Systems and methods for factory or incomplete calibration of indwelling sensors based on sensitivity profiles

Abstract

To provide a "factory-calibrated" sensor.SOLUTION: Systems and methods are disclosed which provide a "factory-calibrated" sensor. In doing so, the systems and methods include predictive prospective modeling of sensor behavior, and also include predictive modeling of physiological functions. With these two correction factors, a consistent determination of sensitivity can be achieved, thus achieving factory calibration.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] INCORPORATION-BY-REFERENCE TO RELATED APPLICATIONS Any and all priority claims identified in the Application Data Sheet, or any corrections thereto, are incorporated herein by reference under 37 CFR 1.57. This application claims the benefit of U.S. Provisional Application No. 62 / 272,975, filed December 30, 2016. The foregoing application is incorporated herein by reference in its entirety and hereby expressly made a part hereof.

[0002] A system and method for processing sensor data from a continuous analyte sensor and for factory calibration of the sensor. [Background technology]

[0003] Diabetes mellitus is a disease in which the pancreas cannot make enough insulin (Type 1, or insulin-dependent) and / or insulin is ineffective (Type 2, or non-insulin-dependent). In the diabetic state, the victim suffers from hyperglycemia, which can lead to a number of physiological disorders associated with deterioration of small blood vessels, such as kidney failure, skin ulcers, or bleeding into the vitreous humor of the eye. Hypoglycemic reactions (low blood sugar) can be precipitated by inadvertent overdosing of insulin or after normal administration of insulin or glucose-lowering drugs accompanied by abnormal exercise or inadequate food intake.

[0004] Traditionally, people with diabetes carry self-monitoring blood glucose (SMBG) monitors, which typically require an uncomfortable finger-prick method. Due to the lack of comfort and convenience, people with diabetes typically measure their glucose levels only two to four times per day. Unfortunately, these time intervals are spread so far apart that people with diabetes may not know about their hyperglycemic or hypoglycemic condition until it is too late, sometimes resulting in dangerous side effects. Glucose levels can alternatively be continuously monitored by a sensor system including an on-skin sensor assembly. The sensor system may have a wireless transmitter that transmits measurement data to a receiver, which can process and display information based on the measurements.

[0005] Various glucose sensors have been developed to continuously measure glucose levels. Many implantable glucose sensors suffer from internal complications and provide only short-term and inaccurate detection of blood glucose. Similarly, transcutaneous sensors have faced problems in accurately detecting and reporting continuous glucose levels over long periods of time.

[0006] In a continuous glucose monitor (CGM), after the sensor is implanted, it is calibrated, after which it provides substantially continuous sensor data to the sensor electronics. The sensor electronics convert the sensor data, which can continuously provide an estimated analyte value to the user. As used herein, the terms "substantially continuous," "continuously," and the like refer to a data stream of individual measurements taken at spaced intervals, which may range from less than one second up to, for example, one, two, or five minutes or more. As the sensor electronics continue to receive sensor data, the sensor may be recalibrated from time to time to account for possible changes (drift) in sensor sensitivity and / or baseline. Sensor sensitivity may refer to the amount of current generated in the sensor by a given amount of measured analyte. Sensor baseline refers to the signal output by the sensor when no analyte is detected. Over time, sensitivity and baseline change due to various factors, including cellular attack or migration to the sensor, which may affect the ability of analyte to reach the sensor.

[0007] One of the major challenges facing CGM is the need to calibrate CGM sensors multiple times daily due to sensor errors caused by changes in membrane kinetics, electrochemistry, and physiology. While many sensors only require calibration twice daily, there is increasing pressure from users for "factory-calibrated" sensors. Factory calibration means that the sensor is calibrated "in" the factory and does not require external user calibration when the device is in use.

[0008] This Background is provided to introduce a brief context for the Summary and Detailed Description that follow. It is not intended as an aid in determining the scope of the claimed subject matter, nor is it to be construed as limiting the claimed subject matter to implementations that solve any or all of the disadvantages or problems discussed above. Summary of the Invention

[0009] Systems and methods according to this principle fulfill the above-mentioned needs in several ways. Specifically, and in one implementation, the present systems and methods provide "factory calibrated" sensors. To this end, the present systems and methods include predictive forward modeling of sensor behavior and also include predictive modeling of physiology. These two correction factors allow for consistent determination of sensitivity, thereby achieving factory calibration. [Means for solving the problem]

[0010] Without wishing to be bound by theory, it is believed that two major challenges have been met in systems and methods according to the present principles in achieving factory calibration for continuous monitoring of an analyte, e.g., glucose. The first challenge is that the sensitivity and background signal of a glucose sensor change as a function of time once the sensor is immersed in an electrolyte. To a large extent, this time-dependent behavior is repeatable across sensors and can be described by a mathematical model according to the present principles defined by a small number of parameters. For example, based on the predicted sensitivity and baseline signal in an implementation according to the present principles, the glucose sensor signal can be calibrated to estimate blood glucose concentration in real time.

[0011] Second, considerable variability exists between glucose sensors in their inherent characteristics and / or how they are characterized at the bench, resulting in differences in the in vivo time course of sensitivity and background signal for each sensor. Tests called "calibration checks" have been used to determine initial or in vitro values ​​of sensitivity and have been found to be reliable predictors of these in vivo characteristics. Systems and methods according to the present principles have established a way to utilize calibration check sensitivity information to adjust mathematical models and provide a fit for individual sensors.

[0012] The factory calibration workflow, when combined with an appropriate algorithm, such as that described in US PGP 2014 / 0278189, owned by the present applicant and incorporated herein by reference in its entirety, attempts to parameterize a number of sensor performance metrics, such as initial / final sensitivity, drift performance, baseline shift, etc., and compartmental effects, in order to prospectively model future performance. In one particular implementation, 14 parameters are identified and used.

