Automated 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 priority claims, or any modifications thereof, identified in the application datasheet are incorporated herein by reference under Rule 1.57 of the U.S. Patent Act. This application claims the benefit of U.S. Provisional Application No. 62 / 666,606, filed on 3 May 2018. The aforementioned application is incorporated herein by reference in its entirety and is expressly created as part of this specification.

[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 is unable to produce enough insulin (Type 1 or insulin-dependent) and / or insulin is ineffective (Type 2 or non-insulin-dependent). In a diabetic state, the patient or user suffers from hyperglycemia, which can lead to many physiological disorders associated with the deterioration of small blood vessels, such as renal failure, skin ulcers, or bleeding into the vitreous humor of the eyeball. A hypoglycemic reaction (low blood sugar) can be induced by inadvertent overdose of insulin or after normal administration of insulin or glucose-lowering drugs accompanied by abnormal exercise or insufficient food intake.

[0004] Traditionally, people with diabetes carry a self-monitoring blood glucose (SMBG) monitor, which typically requires an uncomfortable finger-prick method. Due to the lack of comfort and convenience, people with diabetes usually only measure their glucose levels 2-4 times a day. Unfortunately, such time intervals are too far apart, and people with diabetes may be too late to detect hyperglycemia or hypoglycemia, sometimes leading to dangerous side effects. Not only is it less likely that people with diabetes will notice and prevent dangerous conditions in time, but they may also be unaware that their blood glucose levels are rising (higher) or falling (lower) based on conventional methods. Thus, people with diabetes may be hindered from making experience- and knowledge-based insulin therapy decisions.

[0005] Another device used by some diabetic patients to monitor their blood glucose levels is a continuous analyte sensor, such as a continuous glucose monitor (CGM). A CGM typically involves a sensor that is placed invasively, minimally invasively, or non-invasively. This sensor measures the concentration of a given analyte in the body, such as glucose, and uses the electronics associated with the sensor to generate a raw signal. This raw signal is converted into an output value that is displayed on a screen. The output value obtained from the conversion of the raw signal is typically presented in a format that provides meaningful information to the user, in a format that the user has become accustomed to analyzing, such as blood glucose expressed in mg / dL units.

[0006] The above explanation assumes that the output values ​​are reliable and true, and that these same output values ​​generally require a considerable degree of user interaction to ensure proper calibration. Typically, calibration checks are performed before the analyte sensor leaves the factory, i.e., sensitivity values ​​are derived in vitro during the calibration check. However, this calibration check only provides a snapshot at a specific 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 behave differently when used on a particular patient, because each sensitivity value can change over time depending on the conditions before and after use.

[0007] One way to explain this is by using reference value checks during use, for example, with self-monitoring blood glucose meters. Many current CGMs rely heavily on such user interaction, checking glucose concentration values ​​before administering insulin. However, additional user actions add significant sources of error to monitoring and reduce convenience by requiring the user to perform actions beyond what is necessary. [Overview of the project] [Problems that the invention aims to solve]

[0008] Systems and methods following this principle address many of the aforementioned problems related to the need for frequent calibration of analyte sensors, depending on the implementation. More specifically, the systems and methods provide a pre-connected analyte sensor system in which the analyte sensor is physically combined with measuring electronics during the sensor manufacturing phase and, optionally, during subsequent life phases of the sensor. [Means for solving the problem]

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

[0010] Furthermore, the following are some optional features, namely, a sensor interconnection module capable of fixing an analyte sensor in place and / or in a robust electrical coupling, and a measuring electronics module that may include one or more of the following: a temperature measuring circuit capable of obtaining temperature readings from one or more temperature sensors; an impedance measuring circuit capable of detecting impedance values ​​from an analyte sensor or other electrical component; a capacitance measuring circuit capable of detecting capacitance values ​​from an analyte sensor or other electrical component; a motion detection circuit using one or more sensors such as an accelerometer or gyroscope for detecting and quantifying physical motion and / or orientation; a humidity measuring circuit having one or more sensors capable of measuring humidity; a clock capable of keeping time measurements, and / or a pressure measuring circuit having one or more pressure processors capable of measuring changes in gas pressure (e.g., atmospheric pressure) or pressure applied to a device (e.g., force applied to the surface of the housing); and one or more processors capable of processing data. Other features may include one or more wireless devices capable of transmitting data wirelessly, one or more display / status indicators capable of communicating data to the user, one or more data storage units capable of storing appropriate information for future access, and one or more power supplies (e.g., batteries) capable of providing reliable power for use by the measuring electronics.

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

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

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

[0014] A pre-connected analyte sensor system can compensate for errors caused by the precision and accuracy of the manufacturing equipment. Variations in the manufacturing process can result in different values ​​for various parameters being measured (e.g., analyte sensitivity, baseline, impedance, capacitance, interference sensitivity, etc.), and the errors arising from these different parameter values ​​are combined into the overall system error. The greater the variability during manufacturing startup, the more pronounced the impact on the system error. These variations may include changes in equipment over time, frequency of equipment calibration, number of different measurement points, multiple production lines, multiple production locations, equipment accuracy, calibration accuracy, and equipment cleanliness.

[0015] A pre-connected analyte sensor system limits errors that would otherwise occur by physically connecting the analyte sensor to a portion of the sensor's electronics, where the portion of electronics includes measuring electronics, allowing measurements to be taken during and after the manufacturing process. Several possible types of measurements that can be taken by the measuring electronics are sensitive to factors such as, for example, contact resistance, leakage current, length of electrical paths, volume of components, manufacturing tolerances, and material properties.

[0016] A pre-connected analyte sensor system limits the errors introduced by the measuring electronics. Measuring electronics are limited by their own manufacturing tolerances and design constraints. Typically, calibration equipment is used to characterize the measuring electronics system. Accuracy and precision are measured, and correction factors (e.g., gain, offset, linearity, temperature, resolution, etc.) are used by the circuit to compensate for absolute errors. This adds cost and complexity to the manufacturing phase, as it requires test and programming time to be added to the process. Furthermore, various characteristic changes may arise from the calibration time, depending on the period and equipment used to calibrate the system. Therefore, it is advantageous in the manufacturing process to calibrate the system as late as possible.

[0017] In manufacturing, reducing the number of process steps has the advantages of improving efficiency and reducing the chance of errors. By performing sensor calibration using the measurement electronics that will be used in the final product, the calibration can be completed as a system. For example, to calibrate electronics and sensors as a single step with a known calibration solution, only the value of the calibration solution needs to be controlled. The measurement electronics at least applies a voltage bias to the sensor, measures the analog value of the current, and converts the analog value into 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 with the analyte concentration of the calibration solution is thereafter associated with a digital value that is corrected for variations in individual measurement components (e.g., potentiostat variations, analog-to-digital converter errors, leakage errors, connection resistance variations, etc.). This system also eliminates errors from manufacturing measurement electronics introduced by calibration equipment.

[0018] Such direct calibration solution-based system calibration can be performed over a wide range of analyte values, interfering materials, and other factors that affect sensor performance (e.g., hypoxia). Using this correlation of digital values to analyte concentrations in solutions over a range allows an accurate compensation model to be constructed for in vivo sensor performance.

[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 measurement electronics to an analyte sensor during manufacturing can be extended 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 obtained before, during, or after one or more of the following steps: sensor connection, membrane coating, curing, environmental shift, sterilization, shipping, storage, insertion, in vivo, etc.

[0021] In currently commercially available transcutaneous analyte measurement systems, the sensor and measurement electronics are coupled immediately before or during sensor insertion. This configuration prevents measurement of the coupled system during any system phase prior to the coupling of measurement electronics and the analyte sensor. Additional measurements, which can only be captured using a pre-connected system, can be provided to the analyte processing algorithm. These measurements can correlate with in vivo performance, fault detection, sensor lifespan, sensitivity shift, calibration shift, sensor performance indicators, accuracy, and the like. The correlation of measurements can be used to identify or compensate for system experience over an extended period of time, which is useful between in vivo system phases.

[0022] A multivariate model can be established for multiple measurements at different time points and system phases. The frequency and range of this data collection allow more accurate modeling of system characteristics. Part of this analysis can be achieved using measurements obtained by manufacturing equipment or calibration equipment. These input measurements may optionally be incorporated in addition to measurements obtained by measurement electronics. In other embodiments, the model may simply include inputs from manufacturing equipment and / or calibration equipment. Output measurements can be obtained during the in vivo phase by manufacturing equipment and / or calibration equipment, or by reference measurement of blood analyte levels (e.g., YSI, finger-stick glucose meter, laboratory analysis, etc.).

[0023] For example, measurements such as impedance, temperature, current measurements, and time may be acquired by pre-connected measuring electronics during various phases of manufacturing, such as pre-sensor installation, post-sensor installation, membrane coating, curing, and calibration. The pre-connected system can collect spatial information such as location within the facility, location within the equipment, or equipment identifier. This dataset can be combined with additional datasets from sensors placed within the manufacturing equipment, for example, collecting variables such as humidity, temperature, material viscosity, time, and equipment identifier. Additional datasets can also be collected to track external variables such as time, date, room temperature, room humidity, manufacturing equipment, calibration equipment, operator, production line, and manufacturing location.

[0024] The matched measurements can be immediately interpreted or stored for further processing. This information can be used to adjust manufacturing parameters, construct correction factors, determine lot classifications, reject sensors, or be used by analyte processing algorithms. This vast amount of data can be fed into tools such as machine learning algorithms to identify correlations.

[0025] Multivariate models can be used to identify and correct relationships between input and output parameters. Some of these relationships are well known (e.g., the relationship between temperature and analyte sensitivity measurements), while others need to be further identified. Tools used to identify and model these relationships may include linear regression additive models, generalized linear modeling that selectively incorporates one or more nonlinear functions, nonparametric data fitted to empirical models, nonlinear regression models, neural network models, or other suitable models. This enumeration is merely illustrative, and system relationships can be modeled using any suitable statistical or analytical tool. 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 of the system measurements that can be obtained have correlations with additional system parameters. In this way, it is possible to extract correlations of parameters that are not directly measured but may be useful to input or process using an analyte algorithm processing unit. This has several advantages, including not requiring as many physical sensor components that add cost and complexity, collecting information that is not easily measured by position or sensor size, and providing redundancy or improved accuracy to additional sensors (e.g., compensating for temperature in a current measurement circuit).

[0027] Exemplary applications of using predicted measurements may include some of the following: predicting extracorporeal humidity levels using temperature and sensor impedance measurements; calculating temperature gradients using one or more temperature sensors; estimating the temperature of non-measured points, such as the tip of an analyte sensor in a living organism, using temperature gradient data; and estimating body movement using temperature and accelerometer data. This is not an exhaustive list, but rather a combination of any of the detected measurements to estimate one or more undetected measurements.

[0028] By pre-connecting sensors to some or all of the sensor electronics, sensors can be monitored for all or part of their lifespan, particularly during the portion of their lifespan after they leave the factory. Sensor monitoring can be advantageous for several reasons. In particular, sensor monitoring can address variability (time-dependent deviations from the factory-assigned sensor calibration values), accuracy (errors added to the entire analyte sensor system resulting from variations within the individual components that make up the system), and manufacturing process-related issues that reduce consistency from sensor to sensor and from sensor lot to sensor lot. Additionally, pre-connected sensors can improve sensor safety by facilitating data transfer from the sensor to external devices and detecting when field-deployed sensors are potentially in a dangerous state.

[0029] In one embodiment, the variability problem is addressed by performing various active measurements acquired after manufacturing. For example, in one embodiment, the environmental conditions (e.g., temperature, humidity) maintained while the sensor and pre-connected electronics are sealed during storage and when packaged before use can be monitored. In the case of temperature, an onboard electronic temperature sensor such as a thermistor or thermocouple can be used to measure and store temperature data. Similarly, an onboard electronic 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 individual temperature and / or humidity sensors assigned to each analyte sensor. Alternatively, there may be a single temperature and / or humidity sensor assigned to each box / delivery / pallet of analyte sensors. In another implementation embodiment, the analyte sensor wire itself can be used to determine temperature and / or humidity by inference via impedance or current measurements, and these measurements can be stored in the pre-connected electronics.

[0030] In some embodiments, another environmental condition that can be monitored is the amount of radiation applied to the sensor for sterilization purposes after the sensor and any pre-connected electronics have been sealed in the packaging. In one example, a sterilization detector is provided in the electronics, and as a result, the detector can quantify the dose using active electronics. In some cases, a material that is sensitive to the sterilization dose and can send an electronically responsive command signal indicating that it is sterilized by the electronics can be added to the packaging 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. Alternatively, a batch detection of the sterilization dose may be obtained for each box / delivery / pallet of the analyte sensor. Using the measured application, a value can be assigned to the analyte sensor via wireless communication with the pre-connected electronics, and this value is later used in deriving subsequent calibration parameters.

[0031] In an additional embodiment, another environmental condition that can be monitored is the movement of the analyzer sensor using an accelerometer, a triggering fuse, or other motion sensor. In this way, vibrations or shocks caused by drops or other events can be detected, which may cause damage to the sensor membrane or applicator mechanism.

[0032] Further environmental conditions that can be monitored include exposure to ambient gases and the time elapsed since the sensor was manufactured.

[0033] In addition to the active monitoring techniques described above, or alternatively, passive techniques can also be used to address fluctuation issues. For example, in one implementation described in U.S. Patent Application No. 62 / 521969, filed June 19, 2017, entitled "Applicators for Applying Transcutaneous Analyte Sensors and Associated Methods of Manufacture," the packaging material used has a water vapor transmission rate below a certain threshold level, for example, 10 grams / 100 inches. 2 Less than / day, or 1 gram / 100 inches 2 It can provide a humidity barrier that can be maintained at less than 1 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, metal coated polymer substrates coated via metal vapor deposition, or low MVTR polymers (e.g., PET, HDPE, PVC, PP, PLA).

[0034] 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 when exposed to temperature and / or humidity over time. Alternatively, instead of a visual indicator, the indicator may 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 monitoring screens determine that environmental conditions have exceeded acceptable limits at any given time for a certain duration, the packaging may be equipped with a mechanism to physically prevent the sensor in the packaging from being used. For example, using a material whose dimensions change with temperature and / or humidity, such as bimetal (similar to those used in thermostats), metal, or polymer, and combining it with an interlock mechanism in the applicator, can physically prevent the applicator from unfolding (either permanently or temporarily), preventing the packaging from being opened and / or preventing buttons, etc., from being activated. The physical change in material dimensions will automatically enable this function when certain environmental conditions are exceeded.

[0036] In another embodiment, system-level compensation can be achieved that allows for larger parameter variations between individual system components while reducing overall errors. This can be achieved by using data stored within pre-connected electronics, related to the monitored environmental conditions, as input to an algorithm used to tune the sensor calibration model. This tuning can be performed for initial and / or final sensor sensitivity, background signal, and / or equilibrium velocity. In some cases, data collected and stored for individual sensors or sensor lots may be adapted to individual patients. Furthermore, the necessary tuning can also be used as additional input information previously acquired over time for a vast number of sensors and patients to calculate calibration compensation values ​​based on the performance of sensors that have experienced similar conditions.

[0037] The algorithm used to tune the sensor calibration model may also include a time component, which uses data obtained by examining the sensor's sensitivity and background signal profiles over time, from insertion (when the factory calibration initial sensitivity and background signal are used) to the transition to the stable final sensitivity and background signal. The sensor calibration model can be compensated based on the difference between the factory calibration value and the rate of change during the sensitivity transition period. Typical break-in curves can be obtained for each sensor from this data and the resulting curves 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 embodiment, sensor calibration values ​​can be adjusted before inserting the analyte sensor into the patient, using a sensor calibration model updated based on data stored in pre-connected electronics relating to monitored environmental conditions. For example, the voltage bias applied to the analyte sensor may be adjusted based on stored data. In some cases, the voltage bias may be applied while the analyte sensor is in its packaging to modify the sensor characteristics, allowing the sensor to undergo a break-in period, for example, while in packaging. Furthermore, the packaging may include a calibration solution that can be embedded in foam, gel, etc., to prevent leakage. The calibration solution can be released immediately before or while the package is opened to facilitate the calibration of the sensor within the package. In yet another embodiment, the estimated break-in time required before the sensor starts up can be adjusted based on stored data, including the elapsed time of the sensor and the measured impedance of the sensor. The thus estimated break-in time can be displayed on the system's display.

