of value pairs that specify the parameter number and the difference value from the default value.

[0363] Exemplary Sensor System Configurations The embodiments of the present invention are described above and below with reference to flowchart illustrations of methods, apparatus, and computer program products. It will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by the execution of computer program instructions. These computer program instructions can be loaded onto a computer or other programmable data processing device (such as a controller, microcontroller, microprocessor, etc.) in a sensor electronics system to manufacture a machine, such that the instructions executing on the computer or other programmable data processing device create instructions for implementing the functions specified in one or more blocks of the flowcharts. These computer program instructions can also be stored in a computer-readable memory that can directly instruct the computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable memory generate an article of manufacture that includes instructions that implement the functions specified in one or more blocks of the flowcharts. The computer program instructions can also be loaded onto a computer or other programmable data processing device to enable a series of operable steps executed on the computer or other programmable device to generate a computer-implemented process, such that the instructions executing on the computer or other programmable device provide steps for implementing the functions specified in one or more blocks of the flowcharts presented herein.

[0364] In some embodiments, a sensor system is provided for continuous measurement of an analyte (e.g., glucose) in a host, comprising a continuous analyte sensor configured to continuously measure the concentration of the analyte in the host, and a sensor electronics module physically connected to the continuous analyte sensor during sensor use. In one embodiment, the sensor electronics module includes electronics configured to process a data stream associated with the analyte concentration measured by the continuous analyte sensor, thereby processing the sensor data and generating displayable sensor information including, for example, raw sensor data, transformed sensor data, and / or any other sensor data. The sensor electronics module can include electronics configured to process a data stream associated with the analyte concentration measured by the continuous analyte sensor, thereby processing the sensor data, which can include, for example, raw sensor data, algorithmically processed and transformed sensor data, and / or any other sensor data. The sensor electronics module can include a processor and computer readable program instructions for implementing the processes described herein, including the functions indicated in one or more blocks of the flowcharts presented herein.

[0365] In some embodiments, the receiver, which may also be referred to as a display device, is in communication with the sensor electronics module (e.g., via wired or wireless communication). The receiver may be an application-specific portable device or a general-purpose device, such as, for example, a PC, a smartphone, a tablet computer, a smartwatch, a wearable display, and a haptic device. In one embodiment, the receiver may be in data communication with the sensor electronics module for receiving sensor data, such as raw data and / or processed data, and may include a processing module for processing and / or displaying the received data. The receiver may also include an input module configured to receive input, such as calibration codes, reference analyte values, and any other information described herein, from a user via an input method (e.g., a keyboard or a touch-sensitive display screen), and may also be configured to receive information from external devices, such as insulin pumps, insulin pens, wearable sensors, connected devices, accelerometers, and fiducial meters, via wired or wireless data communication. This input may be processed alone or in combination with information received from the sensor electronics module. The receiver's processing module may include a processor and computer program instructions for implementing any of the processes discussed herein, including the functions indicated in one or more blocks of the flowcharts presented herein.

[0366] While the present disclosure has been illustrated and described in detail in the drawings and the above description, such illustrations and descriptions are to be considered as illustrative or exemplary rather than restrictive. The present disclosure is not limited to the disclosed embodiments. Variations of the disclosed embodiments can be understood and effected by those skilled in the art practicing the disclosure of the claims from a consideration of the drawings, the disclosure, and the appended claims.

[0367] All references cited herein are incorporated herein by reference in their entirety. To the extent that publications and patents or patent applications incorporated by reference conflict with the disclosure contained herein, the present specification is intended to supersede and / or take precedence over any such conflicting material.

[0368] Unless otherwise defined, all terms (including technical and scientific terms) shall have their ordinary and customary meanings indicated to those skilled in the art, and shall not be limited to any special or customized meanings unless expressly defined herein. It should be noted that the use of a particular term when describing a particular feature or embodiment of the present disclosure should not be interpreted as implying that the term is redefined herein to include any particular characteristic of the feature or embodiment of the present disclosure to which it pertains. Particularly in the appended claims, the terms and phrases used in this application, and variations thereof, should be interpreted as open-ended as opposed to limiting, unless expressly stated otherwise. As examples of the above, the term "including" should be interpreted to mean "including without limitation," "including but not limited to," etc.; the term "comprising," as used herein, is synonymous with "including," "containing," or "featuring," is inclusive or open-ended, and does not exclude additional, unrecited elements or method steps; the term "having" should be interpreted as "having at least," the term "including" should be interpreted as "including but not limited to," the term "example" is used to provide illustrative examples of the items under discussion rather than an exhaustive or exclusive list of them, and should not be construed as meaning "known," "conventional," "ordinary ... Adjectives such as "standard" and terms of similar import should not be construed to limit the subject matter described to that available in a given period or at a given time, but rather should be construed to encompass known, conventional, or standard technology that may be available or known at any time now or in the future, and the use of terms such as "preferably," "preferred," "desired," or "desirable" and terms of similar import should not be understood to imply that a particular feature is critical, essential, or even important to the structure or function of the invention, but rather should be intended merely to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the invention.Similarly, a group of items joined by the conjunction "and" should not be construed as requiring the presence of every single one of those items in the group, but rather, unless otherwise stated, should be construed as "and / or." Similarly, a group of items joined by the conjunction "or" should not be construed as requiring mutual exclusivity between the groups, but rather, unless otherwise stated, should be construed as "and / or."