[0013] In accordance with the present principles, the factory calibration workflow in one implementation includes two models that can be used together or separately: a sensitivity profile model and a baseline model. There are two exemplary versions of the sensitivity model (e.g., both a single-parameter exponential model and a double-parameter exponential model) and one exemplary version of the baseline model. These two models have several parameter inputs, such as initial sensitivity, final sensitivity, exponential drift rate, drift rate due to membrane degradation, drift rate due to electrochemical break-in, initial and final amounts of compartmental bias, and drift rate of vanishing compartmental bias. Overall, most of these parameters may be determined based on the sensor / membrane calibration, and data may be culled largely from calibration check data and data from long-term drift tests performed on a subset of sensors from a manufactured lot.

[0014] Without limiting the scope of the present embodiments as expressed by the following claims, salient features of systems and methods in accordance with the present principles will now be briefly discussed. After considering this discussion, and particularly after reading the section entitled "Detailed Description of the Invention," one will understand how the features of the present embodiments provide the advantages described herein.

[0015] In a first aspect, there is provided a method of calibrating an analyte concentration sensor, the sensors being part of a manufactured lot of sensors, the lot being of a given type of sensor, one or more operating parameters of the sensors of that type in the lot being determined, the determination of the operating parameters being based on retrospective data, the operating parameters corresponding to at least the sensitivity of the sensors, the operating parameters representing in vivo values, the method comprising: a. measuring one or more long-term drift characteristics of the sensitivity of a subset of sensors in the lot; b. measuring values ​​of initial measurable parameters for the target sensors; and c. calibrating the target sensors. a. relating a subset of at least one or more determined in vivo operating parameters to the measurements of the initial measurable parameters by a first set of coefficients; b. relating a subset of at least one or more determined in vivo operating parameters to the measurements of the initial measurable parameters by a second set of coefficients; and c. using the first and second sets of coefficients to prospectively determine an estimate of at least a final in vivo sensitivity of the target sensor, wherein an in vivo sensitivity value for the target sensor can be estimated given the measurements of the initial measurable parameters of the target sensor.

[0016] Implementations of embodiments may include one or more of the following.

[0017] Measuring one or more long-term drift characteristics of the sensor subset sensitivities in the lot includes measuring at least two long-term drift characteristics for the sensors in the lot, and the long-term drift characteristics may include an initial long-term drift test sensitivity and a final long-term drift test sensitivity. Determining the at least two drift characteristics of the sensors may further include determining a third drift characteristic, which is a long-term drift test rate of change sensitivity. Determining the at least two drift characteristics of the sensors may further include determining the at least two long-term drift characteristics by placing the sensors in a solution containing a known concentration of analyte for a period of time. The drift characteristics may be characterized by a sensitivity model defined by a set of sensitivity parameters. The sensitivity model may include an exponential function, such as a single exponential function or a double exponential function. The set of sensitivity parameters may include m0 or m F , or both of these, m R The step of determining a predicted final in vivo sensitivity of the target sensor using the first and second sets of coefficients may further include determining a predicted initial in vivo sensitivity of the target sensor using the first and second sets of coefficients. The initial measurable parameter may be sensitivity. The initial measurable parameter may be calibration check sensitivity.

[0018] Determining the calibration check sensitivity of the sensor may further include: a. measuring the output signal of the analyte sensor at multiple analyte concentration values; and b. performing a linear regression procedure using the measured output signal and the measured analyte concentrations. The initial measurable parameter may be sensor film thickness. Determining the one or more operating parameters may include determining in vivo operating parameters using patient data. Determining the one or more operating parameters may include determining one or more operating parameters corresponding to a baseline signal and / or one or more operating parameters corresponding to sensitivity. The one or more operating parameters corresponding to sensitivity may include an operating parameter corresponding to a final value of sensitivity. The one or more operating parameters corresponding to sensitivity may include an operating parameter corresponding to an initial value of sensitivity. The set of sensitivity parameters and / or the set of baseline signal parameters may be subjected to a parameter fit to determine a best-fit parameter set by minimizing a cost function. The cost function may be an absolute relative difference or an average absolute relative difference. The parameter fits may be unconstrained or constrained, where, for example, the constraints are based on the absolute value of the difference between each parameter and the initial value plus the absolute relative difference, or the constraints are based on the absolute value of the difference squared. The set of sensitivity parameters is defined as m0 and m F may include m R The set of baseline parameters may have a number of elements between 5 and 15. The set of baseline parameters may include operational parameters that can be predicted by bench testing or manufacturing parameters. The parameter fitting may include concatenating the sensitivity parameters and the baseline parameters into a single vector. The operational parameters may further correspond to a baseline model defined by the set of baseline parameters. The method may further include, when the target sensor or sensor lot undergoes the step of determining values ​​of the initial measurable parameters, performing a step of correcting the sensor calibration if the initial measurable parameters differ from the predetermined value by more than a predetermined threshold, or if a parameter derived from the initial measurable parameters differs from the predetermined value by more than a predetermined threshold.

[0019] The initial measurable parameter may be the sensitivity, and the correction may be achieved by modifying the calibration to obtain m0 and m F A line on a graph of initial calibration check sensitivity versus m0 and m F and the initial calibration check sensitivity so that it intersects with the line determined for the previous calculation at a point where the initial calibration check sensitivity is zero.

[0020] The initial measurable parameter may be the sensitivity, and the correction may be achieved by modifying the calibration to obtain m0 and m F A line on a graph of initial calibration check sensitivity versus m0 and m F and the initial calibration check sensitivity is modified to match the line determined for the previous calculation with the lines m0 and m F may include crossing at points where the values ​​of

[0021] In a second aspect, a method of calibrating an analyte concentration sensor is provided, the sensor being part of a manufactured lot of sensors of a given type, the method comprising: a. determining, receiving, or measuring an in vitro sensitivity of a first sensor; b. determining, receiving, or measuring at least one drift characteristic for a subset of sensors of the lot, the drift characteristic including at least a final drift test sensitivity, the determined characteristic being statistically representative of the sensors in the lot; c. determining a first set of coefficients that describe a relationship between the initial in vitro sensitivity and the drift characteristic of at least the first sensor; and d. .The method may include at least the steps of: determining a second set of coefficients that describe the relationship between the retrospective in vivo sensitivity, the first set of determined coefficients, and the initial in vitro sensitivity of the first sensor; e. calculating a predicted in vivo sensitivity of the second sensor using the first or second set of determined coefficients, or both, given the measured initial in vitro sensitivity of the second sensor; and f. storing the calculated predicted in vivo sensitivity of the second sensor for later transmission to sensor electronics associated with the second sensor or transmitting the calculated predicted in vivo sensitivity of the second sensor to sensor electronics associated with the second sensor.