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

[0040] The accuracy of pre-connected sensors depends in part on the errors added to the system within the factory by combining the individual variations of the components. Such errors, which can significantly affect system-level calibration, can arise from sensor sensitivity (e.g., gradient, baseline, and O2), membrane defects (e.g., impedance detection), electronics (e.g., voltage bias accuracy, current measurement linearity, leakage current), calibration processes (e.g., solution accuracy, measuring instrument accuracy), and the interconnection coupling of analyte sensors and electronics (e.g., resistance values ​​and variations between analyte sensors and measuring electronics, and between analyte sensors and calibration electronics).

[0041] In another embodiment, manufacturing improvements can be achieved by pre-connecting analyte sensors and various electronic components. For example, such pre-connection can improve sensor tracking and serialization by providing components attached to sensors having a surface on which a code (e.g., barcode, label, etc.) can be placed for identification purposes. This code can serve as a unique identifier and can be applied during or before manufacturing. This code may also include sensor data such as calibration codes and sensitivity values, which can be acquired during manufacturing. In some cases, wireless communication can be established with pre-connected sensors during the manufacturing process. For example, sensors can be identified and tracked via wireless response command signals using short-range wireless communication protocols such as RFID, NFC, and Bluetooth®. Similarly, analyte sensors can actively broadcast data or identifiers of the analyte sensor using short-range wireless communication protocols. In this way, the handling efficiency of analyte sensors during manufacturing can be improved when sensors are moved, connected, and disconnected multiple times. The pre-connected electronic components can also serve as anchor bodies for connection and alignment, improving the manufacturing flow. Further improvements can be achieved by replacing physical electrical connections with contactless wireless methods.

[0042] In another embodiment, calibration codes attached to sensors, transmitters, packaging, or other components may be dynamic calibration codes that change with environmental conditions. For example, parts of a printed code (e.g., a barcode) may become invisible due to environmentally responsive dyes such as thermochromic dyes, causing the code's value to change. In the shipping industry, responsive dyes are used, which change to black (or some other color) or change from transparent to black when exposed to heat, cold, humidity, or impact (e.g., by being dropped). Therefore, for example, if a calibration code is printed on the packaging, the calibration code may include a base calibration code that adjusts the sensor's calibration curve. By printing additional digits, those digits may either disappear or reappear when exposed to environmental factors that significantly affect calibration.

[0043] For example, in one instance of a dynamic calibration code in barcode format, a predetermined number, such as "3," may indicate thermal exposure. In this example, if the package is exposed to heat exceeding the threshold, the number 3 disappears, as does its corresponding portion of the barcode. 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 barcode. When software in a patient's mobile device or other receiver scans or otherwise inputs the code, a calibration curve offset or adjustment may be generated. Additionally, this information can be returned to the manufacturer to determine lot variability, as well as variability in shipment, thereby identifying sensors that have not been stored properly. This information can also be fed back to account for inventory loss. Additional responsive dyes may be placed around the periphery of the code and include a "cutoff" threshold that appears or disappears if the sensor is exposed to something that renders it unusable. This same information can be used to account for end-user credit and reshipment, as well as the aforementioned inventory loss.

[0044] In another embodiment, pre-connecting analyte sensors and various electronic components enables manufacturing improvements by using closed-loop manufacturing feedback, allowing for real-time monitoring of manufacturing variables to modify the manufacturing process and improve the resulting sensors. These 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 specific coating solution into 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 enable the acquisition of a vast sensor dataset related to the manufacturing process, which can then be used to create outcome-based predictors. For example, if, as a result of this process, it is determined that at some point in the manufacturing process the temperature is higher than its average, the humidity is lower than its average, and the sensor sensitivity is higher than its average, then updates to the manufacturing process can be implemented based on this forecast to reduce the deviation of the sensor sensitivity from its average. Furthermore, because these processes can be continuously monitored, it is possible to determine whether updating the manufacturing process has 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 acquired and stored. In this way, additional information that can be used as feedback during the manufacturing process can be acquired. For example, long-term testing of sensor lots, such as sensitivity shifts, can be stored in the cloud for use in a suitable algorithm. Similarly, information related to the sensor shipping process (geographic information, means of transport used, duration of the shipping process, etc.) can be acquired and stored, and this information can then be correlated with sensor data to determine the impact of environmental exposure.

[0046] In another embodiment, in addition to using data collected during manufacturing as part of a closed-loop feedback process, data related to the sensor and the patient can also be used while the sensor is in vivo. For example, analysis from the performance of individual sensors within a patient may be used as input data to any number of algorithms used during the manufacturing process. Such data can be obtained from a device such as a mobile phone or other receiver communicating with the sensor in use. The data obtained may be temperature, humidity, sensor movement (e.g., indicating whether the patient is sleeping, exercising, etc.), compressive forces that can be determined from the accelerometer and that may act on the sensor while the user is in different positions (e.g., sitting, standing, lying down), and any available information such as the patient near a known location (e.g., Wi-Fi beacon, cell phone base station, Internet of Things (IoT) device).

[0047] In another embodiment, stored data acquired from sensors during or after manufacturing can be used to reduce the risk of potentially hazardous sensors lurking in the field. Using such data, the effectiveness of various storage conditions (e.g., packaging barriers and packaging accusers) and sterilization conditions (e.g., by sampling sensor lots undergoing sterilization) can be investigated, and based on available data relating to the age of the sensor, as well as manufacturing, storage, and other environmental conditions the sensor has experienced, it can be more accurately determined when the sensor is expected to expire. In this way, patients can be automatically notified of when their sensor is expected to expire (e.g., by app pop-ups, email, or automated phone calls).

[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 such that the analyte sensor system has an initial characteristic criterion determined in a first time; using the sensor electronics in a second time following the first time to determine a change in the initial characteristic sensitivity criterion of the analyte sensor system based at least in part on one or more manufacturing parameters and / or environmental parameters; and using the sensor electronics to automatically calibrate the analyte sensor system based at least in part on the determined change in the initial characteristic criterion, without user intervention.

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

[0050] In an embodiment of the first aspect, or any other embodiment, monitoring one or more environmental parameters includes measuring the impedance of an analyte sensor by applying a stimulus signal to the analyte sensor, measuring the signal response to the stimulus 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 thereof, the first time is following the fabrication of the sensor, and the second time is prior to the use of the sensor in vivo.

[0052] In an embodiment of the first aspect, or any other embodiment thereof, the first time is following the fabrication of the sensor, and the second time is following the commencement of the use of the sensor in a living organism.

[0053] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial characteristic measurement criterion 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 sensor electronics.

[0054] In embodiments of the first aspect, or any other embodiment thereof, the initial characteristic criterion is further determined by measuring the in vitro sensitivity characteristics of the analyte sensor.

[0055] In an embodiment of the first aspect, or any other embodiment of that aspect, the initial characteristic criterion 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 embodiments of the first aspect, or any other embodiment of that aspect, one or more components include 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 without interruption between a first time and a second time.

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

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

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

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

[0062] In embodiments of the first aspect, or any other embodiment of that aspect, monitoring temperature includes measuring temperature using a temperature sensor contained within a sterile package, wherein the temperature sensor is operably coupled to sensor electronics.

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

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

[0065] In embodiments of the first aspect, or any other embodiment of that aspect, monitoring humidity includes measuring humidity using a humidity sensor contained within a sterile package, wherein the humidity sensor is operably coupled to sensor electronics.

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

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

[0068] In an embodiment of the first aspect, or any other embodiment of that aspect, the manufacturing parameters are obtained from the identifier of the analyte sensor.

[0069] In an embodiment of the first aspect, or any other embodiment of that aspect, the identifier is attached 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 sending a response command signal to the analyte sensor.

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

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

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

[0074] In an embodiment of the first aspect, or any other embodiment of that aspect, the values ​​of the monitored environmental parameters are stored for subsequent use when the analyte sensor system is automatically calibrated.

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

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

[0078] In embodiments of the first aspect, or any other aspect thereof, the initial characteristic measurement criterion reflects the initial sensor sensitivity and baseline value.

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

[0080] In an embodiment of the first aspect, or any other embodiment thereof, the initial calibration coefficient is derived from a sensor characteristic measurement criterion.

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

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

[0083] In an embodiment of the first aspect, or any other embodiment of that aspect, one or more manufacturing parameters are measured before a first time.

[0084] In a second aspect, a method is provided for self-calibration of an analyte sensor system, which includes 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, wherein the analyte sensor system has 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 in 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 parameters and / or environmental parameters, the second time being before or during use of the sensor in vivo; and using the sensor electronics to automatically calibrate the analyte sensor system based at least in part on the determined change in the initial characteristic metric, without user intervention.

[0085] A third aspect provides a method for self-calibrating an analyte sensor system, which includes an analyte sensor operably coupled to sensor electronics, the method comprising: using sensor electronics to apply a bias voltage to the analyte sensor to generate sensor data, such that the analyte sensor system has an initial calibration coefficient used to convert the sensor data into an analyte concentration value; using sensor electronics to update the calibration coefficient of the analyte sensor system multiple times during one or more life phases of the analyte sensor, at least in part on one or more manufacturing parameters and / or environmental parameters monitored during one or more life phases of the analyte sensor; and using sensor electronics to automatically calibrate the analyte sensor system, at least in part on the updated calibration coefficient, without user intervention.

[0086] In embodiments of the third aspect, or any other embodiment of that aspect, one or more life phases include multiple life phases.

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

[0088] In embodiments of the third aspect, or any other embodiment of that aspect, using sensor electronics to update the calibration coefficient of an analyte sensor system involves determining a complex adaptive calibration value that is at least partially based on the manufacturing and environmental conditions experienced by the analyte sensor during multiple life phases of the analyte sensor.

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

[0090] In embodiments of the third aspect, or any other embodiment of that aspect, the manufacturing parameters include process parameters, which include temperature, humidity, curing time, and immersion time.

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

[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 coefficient.

[0093] In an embodiment of the third aspect, or any other embodiment of that aspect, the received, remotely stored sensor performance data 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.

[0094] A fourth aspect provides a method for a sensor to experience multiple life phases, including manufacturing, shipping, storage, and insertion, as well as use by a user as part of a sensor session, the method comprising: arranging measuring electronics operably connected to the sensor; determining a first calibration coefficient during the manufacturing life phase in a factory where the sensor is manufactured using multiple manufacturing parameters; determining a second calibration coefficient during the shipping or storage phase; and calibrating the signal from the sensor using a combined calibration coefficient of a user monitoring device once inserted by a user, the combined calibration coefficient being based on both the first and second calibration coefficients.

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

[0096] In embodiments of the fourth aspect, or any other embodiment of that aspect, the measuring electronics form part of the sensor electronics.

[0097] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the measuring electronics are separated from the sensor electronics.

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

[0099] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the measuring electronics are housed in a separate package from the sensor electronics.

[0100] In embodiments of the fourth aspect, or any other embodiment of that aspect, the sensor assembly and measuring electronics are arranged in a package for shipment.

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

[0102] In embodiments of the fourth aspect, or any other embodiment of that aspect, transmission is from sensor electronics or measuring 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 within the measuring electronics or sensor electronics.

[0104] In the 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, a combined calibration coefficient, or a second calibration coefficient, or both, is sent to the factory to cause a change in one of several manufacturing parameters.

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

[0107] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration coefficient is a system-level calibration coefficient belonging to the calibration of all components within the sensor assembly.

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

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

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

[0111] In an embodiment of the fourth aspect, or any other embodiment of that aspect, the calibration coefficients stored in 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 measuring electronics are configured to detect an electrical signal from a sensor wire, sensor electronics, housing, or combination thereof.

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

[0114] In the embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration coefficient or the second calibration coefficient, or both, represent the measured impedance.

[0115] In embodiments of the fourth aspect, or any other embodiment of that aspect, impedance measurement is performed by measuring the step response at one or more frequencies.

[0116] In the embodiment of the fourth aspect, or any other embodiment of that aspect, the third calibration coefficient is measured before shipment, and the third calibration coefficient represents the impedance.

[0117] In the embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration coefficient or the second calibration coefficient, or both, represent the measured temperature.

[0118] In the embodiment of the fourth aspect, or any other embodiment of that aspect, the first calibration coefficient or the second calibration coefficient, or both, represent the measured humidity.

[0119] In embodiments of the fourth aspect, or any other embodiment of that aspect, the combined calibration coefficient is 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 embodiments of the fourth aspect, or any other embodiment of that aspect, the combined calibration coefficient is a complex adaptive value that combines calibration values ​​collected among sensor manufacturers with conditions experienced during the time from the sensor manufacturer to sensor insertion.

[0121] In an embodiment of the fourth aspect, or any other embodiment thereof, a user receiving the sensor is selected based on a first calibration coefficient, thereby determining that the population data or individual user data is optimized for the user to have a sensor with the first calibration coefficient.

[0122] In the fourth embodiment, or any other embodiment thereof, the user is known to have a high average glucose level, and the first calibration coefficient has relatively low sensitivity.

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

[0124] A fifth aspect provides an improved method for calibrating a sensor associated with a pre-connected sensor assembly, the sensor experiencing multiple life phases including manufacturing, shipping, storage, and insertion, as well as use in a user as part of a sensor session, the method comprising: arranging measuring electronics by operably connecting to the sensor electronics; determining a first calibration coefficient during the manufacturing life phase in the factory, manufacturing the sensor using multiple manufacturing parameters; determining a second calibration coefficient during the shipping or storage phase; and, once inserted into a user, calculating a combined calibration coefficient and storing the same combined calibration coefficient within the sensor electronics, the combined calibration coefficient being based on both the first and second calibration coefficients, and the combined calibration coefficient being configured to provide a conversion between the signal detected from the sensor wire and the analyte concentration in the user.

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

[0126] In embodiments of the fifth aspect, or any other embodiment of that aspect, the display occurs on a user monitoring device that transmits signals with sensor electronics.

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

[0128] A sixth aspect provides an improved method for manufacturing a sensor assembly including sensor wires, a housing, and sensor electronics, the method comprising pre-connecting at least sensor wires to at least a portion of the sensor electronics sufficient to monitor manufacturing parameters; monitoring manufacturing parameters while completing the manufacturing of a sensor assembly; and modifying one or more of the manufacturing parameters between subsequent manufacturing processes used to manufacture additional sensor assemblies, wherein the modification is at least partially based 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 thereof, the radio is pre-connected to a battery and sensor wires, as well as a portion of 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 internally is less than the combined or reflected error of the sensor wire and the portion of sensor electronics sufficient to monitor the manufacturing parameter, which is considered separately.

[0133] In a seventh aspect, an improved pre-connected sensor assembly is provided, comprising a sensor wire, a housing, and sensor electronics, wherein the sensor is pre-connected to the housing and / or sensor electronics, and the sensor wire is at least pre-connected to an interposer configured to enable measurement of the physical properties 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 sensor electronics.

[0135] In an embodiment of the seventh aspect, or any other embodiment of that aspect, the radio is pre-connected to a battery and sensor wires and housing, and / or 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 an analyte sensor to one or more components of sensor electronics in a first time so as to define a packageable analyte sensor array having an initial sensitivity criterion determined thereafter; applying an analyte response command signal to the analyte sensor using one or more components of sensor electronics in a second time following the first time; measuring the signal response to the stimulus signal; determining a second sensitivity criterion at least in part on the measured signal response; and automatically calibrating the packageable sensor array at least in part on the initial sensitivity criterion and the second sensitivity criterion without user intervention.

[0137] In an embodiment of the eighth aspect, or any other embodiment thereof, the analyte sensor is operably coupled to one or more components of the sensor electronics without interruption between a first time and a second time.

[0138] In an embodiment of the eighth aspect, or any other embodiment of that aspect, applying an analyte response command signal includes applying a stimulus signal to an analyte sensor, and measuring the signal response includes measuring the impedance of a packageable analyte sensor array.

[0139] In an embodiment of the eighth aspect, or any other embodiment of that aspect, the automatic calibration of a packageable sensor array includes, based on an established relationship between impedance and analyte sensor sensitivity, the automatic calibration of a packageable sensor array in vivo.

[0140] A ninth aspect provides a method for performing an operation using an analyte sensor system, which includes 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, wherein the analyte sensor system has 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 in 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 parameters and / or environmental parameters, wherein the second time is before or during use of the sensor in a biological system; and performing an operation selected from the group, which includes generating a message, initiating a recalibration process, using a default calibration value, and using a calibration value compensated for temperature and / or humidity, based at least in part on the determined change in the initial characteristic metric.