[0369] When a range of values ​​is provided, it is understood that the upper and lower limits, and each intervening value between the upper and lower limits of that range, are included within an embodiment.

[0370] With respect to virtually any plural and / or singular term herein, one of ordinary skill in the art can convert from plural to singular and / or from singular to plural as appropriate to the context and / or application. Various singular / plural permutations may be expressly set forth herein for clarity. The indefinite articles "a" or "an" do not exclude plurals. A single processor or other unit may accomplish the functions of several items recited in the claims. The mere fact that certain criteria are recited in mutually distinct independent claims does not indicate that a combination of these criteria cannot be used to advantage. Reference signs in the claims should not be construed as limiting the scope.

[0371] When a specific number is intended in an introduced claim statement, such intention shall be clearly stated in the claim, and it will be further understood by those skilled in the art that in the absence of such statement, no such intention exists. For example, to aid in understanding, the following appended claims may include the use of the introductory phrases "at least one" and "one or more" to introduce the claim statement. However, the use of such phrases should not be construed as implying that the introduction of a claim statement with the indefinite article "a" or "an" limits a particular claim that includes such an introduced claim statement to an embodiment that includes only one such statement (e.g., "a" and / or "an" should typically be construed to mean "at least one" or "one or more"), even when the same claim includes the introductory phrase "one or more" or "at least one" and an indefinite article such as "a" or "an" (e.g., "a" and / or "an" should typically be construed to mean "at least one" or "one or more"), which also applies to the use of definite articles used to introduce claim statements. In addition, even if a specific number of recitations of an introduced claim is explicitly recited, a person skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., a mere recitation of "two recitations" without other qualifiers typically means at least two recitations, or more than two recitations). Furthermore, when a conventional expression similar to "at least one of A, B, and C, etc." is used, such a structure is generally intended in the sense that a person skilled in the art would understand the conventional expression (e.g., "a system having at least one of A, B, and C" includes, but is not limited to, a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.).When a conventional expression similar to "at least one of A, B, or C, etc." is used, such a structure is generally intended in the sense that one of ordinary skill in the art would understand the conventional expression (e.g., "a system having at least one of A, B, or C" includes, but is not limited to, a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.). It will be further understood by those of ordinary skill in the art that virtually any disjunction and / or phrase indicating two or more alternative terms, whether in the specification, claims, or drawings, should be understood to contemplate the possibility of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" is understood to include the possibilities of "A" or "B" or "A and B."

[0372] All numbers expressing quantities of ingredients, reaction conditions, and the like used herein should be understood to be modified in all instances by the term "about." Accordingly, unless otherwise indicated, the numerical parameters set forth herein are approximations that may vary depending on the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of any claims in any application claiming priority to this application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

[0373] Moreover, while the foregoing has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be apparent to those skilled in the art that certain changes and modifications may be practiced. Thus, the description and examples should not be construed as limiting the scope of the invention to the specific embodiments and examples described herein, but rather as embracing all modifications and alternatives that fall within the true scope and spirit of the invention. [Explanation of symbols]