[0022] Implementations of embodiments may include one or more of the following.

[0023] The retrospective in vivo sensitivity may include at least the final in vivo sensitivity. The retrospective in vivo sensitivity may further include the initial in vivo sensitivity. The retrospective in vivo sensitivity may be determined using linear regression of prior data.

[0024] In a third aspect, a sensor electronics device is provided, the sensor electronics device including: a. a processor; b. a first input port configured to receive data from a sensor; c. a second input port configured to receive calibration data, the calibration data corresponding to at least a sensor sensitivity; and d. an output port configured to transmit sensor data to a mobile device.

[0025] Implementations of embodiments may include one or more of the following.

[0026] The device may be a transmitter. The device may be mechanically configured to physically couple with the sensor. The output port may be configured for wired communication. The first input port may include at least two electrode contacts. The second input port and the output port may correspond to a common communication port. The second input port may be configured to receive a code, the code configured to provide input calibration data to the sensor electronics device and / or the mobile device. The second input port may be configured to receive the code via user input, network communication, NFC, RFID, barcode, mechanical means, or optical means. The second input port may be configured to receive the code via user input, the sensor electronics device may be configured to receive the user input from a mobile device. The sensor electronics device may further include a memory for storing a lookup table, the second input port may be configured to receive a code convertible to at least a sensor sensitivity using the lookup table stored in the memory, the conversion occurring in the sensor electronics device or the mobile device. The code may be further convertible to at least a sensor baseline signal using the lookup table stored in the memory.

[0027] In a fourth aspect, a kit for monitoring analyte concentrations is provided, the kit including: a. the sensor electronics device described above; b. a sensor, wherein the sensor electronics device is mechanically configured to physically couple to the sensor; and c. a calibration indicator, wherein the calibration indicator is configured to provide input calibration data to the sensor electronics device and / or a mobile device, the calibration data can be used to calibrate the sensor such that the mobile device is configured to present the sensor measurements in clinical units.

[0028] In a fifth aspect, a kit for monitoring analyte concentrations is provided, the kit including: a. a sensor, wherein the sensor is mechanically configured to physically couple to sensor electronics; and b. a calibration indicator, wherein the calibration indicator is configured to provide calibration data for input to a sensor electronics device and / or a mobile device, the calibration data can be used to calibrate the sensor such that the mobile device is configured to present sensor measurements in clinical units.

[0029] In a sixth aspect, a system for providing calibrated sensors is provided, wherein a lot of sensors of a given type have been manufactured and one or more operating parameters of the sensors of that type in the lot have been determined, wherein the determination of the operating parameters is based on retrospective data, and wherein the operating parameters correspond to statistical representations of the sensitivities of at least the sensors in the lot, and wherein the operating parameters represent in vivo values, the system comprising: a. a first device for manufacturing the lot of analyte concentration sensors; and b. a second device for prospectively determining the calibration of subject sensors from the lot, wherein the second device performs the steps of: i. measuring one or more long-term drift characteristics of the sensitivities of a subset of sensors in the manufactured lot; and ii. iii. correlating the measured values ​​of the initial measurable parameters of the target sensor with the measured one or more long-term drift characteristics using a first set of coefficients; iv. correlating at least a subset of the one or more determined in-vivo operating parameters with the measured values ​​of the initial measurable parameters by a second set of coefficients; and v. determining an estimated forward value of at least a final in-vivo sensitivity of the target sensor using the first and second sets of coefficients, wherein given the measured values ​​of the initial measurable parameters of the target sensor, the in-vivo sensitivity value for the target sensor is calculated.

[0030] Implementations of embodiments may include one or more of the following.

[0031] The second device may be further configured to create a data file corresponding to a sensitivity profile, the sensitivity profile including or indicating at least the in vivo final sensitivity of the sensor. The second device may be further configured to encode the in vivo final sensitivity of the subject sensor in a calibration indicator, and further configured to package the subject sensor with the calibration indicator in a kit. Configuring the second device to encode the in vivo final sensitivity of the subject sensor in a calibration indicator may further include configuring the second device to output data having the encoded sensitivity data. The calibration indicator may be embodied in a printed code, whereby a user may input the printed code into a mobile device or sensor electronics to set the calibration. The printed code may interface with a lookup table stored in the sensor electronics device or mobile device, such that when the printed code is input by a user, calibration information is received from the lookup table and used to calibrate the sensor and sensor electronics. The calibration indicator may be embodied in an RFID or NFC device that may be swiped by the sensor electronics or mobile device to transfer the calibration data.

[0032] In a seventh aspect, a method of calibrating an analyte concentration sensor is provided, the sensor being part of a manufactured lot of sensors of a given type, the method comprising: a. determining, receiving, or measuring an in vitro sensitivity of a first sensor; b. determining, receiving, or measuring at least one drift characteristic for a subset of sensors of the lot, the drift characteristic including at least a final drift test sensitivity, the determined characteristic being statistically representative of the sensors in the lot; c. determining a first set of coefficients that describe a relationship between the initial in vitro sensitivity and the drift characteristic of at least the first sensor; and d. .The method may include at least the steps of: determining a second set of coefficients that describe the relationship between the retrospective in vivo sensitivity, the determined first set of coefficients, and the initial in vitro sensitivity of the first sensor; e. calculating a sensitivity profile of the second sensor using the determined first or second sets of coefficients, or both, given the measured initial in vitro sensitivity of the second sensor; and f. storing the calculated predicted in vivo sensitivity of the second sensor for later transmission to sensor electronics associated with the second sensor or transmitting the calculated predicted in vivo sensitivity of the second sensor to sensor electronics associated with the second sensor.