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

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

[0143] These, as well as other features and advantages, will be better understood by referring to the detailed description below, when considered in conjunction with the attached drawings. [Brief explanation of the drawing]

[0144] [Figure 1] This is a schematic diagram of an analyte sensor system, according to several embodiments, that is mounted on a host and communicates with multiple exemplary devices. [Figure 2] This is a block diagram showing the electronics related to the sensor system of Figure 1, according to several embodiments. [Figure 3] Perspective views of several embodiments of wearable devices having an analyte sensor are shown. [Figure 4] Schematic diagrams of pre-connected analyte sensors according to several embodiments are shown. [Figure 5] Block diagrams of a manufacturing system for an analyte sensor and a system having a wearable device are shown according to several embodiments. [Figure 6] A schematic diagram of the sensor sensitivity as a function of time during a sensor session, according to one embodiment, is shown. [Figure 7] Figure 6 shows a schematic diagram of the transformation function at various time periods of the sensor session according to the embodiment. [Figure 8A] This section illustrates various phases in the lifecycle of an analyte sensor system. [Figure 8B]This section illustrates various phases in the lifecycle of an analyte sensor system. [Figure 9] A schematic block diagram of one specific example of a pre-connected analyte sensor system is shown. [Figure 10] This is a 5000-sample Monte Carlo simulation comparing a pre-connected system with an unconnected system, using a randomly selected number of input variables within a statistical distribution. [Figure 11] This example illustrates an automated calibration process that can be performed by sensor electronics within an analyte monitoring system without user intervention. [Figure 12] The data includes time curves showing the monitored temperature (a), humidity (b), and sensitivity (c) throughout the lifespan of the analyte sensor. [Figure 13] This shows the sensor output signals acquired from the analyte sensor at various steps in the manufacturing process. [Figure 14] The NMR spectrum of PVP in DMSO-d6 is shown. [Figure 15] The HNMR spectrum of Carbosil in DMSO is shown. [Figure 16] The HNMR spectrum of the RL film (Carbosil / PVP mixture with solvent removed) is shown. [Figure 17] The compositions of RL solutions prepared using different Carbosil / PVP ratios are shown. [Figure 18] The HNMR calibration curve is shown. [Figure 19] This graph shows the initial sensor drift when ethylene oxide (ETO) sterilization is used. [Figure 20] This section illustrates various life phases that an analyte sensor may experience. [Modes for carrying out the invention]

[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, its ordinary and conventional meaning given to those skilled in the art (and not limited to any special or customized meaning), referring to, but not limited to, substances or chemical components in a bodily fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymph, or urine) that can be analyzed. Analytes may include naturally occurring substances, artificial substances, metabolites, and / or reaction products. In some embodiments, the analyte subjected to measurement by the sensor heads, devices, systems, and methods disclosed herein is glucose. However, other analytes can be considered similarly, including acarboxyprothrombin, acylcarnitine, adenine phosphoribosyltransferase, adenosine deaminase, albumin, α-fetoprotein, amino acid composition (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan), andrenostenedione, antipyrine, arabinitol enantiomer, 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 isozyme, and cyclosporine A d-penicillamine, de-ethylchloroquine, dehydroepiandrosterone sulfate, DNA (acetylated polymorphism, alcohol dehydrogenase, α1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobinopathy, A, S, C, E, D-Punjab, β-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber's hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sex differentiation, 21-deoxycortisol), desbutylhalofantrin, dihydropteridine reductase, diphtheria / tetanus antitoxin, erythrocyte arginase, erythrocyte protoporphyrin, esterase D, fatty acids / acylglycine, free β-human chorionic gonadotropin, free erythrocyte porphyrin, free thyroxine (FT4),Free triiodothyronine (FT3), fumaryl acetase, galactose / gal-1-phosphate, galactose-1-phosphate uridyltransferase, gentamicin, analyte-6-phosphate dehydrogenase, glutathione, glutathione peroxidase, glycocholic acid, glycosylated hemoglobin, halofantrin, hemoglobin variant, hexosaminidase A, human erythrocyte carbonic anhydrase I, 17α-hydroxyprogesterone, hypoxanthine phosphoribosyltransferase, immunoassay Responsive trypsin, lactate, lead, lipoprotein ((a), B / A-1, β), lysozyme, mefloquine, netylmycin, phenobarbiton, phenytoin, phytanic acid / pristanic acid, progesterone, prolactin, prolidase, purine nucleoside phosphorylase, kinin, inverted triiodothyronine (rT3), selenium, serum pancreatic lipase, shisomecin, somatomedin C, specific antibodies (adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, Aujeszky's disease virus, dengue virus, meadowsinus) Insects, Echinococcus granulosus, Entamoeba histolytica, Enterovirus, Giardia lamblia, Helicobacter pylori, Hepatitis B virus, Herpesvirus, HIV-1, IgE (atopic disease), Influenza virus, Donovan's leishmania, Leptospirosis, Measles / Mumps / Rubella, Mycoplasma leprae, Mycoplasma pneumoniae, Myoglobin, Irocystitis rotundifolia, Parainfluenza virus, Plasmodium falciparum, Poliovirus, Pseudomonas aeruginosa, Respiratory rash virus, Rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Plum Examples of analytes include, but are not limited to, venomous treponema, Cruz / Langer's trypanosome, vesicular stomatitis virus, Bancroft's filariasis, 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 spontaneously in blood or interstitial fluid may also constitute analytes in certain embodiments.For example, metabolites, hormones, antigens, and antibodies can naturally exist in bodily fluids. Alternatively, analytes, such as contrast agents for imaging, radioisotopes, chemical agents, fluorocarbon-based artificial blood, or drugs or pharmaceutical compositions may be introduced into the body, including insulin, ethanol, cannabis (marijuana, tetrahydrocannabinol, hashish), inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons), cocaine (crack cocaine), stimulants (amphetamine, methamphetamine, Ritalin, Sylart, Preludin, Zydrex, Prestate, Boranil, Sandrex, Prezin), and inhibitors (barbiturates, methacaron, psychiatric tranquilizers). Constipulation agents (e.g., Valium, Librium, Miltown, Serax, Equenyl, Tranxine), hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin), narcotics (heroin, codeine, morphine, opium, meperidin, Percocet, Percodan, Tasionex, fentanyl, Dalvon, Talwin, Lomorthyl), designer drugs (fentanyl, meperidin, amphetamine, methamphetamine, and analogs of phencyclidine, e.g., ecstasy), anabolic steroids, and nicotine can be introduced into the body. Metabolites of drugs and pharmaceutical compositions are also conceived as analytes. Analytes of neurochemical drugs and other chemicals produced in the body may also be analyzed, for example, ascorbic acid, uric acid, dopamine, norepinephrine, 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, and their ordinary and conventional meanings are given to those skilled in the art (and are not limited to any special or customized meanings), referring to, but not limited to, a device that continuously or continuously measures the concentration of an analyte / glucose, and / or a device that calibrates the device (for example, by continuously or continuously adjusting or determining the sensitivity and background of the sensor) at time intervals ranging from 0.1 seconds to a maximum of, for example, 1, 2, or 5 minutes or more.

[0148] As used herein, the term “biological sample” is a broad term, and its ordinary and customary meaning is given to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, samples derived from the body or tissues of a host, such as, for example, blood, interstitial fluid, cerebrospinal fluid, saliva, urine, tears, sweat, or other similar fluids.

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

[0150] As used herein, the term “membrane system” is a broad term whose ordinary and conventional meaning is given 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 permeable or semipermeable membrane that can consist of two or more domains and can typically be constructed of a material several microns or more thick, which is permeable to oxygen and, in some cases, glucose. For example, a membrane system may include an immobilized glucose oxidase enzyme, which enables an electrochemical reaction to be carried out to measure the concentration of glucose.

[0151] As used herein, the term “domain” is a broad term whose ordinary and conventional meaning is given 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 layer, a uniform or non-uniform gradient (e.g., anisotropy), a functional aspect of a material, or a region of a film that may be provided as a portion of a film.

[0152] As used herein, the term “detection region” is a broad term whose ordinary and conventional meaning is given 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 area of ​​a monitoring device responsible for detecting a particular analyte. In one embodiment, the detection region generally includes a non-conductive body, at least one electrode, a reference electrode, an optional counter electrode fixed within the body passing through the body to form an electroactive surface at one location on the body and an electronic connection at another location on the body, and a membrane system attached to the body and covering the electroactive surface.

[0153] As used herein, the term “electroactive surface” is a broad term whose ordinary and customary meaning is given to those skilled in the art (and not limited to any special or customized meaning), referring to, but not limited to, the surface of an electrode on which an electrochemical reaction occurs. In one embodiment, the working electrode measures hydrogen peroxide (H2O2) to generate a measurable electron current.

[0154] As used herein, the term “baseline” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), referring to, but not limited to, a component of the analyte sensor signal independent of the analyte concentration. In one embodiment of a glucose sensor, the baseline is substantially composed of signal contributions from factors other than glucose (such as interfering species, non-reactive related hydrogen peroxide, or other electroactive species with oxidation potentials overlapping with hydrogen peroxide). In some embodiments where 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 nearly zero. This may be, for example, a result of a baseline subtraction electrode or a low bias potential setting. Consequently, in these embodiments, calibration can be defined by solving the equation y = mx.

[0155] As used herein, the term “inactive enzyme” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), referring to, but not limited to, an enzyme that is taken in an inactive state (e.g., by denaturation of the enzyme) and is substantially devoid of enzymatic activity (e.g., glucose oxidase, GOx). Enzymes can be inactivated using a variety of techniques known in the art, such as heating, freeze-thaw cycles, denaturation in organic solvents, acids or bases, cross-linking, and genetically altered enzymatically important amino acids. In some embodiments, a solution containing an active enzyme can be applied to a sensor, and the applied enzyme is subsequently inactivated by heating or treatment with an inactivating solvent.

[0156] As used herein, the term “non-enzymatic” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), referring to, but not limited to, a lack of enzymatic activity. In some embodiments, the “non-enzymatic” membrane portion does not contain enzymes, while in other embodiments, the “non-enzymatic” membrane portion contains an inactive enzyme. In some embodiments, an enzyme solution containing an inactive enzyme or containing no enzymes at all is applied.

[0157] As used herein, the term “substantially” is a broad term whose ordinary and customary meaning is given to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but not necessarily to, all, of the specified subjects.

[0158] As used herein, the term “about” is a broad term whose ordinary and customary meaning is given to those skilled in the art (and not limited to any special or customized meaning), and where it is associated with any number or range, it means understanding that the quantity or condition modified by the term may be modified to some extent beyond the quantity described, insofar as the function of the disclosure is achieved.

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

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

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

[0162] As used herein, the terms “raw data stream” and “data stream” are broad terms, and their ordinary and conventional meanings are given to those skilled in the art (and are not limited to any special or customized meanings), referring to, but not limited to, analog or digital signals directly relating to analyte concentrations measured by an analyte sensor. In one embodiment, the raw data stream is digital data (e.g., voltage or amperage) in counts converted from an analog signal by an A / D converter, representing the analyte concentration. These terms broadly encompass multiple time-interval data points from a substantially continuous analyte sensor, which includes individual measurements acquired at time intervals ranging from 0.1 seconds to, for example, 1, 2, or 5 minutes, or longer.

[0163] As used herein, the term “count” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), referring to, but not limited to, a unit of measurement of a digital signal. In one embodiment, the raw data stream measured in the count 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 whose ordinary and conventional meaning is evident to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, components of a device configured to process data (e.g., hardware and / or software). 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. Patents 4,757,022, 5,497,772, and 4,787,398 describe suitable electronic circuits that may be used with devices of a particular embodiment.

[0165] As used herein, the term “potentiostat” is a broad term whose ordinary and customary meaning is given to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, an electrical system that applies a preset potential between a working electrode and a reference electrode of a two-electrode or three-electrode cell and measures the current flowing through the working electrode. A potentiostat forces the current necessary to flow between the working electrode and the counter electrode to maintain a required potential, provided that 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 whose ordinary and conventional meaning is given to those skilled in the art (and not limited to any special or customized meaning), referring to, but not limited to, the coupling of one or more components to another component in a manner that enables the transmission of signals between components. For example, one or more electrodes can be used to detect the amount of glucose in a sample, convert that information into a signal, and then transmit that signal to an electronic circuit. In this case, the electrodes are “operably connected” 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 whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, any modification of a data set that makes the data set smoother or more continuous, or removes or reduces outliers, for example, by performing a moving average of the raw data stream.

[0168] As used herein, the term “algorithm” is a broad term, its ordinary and conventional meaning given to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, any computational process (e.g., a program) required to transform information from one state to another, for example, using computer processing.

[0169] As used herein, the term “calibration” is a broad term whose ordinary and conventional meaning is given 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 scale of a sensor that gives a quantitative measurement (e.g., analyte concentration). In one embodiment, 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. Furthermore, sensor calibration may include, for example, automatic, self-calibration that does not use a reference analyte value after the time of use.

[0170] As used herein, the term “sensor data” is a broad term, its ordinary and conventional meaning given to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, data received from a continuous analyte sensor, including one or more spatially and temporally placed sensor data points.

[0171] As used herein, the terms “reference analyte value” and “reference data” are broad terms, and their ordinary and conventional meanings are given to those skilled in the art (and are not limited to any special or customized meanings), referring to, but not limited to, reference data from a reference analyte monitoring device, such as a blood glucose meter, including one or more reference data points. In some embodiments, reference glucose values ​​are 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 “interfering substance” and “interfering species” are broad terms, and their ordinary and conventional meanings are given to those skilled in the art (and are not limited to any special or customized meanings), referring to, but not limited to, the influence and / or type of influence that interferes with the measurement of the analyte in the sensor, producing a signal that does not accurately represent the analyte measurement. In an 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 false signal.

[0173] As used herein, the term “sensor session” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), referring to, but not limited to, the period during which a sensor is applied to a host (e.g., implanted therein) or during which sensor values ​​are obtained using the sensor. For example, in some embodiments, a sensor session extends from the time of sensor implantation (e.g., including inserting the sensor into subcutaneous tissue and positioning the sensor to be in fluid communication with the host’s circulatory system) to the time of sensor removal.

[0174] As used herein, the terms “sensitivity” or “sensor sensitivity” are broad terms whose ordinary and customary meanings are shown to those skilled in the art (and are not limited to any special or customized meanings) and refer to, but are not limited to, the amount of signal produced by a certain concentration of the analyte or a sample (e.g., H2O2) associated with the analyte (e.g., glucose). For example, in one embodiment, the sensor has a sensitivity of about 1 to about 300 picoamperes of current for every 1 mg / dL of glucose analyte.

[0175] As used herein, the terms “sensitivity profile” or “sensitivity curve” are broad terms whose ordinary and conventional meanings are given to those skilled in the art (and are not limited to any special or customized meanings), and refer to, but are not limited to, a representation of a change in sensitivity over time.

[0176] As used herein, the term “process setpoint” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and not limited to any special or customized meaning), and refers to a desired or target value for a variable or process of a system.

[0177] As used herein, the term “process variation” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and not limited to any special or customized meaning), referring to the degree of deviation from a setpoint, usually expressed as a standard deviation, but not limited thereto.

[0178] As used herein, the term “Monte Carlo simulation” is a broad term, its ordinary and conventional meaning given to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, defining a range of possible inputs, generating inputs randomly from probability distributions across that range, performing deterministic calculations on those inputs, and aggregating the results. A Monte Carlo simulation samples from the probability distribution of each variable to generate hundreds or thousands of possible outcomes. These outcomes are then analyzed to obtain the probabilities of different outcomes occurring.

[0179] As used herein, the term “accuracy” is a broad term, its ordinary and conventional meaning given to those skilled in the art (and not limited to any special or customized meaning), referring to, but 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, its ordinary and conventional meaning given to those skilled in the art (and not limited to any special or customized meaning), referring to the degree to which repeated measurements under invariant conditions yield the same results.

[0181] overview Commercially available transcutaneous analyte measurement systems consist of individual modules that are physically interconnected immediately before or at the time of final sensor placement. Generally, analyte sensor modules are 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 test configurations such as placing the analyte sensor in one or more solutions of known analyte concentrations.