[0374] 100 Sensor System 101 Analyte Sensor System 102 Drug delivery pump 104 Glucose Meter 112 Sensor Electronics 114 Display Devices 116 Display Devices 118 Display Devices 138 Analyte Sensors 205 Application Specific Integrated Circuits (ASIC) 210 Potentiostat 211 First input port 212 Data Line 214 Processor Module 216 Program Memory 218 Memory 220 Data storage memory 222 User Interface 224 User Button 226 Liquid Crystal Display (LCD) 228 Vibrator 230 Audio transducers (e.g., speakers) 232 Telemetry Module 234 Battery 237 Second Input Port 238 communication port 239 Output Port 400 Sensors 402 Sensor Interposer 404 Substrate 406 First Contact 408 Second Contact 409 Network 410 External Contact 412 External Contact 490 Cloud-Based Analyte Processor 500 Electronics Unit 600 On-skin sensor assembly 5000 Systems 5002 Test Station 5004 Calibration Station 5006 Receptacle 5008 Contact 5010 Contacts 5011 Leader 5012 Processing circuit 5013 Leader 5014 Receptacle 5016 Contact 5018 Contact 5020 Processing circuit 5091 Manufacturing Station 5092 Processing Circuit 5093 Gripping structure 5094 Mechanical components 5095 Gripping structure 5097 direction 5602 Analyte Sensor 5604 Sensor Interconnect Module 5608 Measuring Electronics 5610 Potentiostat 5612 Temperature measurement circuit 5614 Impedance measurement circuit 5616 processor 5618 Radio 5620 Humidity measurement circuit 5622 Pressure measurement circuit 5624 Motion Detector Circuit 5626 Capacitance Measurement Circuit 5628 Status Indicator 5630 Data Storage 5632 Power supply 5634 Clock 5640 Potential Errors 5650 Potential Errors 5702 Sensor Manufacturing Phase 5704 Sensor Packaging Phase 5706 Sensor Package Sterilization Phase 5708 Sensor Shipping Phase 5710 Sensor Storage Phase 5712 Insertion Phase 5714 Sensor In vivo Phase

Claims

1. A method for self-calibration of an analyte sensor, comprising: determining a first sensor characteristic during a first sensor life phase associated with manufacturing the analyte sensor by applying a bias voltage to at least the analyte sensor; determining a second sensor characteristic during a second sensor life phase occurring after the first sensor life phase; determining a change to the first sensor characteristic based on at least one or more parameters affecting a sensitivity of the analyte sensor during the first sensor life phase and a second sensor life phase; calibrating the analyte sensor based at least in part on the determined change to the first sensor characteristic without user involvement; A method comprising:

2. The method of claim 1, wherein the first sensor characteristic is based at least on one or more manufacturing parameters related to the first sensor life phase and / or one or more environmental parameters related to the first sensor life phase.

3. The method comprising:

2. The method of claim 1, further comprising: storing information associated with the first sensor life phase and a second sensor life phase; and determining the change to the first sensor characteristic based at least on the stored information.

4. The method of claim 2, wherein the one or more manufacturing parameters include process parameters, the process parameters including temperature, humidity, curing, time, and soaking time.

5. The method of claim 2, wherein the one or more manufacturing parameters include design parameters, the design parameters including a thickness of the analyte sensor membrane and properties of an unprocessed material.

6. The method of claim 1, further comprising receiving remotely stored sensor performance data for use in calibrating at least the analyte sensor.

7. The method of claim 6, wherein the remotely stored sensor performance data relates to analyte sensors that have experienced or been exposed to manufacturing and / or environmental parameters that are most similar to one or more of the monitored manufacturing and / or environmental parameters.

8. A method for self-calibrating an analyte sensor system including an analyte sensor operably coupled to sensor electronics, comprising: generating sensor data associated with the analyte sensor; storing information describing a plurality of life phases of the analyte sensor and a plurality of times corresponding to the plurality of life phases, the plurality of life phases including a first life phase associated with manufacturing of the analyte sensor and a second life phase occurring after the first life phase; updating initial calibration coefficients of the analyte sensor system at the plurality of times based at least in part on one or more parameters monitored during one or more of the plurality of life phases, the one or more parameters affecting a sensitivity of the analyte sensor; automatically calibrating the analyte sensor system based at least in part on the updated calibration coefficients without user intervention; A method comprising:

9. The method of claim 8, further comprising converting the sensor data to an analyte concentration value based at least in part on the updated calibration coefficient.

10. The method of claim 8, wherein the sensor data is generated by applying at least a bias voltage to the analyte sensor.

11. The method of claim 8, wherein the second life phase includes one of shipping, storage, pre-in vivo, and sensor session.

12. The method of claim 8, wherein updating the initial calibration coefficients of the analyte sensor system includes determining complex adaptive calibration values ​​based at least in part on manufacturing and environmental conditions experienced by the analyte sensor during one or more of the multiple life phases of the analyte sensor.

13. The method of claim 8, wherein the one or more manufacturing parameters include process parameters, the process parameters including temperature, humidity, curing, time, and soak time.

14. The method of claim 8, wherein the one or more manufacturing parameters include design parameters, the design parameters including a thickness of the analyte sensor membrane and properties of an unprocessed material.

15. The method of claim 8, further comprising receiving remotely stored sensor performance data to update the initial calibration coefficients.

16. The method of claim 15, wherein the remotely stored sensor performance data received relates to an analyte sensor that has experienced or been exposed to manufacturing and / or environmental parameters that are most similar to one or more of the monitored manufacturing and / or environmental parameters.

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