[0033] In an eighth aspect, there is provided a system for calibrating analyte concentration sensors, the sensors being part of a manufactured lot of sensors, the lot being of a given type of sensor, one or more operating parameters of the sensors of that type of lot having been determined, the determination of the operating parameters being based on retrospective data, the operating parameters corresponding to at least the sensitivity of the sensors, the operating parameters representing in vivo values, and one or more long-term drift characteristics of the sensitivity having been measured from a subset of the sensors of the lot, the system comprising: a. a sensor calibration module configured to: i. measure values ​​of initial measurable parameters for the sensors of interest; ii. associate the measured values ​​of the initial measurable parameters of the sensors of interest with the measured one or more long-term drift characteristics by a first set of coefficients; iii. correlating at least a subset of the one or more retrospectively determined in vivo operating parameters with measurements of the initial measurable parameters by a second set of coefficients; and iv. using the first and second sets of coefficients to prospectively determine an estimate of at least a final in vivo sensitivity of the subject sensor, wherein an in vivo sensitivity value for the subject sensor may be estimated given the measurements of the initial measurable parameters of the subject sensor; b. a coding module configured to code the final in vivo sensitivity of the subject sensor as a calibration indicator; and c. a packaging module configured to package the calibration indicator and the subject sensor into a kit.

[0034] In a ninth aspect, there is provided a downloadable application configured to run on a mobile device, the application performing a method for receiving and displaying calibrated analyte concentration data, the method comprising: a. receiving a code associated with a sensitivity value; b. receiving a current or count sensor signal; c. calculating a calibrated analyte concentration value using the code or sensitivity value and the sensor signal; and d. displaying the calculated value, wherein the code is associated with the sensitivity value by a lookup table stored in the mobile device or sensor electronics in data communication with the sensor and the mobile device, or the code includes the sensitivity value; and f. the sensitivity value includes at least a final sensitivity value and an initial sensitivity value.

[0035] In a tenth aspect, there is provided a method for calibrating an analyte concentration sensor, the sensor being a member of a manufactured lot of sensors, the lot being of a given type of sensor, one or more operational parameters of the lot of sensors of that type having been determined, the determination of the operational parameters being based on retrospective data, the operational parameters corresponding to at least the sensitivity of the sensor, the operational parameters representing in vivo values, the method comprising: a. measuring values ​​of initial measurable parameters for the subject sensor; b. relating the measured values ​​of the initial measurable parameters of the subject sensor to the one or more determined operational parameters by at least one coefficient; and c. using the coefficient to prospectively determine an estimate of at least a final in vivo sensitivity of the subject sensor, wherein given the measured values ​​of the initial measurable parameters of the subject sensor, an in vivo sensitivity value for the subject sensor can be estimated.

[0036] In one exemplary implementation, the method further includes measuring one or more long-term drift characteristics of the sensitivity of a subset of sensors in the lot, and the correlating further relates the measured values ​​of the initial measurable parameters to the measured one or more long-term drift characteristics by at least another coefficient, and uses the coefficient in conjunction with the other coefficients to prospectively determine an estimate of at least the final in vivo sensitivity of the target sensor.

[0037] In an eleventh aspect, there is provided a method for calibrating an analyte concentration sensor, the sensor being part of a manufactured sensor lot, the lot being of a given type of sensor, and one or more in vivo operating parameters of the sensors of that type of lot having been determined, the method comprising: a. measuring values ​​of initial measurable parameters for the sensor of interest; b. relating the measured values ​​of the initial measurable parameters of the sensor of interest to the one or more in vivo operating parameters by at least one coefficient; and c. using the coefficient to prospectively determine an estimate of at least a final in vivo sensitivity of the sensor of interest, wherein an in vivo sensitivity value for the sensor of interest can be estimated given the measured values ​​of the initial measurable parameters of the sensor of interest.

[0038] In further aspects and embodiments, the above-described method features of various aspects are described in terms of systems, such as those in various aspects, configured to implement the method features. Any feature of any embodiment of any of the aspects, including but not limited to any embodiment of any of the above-mentioned aspects 1-11, is applicable to all other aspects and embodiments identified herein, including but not limited to any embodiment of any of the above-mentioned aspects 1-11. Furthermore, any feature of any embodiment of various aspects, including but not limited to any embodiment of any of the above-mentioned aspects 1-11, may be independently combinable in any way, partially or wholly, with other embodiments described herein; for example, one, two, three, or more embodiments may be combinable in whole or in part. Furthermore, any feature of any embodiment of various aspects, including but not limited to any embodiment of any of the above-mentioned aspects 1-11, may be optional with respect to other aspects or embodiments. Any aspect or embodiment of the method may be performed by a system or apparatus of another aspect or embodiment, and any aspect or embodiment of the system or apparatus may be configured to perform the method of another aspect or embodiment, including, but not limited to, any embodiment of any of the first to eleventh aspects mentioned above.

[0039] Advantages of embodiments may, in certain embodiments, include one or more of the following: The difficulty of achieving factory calibration for continuous glucose monitoring is effectively resolved in some implementations. For example, the sensitivity and background signal of a glucose sensor change as a function of time once the sensor is immersed in an electrolyte, and this time-dependent behavior can be described by a mathematical model defined by a small number of parameters. Based on the sensitivity and baseline signal predicted by systems and methods according to present principles, calibration of the glucose sensor signal can occur, allowing for real-time estimation of blood glucose concentration. Systems and methods according to present principles establish a method for utilizing measurable parameters, such as calibration check sensitivity information (described below), to correct the mathematical model and allow for calibration tailored to individual sensors.

[0040] Other advantages will be understood from the following description, including the drawings and claims.