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

[0183] Conventional in vivo continuous analyte detection techniques typically rely on reference measurements taken during sensor sessions for the calibration of the continuous analyte sensor. These reference measurements are matched with sensor data that corresponds substantially to time to create matched data pairs. Regression is then performed on these 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 critical settings, calibration of continuous analyte sensors is often performed by using a calibration solution with a known concentration of the analyte as a reference. This calibration procedure can be cumbersome, as it involves a calibration bag separate from (and an add-on to) the typically used IV (intravenous) bag. In outpatient settings, continuous analyte sensor calibration has traditionally been performed using capillary blood glucose measurements (e.g., finger-prick glucose tests), through which reference data is acquired and input into the continuous analyte sensor system. This calibration procedure typically involves frequent use of finger-prick measurements, which can be inconvenient and painful.

[0185] Traditionally, systems and methods for in vitro calibration (e.g., factory calibration) of continuous analyte sensors by manufacturers have been, in most cases, inaccurate in terms of 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 can occur during sensor use. Therefore, calibration of continuous analyte sensors typically requires periodic input of reference data, whether or not that input is associated with calibration solutions or finger puncture measurements. As mentioned above, such input, regardless of the setting, can be very burdensome for the patient.

[0186] This specification describes a continuous analyte sensor that is either factory calibrated or enables continuous automatic self-calibration during a sensor session, achieving a high level of accuracy without relying on (or reducing) reference data from a reference analyte monitor (e.g., from a blood glucose meter). Factory calibration typically refers to an initial calibration performed before the sensor leaves the factory and that does not change over time. Automatic self-calibration, in contrast, refers to a process in which the calibration is updated without user intervention at one or more time intervals following the factory calibration, in which case the update is based on information acquired during the manufacturing and / or subsequent life phases of the analyte sensor. This calibration update is generally achieved by, for example, transmitting a signal from the cloud to the sensor or sensor electronics.

[0187] Figure 20 shows examples of the various life phases that an analyte sensor may undergo, including the sensor manufacturing phase, sensor packaging phase, sensor storage phase, in vivo pre-phase, and sensor session phase. As will be discussed in more detail below, in some cases the sensor may go through additional or fewer life phases. At various time points t during these phases, a complex adaptive calibration coefficient C(t,p i ) can be generated, and its coefficient is determined by the time t since the sensor was manufactured, and various parameters p i This is a function of where i > 1. These parameters p i This represents, for example, the environmental conditions experienced by the analyte sensor (and any pre-connected electronics, if present) from sensor manufacturing to sensor use during the sensor session phase, combined with additional information, possibly patient-specific data. This complex adaptive calibration coefficient is one or more initial calibration coefficients C m Changes acquired during sensor manufacturing can be reflected in the analyte sensor (and any pre-connected electronics, if present). For example, in Figure 20, the initial calibration coefficient C Mis obtained by a “calibration check” procedure in a 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), for example, the complex adaptive calibration coefficient C t1 and C t2 can be obtained respectively using C M1 and measured values of parameters. Additional complex adaptive calibration coefficients can be obtained during the shipping phase (e.g., C S1 , C S2 , ..., C Sn ), the storage phase (C ST1 and C ST2 ), the pre-in-vivo phase (e.g., C P ), and the sensor session phase (e.g., C SS1 , C SS2 , ..., C SSn ). In this way, the experience of the analyte sensor over its lifetime is encoded in a form that enables the analyte sensor to be used by a suitable calibration algorithm to determine, for example, the sensitivity of the sensor and / or the baseline value of 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, transdermal, 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 a subcutaneous device and a transdermal device, a combination of a subcutaneous device and an intravascular device, a combination of a transdermal device and an intravascular device, or a combination of a subcutaneous device, a transdermal device, and an intravascular device. In some embodiments, the continuous analyte sensor is capable of analyzing a plurality of intermittent biological samples (e.g., blood samples). The continuous analyte sensor can use any glucose measurement method, and these methods include methods that use mechanisms such as enzymatic, chemical, physical, electrochemical, spectroscopic analysis, polarimetric analysis, calorimetry, ionophoresis, and radiometric analysis.

[0189] In certain embodiments, a continuous analyte sensor includes one or more working electrodes and one or more reference electrodes, which work together to measure a signal associated with the concentration of an analyte in a host. The output signal from the working electrodes 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 may measure additional signals associated with the sensor's baseline and / or sensitivity, thereby enabling baseline monitoring and / or additional monitoring of any changes or drifts in sensitivity that may occur within the continuous analyte sensor over time.

[0190] In some embodiments, the sensor extends through a housing that maintains the sensor on the skin and provides an electrical connection to the sensor's electronics. In one embodiment, the sensor is formed from a wire. For example, the sensor may 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 may be long and thin, but still flexible and strong. For example, in some embodiments, the minimum dimensions of the elongated conductive body are 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. Another embodiment of the elongated conductive body is disclosed in U.S. Patent Application No. 20110027127A1, which is incorporated herein by reference in its entirety. It is desirable that the membrane system be deposited over at least a portion of the electrically active surface of the sensor 102 (including the working electrode and optionally the reference electrode) to provide protection for the exposed electrode surface from the biological environment, diffusion resistance (limitation) of analytes if necessary, catalysts for enabling enzymatic reactions, limitation or blocking of interfering substances, and / or hydrophilicity on the electrochemically reactive surface of the sensor interface. Disclosures relating to different membrane systems that may be used in conjunction with the embodiments described herein are found 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 generally required defining a relationship between the sensor-generated measurements (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 acquired shortly after the manufacture of the analyte sensor and before the sensor is used are used for calibration. These reference measurements may have been acquired in many forms. For example, in certain cases, the reference measurements may be determined from in vivo analyte concentration measurements.

[0192] Using factory calibration or automated self-calibration, the need for recalibration can be eliminated or reduced by using reference data during a sensor session, resulting in recalibration being invoked only in specific, limited circumstances, such as when a sensor failure is detected. Additionally or alternatively, in some embodiments, a continuous analyte sensor can be configured to request and accept one or more reference measurements (e.g., finger-prick glucose measurements or from a calibration solution) at the start of a sensor session. In some embodiments, using reference measurements at the start of a sensor session in conjunction with a predetermined sensor sensitivity profile can eliminate or substantially reduce the need for further reference measurements.

[0193] Turning to a basic explanation of the functionality of glucose sensors, when using certain implantable enzyme-based electrochemical glucose sensors, the detection mechanism depends on a specific phenomenon that has a roughly linear relationship with glucose concentration, which is, for example, (1) the diffusion of the analyte through a membrane system located between the implantation site (e.g., subcutaneous space) and the electrically active surface, (2) the rate of the enzyme-catalyzed reaction of the analyte to generate the analyte 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 analyte (e.g., H2O2) to the electrically active surface. Because of this roughly linear relationship, the calibration of the sensor is obtained by solving the following equation.

[0194] y = mx + b

[0195] In the formula, y represents the sensor signal (count), x represents the estimated glucose concentration (mg / dL), m represents the sensor sensitivity to the analyte concentration (count / mg / dL), and b represents the baseline signal (count). As described elsewhere in this specification, in certain embodiments, the value b (i.e., baseline) can be zero or near zero. Consequently, in these embodiments, calibration can be defined by solving the formula y = mx.

[0196] In some embodiments, a continuous analyte sensor system is configured to evaluate the change or drift in sensor sensitivity over an entire sensor session as a function of time (e.g., elapsed time 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 sensor sensitivity or provide additional information about sensor sensitivity. These parameters may affect sensor sensitivity or provide additional information about 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 may include information that is acquired prior to the sensor session and can be described by a calibration code associated with a particular sensor lot. Other parameters can be associated with the conditions surrounding the sensor, such as the level of sensor exposure to specific humidity or temperature levels, after sensor manufacturing but before sensor sessions, for example, while the sensor is in the package during the transition period from the manufacturing facility to the patient. Further parameters (e.g., sensor membrane permeability, temperature at the sample site, pH at the sample site, oxygen level at the sample site, etc.) may affect sensor sensitivity or provide additional information regarding sensor sensitivity during sensor sessions.

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

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

[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 changes above a certain threshold can trigger calibration, update calibration, and / or provide a basis for determining whether inaccurate reference analyte values ​​from reference analyte monitoring are acceptable. In an example of a continuous glucose sensor, the background noise consists substantially of signal contributions from non-glucose factors (e.g., interfering species, non-reactive related hydrogen peroxide, or other electroactive species with oxidation potentials overlapping with hydrogen peroxide). That is, the continuous glucose sensor is configured to measure the 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 placed directly beneath the non-enzymatic portion of the membrane system is used to measure the baseline signal. This baseline signal can be subtracted from glucose + baseline signal to obtain a signal that is fully or substantially fully associated with glucose concentration. The subtraction can be achieved electronically in the sensor, digitally in the receiver, and / or otherwise in the hardware or software of the sensor or receiver, using a differential amplifier, as described in more detail elsewhere herein.

[0200] Simultaneously, by determining the sensor sensitivity based on the sensitivity profile and by measuring the baseline signal, the continuous analyte sensor can continuously self-calibrate during a sensor session, independently of (or with reduced reliance on) reference measurements from a reference analyte monitor or calibration solution.

[0201] Sensor system Figure 1 shows an exemplary system 100 in several exemplary implementation configurations. System 100 includes an analytic sensor system 101, which includes sensor electronics 112 and an analytic sensor 138. System 100 may include other devices and / or sensors, such as a drug delivery pump 102 and a glucose meter 104. The analytic sensor 138 may be physically connected to the sensor electronics 112, and may be integrated with the sensor electronics (e.g., permanently mounted) or detachably mounted. For example, a continuous analytic sensor 138 may be connected to the sensor electronics 112 via a sensor interposer that mechanically and electrically interfaces the analytic sensor 138 to the sensor electronics. The sensor electronics 112, drug delivery pump 102, and / or glucose meter 104 may be coupled with one or more devices, such as display devices 114, 116, 118, and / or 120.

[0202] In some exemplary implementations, system 100 may include a cloud-based analyte processor 490 configured to analyze analyte data (and / or other patient-related data) provided via network 409 (e.g., via wired, wireless, or a combination thereof) from a sensor system 101 associated with a host (also called a patient) and other devices such as display devices 114, 116, 118, and / or 120, to generate a report that provides high-level information, such as statistics, regarding analytes measured within a specific time frame. A full discussion of the use of a cloud-based analyte processing system can be found in U.S. Patent Application No. 13 / 788,375, filed on March 7, 2013, and published as U.S.20130325352A1, entitled “Cloud-Based Processing of Analyte Data,” which is incorporated herein by reference in its entirety. In some implementations, one or more steps of a factory calibration algorithm or an automated self-calibration algorithm can be performed in the cloud.

[0203] In some exemplary implementations, the sensor electronics 112 may include electronic circuits related to the measurement and processing of data generated by the analyte sensor 138. This generated analyte sensor data may include algorithms that can be used for processing and calibration of the analyte sensor data, although these algorithms may be provided in other ways. The sensor electronics 112 may include hardware, firmware, software, or a combination thereof that provides measurement of analyte levels via an analyte sensor such as a glucose sensor. Exemplary implementations of the sensor electronics 112 are further described below with reference to Figure 2.

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

[0205] As described above, the sensor electronics 112 may be coupled (for example, wirelessly) with one or more devices such as display devices 114, 116, 118, and / or 120. The display devices 114, 116, 118, and / or 120 may be configured to present (and / or alarm) 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 algorithm or self-calibration algorithm described herein can be performed at least partially by the display device.

[0207] In some exemplary implementations, the relatively small key fob-like display device 114 may include a wristwatch, belt, necklace, pendant, jewelry, adhesive patch, pager, key fob, plastic card (e.g., credit card), identification (ID) card, and / or similar. This small display device 114 may include a relatively small display (e.g., smaller than the larger display device 116) and may be configured to display certain types of displayable sensor information, such as numbers, arrows, or color codes.

[0208] In some exemplary implementations, the relatively large portable display device 116 may include a smartphone, a portable receiver device, and / or a palmtop computer. 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 graphic representation of sensor data, including current and historical sensor data output by the sensor system 100.

[0209] In some exemplary 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 enzyme, chemical, physical, electrochemical, spectrophotometric, polarization, colorimetric, iontophoresis, radiometric, or immunochemistry. In implementations where the analyte sensor 138 comprises a glucose sensor, the glucose sensor may comprise any device capable of measuring glucose concentration and may measure glucose using a variety of techniques, including invasive, minimally invasive, and non-invasive detection techniques (e.g., fluorescence monitoring), to provide data such as a data stream indicating glucose concentration in the host. The data stream may be sensor data (raw and / or filtered) that can be converted into a calibrated data stream used to provide glucose values ​​to the host, such as a user, patient, or caregiver (e.g., parent, relative, guardian, teacher, physician, nurse, or any other individual interested in the host's health). Furthermore, the analyte sensor 138 can be implanted as at least one of the following types of analyte sensors: an implantable glucose sensor, a transcutaneous glucose sensor implanted intravascularly or extracorporeally in a host blood vessel, a subcutaneous sensor, a refillable subcutaneous sensor, or an intravascular sensor.

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

[0211] Figure 2 shows examples of 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 the manufacture of device 101, according to several exemplary implementation embodiments. 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 can 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., which can be determined by a factory calibration algorithm or self-calibration algorithm, a sensor data smoothing algorithm and / or filtering algorithm, such as those disclosed herein.

[0212] In some embodiments, the processor module 214 is configured to perform a substantial, if not all, portion, of the data processing, including data processing that falls under factory calibration or self-calibration. The processor module 214 may be integrated with the sensor electronics 12 and / or located remotely, such as in one or more of the devices 114, 116, 118, and / or 120 and / or 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 sensor data, and the data storage memory 220 may store calibrated sensor data points as converted sensor data. Also, in some exemplary implementations, the processor module 214 may be configured to wirelessly receive calibration information from display devices such as devices 114, 116, 118, and / or 120 to enable calibration of sensor data from sensor 138. Furthermore, the processor module 214 may be configured to perform additional algorithmic processing on sensor data (e.g., calibrated and / or filtered data and / or other sensor information), and the data storage memory 220 may be configured to store converted sensor data and / or sensor diagnostic information related to the algorithms. The processor module 214 may be further configured to store and use calibration information determined from factory calibration or self-calibration, as described below.

[0214] In some exemplary 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). Figure 2 depicts the ASIC 205, but other types of circuitry may also be used, including field-programmable gate arrays (FPGAs), one or more microprocessors configured to provide some (if not all) of the processing performed by the sensor electronics 12, analog circuits, digital circuits, or a combination thereof.

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

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

[0217] The telemetry module 232 may be operably connected to the processor module 214 and may provide hardware, firmware, and / or software that enables wireless communication between the sensor electronics 112 and one or more other devices such as a display device, a processor, or a network access device. Various wireless technologies that can be implemented in the telemetry module 232 include Bluetooth®, Bluetooth® Low-Energy, ANT, ANT+, ZigBee®, IEEE 802.11, IEEE 802.16, cellular wireless access technology, radio frequency (RF), infrared (IR), paging network communication, magnetic induction, satellite data communication, spread spectrum communication, frequency hopping communication, short-range wireless communication, and / or similar. In some exemplary implementations, the telemetry module 232 includes a Bluetooth® chip, but Bluetooth® technology may be implemented in combination with 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 from the sensor (e.g., counts), filter the data, calibrate the data, perform fail-safe checks, and / or do such things.

[0219] The potentiostat 210 can measure analytes (e.g., glucose) 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 display devices 114, 116, 118, and / or 120. Furthermore, the processor module 214 may generate data packets for transmission to these external sources via the telemetry module 232. In some exemplary implementations, the data package may include identifier codes for the sensor and / or sensor electronics 112, raw data, filtered data, calibrated data, rate of change information, trend information, error detection or correction, and / or similar.

[0221] The processor module 214 may also include program memory 216 and other memory 218. The processor module 214 may be connected to a communication interface such as a communication port 238 and a power source such as a battery 234. Furthermore, the battery 234 may be further connected 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 quasi-static memory for storing data such as an identifier for the coupled sensor 138 (e.g., a sensor identifier (ID)) and for storing code (also called 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, and perform fail-safe checks.

[0223] Memory 218 may also be used to store information. For example, a processor module 214 including memory 218 may be used as system cache memory, where temporary storage is provided for recent sensor data received from the sensor. In some exemplary 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), rewritable ROM, and flash memory.

[0224] The data storage memory 220 may be connected to the processor module 214 and may be configured to store various sensor information. In some exemplary 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: timestamp, 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., previous calibration information such as reference BG value and / or 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 similar. The components constituting the user interface 222 may provide controls for interacting with a user (e.g., a host).

[0226] The battery 234 may be operably connected to the processor module 214 (and possibly other components of the sensor electronics 12) to provide the necessary power to the sensor electronics 112. In other implementations, power can be supplied transcutaneously to the receiver, 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 exemplary implementations, the battery 234 (or more batteries) is configured to be charged via an inductive and / or wireless charging pad, but other charging and / or power mechanisms may also be used.

[0228] One or more communication ports 238, also called external connectors, may be provided to enable communication with other devices. For example, a PC communication (com) port may be provided to enable communication with a system separate from or integrated with the sensor electronics 112. The communication ports may include, 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 exemplary implementations, factory information or other data can be sent to and received from sensors, algorithms, or cloud data sources.

[0229] One or more communication ports 238 may further include a second input port 237 capable of receiving 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 schematically illustrates these embodiments. The ports may be physically separated, but in alternative implementations, it will be understood that a single communication port may provide the functionality of both a second input port and an 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, providing raw, calibrated, and / or filtered data to a display device configured to perform calibration and other algorithms necessary for displaying sensor data. However, the sensor electronics 112 (e.g., via a processor module 214) may be implemented to perform predictive algorithms used to generate the sensor data to be converted and / or displayable sensor information, such predictive algorithms may include, for example, algorithms to assess the clinical validity of optional criteria and / or sensor data, evaluate calibration data for best calibration based on ingestion criteria, assess calibration quality, compare estimated analyte values ​​with analyte values ​​measured over time, analyze fluctuations in estimated analyte values, assess the stability of the sensor and / or sensor data, detect signal artifacts (noise), replace signal artifacts, determine the rate of change and / or trend of sensor data, perform dynamic and intelligent analyte value estimation, perform diagnostics regarding the sensor and / or sensor data, set operating modes, and evaluate abnormal data. A connected receiver, smart device, or wearable device can perform one or more such calculations.

[0231] Figure 3 shows a perspective view of an exemplary implementation of the analyte sensor system 101 as a wearable device, such as a skin sensor assembly 600. As shown in Figure 3, the skin sensor assembly includes a base 128. An adhesive 126 can bond the base 128 to the host skin. This adhesive 126 may be an adhesive suitable for skin bonding, but is generally not, for example, a foamed resin 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 coupling mechanism such as a snap-fit ​​and / or interlocking mechanism). The electronics unit 500 may include sensor electronics 112 that are operable to measure and / or analyze a glucose indicator detected by the glucose sensor 138. The sensor electronics 112 within the electronics unit 500 can transmit information (e.g., measured values, analyte data, and glucose data) to a remotely located device (e.g., 114-120 shown in Figure 1).

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

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

[0235] As shown in Figure 4, contact 406 is coupled to external contact 410, and contact 408 is coupled to external contact 412. As will be described in more detail later in this specification, the external contacts 410 and 412 are sized, shaped, and positioned to electrically interface with the sensor electronics 112 in the electronics unit 500, in addition to electrically interface with one or more test stations and / or one or more calibration stations to the processing circuitry of the manufacturing equipment. Various examples in which two contacts 410 and 412 on the interposer are coupled to two corresponding contacts 406 and 408 on the sensor 138 are described herein, but these are merely illustrative. In other implementations, the interposer 402 and the sensor 138 may be provided with a single contact or with three or more contacts. In some implementations, the interposer 402 and the sensor 138 may have the same number of contacts. In some implementations, the interposer 402 and the 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 the manufacturing station.

[0236] The substrate 404 is sized and shaped to mechanically interface with the base 128 and / or electronics unit 500, in addition to mechanically interface with one or more assembly devices, test stations, and / or one or more calibration stations to manufacturing equipment. The interposer 402 may be attached to and / or electrically coupled to the sensor 138. The interposer 402 may be attached to the sensor 138 using, for example, adhesive, 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 before or during assembly, manufacturing, testing, and / or calibration operations. The interposer 402 can be attached to the sensor 138 by, for example, spot welding, swaging, crimping, clipping, soldering or brazing, plastic welding, overmolding, or by other preferred methods for mechanically and electrically attaching the interposer 402 to the sensor 138 before or during assembly, manufacturing, testing, and / or calibration operations. The substrate 404 may include reference mechanisms (sometimes called reference structures) such as recesses, openings, surfaces, or protrusions for aligning, positioning, and oriented the sensor 138 relative to the interposer 402. The substrate 404 may also include one or more anchoring mechanisms for fixing and aligning the analyte sensor during manufacturing (e.g., relative to a manufacturing station), or the substrate itself may form the anchors.

[0237] Figure 5 shows a block diagram of an exemplary system 5000, which has one or more manufacturing stations 5091, one or more positioning or testing stations 5002, and / or another calibration station 5004, and each has a skin sensor assembly 600 configured to receive a sensor interposer 402 and to be communicably coupled to a sensor 138 via the sensor interposer 402.

[0238] The system 5000 may include one or more positioning stations or test stations 5002 having a processing circuit 5012 configured to perform test operations using the sensor 138 to determine parameters and / or verify the operational integrity of the sensor 138. The test operations may include verifying the electrical characteristics of the sensor 138, verifying communication between the working electrode and contact 408, verifying communication between the reference electrode or additional electrode and contact 406, and / or other electronic verification operations of the sensor 138. The processing circuit 5012 may be communicatively connected to the sensor 138 for test operations by inserting the substrate 404 into the receptacle 5006 (e.g., a recess in the housing of the test station 5002) until contact 410 is coupled to contact 5010 of the test station 5002 and contact 412 is coupled to contact 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 a calibration operation on the sensor 138 and acquire calibration data of the in vivo operation of the sensor 138. The calibration data acquired by the calibration equipment 5004 may be provided to a skin sensor assembly 600 used during the in vivo operation of the sensor 138. The processing circuit 5020 may be communicatively connected to the sensor 138 for calibration operations by inserting a substrate 404 into a receptacle 5014 (e.g., a recess in the housing of the calibration station 5004) until contact 410 is coupled to contact 5018 of the test station 5002 and contact 412 is coupled to contact 5016 of the test station 5002.

[0240] The system 5000 may include one or more manufacturing stations 5091. A manufacturing station 5091 may also serve to provide the functions of a test station, a calibration station, or another manufacturing station as described herein. A manufacturing station 5091 may include a processing circuit 5092, and / or mechanical components 5094 that can be operated to perform other manufacturing operations such as test operations, calibration operations, and / or sensor linearization operations, membrane application operations, baking operations, calibration check operations, glucose sensitivity operations (e.g., sensitivity gradient, baseline, and / or noise calibration operations), and / or visual inspection operations. Manufacturing parameters that may be measured during these various operations may include, for illustrative purposes, temperature, humidity, the contents of the particular coating solution into which the sensor is immersed (which can be determined from the refractive index of the solution) (e.g., PVP, ethanol, etc.), 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] In the example in Figure 5, the test station 5002 and calibration station 5004 include receptacles 5006 and 5014. However, this is merely illustrative, and the interposer 402 can be mounted to the test station 5002 and calibration station 5004, and / or the manufacturing station 5091, using other mounting mechanisms such as gripping, clipping, or clamping styles. For example, the manufacturing station 5091 may 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). Structure 5093 may be a stationary mechanism having one or more electrical contacts, such as contact 5008. Structure 5095 may be a movable mechanism that moves (e.g., slides in direction 5097) to grip and fix the interposer 402 in a position electrically coupled 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, for example, Figure 4). The identifier 450 may be formed on the substrate 404 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 an identifier, as well as a microstorage circuit programmable with other data before, during, or after testing and calibration (e.g., an integrated circuit or other circuit in which the identifier is encoded in the memory of that identifier)). The identifier 450 may be used to track each sensor throughout the manufacturing process of its sensors (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 unique raw value, or it may encode information in addition to an identification number. Identifier 450 may be used to digitally store data in the non-volatile memory of the substrate 404, or to store data outside 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. The test data obtained by the test station 5002 may be stored and / or transmitted along with the identifier of 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 together with the identifier of the sensor 138. In some implementations, the calibration data obtained by the calibration station 5004 may be appended 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 together with the identifier 450 to a remote system or device.

[0245] As shown in Figure 5, the skin sensor assembly 600 may include one or more contacts, such as contact 5022, configured to couple the electronics unit 500 to contacts 410 and 412 of the interposer 402, and thus to the sensor 138. The interposer 402 may be sized and shaped to be fixed within a cavity 5024 between the base 128 and the electronics unit 500, so that the sensor 138 is coupled to the electronics unit 500 via the interposer 402, the identifier 450 is accessible by the reader 5013, and the sensor 138 is fixed in place and extends through an 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 System 5000. Although calibration station 5004 and test station 5002 are shown as separate stations in Figure 5, it should be understood that in some implementations, the calibration station and test station can be combined into one or more calibration / test stations (e.g., a station in which processing circuits for performing test and calibration operations are located in a common housing and coupled to a single interface 5006). Furthermore, data from one or more manufacturing stations may be compiled and stored internally, as well as stored and associated with sensors and interposers.

[0247] The skin-surface 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 within the electronics unit 500 can obtain calibration data for the in vivo operation of the sensor 138 based on the read identifier 450. The calibration data may be stored in the identifier 450 itself and retrieved from there, 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] Further details relating to the example sensor system shown in Figures 1 to 5 can be found in U.S. Patent Application No. 62 / 576,560, “Preconnected Analyte Sensors,” filed on 24 October 2017, which is incorporated herein by reference in its entirety.

[0249] Determining sensor sensitivity As described elsewhere in this specification, in certain embodiments, self-calibration of the analyte sensor system may be performed by determining the sensor sensitivity based on the sensitivity profile (and measured or estimated baseline), thereby solving the following equation:

[0250] y = mx + b

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

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

[0253] Returning to the discussion associated with Figure 6, the sensors were fabricated under substantially the same conditions and in substantially the same manner. The sensor sensitivity associated with the plot on the y-axis is expressed as a percentage of the substantially steady-state sensitivity achieved approximately 3 days after the start of the sensor session. Furthermore, these sensor sensitivities correspond to measurements obtained from the YSI test. As shown in the plot, the sensitivity of each sensor (expressed as a percentage of steady-state sensitivity) is remarkably close to the sensitivity of other sensors in the group at an arbitrarily given time in the sensor session, as measured. Although we do not wish to be constrained by theory, the observed increasing trend in sensitivity (over time), which is particularly noticeable in the early stages of the sensor session, may be attributable to the adjustment and hydration of the sensing area of ​​the working electrode. Additionally, the glucose concentration of the fluid surrounding the continuous glucose sensor during sensor startup may also influence sensitivity drift.

[0254] In the case of the sensors tested in this study, the change in sensor sensitivity (expressed substantially as a percentage of steady-state sensitivity) resembled a logarithmic growth curve over time defined by the sensor session. It should be understood that other continuous analyte sensors fabricated with different techniques, 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 linear functions). Nevertheless, with improved control over the operating conditions of the sensor fabrication process, a high level of reproducibility is achieved, resulting in the sensitivity profiles exhibited by individual sensors within a sensor population (e.g., a sensor lot) being substantially similar, and sometimes nearly identical.

[0255] It was found that the change or drift in sensitivity over a sensor session is substantially consistent among sensors manufactured under substantially the same conditions and in substantially the same manner, and that its modeling can be performed through a mathematical function that can accurately estimate this change or drift. As shown in Figure 6, the relationship between time during a sensor session and sensor sensitivity can be defined using an estimable algorithmic function 120. The estimable algorithmic function can be generated by testing a sample set (containing one or more sensors) from a sensor lot under in vivo and / or in vitro conditions. Alternatively, the estimable algorithmic function can be generated by testing each sensor under in vivo and / or in vitro conditions.

[0256] In some embodiments, a sensor can undergo an in vitro sensor sensitivity drift test, in which the sensor is exposed to changing conditions (e.g., a stepwise change in glucose concentration in solution) to generate an in vitro sensitivity profile of the sensor over a specific period. The test period can substantially coincide with the entire sensor session of the corresponding sensor in vivo, or it can include a portion of the sensor session (e.g., the first day, the first two days, or the first three days of the sensor session). The above test can be performed on each individual sensor, or alternatively, on one or more sample sensors from 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 tests, one or more datasets can be generated and plotted, each containing data points that relate sensitivity to time. A sensitivity profile or curve can then be fitted to these data points. If the curve fit is deemed satisfactory (for example, if 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, a reference table. The algorithmic function or reference table can, for example, be stored in computer-readable memory and accessed by a computer processor.

[0258] An estimable algorithmic function may be formed by applying a curve-fitting technique, which recursively fits a curve to the available data points by adjusting the function (e.g., by adjusting the constants of the function) until the best 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, and the parameters of the curve are selected so that the curve estimates the relationships between the data values. For example, selecting the parameters of a 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 relationships between the data values ​​to determine the best fit. As used herein, the term “curve” is broad, and its ordinary and conventional meaning is given to those skilled in the art (and not limited to any special or customized meaning), referring 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 can be formed by any of the various curve fitting techniques, such as linear least squares fitting, nonlinear least squares fitting, Nelder-Mead simplex method, Levenberg-Marquard method, and variations thereof. Furthermore, the curve may be fitted using any of the various functions, which include, but are 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, as well as combinations thereof. For example, in some embodiments, the estimable algorithm includes a linear function component given a first weight w1, a logarithmic function component given a second weight w2, and an exponential function component 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 of the estimable algorithmic function (e.g., R² value) is a measure of the quality of the curve fitting to data points with respect to data obtained from a sample sensor, and can be one metric used to determine whether the function is optimal. In certain embodiments, the estimable algorithmic function formed from the curve fitting analysis can be adjusted to describe other parameters, such as other parameters that may affect sensor sensitivity or provide additional information about sensor sensitivity. For example, the estimable algorithmic function can be adjusted to describe the sensor's sensitivity to hydrogen peroxide or other chemical species.

[0261] At any point during a sensor session, an estimable algorithm formed and used to accurately estimate the sensitivity of an individual sensor can be based on factory calibration and / or a single initial baseline measurement (e.g., using a single-point blood glucose monitor). In some embodiments, sensors across a population of continuous analyte sensors manufactured substantially in substantially the same manner under substantially the same conditions present a substantially fixed in vivo sensitivity relationship to in vivo sensitivity. For example, in one embodiment, the in vivo sensor sensitivity at a specific time after the start of sensor use (e.g., at approximately 5, 10, 15, 30, 60, 120, or 180 minutes after sensor use) is consistently equal to the in vivo measured sensitivity of the sensor or an equivalent sensor. From this relationship, an initial value of in vivo sensitivity can be generated, and from that initial value, 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 in vivo sensitivity can be associated with any part of the sensor sensitivity profile. In certain embodiments, multiple initial values ​​of in vivo sensitivity, spaced far apart in time, correspond to multiple in vitro sensitivities, but these initial values ​​can be calculated and combined together to generate a sensor sensitivity profile.

[0262] In some embodiments, it has been found that not only does the sensor sensitivity tend to drift over time, but the sensor baseline also drifts over time. Therefore, in certain embodiments, it is also possible to create models that predict baseline drift over time by applying the concepts behind the methods and systems used to predict sensitivity drift. Although we do not wish to be constrained by theory, it is assumed that the overall signal received by the sensor electrode consists of a glucose signal component, an interference signal component, and an electrode-related baseline signal component, which is a function of the electrode and substantially dependent on the environment surrounding the electrode (e.g., the extracellular matrix). As stated 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 consists of the interference signal component and the electrode-related baseline signal component, as the term is defined herein. Again, although we do not wish to be constrained by theory, it is assumed that as membrane permeability increases, typically not only does the glucose diffusion rate across the sensor membrane increase, but the interference diffusion rate across the sensor membrane also increases. Therefore, as the permeability of the sensor membrane changes over time, the sensor sensitivity drifts, and similarly, the baseline interference signal component is also likely to drift. Simply put, the baseline interference signal component is not static, but typically changes as a function of time, and consequently, the baseline also drifts over time. By analyzing how each of the aforementioned baseline components responds to changing conditions and time (e.g., as a function of time and temperature), predictive models can be developed to predict how the sensor baseline will drift during a sensor session. By being able to predict both the sensor sensitivity and the baseline in advance, it is thought that it may be possible to achieve factory-calibrated or automatically self-calibrated continuous analyte sensors, i.e., sensors that do not require the use of reference measurements for calibration (e.g., fingertip puncture measurements).