[0041] This Summary is provided to introduce a selection of concepts in a simplified form. The concepts are further described in the Detailed Description. Elements or steps other than those described in this Summary are contemplated, and no element or step is necessarily required. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended for use as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all of the disadvantages noted in any part of this disclosure.

[0042] The present embodiments will now be discussed in detail with an emphasis on highlighting advantageous features. These embodiments illustrate novel and non-obvious sensor signal processing and calibration systems and methods as illustrated in the accompanying drawings, which are for illustrative purposes only and are not to scale, but rather emphasize the principles of the disclosure. These drawings include the following figures, in which like numerals indicate like parts: [Brief explanation of the drawings]

[0043] [Figure 1] FIG. 1 is a schematic diagram of a continuous analyte sensor system attached to a host and in communication with multiple exemplary devices. [Figure 2] FIG. 2 is a block diagram showing the electronics associated with the sensor system of FIG. 1. [Figure 3] 1 shows a graph illustrating the linear relationship between measured sensor counts and analyte concentration. [Figure 4] 1 shows an exemplary flow chart of a method according to the present principles. [Figure 5] 1 shows an exemplary change in sensitivity over time, specifically a single exponential model. [Figure 6] 1 shows an exemplary change in sensitivity over time, specifically a double exponential model. [Figure 7] The effect of non-enzymatic break-in (measured without enzyme) is shown, specifically the data corresponding to the non-enzymatic break-in model B1(t). [Figure 8] An example of the physiological negative bias model B2(t) is shown. [Figure 9(A)] 1 shows a retrospective fit using a single exponential sensitivity model. [Figure 9(B)] 1 shows a retrospective fit using a single exponential sensitivity model. [Figure 10(A)] 1 shows a retrospective fit using a double exponential sensitivity model. [Figure 10(B)] 1 shows a retrospective fit using a double exponential sensitivity model. [Figure 11(A)] We present two strategies for handling scenarios with significant deviations from the expected linear relationship between the calibration check procedure and the in vivo sensitivity parameters. [Figure 11(B)] We present two strategies for handling scenarios with significant deviations from the expected linear relationship between the calibration check procedure and the in vivo sensitivity parameters. [Figure 12] 1 shows a system for manufacturing and calibrating a sensor lot. [Figure 13]10 shows a schematic representation of code transmission to the sensor electronics or receiver / mobile device. [Figure 14] 1 illustrates generally a kit into which systems and methods according to the present principles may be packaged. [Figure 15] 1 illustrates schematically a kit into which systems and methods according to the present principles may be packaged. Like reference numerals refer to like elements throughout. Elements are not drawn to scale unless otherwise indicated. DETAILED DESCRIPTION OF THE INVENTION

[0044] The following description and examples illustrate in detail some exemplary implementations, embodiments, and procedures of the disclosed invention. Those skilled in the art will recognize that there are many variations and modifications of the invention that are encompassed by its scope. Therefore, the description of a particular exemplary embodiment should not be considered as limiting the scope of the invention.

[0045] definition To facilitate understanding of the preferred embodiments, a number of terms are defined below.

[0046] As used herein, the term "analyte" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, a substance or chemical constituent in a bodily fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymph, or urine) that may be analyzed. Analytes may include naturally occurring substances, man-made substances, metabolites, and / or reaction products. In some embodiments, the analyte for measurement by the sensor head, devices, and methods is an analyte. However, other analytes are contemplated as well, including acarboxyprothrombin, acylcarnitines, adenine phosphoribosyltransferase, adenosine deaminase, albumin, alpha-fetoprotein, amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan), andrenostenedione, antipyrine, arabinitol enantiomers, arginase, benzoylecgonine (cocaine), biotinidase, biopterin, c-reactive protein, carnitine, carnosinase, CD4, ceruloplasmin, chenodeoxycholic acid, chloroquine, cholesterol, cholinesterase, conjugated 1-beta hydroxycholic acid, cortisol, creatine kinase, creatine kinase MM isoenzyme, cyclosporine A, d-penicillamine, de-ethylchloroquine, dehydroepiandrosterone sulfate, DNA (acetylation polymorphism, alcohol dehydrogenase, α1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D Punjab, beta-thalase MIA, Hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber's hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sex differentiation, 21-deoxycortisol), desbutylhalofantrine, dihydropteridine reductase, diphtheria / tetanus antitoxin, erythrocyte arginase, erythrocyte protoporphyrin, esterase D, fatty acids / acylglycines, free β-human chorionic gonadotropin,Free erythrocyte porphyrins, free thyroxine (FT4), free tri-iodothyronine (FT3), fumarylacetoacetase, galactose / gal-1-phosphate, galactose-1-phosphate uridyltransferase, gentamicin, analyte-6-phosphate dehydrogenase, glutathione, glutathione peroxidase, glycocholate, glycosylated hemoglobin, halofantrine, hemoglobin variants, hexosaminidase A, human erythrocyte carbonic anhydrase I, 17-α-hydroxyprogesterone, hypoxanthine phosphoribosyltransferase, immunoreactive trypsin Calcium, lactate, lead, lipoproteins ((a), B / A-1, β), lysozyme, mefloquine, netilmicin, phenobarbitone, phenytoin, phytanic acid / pristanic acid, progesterone, prolactin, prolidase, purine nucleoside phosphorylase, quinine, inverted tri-iodothyronine (rT3), selenium, serum pancreatic lipase, sisomicin, somatomedin C, specific antibodies (adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, pseudorabies virus, dengue virus, guinea worm, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia lamblia (giardia duodenalisa), Helicobacter pylori, Hepatitis B virus, Herpes virus, HIV-1, IgE (atopic disease), Influenza virus, Leishmania donovani, Leptospirosis, Measles / Mumps / Rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerciasis volvulus, Parainfluenza virus, Plasmodium falciparum, Poliovirus, Pseudomonas aeruginosa, Respiratory syncytial virus, Rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Treponema pallidum, Trypanosoma cruzi / Langer, Vesicular stomatitis virus virus), Wuchereria bancrofti, Yellow fever virus), specific antigens (Hepatitis B virus, HIV-1), succinylacetone, 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 naturally occurring in blood or interstitial fluid may also constitute analytes in certain embodiments. Analytes, such as metabolites, hormones, antigens, antibodies, etc., may be naturally present in bodily fluids. Alternatively, analytes, such as contrast agents for diagnostic imaging, radioisotopes, chemical agents, fluorocarbon-based artificial blood, or drugs or pharmaceutical compositions, may be introduced into the body, including insulin, ethanol, cannabis (marijuana, tetrahydrocannabinol, hashish), inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons), cocaine (crack cocaine), stimulants (amphetamines, methamphetamines, Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine), depressants (barbiturates, methaqualone, tranquilizers, e.g., , Valium, Librium, Miltown, Serax, Equanil, Tranxene), hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin), narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, Fentanyl, Darvon, Talwin, Lomotil), designer drugs (fentanyl, meperidine, amphetamine, methamphetamine, and phencyclidine analogs, e.g., Ecstasy), anabolic steroids, and nicotine. Metabolites of drugs and pharmaceutical compositions may also be contemplated as analytes. For example, analytes such as neurochemicals and other chemicals produced in the body, such as ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), and 5-hydroxyindoleacetic acid (FHIAA), can be analyzed.