[0263] Calibration code The process for manufacturing continuous analyte sensors may sometimes experience some degree of variation between sensor lots, as is described in more detail below. To compensate for this variation, one or more calibration codes may be assigned to each sensor or sensor set to define parameters that may affect sensor sensitivity or provide additional information regarding the sensitivity profile. These calibration codes can reduce the variation between different sensors and ensure that results obtained from using sensors from different sensor lots are substantially equal and consistent by applying algorithms to adjust for their differences. In one embodiment, the analyte sensor system may be configured so that one or more calibration codes can be manually entered into the system by the user. In other embodiments, the calibration codes may be part of a calibration coding label that is attached (or inserted) to a package of multiple sensors. This calibration coding label may itself be read by any of a variety of techniques or to which a response command signal is sent, including but not limited to optical techniques, RFID (radio frequency identification), and combinations thereof. These techniques for transferring the codes to the sensor system tend to be more automated, more accurate, more convenient for the user, and less error-prone compared to manual input devices. Manual input devices have an inherent risk of errors, for example, caused by patients or hospital staff entering incorrect codes, which can lead to incorrect calibration and, consequently, inaccurate glucose concentration readings. This, in turn, could lead to inappropriate actions by patients or hospital staff (e.g., administering insulin when hypoglycemic).

[0264] In some embodiments, the calibration codes assigned to the sensor may include a first calibration code associated with a predetermined logarithmic function corresponding to a 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 speculative information or parameters described elsewhere herein, and / or any parameters that help define a mathematical relationship between the measured signal and the analyte concentration. The calibration codes may be developed from these measurements, or, for example, based on, or by combination thereof, manufacturing parameters known, determined, or measured during lot production.

[0265] In some embodiments, a package used to store and transport a continuous analyte sensor (or sensor set) may include a detector configured to measure specific parameters, the detector of which may affect sensor sensitivity or provide additional information regarding sensor sensitivity or other sensor characteristics. For example, in one embodiment, the sensor package may include a temperature detector configured to provide calibration information relating to whether the sensor has been exposed to a temperature condition above (and / or below) one or more predetermined temperature values. In some embodiments, one or more predetermined temperature values ​​may be above about 75°F, above about 80°F, above about 85°F, above about 90°F, above about 95°F, above about 100°F, above about 105°F, and / or above about 110°F. Additionally or alternatively, 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 relating to whether the sensor has been exposed to a humidity level above or below one or more predetermined humidity values. In some embodiments, 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, 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] When the sensor detects exposure to a specific level of temperature and / or humidity, the corresponding calibration code can be changed to compensate for any potential effects of this exposure on the sensor's sensitivity or other sensor characteristics. This change in calibration code can be performed automatically by a control system associated with the sensor package. Alternatively, in other embodiments, an indicator (e.g., a color-changing indicator) adapted to undergo a change (e.g., a color change) when exposed to a specific environment can be used. For example, the sensor package may include an indicator that irreversibly changes color from blue to red when the package is exposed to a temperature above approximately 85°F, and may also include a command to the user to input a specific calibration code when the indicator is red. Exposure to temperature and humidity is described herein as an example of conditions that may be detected by the sensor package and used to activate a change in calibration code information, but it should be understood that other conditions may also be detected and used to activate a change in calibration code information.

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

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

[0269] Furthermore, in some cases, the various lifecycle phases enumerated above can 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 can be pre-connected to one or more components of sensor electronics, or even to all of the sensor electronics. Where the analyte sensor system uses a sensor interface, such as sensor interposer 402 shown in the embodiment of FIG. 4, the sensor can be pre-connected to the sensor interface and, in some cases, further to one or more components of the sensor electronics. Alternatively, instead of addressing the pre-connection process as part of the sensor manufacturing phase, the sub-phase may be identified as a separate phase occurring before or after the sensor manufacturing phase.

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

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

[0272] FIG. 9 shows a schematic block diagram of one specific example of a pre-connected analyte sensor system, wherein the analyte sensor system includes an analyte sensor 5602, a sensor interconnection module 5604 (e.g., a sensor interposer), and measurement electronics 5608. The measurement electronics 5608 includes a potentiostat 5610 and some 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, a data storage 5630, a power supply 5632, and a clock 5634.

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

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

[0275] The comparison uses unitless current measurements (counts) and is calibrated against a known glucose calibration solution. This is an illustrative model and does not necessarily consider all variables affecting the system. Variations induced by components and measurement variability are excluded. In particular, the gain and offset values ​​are not measured and are not calibrated to unit values, so their induced errors are excluded. This system is calibrated using precise components that affect the values ​​of contact resistance, leakage current, and bias voltage. Therefore, their variations are excluded, and they can be modeled as fixed values.

[0276] In some embodiments, some of the electronics may be incorporated within an interposer or other interconnection component connected to the sensor. Thus, by pre-connecting the interposer to the sensor, some or all of the electronics are also 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) can be used for other purposes as well. For example, the interposer can be used to store a code that allows tracking of the sensor during the manufacturing phase and / or other life phases. This code can be embodied, for example, in a series of resistors printed on the interposer. This code can be programmed by a laser-cut selection pattern to partially impart the final resistance to or on the printed resistors. These resistors can be read by a transmitter if the transmitter is installed on the interposer via spring contacts or the like.

[0277] In some embodiments, any of these or other components that can be pre-connected to the analyte sensor can be configured such that the connection is maintained over multiple periods during the sensor's lifecycle. In this way, the pre-connection can be maintained over the entire lifecycle of the analyte sensor or over multiple sequences. Therefore, system-level calibration (i.e., the analyte sensor and the pre-connected components of the sensor electronics) performed over the lifecycle of the analyte sensor and / or the lifecycle of the sensor electronics must correlate with changes in the system between one or more phases.

[0278] Components with pre-connected analyte sensors can be packaged together with the analyte sensor in a sterile package used for shipping and storing the analyte sensor. Therefore, in these embodiments, it may be advantageous if the pre-connected components are single-use, disposable components.

[0279] Parameters that may uniquely have a significant impact on analyte sensor sensitivity during the manufacturing phase may include, but are not limited to, the materials 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, the enzyme activity level, and the amount of coating applied. Parameters that may uniquely have a significant impact on analyte sensor sensitivity during the packaging phase may include, but are not limited to, the amount of sterilization applied, the sterilization method, enzyme activity, and packaging materials. Additional parameters that may significantly impact analyte sensor sensitivity during any and all phases may include temperature and humidity, as well as various environmental parameters such as 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 one or more measurements of various parameters that significantly affect the analyte sensor sensitivity during two or more phases of the analyte sensor's lifecycle. Here, an exemplary calibration process 2400 according to some embodiments is described with reference to Figure 11. The calibration process can be performed by the sensor electronics within the analyte monitoring system without user involvement, thereby avoiding the need for external user calibration while 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. Next, in block 2404, the analyte sensor undergoes an initial calibration process together with the pre-connected sensor electronic component(s). This initial calibration process can use any available prior information, including sensor sensitivity information, to obtain a calibration coefficient that can be used to convert sensor data (e.g., in units of current or counts) to estimated analyte values ​​(e.g., in units of analyte concentration).

[0281] Several advantages can arise from performing an initial calibration process after the analyte sensor has been pre-connected to one or more components of the sensor electronics. Measurements can be acquired during the manufacturing process phase and, in later periods, establish reference values ​​for comparison. These reference values ​​can be used by processing algorithms to quantify scale and offset values ​​from known states. In some cases, the reference measurement depends on the sensor characteristics affected by the connection characteristics. This can enable measurements that would not be possible in separable systems. For example, errors that may occur separately in the analyte sensor and sensor electronics can be reduced or eliminated by calibrating them as a single unit. Furthermore, errors that may arise from the act of connecting (and disconnecting) the analyte sensor to the sensor electronics can 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 lifecycle following the manufacturing phase. For example, environmental parameters may be monitored during the sensor packaging phase, the sensor package sterilization phase, the sensor shipping phase, the sensor storage phase, the sensor insertion phase, and / or the sensor use phase.

[0283] Monitoring of environmental parameters can be achieved in several different ways. For example, if an analyte sensor is pre-connected to at least one component of sensor electronics, and that component applies a stimulus signal to the analyte sensor and measures the signal response to the stimulus signal, the impedance value of the analyte sensor can be determined using that signal response. Various techniques for calculating the impedance value of an analyte sensor based on the signal response are described elsewhere herein, such as one or more of the techniques described in U.S. Patent Application No. 14 / 144,343, entitled “Advanced Analyte Sensor Calibration and Error Detection,” published as U.S.20140114156A1, which is incorporated herein by reference in its entirety. The determined impedance can then be compared to a pre-established impedance-to-environmental parameter relationship, such as a pre-established impedance-to-temperature relationship, a pre-established impedance-to-humidity relationship, or a pre-established impedance-to-membrane damage relationship, as described in the aforementioned patent document. In this way, environmental parameters can be monitored.

[0284] In alternative embodiments, 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 sensor is stored when it leaves the factory. Alternatively, monitors such as temperature monitors may be incorporated directly into the sensor electronics themselves. In some cases, the monitor does not need to provide numerical values ​​for the environmental parameters and can simply indicate whether the environmental parameters are outside a specified range in which the sensitivity of the analyte sensor is known to remain relatively stable. In this way, relatively simple environmental monitors can be used.

[0285] Once the manufacturing parameters and / or environmental parameters are obtained, the calibration process 2400 proceeds to block 2408, where the updated calibration coefficients are determined based on the previously established relationship between the environmental and / or manufacturing parameters and the analyte sensor sensitivity. In determining the coefficients to be updated and calibrated, information other than the measured parameters may be taken into consideration. For example, the initial calibration coefficients may also be used. Using the updated calibration coefficients, the analyte sensor can be properly calibrated, and as a result, the sensor data (e.g., in units of current or counts) can be converted into analyte values ​​(in units of analyte concentration).

[0286] The updated calibration coefficient can be determined at any suitable time after the environmental parameters have been acquired. Partially, this will depend on the specific 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 supply (e.g., a battery), the updated calibration coefficient can be determined as soon as the environmental parameters are acquired, for example, while the analyte is in or stored in a sterile package. Alternatively, if such a lossator is unavailable, the environmental parameters may be stored in one of the pre-connected components and transmitted when the rest 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 may be transmitted to the rest of the sensor electronics or to another connected device.

[0287] In some embodiments, the updated calibration parameters can be determined by a processor and associated algorithms not integrated into the sensor electronics. Conversely, the updated calibration parameters can be stored in a pre-connected electronic component and uploaded at a suitable time to a device that communicates with the sensor electronics (e.g., display devices 114, 116, 118, and / or 120 in Figure 1) or to a cloud-based processor (e.g., the cloud-based analyte processor 490 in Figure 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 mentioned above, environmental parameters can be acquired multiple times throughout the various phases of the analyte sensor's lifecycle, and even multiple times during a single phase (e.g., storage). These 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 manufacturing, individual patient factors) to determine the final updated calibration coefficient.

[0289] In some embodiments, instead of, or in addition to, periodically updating calibration coefficients at multiple times throughout various lifecycle 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 coefficient obtained during sensor manufacturing, along with the environmental conditions experienced by the analyte sensor (and, if any, pre-connected electronics) from sensor manufacturing to sensor insertion. In this way, the experience of the analyte sensor over 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, as well as sensor characteristics, such as impedance, individually, a single encoded value or profile can be provided to a calibration algorithm that encapsulates all manufacturing and / or environmental parameters, as well as sensor characteristics.

[0290] Figures 12(a)–12(c) are timetables showing various phases over the lifespan of the analyte sensor. This example shows the manufacturing phase, sterilization phase, shipping / storage phase, and in vivo phase. The temperature and humidity experiences monitored by the sensor over these phases are shown in Figures 12(a) and 12(b), respectively, and Figure 12(c) shows the change in the sensitivity of the analyte sensor determined based on these environmental parameters. The temperature spikes shown in the manufacturing phase occur during the curing of the analyte sensor. It should be noted that sensitivity does not change so 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 changes in sensitivity, and therefore indicate that recalibration may be required when 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 very significant impact on sensitivity.

[0291] It should be noted that all of the above parameters, which significantly affect the sensitivity of the analyte sensor and are monitored at various points in time, can also significantly affect the sensor's baseline signal. Therefore, in addition to monitoring these parameters to calibrate or adjust the sensitivity of the analyte sensor, it is also possible to monitor these parameters to calibrate or adjust the baseline of the analyte sensor. More generally, the monitored parameters can be used to adjust not only the sensitivity and / or baseline, but also any characteristic metric of the analyte sensor. 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 dual-exponential sensitivity model, non-glucose baseline, segmental bias between glucose concentration in local tissue surrounding the sensor and blood glucose, constant baseline, asymptotic increase in baseline magnitude due to membrane degradation, asymptotic increase in baseline magnitude 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 break-in, drift rate of fast electrochemical break-in, initial magnitude of slow electrochemical break-in, drift rate of slow electrochemical break-in, initial magnitude of segmental bias, final magnitude of segmental bias, and drift rate of invisible segmental bias.

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

[0293] For example, Figure 13 shows the sensor output signals acquired from a sensor during each step of the manufacturing process, which include at least one curing step, a membrane coating step in which the sensor is coated within a specific membrane, and a manufacturing calibration measurement step to determine initial or in vitro values ​​such as analyte sensitivity, baseline, interference sensitivity, and impedance value. As shown in the figure, the sensor output signals change between each step. The shape of this signal over all or some of these steps defines the sensor signature, which can be acquired for a great many sensors during the manufacturing phase. By investigating the behavior of these sensors during each subsequent phase, particularly during the use phase, statistically useful correlations can be found between the sensor signature and the sensor behavior. In this way, by measuring the sensor signature of a particular sensor during various steps of the manufacturing phase, it may be possible to predict the behavior of the sensor afterward (e.g., one or more characteristic metrics). For example, it may be possible to obtain a predictive sensitivity profile of sensitivity changes over time for a particular sensor.

[0294] The preceding discussion focuses on the use of manufacturing parameters and / or environmental parameters monitored during various life cycle phases of an analyte sensor to facilitate sensor calibration, but these monitored parameters can also be used for still other purposes. For example, based on the monitored parameters, various actions can be performed by the analyte monitoring system. For example, if one or more of the environmental parameters exceeds a specified threshold for a particular period of time, a message can be generated on a receiver (e.g., the user's mobile communication device), which message notifies the user, for example, that the scheduled end of the sensor life has been advanced, that the calibration quality is below a recommended value, that the confidence level of sensor reliability is below a recommended value, or that the sensor is only suitable for a particular operation mode (e.g., the sensor life, accuracy, outlook, trend, analyte values and alarms are suitable for monitoring patients with type 1 diabetes, but not for monitoring patients with type 2 diabetes, and vice versa), or that the sensor is only suitable for implantation in a specific site such as the abdomen or arm. Alternatively or additionally, other actions that may be performed as a result of monitoring environmental parameters include adjusting various starting parameters of the analyte monitoring system (e.g., requiring a longer period than the normal break-in period of the sensor), switching to an operation mode in which the glucose concentration is only reported as being in a range (e.g., low, medium or high) rather than in an operation mode in which a specific glucose concentration is reported, initiating an in-vivo calibration process, using default calibration values, and using temperature, humidity, and / or complex compensated calibration values.

[0295] The reduction in errors that can be achieved by calibrating a pre-connected system compared to a conventional (unconnected) system is modeled by considering a subset of the variables that affect the system. This model uses only unitless measurements, namely current (counts), and calibrates against a known glucose calibration solution. Variations induced by the components and variability in the measurements are eliminated. In particular, the gain and offset values ​​are not measured and are calibrated against unit values, so their induced errors are eliminated. This system is calibrated using precise components that affect the values ​​of contact resistance, leakage current, and bias voltage. Therefore, variations in these components are eliminated and can be modeled as fixed values.

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

[0297] Sensor manufacturing processes, sensor bench characterization, and in vivo sensor characteristics have all been found to be loosely interconnected. That is, while sensor process parameters are monitored to ensure they are within limits, and each sensor is then evaluated to determine whether it meets predefined criteria, process parameters and in vitro characteristics do not predict in vivo sensor characteristics (e.g., sensitivity) to a very high degree. Typically, in vivo characteristics are less estimated from manufacturing process variables and more estimated from calibration, because in conventional systems, each sensor is typically calibrated every 12 hours using a blood glucose meter.

[0298] When automated calibration techniques are employed, it may become increasingly necessary to rely on sensor characteristics and little on calibration via instruments. Therefore, the mathematical relationships between manufacturing process variables, the sensor's in vitro (or bench) characteristics, and its in vivo characteristics become important. Furthermore, as the number of sensors manufactured increases, the necessary resources and time may not be available to thoroughly test each sensor against acceptance criteria or to estimate their in vivo characteristics. Therefore, mathematical / statistical frameworks can provide alternative methods for relating the sensor manufacturing process to the sensor's in vitro sensitivity and predicted in vivo sensitivity. Ideally, process variables could be set to produce sensors with specific sensitivities.

[0299] There are several sensor process and design parameters that can be adjusted to construct sensors with specific characteristics. These include relative humidity, temperature, curing time, immersion time, thickness of each layer, and the properties / proportions of the raw material. The behavior of sensors in vitro and in vivo depends on these process variables and can be modeled using mathematical and statistical models. A hierarchical model is a type of multi-level statistical model in which different random effects that significantly influence the process and measurement are quantified at multiple levels as conditional probabilities. For example, variability in the process at a particular setpoint can be modeled at level 1 (the highest level), variability in sensor behavior in vitro at level 2, and variability in sensor behavior in vivo at level 3. This model can ultimately be used to estimate variables at different levels (e.g., level 1) using variables from one level (e.g., level 2 or 3) in relation to these levels.

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

[0301] In other words, the characteristics of a sensor are a function of all the sensor's design and process parameters. The overall distribution of sensor characteristics has a mean process setpoint or target with a variance of Σ². Note that non-normal distributions are also possible.

[0302] The vector of sensor characteristics verified on the bench is given by the following equation. Mb=N(Mp,Γ 2 )

[0303] For example, if a batch of 10,000 sensors is manufactured, 100 of them can be sampled to estimate process characteristics. Therefore, the distribution of the benches whose characteristics were verified will have an average target lot size Mp and a variance Γ. 2 It is normalized using [this method]. 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 extracellular properties into in vivo properties. This is also called the in vivo-to-extracellular correlation. A simple example of the function "g" is the constant of proportionality from extracellular to in vivo. In general, the function "g" is an extracellular-to-in vivo transformation involving multiple factors. In matrix form, this can be written as follows:

[0305] Mi = G * Mb,

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

[0307]

number

[0308] Here, the diagonal terms s, d, and b are the sensitivity, drift, and baseline related to the in vivo factors for the extravivo 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 may change over time.

[0309] Once this model is developed, several different applications will emerge 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 stochastic algorithm), thus enabling reduced factory calibration, as described in US20140278189A1, titled "Advanced Calibration for Analyte Sensors," which is incorporated by reference in its entirety. In another application, where large-scale sampling of the manufacturing process is cumbersome and costly, this hierarchical model can be used to estimate process parameters and target sensor characteristics through sampling of multiple lots from in vitro and in vivo. A third application involves tracking field performance and directly correlating that performance with manufacturing. This model can help proactively track process parameters based on field data, allowing for faster corrective actions.

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

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

[0312] Individual users may have stable glucose fluctuation patterns over weeks or months, and these patterns are consistent with the patient's treatment approach, assuming that the patient's underlying physiological function does not change dramatically. As a result, differences observed in the raw statistical data of sensor signals for each sensor (e.g., mean, standard deviation, median, percentile, skewness, etc.) may reveal differences in sensor characteristics such as sensitivity. While sensitivity estimated in this way is not as reliable as sensitivity measured through comparison with accurate baseline glucose measurements, if a sufficiently large dataset is available, the information may be useful in detecting patterns in sensor behavior and can be used to build predictive models of field sensor behavior or to detect unexpected shifts in field sensor behavior.

[0313] For example, when wire obtained from a new wire supplier is introduced into production, it is generally expected that this wire will not have any effect on sensor sensitivity. However, field data shows that across thousands of users, the standard deviation of raw sensor readings is approximately 2% higher for sensors from lots using a new supplier than for each user's previous standard deviation. This pattern could prompt further investigation into the significant impact of wire suppliers, or the data could be incorporated into the factory calibration model. In this way, the sensitivity predicted by the factory calibration algorithm can be adjusted to account for the significant impact of wire suppliers, thereby improving accuracy.

[0314] NMR method for characterizing the Carbosil / PVP ratio in a diffusion-resistant layer solution In yet another variation of the subject described herein, this subject can be used to improve the accuracy of sensors under manufacture, and a method may be employed to characterize the Carbosil / PVP ratio in the diffusion resistance layer of the sensor.

[0315] This diffusion resistance layer is one of the most important layers in the sensor's CGM membrane, providing stable, predictable glucose and oxygen penetration and blocking some interferants. Currently, certain sensors use a mixed system of Carbosil 2090A and PVP (K90). Carbosil is soluble in THF but not in ethanol. However, PVP is soluble in ethanol but not in THF. Therefore, current diffusion resistance layer solutions are prepared by using a THF / ethanol mixed solvent to dissolve both Carbosil and PVP.

[0316] Sensor performance is related to the Carbosil / PVP ratio (for example, a high PVP ratio results in higher sensitivity). In particular, the uniformity of the immersion coating is affected by the Carbosil / PVP ratio. Also, the viscosity of the diffusion resistance layer immersion solution is affected by changes in the Carbosil / PVP ratio. Overall, sensor stability is affected by the Carbosil / PVP ratio.

[0317] To fabricate reproducible sensors, a consistent and accurate Carbosil / PVP ratio in the RL immersion solution is a critical parameter to control. However, to date, no method has been developed to estimate the Carbosil / PVP ratio in the RL solution. Therefore, to improve sensor accuracy and thus enhance the ability to automate, 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 resistance layer solution. In particular, proton NMR techniques can be employed.

[0319] One specific example of a process that can be employed to determine the Carbosil / PVP ratio is described in the following steps.

[0320] 1. Prepare the sample. 1.1 A C2090A / PVPTHF / EtOH solution containing 22% by weight of EtOH and 13.6% by weight of PVP. 1.2 Form a film using RL solution and dry overnight at 50°C until it reaches a certain weight. Remove the solvent. 1.3 Cut out one thin film and dissolve it in DMSO-d6 at a concentration of 10 mg / mL. (20 mg / mL, 50 mg / mL) 2. Perform proton NMR to obtain the FID signal, then perform baseline correction and phase adjustment to obtain the spectrum. 3. Integrate the MDI peak within Carbosil; calculate the integral for each proton. 4. Integrate the H2 peak within the PVP; calculate the integral for each proton. 5. Calculate the molar ratio of Carbosil and PVP. 6. Obtain a calibration curve for the Carbosil / PVP mixture. 7. Calculate the Carbosil / PVP weight% / weight% ratio 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 the RL film (Carbosil / PVP mixture with solvent removed). The Carbosil / PVP ratio was calculated by selecting the MDI peak for Carbosil and the H2 peak for PVP.

[0322] This method was validated by performing an HNMR calibration process. First, RL solutions with different Carbosil / PVP ratios were prepared, as shown in the table in Figure 17, with a predetermined Carbosil / PVP ratio. Then, HNMR was performed using DMSO-d6 as the solvent. Figure 18 shows the obtained HNMR calibration curves.

[0323] Detection of temperature and humidity during storage As described above, impedance measurements of the analyte sensor can be acquired during the shipping and storage phases to monitor the humidity of the sensor pre-connected to the electronics. Furthermore, a temperature sensor within the transmitter can record the temperature, and thus the temperature and humidity sensors can indicate whether the analyte sensor was outside its recommended humidity and temperature range during shipping and storage. Additionally, algorithms can be created to compensate for initial factory calibration parameters based on temperature and humidity conditions, as well as the duration of exposure. (Note that initial factory calibration can be performed on a single sensor using a single bathtub, or on a lot or batch of sensors that can be calibrated simultaneously using a single large bathtub, e.g., 30 sensors).

[0324] In one variation, measuring current alone may be sufficient to indicate humidity or extreme humidity. Several embodiments of the sensor system can be periodically activated and perform measurements to identify when the sensor has a signal to indicate system activation (by hydration after deployment). Also, a fully pre-connected sensor may measure current even when only humidity is present. This can therefore 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's electrodes, which can conduct current when humidity is present. This would allow the transmitter to measure humidity. This tab would be removed before using the transmitter.

[0325] In another variation, the sensor's storage conditions can be determined using a resistor or other material that has a known response to temperature, humidity, or a combination thereof, and generates electrical characteristics (e.g., resistance, current). In addition to the circuit that activates the transmitter when the sensor detects something, the same or another circuit can be placed to be activated whenever the temperature and / or humidity exceed a threshold. Based on the duration of activation and the magnitude of the measured values ​​(reflecting temperature and / or humidity), the system can be adjusted to better predict in vivo performance by inferring changes due to environmental conditions. In one particular implementation, a piece of environmentally sensitive material is placed at both ends of the transmitter electrodes so that the piece can conduct current only under specific environmental conditions. In some cases, this functions as an irreversible circuit or material change that is activated only when a threshold is exceeded, shifting the predicted sensor response to a new performance range or creating an on / off indicator to prevent product use if extreme conditions are reached.

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

[0327] In yet another variation, the calibration parameters used by the calibration algorithm may differ from sensor to sensor based on sensor manufacturing details and other factors. From the transmitter's perspective, the user inserts the sensor and enters the "sensor ID" into the display, and based on this, the display either transmits the actual set of parameters that need to be used, or transmits a code that causes the user to use one of a predefined set of parameters. To achieve this, the transmitter can store multiple sets of parameters. If this set of parameters is enormous, storing multiple copies of those parameters may occupy 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 difference between the default set and the updated set needs to be transmitted. This can be more efficient, as these differences are usually small. This approach also provides the flexibility to change any individual parameters; that is, the set of parameters does not need to be fixed, and those parameters can be changed during the factory calibration process. If the set of parameters is an ordered list, its changes can be specified as a list of paired values ​​such as (parameter number, new value).

[0329] Calibration code encoding In yet another embodiment, a sensor calibration code or some other code assigned to a sensor in the factory can be associated with a customer account in the following way. In this example, it is assumed that the transmitter shipped with the sensor is reusable and is shipped with enough sensors to cover the lifespan of the transmitter (generally determined by the transmitter's battery). For example, a transmitter that is usable for 3 months would require 6 sensors, each lasting 14 days. In such a system, the factory calibration code associated with the sensor can be transmitted to the user's mobile device using the following method.

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

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

[0332] Strengthening Closed-Loop Manufacturing Feedback Processes In another variation, additional information can be used to supplement available information related to the manufacturing process, which is stored by sensors pre-connected to the sensor electronics. For example, a manufacturing process typically involves a series of steps performed at various stations within a factory. In principle, the amount of time an operator needs to perform any given step should be roughly the same as the time required for each part or component being assembled at that station, or for the process being executed. If these time variations are significantly large, it may indicate an incomplete station or process, in which case the operator would need to over-adjust parts and fixtures during assembly, which can highlight areas for process improvement. In some cases, a small device can be placed at each workstation to investigate the time required to perform the activities required at that station. This device may include actuators (e.g., buttons, motion sensors, light sensors) that provide simple, non-invasive means, by which the operator can quickly interact with the device and have a microcontroller within the device record the time spent interacting with each device. The operator would then be instructed to interact with the device each time a process or other task is completed at those stations. Next, this device stores that time, and those times can be output for later analysis.

[0333] Initial calibration In another variation, when ethylene oxide (ETO) sterilization is used (instead of electron beam sterilization), the initial drift profile for some conditions is found to be extremely flat (see the graph in Figure 19, where group 4 (left) is ETO conditions and group 6 (right) is non-sterile conditions, using the same timescale with approximately 12 sensor drift profiles for each group). Therefore, ETO treatment can be used to stabilize sensors against high-humidity storage or shipping.

[0334] In another variation, the sensor can undergo ETO sterilization using a desiccant that is temporarily reusable in the packaging during the ETO process. This desiccant can then be "burned out" after ETO to restore its drying capacity. After ETO sterilization, additional desiccant may be added to the final sensor packaging, and / or the final packaging may employ a moisture-resistant material to minimize humidity. In this way, some sensors can be sterilized using bulk packaging containing the sensor and desiccant.

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

[0336] Electronic hardware correction Factory calibration and correction techniques for continuous analyte monitoring systems typically employ digital technology to store and adjust for variations between sensor batches. In some embodiments, it is useful to modify the sensor signal using analog electronic circuits. Using resistors of known values ​​helps to modify the analog signal, correcting the amount of current or the measured voltage. In one example, this resistor can be combined as part of a gain circuit with an operational amplifier to adjust the gain of the output signal. This resistor can be selected from a range of known resistance values ​​or configured through a process (e.g., a laser-trimmed resistor).

[0337] Coefficients that affect sensitivity and impedance A set of non-limiting coefficients found to affect the impedance and / or sensitivity of pre-connected analyte sensors is shown in the table in Figure [IFD1675]. These coefficients are noted for selecting manufacturing and storage conditions, but can be measured for individual sensors and / or sensor lots and correlated with sensitivity and impedance measurements at various points in the sensor's life. In this way, the values ​​of these coefficients, individually and / or combined, can be used to determine the relationship between measured impedance and sensitivity at any point in the sensor's life, thereby adjusting the calibration coefficient used to calibrate the sensor at any point in the sensor's operational life.

[0338] Periodic updating of gradient parameters Currently, the "calibration check" procedure is performed within the factory, where sensors undergo in vitro calibration and gradient values ​​are obtained. These values ​​are then used to sow seeds for a joint stochastic algorithm using initial and final sensitivity values, employing a linear transformation. That is, Average initial gradient = calcheck * mstart + bstart Mean end-to-end gradient = calcheck * mfinal + bfinal

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

[0340] Mean end-time 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 end-of-life gradient include the mean sensor current, the root mean square of the sensor current, and the sensor current acquired at a specified percentile within the distribution of sensor current values. A similar approach can be used to adjust the mean of the initial sensitivity. By using a combination of factory measurements and real-time measurements in this way, the system performance can be improved because the linear combination makes it possible to correlate factory information with in vivo sensor measurements. The parameters describing the following formulas may be updated periodically (e.g., daily) during sensor wear to ensure that the end-of-life gradient estimate is updated regularly.

[0342] Retrospective calibration of CGM signals Retrospective calibration of CGM signals with or without SMBG 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. Retrospective calibration presents an opportunity to eliminate certain artifacts that corrupt real-time CGM signals, such as time delays, intermittent faults, 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 most effectively after removing time delays from the signal and smoothing it.

[0343] It is generally believed that blood glucose levels can be predicted fairly reasonably up to approximately 30 minutes in advance. The accuracy of the prediction signal decreases when the prediction range exceeds 30-40 minutes. Therefore, any signal artifacts or data gaps shorter than 30-40 minutes can be replaced with the prediction signal without losing important information necessary for clinical use. Furthermore, assuming retrospective use, any errors in the predicted blood glucose levels can be eliminated by data analysis. Several methods that can achieve this are as follows:

[0344] 1. Identify the region(s) of artifacts within the signal. 2. Replace artifact signals with predicted blood glucose levels. 3. Evaluate the difference between the predicted blood glucose level at the end and initial time points of the signal after artifacts. 4. This error is fed back into the prediction to correct the predicted signal. For example, if there is a 30 mg / dL error between the end-of-term predicted CGM and the initial point in time of the CGM after the artifact, this error can be evenly distributed (or weighted averaged) over the duration of the predicted signal. In this way, the predicted signal is corrected, resulting in a smooth correction of the artifact without discontinuity.

[0345] In another embodiment, predictions can be used bidirectionally to increase the duration of artifacts that can be corrected. How longer-duration artifacts can be corrected is described below.

[0346] 1. Identify the start and end points of artifacts that need to be removed / replaced. 2. Create two CGM time-series signals: a first time-series that is the normal signal (time progressing in the forward direction from the start to the end of the session), and a second time-series that is in the reverse direction (from the end to the start of the session). 3. Using that prediction, we swap both forward and reverse time series artifacts, meaning each artifact will have two possible swaps: one based on the forward time series signal and one based on the reverse time series signal. 4. Select the midpoint between the two swapped 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 coincide or differ at the midpoint. 5. Assuming that the prediction is reasonably accurate in the short term, the best estimate of blood glucose levels at the midpoint is the average of the values ​​from two time series. 6. Here, the error between the mean value from the time series and the actual median value can be fed back into the exchange of predicted artifacts to correct those values. 7. The correction can be weighted according to the quality of the signal before and after the artifact.

[0347] This approach to artifact correction makes the signal more reliable and allows for correction / removal, and can increase the duration of artifacts.