[0047] As used herein, the terms "microprocessor" and "processor" are broad terms that are given their ordinary and customary meanings to those skilled in the art (and are not limited to any special or customized meanings), and further refer to, but are not limited to, computer systems that perform arithmetic and logical operations using logic circuitry that responds to and processes the basic instructions that drive a computer, state machines, and the like.

[0048] As used herein, the terms "raw data stream" and "data stream" are broad terms that are given their ordinary and customary meanings to those skilled in the art (and are not limited to any special or customized meanings), and further refer to, but are not limited to, an analog or digital signal directly related to glucose measured from a glucose sensor. In one example, a raw data stream is digital data in "counts" (e.g., voltage or amperage) converted from an analog signal by an A / D converter and includes one or more data points representing glucose concentration. These terms broadly encompass multiple time-spaced data points from a substantially continuous glucose sensor, including individual measurements taken at time intervals ranging from less than one second up to, for example, one, two, or five minutes or more. In another example, a raw data stream includes integrated digital values, and the data includes one or more data points representing a glucose sensor signal averaged over a period of time.

[0049] As used herein, the term "calibration" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers, without limitation, to the process of determining the relationship between sensor data and corresponding reference data, with or without the use of reference data in real time, which can be used to convert the sensor data into a meaningful value substantially equivalent to the reference data. In some embodiments, i.e., in continuous analyte sensors, the calibration can be updated or recalibrated over time (at the factory, in real time, and / or retroactively) as changes in the relationship between the sensor data and the reference data occur, for example, due to changes in sensitivity, baseline, transport, metabolism, etc.

[0050] As used herein, the terms "calibrated data" and "calibrated data stream" are broad terms that are given their ordinary and customary meanings to those skilled in the art (and are not limited to any special or customized meanings), and further refer to data that has been transformed from its raw state to another state using a function, e.g., a transformation function, including through the use of sensitivities, to provide a meaningful value to a user, but are not limited to such data.

[0051] As used herein, the term "algorithm" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers, for example, but not limited to, a computational process (e.g., a program) involved in transforming information from one state to another by using computational processing.

[0052] As used herein, the term "counts" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, a unit of measurement for a digital signal. In one example, the raw data stream measured in counts is directly related to a voltage (e.g., converted by an A / D converter), which is directly related to a current from the working electrode.

[0053] As used herein, the term "sensor" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, a component or area of ​​a device where an analyte may be quantified. A "lot" of sensors generally refers to a group of sensors manufactured on or about the same day, using the same processes and tools / materials.

[0054] As used herein, the terms "glucose sensor" and "device for determining the amount of glucose in a biological sample" are broad terms and are given their ordinary and customary meanings to those skilled in the art (and are not limited to any special or customized meanings), and further refer to any mechanism (e.g., enzymatic or non-enzymatic) by which glucose may be quantified, without limitation. For example, some embodiments utilize a membrane containing glucose oxidase, which catalyzes the conversion of oxygen and glucose to hydrogen peroxide and gluconate, as exemplified by the following chemical reaction:

[0055] Glucose + O2 → Gluconate + H2O2

[0056] Because there is a proportional change in the co-reactant O2 and product HO2 for each glucose molecule metabolized, electrodes can be used to monitor the change in current of either the co-reactant or product to determine the glucose concentration.

[0057] As used herein, the terms "operably connected" and "operably linked" are broad terms that are given their ordinary and customary meanings by those skilled in the art (and are not limited to any special or customized meanings), and further refer to, but are not limited to, one or more components that are linked to another component(s) in a manner that allows for the transmission of a signal between the components. For example, one or more electrodes may be used to detect the amount of glucose in a sample and convert that information into a signal, e.g., an electrical or electromagnetic signal, which may then be transmitted to an electronic circuit. In this case, the electrodes are "operably linked" to the electronic circuit. These terms are broad enough to include wireless connections.

[0058] The term "determining" encompasses a wide range of actions. For example, "determining" may include calculating, computing, processing, deriving, investigating, searching (e.g., searching in a table, database, or another data structure), ascertaining, etc. Also, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), etc. Also, "determining" may include resolving, selecting, choosing, calculating, deriving, establishing, and / or the like. Determining may also include determining that a parameter matches a predetermined criterion, including meeting, passing, exceeding, etc. a threshold.

[0059] As used herein, the term "substantially" is a broad term having its ordinary and customary meaning given to those skilled in the art (and is not limited to any special or customized meaning), and further refers, without limitation, to what is primarily, but not necessarily entirely, specified.

[0060] As used herein, the term "host" is a broad term that is given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning), and further refers to mammals, particularly, but not limited to, humans.

[0061] As used herein, the term "continuous analyte (or glucose) sensor" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers, without limitation, to a device that measures the concentration of an analyte continuously or continuously, e.g., at time intervals ranging from less than one second up to, e.g., one, two, or five minutes or more. In one exemplary embodiment, the continuous analyte sensor is a glucose sensor such as that described in U.S. Pat. No. 6,001,067, which is incorporated herein by reference in its entirety.

[0062] As used herein, the term "continuous analyte (or glucose) sensing" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, periods during which analyte monitoring is performed continuously or continuously, for example, at time intervals ranging from less than one second up to, for example, one, two, or five minutes or more.

[0063] As used herein, the terms "reference analyte monitor," "reference analyte meter," and "reference analyte sensor" are broad terms that are given their ordinary and customary meanings to those skilled in the art (and are not limited to any special or customized meanings), and further refer to, but are not limited to, a device that measures the concentration of an analyte and may be used as a reference for a continuous analyte sensor, for example, a self-monitoring blood glucose meter (SMBG) may be used as a reference for a continuous glucose sensor for comparison, calibration, etc.

[0064] As used herein, the term "sensing membrane" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers, without limitation, to a permeable or semi-permeable membrane that may consist of two or more regions and is typically composed of a material several microns or more thick that is permeable to oxygen and may or may not be permeable to glucose. In one example, the sensing membrane contains immobilized glucose oxidase enzyme, which allows an electrochemical reaction to occur to measure the concentration of glucose.

[0065] As used herein, the term "physiologically feasible" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, physiological parameters obtained from serial studies of glucose data in humans and / or animals. For example, a maximum sustained rate of change of glucose of about 4-5 mg / dL / min in humans and a maximum sustained rate of change of glucose of about 0.1-0.2 mg / dL / min in humans. 2 The maximum acceleration of the rate of change of glucose is considered a physiologically possible limit. Values ​​outside these limits are considered, for example, non-physiological and likely the result of signal error. As another example, the rate of change of glucose is lowest at the maximum and minimum of the daily glucose range, which is the area of ​​greatest risk in patient care; therefore, the physiologically possible rate of change may be set at the maximum and minimum based on continuous study of glucose data. As a further example, it has been found that the best solution for the shape of the curve at any point along the glucose signal data stream over a certain period of time (e.g., approximately 20-30 minutes) is a straight line, which may be used to set the physiological limits.

[0066] As used herein, the term "frequency content" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to the spectral density, including, but not limited to, the frequencies contained within a signal and their power.

[0067] As used herein, the term "linear regression" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, finding the line where a set of data has the minimum measurement from that line. By-products of this algorithm include the slope, y-intercept, and R-squared value, which determine how well the measured data fit the line.

[0068] As used herein, the term "nonlinear regression" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, fitting a set of data to describe the relationship between a response variable and one or more explanatory variables in a nonlinear manner.

[0069] As used herein, the term "average" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, the sum of the observations divided by the number of observations.

[0070] As used herein, the term "non-recursive filter" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, an equation that uses a moving average of its inputs as its output.

[0071] As used herein, the terms "recursive filter" and "autoregressive algorithm" are broad terms given their ordinary and customary meanings to those skilled in the art (and are not limited to any special or customized meanings), and further refer to, but are not limited to, an equation in which the average of previous outputs is part of the next filtered output. More specifically, the generation of a series of observations in which the value of each observation depends in part on the value of the observation immediately preceding it. An example is a regression structure in which lagged response values ​​act as independent variables for calculating subsequent responses.

[0072] As used herein, the term "variation" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, the amount of difference or variation from a point, line, or set of data. In one embodiment, an estimated analyte value can have a variation that includes a range of values ​​other than the estimated analyte value, for example, representing a range of possibilities based on known physiological patterns.

[0073] As used herein, the terms "physiological parameter" and "physiological boundary" are broad terms that are given their ordinary and customary meanings to those skilled in the art (and are not limited to any special or customized meanings), and further refer to parameters obtained from serial studies of physiological data in humans and / or animals, including, but not limited to, a maximum sustained rate of change of glucose of about 4-5 mg / dL / min and a maximum sustained rate of change of glucose of about 0.1-0.2 mg / dL / min in humans. 2The maximum acceleration of the rate of change of glucose is considered a physiologically possible limit, and values ​​outside these limits are considered non-physiological. As another example, the rate of change of glucose is lowest at the maximum and minimum of the daily glucose range, which is the area of ​​greatest risk in patient care; therefore, the physiologically possible rate of change may be established at the maximum and minimum based on continuous study of glucose data. As a further example, it has been found that the best solution for the shape of the curve at any point along the glucose signal data stream over a certain period of time (e.g., about 20-30 minutes) is a straight line, which may be used to establish physiological limits. These terms are broad enough to include physiological parameters for any analyte.

[0074] As used herein, the term "measured analyte value" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, an analyte value or set of analyte values ​​over a period of time during which the analyte data was measured by an analyte sensor. The term is broad enough to include data from an analyte sensor before or after data processing (e.g., data smoothing, calibration, etc.) in the sensor and / or receiver.

[0075] As used herein, the term "estimated analyte value" is a broad term that is given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, an analyte value or set of analyte values ​​that are algorithmically extrapolated from a measured analyte value.

[0076] As used herein, the term “sensor data” is a broad term, given its ordinary and customary meaning to those skilled in the art (and not limited to any special or customized meaning), and further refers to any data associated with a sensor, such as a continuous analyte sensor, without limitation. Sensor data includes a raw data stream, or simply a data stream, of an analog or digital signal directly related to a measured analyte from an analyte sensor (or other signal received from another sensor), as well as raw data that has been calibrated and / or filtered. In one example, sensor data includes digital data in “counts” (e.g., voltage or amperage) converted from an analog signal by an A / D converter and includes one or more data points representing glucose concentration. Thus, the terms “sensor data point” and “data point” generally refer to a digital representation of sensor data at a particular time. These terms broadly encompass multiple time-spaced data points from a sensor, such as from a substantially continuous glucose sensor, including individual measurements taken at time intervals ranging from less than one second up to, for example, one, two, or five minutes or more. In another example, the sensor data includes an integrated digital value representing one or more data points averaged over a period of time. The sensor data may include calibrated data, smoothed data, filtered data, transformed data, and / or any other data associated with the sensor.