[0348] Replacement sensor Sensors can sometimes fail before their shelf life (e.g., 7 days). In some cases, sensor electronics (e.g., transmitters) can be packaged in a single box, supplying a 3-month supply of sensors (e.g., 6 packs). In one variation, the transmitter can then be coded using a single common sensor code. If one of the sensors in the sensor box fails and a replacement sensor needs to be sent to the customer, the transmitter can send the sensor code to a dedicated app on the customer's mobile device. The customer can then request a replacement sensor through the app. The app then relays the sensor code to a manufacturer, who can send the customer the appropriate sensor with the correct code matching the transmitter that was included in the original sensor box.

[0349] configurable calibration frequencies In one variation, the frequency with which a transmitter issues calibration requests to a dedicated app on a customer's mobile device can be configurable. For example, a transmitter may have a default calibration frequency (e.g., once per day, twice per day, then once per day thereafter) if it has not been supplied 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. Furthermore, the calibration frequency may also be based on the type of app running on the mobile device. The transmitter may also store different default calibration frequencies based on the type of app being used.

[0350] Transfer of 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 intervention could operate as follows: This method uses memory embedded in the sensor to transfer calibration coefficients and / or other information to the transmitter. Information that may be transferred may include, for example, lot number, expiration date, and authentication information that can ensure the manufacturer is using an original sensor. Such authentication information may operate according to cryptographic algorithms such as hashing (e.g., SHA-256) and other algorithms, and / or according to standards such as federal information processing standards.

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

[0352] Without loss of generality, this technique will be described as applicable to a sensor using a low bias voltage (e.g., less than 1 volt) and having at least two electrical connectors (e.g., a reference electrode and a working electrode). The sensor is assumed to consist of a memory element connected by wires or containing stored information. This memory element uses a single wire for power and signaling and is connected to the working electrode of the sensor. A ground connection is made to the reference electrode of the sensor.

[0353] To initiate a session, the transmitter periodically checks for the presence of a new sensor by waking from sleep mode, activating the bias voltage, and searching for a predetermined response from the sensor. If this response indicates the presence of a new sensor, the transmitter will transition to operating mode as described below. If this response is not as expected and indicates the absence of a sensor, the transmitter will return to sleep mode for a predetermined period of time.

[0354] If a given signal indicates the presence of a new sensor, the transmitter attempts to retrieve calibration coefficients and other information from the memory device. The memory device is configured to respond to a signal pulse only if the signal pulse exceeds a given voltage level and is above the sensor's nominal operating bias voltage. The memory device uses the same pin for both power supply and communication. The memory device can operate in active mode, in which case it 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 that pin connected to the memory element at high impedance. Alternatively, the memory device can operate in passive mode, allowing the transmitter to be subjected to high or low loads, and as a result, the transmitter can be used as a master clock to signal the appropriate information back to the transmitter.

[0355] The time required to communicate relevant calibration coefficients and other arbitrary information (such as expiration date and serial number) is generally short compared to the sensor's lifespan, and the high voltage used during such short-term communication will not damage any enzymes used within the sensor. Therefore, short-term overvoltage will not affect the sensor's long-term operation and may even serve as an electrochemical break-in period for the sensor. Once the memory device has passed the required information and the transmitter has decreased to the nominal sensor bias voltage, the memory device may be designed to either enter an extremely high impedance state and inadvertently increase the observed signal current from the sensor, or to 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's lifespan to a memory element, which will then place an indicator in its internal memory that the sensor has expired. This prevents accidental reuse of the sensor because, even if the sensor is disconnected and reconnected, the memory element will inform the transmitter that the sensor has already been used. At this point, the duration of the communication session and the applied voltage are not as important because it does not matter whether the enzyme is damaged, since the sensor has reached the end of its lifespan.

[0357] In an alternative embodiment, upon initiating a session, the transmitter simply sends a connection response command signal regarding the presence of the memory device as soon as it wakes up, and later, after successful transfer of calibration data, it can verify that the sensor is functioning correctly.

[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 calibration can lead to misapplication because the EGV change may be larger than the natural blood glucose change when calibration is performed. When such EGV changes are input into an artificial pancreas algorithm, the algorithm may end up inaccurately predicting the EGV. Current artificial pancreas algorithms accept calibration updates and update the EGV after the update is complete.

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

[0360] Using biometric data to prevent incorrect input of calibration data Manual entry of calibration data or other reference information is prone to errors. One way to detect and prevent the use of incorrectly entered data may be to use the user's biometric data. Such information can be available in 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 app finds that the entered calibration data or other data does not match or conform to the biometric data, it can present an error message or take other appropriate action. As a simple example, if a 35-year-old man is found to have a heart rate of 170 bpm from a biometric sensor, and his 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 sends either parameters to the transmitter, or identifiers corresponding to a predefined set of parameters already stored in the transmitter or otherwise available. In either case, the transmitter may need to store multiple sets of parameters. However, if one or more sets of parameters are enormous, the available memory in the transmitter may not be sufficient to store all the necessary parameters.

[0362] In one modification, the transmitter can store a limited number (e.g., 1) of parameter sets that can function as a set of default parameters. Then, if a newer parameter set is available, only the difference between the values ​​in the default parameter set and the values ​​in the new parameter set needs to be stored in the transmitter. Since these differences are usually small, this can be a more efficient way to store data. This also provides flexibility to change any parameters, as parameters established during factory calibration do not need to remain fixed. In one embodiment, the default parameter set can be provided as an ordered list, and their changes can be provided as a list of pairs of values ​​specifying the parameter number and the difference from the default value.

[0363] Example Sensor System Configuration Embodiments of the present invention are described above and below with reference to flowcharts of methods, apparatus, and computer program products. It will be understood that each block in a flowchart, and combinations of blocks within a flowchart, 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, or microprocessor) within a sensor electronics system to manufacture a machine, and as a result, the instructions executed on the computer or other programmable data processing device create instructions for implementing the functions specified in one or more blocks of the flowchart. These computer program instructions can also be stored in computer-readable memory that can directly direct the computer or other programmable data processing device to function in a particular way, and as a result, the instructions stored in computer-readable memory generate a manufactured article containing instructions for implementing the functions specified in one or more blocks of the flowchart. Computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operable steps executed on the computer or other programmable device can generate a computer-implemented process, and as a result, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more blocks of the flowchart presented herein.

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

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

[0366] Although this disclosure has been illustrated and described in detail in the drawings and the above description, such illustrations and descriptions are to be considered descriptive or illustrative, not restrictive. This disclosure is not limited to the embodiments disclosed. Modifications of the embodiments disclosed can be understood and achieved by those skilled in the art who practice the disclosure of the claims, based on the consideration of the drawings, the disclosure and the appended claims.

[0367] All references listed herein are incorporated herein in their entirety by reference. This Specification is intended to supersede and / or take precedence over any publications and patents or patent applications incorporated by reference to the extent that they conflict with the disclosures contained herein.

[0368] Unless otherwise explicitly defined, all terms (including technical and scientific terms) have their ordinary and customary meanings as indicated to those skilled in the art, and are not 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 aspect of the Disclosure should not be construed as implying that the term is being redefined herein if it is limited to including any particular characteristic of the feature or aspect of the Disclosure to which it relates. In particular in the appended claims, terms and phrases used in this application, and their variations thereof, should be construed as non-restrictive, as opposed to restrictive, unless otherwise explicitly stated. In the examples above, the term “including” should be interpreted as meaning “including without limitation,” “listed but not limited to,” etc., and the term “equipped with,” when used herein, is synonymous with “including,” “containing,” or “characterizing,” and is comprehensive or non-restrictive, without excluding additional unlisted elements or method steps, the term “having” should be interpreted as “having at least,” the term “including” should be interpreted as “listed but not limited to,” and the term “examples” is used to provide illustrative examples of matters in consideration, rather than a comprehensive or restrictive list of such matters, “known,” “common,” Adjectives such as “standard” and similar terms should not be interpreted as limiting the matters described to those available during a given period or at a given point in time, but rather as encompassing known, ordinary, or standard techniques that may be available or known now or at any time in the future; and the use of terms such as “preferred,” “desired,” “desired,” or “coveted” and similar terms should not be understood as implying that certain features are critical, essential, or even important to the structure or function of the invention, but rather should simply be intended to highlight alternative or additional features that may or may not be utilized in particular embodiments of the invention.Similarly, a group of items connected by the conjunction "and" should not be interpreted as requiring each item to exist within the group; rather, unless otherwise specified, it should be interpreted as "and / or." Likewise, a group of items connected by the conjunction "or" should not be interpreted as requiring mutual exclusivity between the items; rather, unless otherwise specified, it should be interpreted as "and / or."

[0369] If a range of values ​​is provided, it is understood that the upper and lower limits, as well as each intermediate value between the upper and lower limits of that range, are included within the embodiment.

[0370] With respect to substantially any plural and / or singular terms herein, a person skilled in the art can convert from plural to singular and / or singular to plural as appropriate to the context and / or use. Various singular / plural substitutions may be explicitly stated herein for clarity. The indefinite articles “a” or “an” do not exclude the plural. A single processor or other unit may perform the functions of several matters described in the claims. The mere fact that certain criteria are described in separate claims that differ from each other does not imply that combinations of these criteria cannot be used for benefit. The reference numerals in the claims should not be construed as limiting the scope.

[0371] If a particular number is intended in the description of an introduced patent claim, such intention is clearly stated in that claim, and if no such statement is present, such intention is not present, as will be understood by those skilled in the art. For example, to aid understanding, the following appended claims may include the use of the introductory phrases “at least one” and “one or more” to introduce the description of a patent claim. However, the use of such phrases should not be interpreted as implying that the introduction of the description of a patent claim by the indefinite article “a” or “an” implies that any particular claim encompassing such introduced description is limited to embodiments containing only one such description (for example, “a” and / or “an” should typically be interpreted as meaning “at least one” or “one or more”), and the same applies to the use of definite articles used to introduce the description of a patent claim. In addition, even when a specific number of descriptions in an introduced patent claim is explicitly stated, a person skilled in the art will recognize that such a statement should typically be interpreted as meaning at least that number (for example, the mere statement “two descriptions” without other modifiers typically means at least two descriptions, or two or more descriptions). Furthermore, when a conventional expression similar to “at least one of A, B, and C, etc.” is used, such a structure is generally intended to mean that a person skilled in the art will understand the conventional expression (for example, “a system having at least one of A, B, and C” includes, but is not limited to, A alone, B alone, C alone, A and B, A and C, B and C, and / or a system having A, B, and C, etc.).Where a conventional expression similar to “at least one of A, B, or C, etc.” is used, such a structure is generally intended to mean that a person skilled in the art will understand the conventional expression (for example, “a system having at least one of A, B, or C” includes, but is not limited to, A alone, B alone, C alone, A and B, A and C, B and C, and / or a system having A, B, and C, etc.). A person skilled in the art will further understand that substantially any disjunct word and / or phrase indicating two or more alternative terms should be understood, whether in this specification, claims, or drawings, to contemplate the possibility of including one of the terms, either of the terms, or both of the terms. For example, the phrase “A or B” is understood to include the possibilities of “A” or “B” or “A and B”.

[0372] All figures used herein to express quantities of components, reaction conditions, etc., should be understood to be modified in all cases by the term “approximately.” Therefore, unless otherwise indicated, numerical parameters described herein are approximations that may vary depending on the desired properties to be obtained. At a minimum, and not as an attempt to limit the application of the equivalence principle of any claims in any application claiming priority to this application, each numerical parameter should be interpreted with regard to a significant number of digits and ordinary rounding methods.

[0373] Furthermore, although the above has been described in some detail as examples and embodiments for the purpose of clarity and understanding, it will be apparent to those skilled in the art that certain changes and modifications may be made. Therefore, 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 encompassing all modifications and substitutes that fall within the true scope and spirit of the invention. [Explanation of symbols]

[0374] 100 Sensor Systems 101 Analytical Sensor System 102 Drug delivery pump 104 Glucose meter 112 Sensor Electronics 114 Display Devices 116 Display Devices 118 Display Devices 138 Analytical Sensor 205 Application-Specific Integrated Circuits (ASICs) 210 potentiostat 211 First input port 212 data lines 214 Processor Modules 216 Program Memory 218 memory 220 data storage memory 222 User Interface 224 User Buttons 226 Liquid Crystal Display (LCD) 228 Vibrator 230 Audio transducers (e.g., speakers) 232 Telemetry Module 234 batteries 237 Second input port 238 communication ports 239 output ports 400 sensors 402 Sensor Interposer 404 circuit board 406 First Contact Point 408 The second point of contact 409 Network 410 External Contacts 412 External Contacts 490 Cloud-based Analytical Processor 500 Electronics Units 600 Skin Sensor Assembly 5000 Systems 5002 Test Station 5004 Calibration Station 5006 Receptacle 5008 Contact 5010 Contact 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 Analytical Sensor 5604 Sensor Interconnection Module 5608 Measurement 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 error 5650 Potential error 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-calibrating an analyte sensor for analyzing glucose contained in a biological sample derived from a user's body or tissue, At least by applying a bias voltage to the analyte sensor, a first sensor characteristic reflecting the initial sensor sensitivity is determined during a first sensor life phase associated with the manufacture of the analyte sensor, During the second sensor life phase that occurs after the first sensor life phase, a second sensor characteristic that reflects the calibrated sensor sensitivity is determined. During the first and second sensor life phases, a change to the first sensor characteristics is determined based on one or more manufacturing parameters and / or environmental parameters that affect the sensitivity of the analyte sensor, in response to one or more of the manufacturing parameters and / or environmental parameters exceeding or falling below a predetermined threshold. The analyte sensor is calibrated based at least in part on the determined change to the first sensor characteristics, Methods that include...

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

3. The method described above is The method according to claim 1, further comprising storing information associated with the first sensor life phase and the second sensor life phase, and determining the changes to the first sensor characteristics based at least on the stored information.

4. The method according to claim 2, wherein the one or more manufacturing parameters include process parameters, the process parameters include temperature, humidity, curing time, and immersion time.

5. The method according to claim 2, wherein the one or more manufacturing parameters include design parameters, the design parameters include the thickness of the analyte sensor membrane and the properties of the raw material.

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

7. The method according to claim 6, wherein the remotely stored sensor performance data relates to a history of the exposure of the analyte sensor to manufacturing conditions and / or environmental conditions relating to one or more of the manufacturing and / or environmental parameters that are most similar to the manufacturing and / or environmental parameters.

8. A method for self-calibrating an analyte sensor system, which includes an analyte sensor operably coupled to sensor electronics for analyzing glucose contained in a biological sample derived from a user's body or tissue, To generate sensor data associated with the change in the sensor sensitivity of the aforementioned analyte sensor, The method involves storing information describing a plurality of life phases of the analyte sensor and a plurality of times corresponding to the plurality of life phases, wherein the storage is characterized in that the plurality of life phases include a first life phase associated with the manufacture of the analyte sensor and a second life phase occurring after the first life phase. Updating the initial calibration coefficient of the analyte sensor system at a plurality of time periods, at least in part, based on the relationship between one or more manufacturing parameters and / or environmental parameters monitored during one or more of the plurality of life phases and the sensor sensitivity of the analyte sensor, wherein the updating is characterized by determining a change to a first sensor characteristic in response to one or more of the manufacturing parameters and / or environmental parameters affecting the sensitivity of the analyte sensor and one or more of the manufacturing parameters and / or environmental parameters exceeding or falling below a predetermined threshold. The analyte sensor automatically calibrates the analyte sensor included in the analyte sensor system based at least partially on the updated calibration coefficient. Methods that include...

9. The method according to claim 8, further comprising converting the sensor data into analyte concentration values ​​based at least in part on the updated calibration coefficient.

10. The method according to 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 updating the initial calibration coefficient of the analyte sensor system includes determining an adaptive calibration value that is at least in part based on the history of exposure of the analyte sensor to manufacturing and environmental conditions during one or more of the plurality of life phases of the analyte sensor.

12. The method according to claim 8, wherein the one or more manufacturing parameters include process parameters, the process parameters include temperature, humidity, curing time, and immersion time.

13. The method according to claim 8, wherein the one or more manufacturing parameters include design parameters, the design parameters include the thickness of the analyte sensor membrane and the properties of the raw material.

14. The method according to claim 8, further comprising receiving remotely stored sensor performance data in order to update the initial calibration coefficient.

15. The method according to claim 14, wherein the received remotely stored sensor performance data relates to a history of exposure of the analyte sensor to manufacturing conditions and / or environmental conditions relating to manufacturing conditions and / or environmental conditions that are most similar to one or more of the monitored manufacturing conditions and / or environmental conditions.

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