[0077] As used herein, the term "matched data pair" or "data pair" is a broad term having its ordinary and customary meaning as given to those skilled in the art (and is not limited to any special or customized meaning), and further refers to, but is not limited to, reference data (e.g., one or more reference analyte data points) matched with substantially time-corresponding sensor data (e.g., one or more sensor data points).

[0078] As used herein, the term "sensor electronics" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, the components (e.g., hardware and / or software) of a device configured to process data.

[0079] As used herein, the term "calibration set" is a broad term given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning) and refers to, but is not limited to, a set of data containing information useful for calibration. In some embodiments, a calibration set is formed from one or more matched data pairs used to determine the relationship between reference data and sensor data, although other data obtained pre-implant, externally, or internally may also be used.

[0080] As used herein, the terms "sensitivity" or "sensor sensitivity" are broad terms given their ordinary and customary meanings 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 particular concentration of a measured analyte or a measured species (e.g., HO) associated with the measured analyte (e.g., glucose). For example, in one embodiment, the sensor has a sensitivity of about 1 to about 300 picoamps of current per 1 mg / dL of glucose analyte.

[0081] As used herein, the terms "sensitivity profile" or "sensitivity curve" are broad terms and are given their ordinary and customary meaning to those skilled in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, a display of change in sensitivity over time.

[0082] Other definitions are provided below, and in some cases from the context of the use of a term.

[0083] As used herein, the following abbreviations apply: Eq and Eqs (equivalents), mEq (milliequivalents), M (mole), mM (millimolar), μM (micromolar), N (normal), mol (mole), mmol (millimolar), μmol (micromolar), nmol (nanomole), g (gram), mg (milligram), μg (microgram), Kg (kilogram), L (liter), mL (milliliter), dL (deciliter), μL (microliter), cm (centimeter), mm (millimeter), μm (micrometer), nm (nanometer), h and hr (hours), min (minute), and sec. (second), °C (degrees Celsius).

[0084] System Overview / Overview Conventional in vivo continuous analyte sensing techniques have typically relied on reference measurements performed during a sensor session for calibration of the continuous analyte sensor. The reference measurements are matched with substantially time-corresponding sensor data to create matched data pairs. Regression is then performed on the matched data pairs (e.g., by using least-squares regression) to generate a transfer function that defines the relationship between the sensor signal and estimated glucose concentration.

[0085] In critical care settings, calibration of continuous analyte sensors is often performed by using a calibration solution with a known concentration of analyte as a reference. This calibration procedure can be cumbersome because a calibration bag separate from (and in addition to) an IV (intravenous) bag is typically used. In outpatient settings, calibration of continuous analyte sensors is traditionally performed by peripheral blood glucose measurements (e.g., fingerstick glucose tests), through which reference data is obtained and entered into the continuous analyte sensor system. This calibration procedure typically involves frequent fingerstick measurements, which can be inconvenient and painful.

[0086] To date, systems and methods for in vitro calibration (e.g., factory calibration) of continuous analyte sensors by manufacturers that do not rely on periodic recalibration have largely fallen short with respect to the high levels of sensor accuracy required for glycemic management. Part of this can be attributed to changes in sensor characteristics (e.g., sensor sensitivity) that can occur during sensor use. Therefore, calibration of continuous analyte sensors has typically involved periodic input of reference data, whether they involve calibration fluids or fingerstick measurements. This can be very cumbersome for users in everyday life, and for patients in outpatient settings or hospital staff in critical care settings.

[0087] Described herein are systems and methods for calibrating continuous analyte sensors that are capable of achieving high levels of accuracy without (or with reduced reliance on) reference data from a reference analyte monitor (e.g., from a blood glucose meter).

[0088] The following description and examples illustrate the present embodiments with reference to the drawings, in which reference numerals indicate elements of the present embodiments, and these reference numerals are reproduced below in connection with the discussion of corresponding drawing features. Sensor System

[0089] 1 illustrates an exemplary system 100 according to some exemplary implementations. System 100 includes a continuous analyte sensor system 8, which includes sensor electronics 12 and a continuous analyte sensor 10. System 100 may include other devices and / or sensors, such as a drug delivery pump 2 and a glucose meter 4. Continuous analyte sensor 10 may be physically connected to sensor electronics 12 and may be integral with (e.g., non-releasably attached to) or releasably attachable to continuous analyte sensor 10. Sensor electronics 12, drug delivery pump 2, and / or glucose meter 4 may be coupled to one or more devices, such as display devices 14, 16, 18, and / or 20.

[0090] 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 over network 406 (e.g., via wired, wireless, or a combination thereof) from other devices associated with the host (also referred to as the patient), such as sensor system 8 and display devices 14-20, and generate reports providing high-level information, such as statistical data about analytes measured over a particular time frame. A thorough discussion of using cloud-based analyte processing systems can be found in U.S. Patent Application No. 13 / 788,375, filed March 7, 2013, 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 may occur in the cloud.

[0091] In some exemplary implementations, sensor electronics 12 may include electronic circuitry associated with measuring and processing data generated by continuous analyte sensor 10. This generated continuous analyte sensor data may also include algorithms that can be used to process and calibrate the continuous analyte sensor data, although these algorithms may be provided in other ways as well. Sensor electronics 12 may include hardware, firmware, software, or a combination thereof to provide measurement of analyte levels via a continuous analyte sensor, such as a continuous glucose sensor. An exemplary implementation of sensor electronics 12 is further described below with respect to FIG. 2.

[0092] In one implementation, the factory calibration algorithms described herein may be performed by the sensor electronics.

[0093] Sensor electronics 12, as described, may be coupled (e.g., wirelessly, etc.) with one or more devices, such as display devices 14, 16, 18, and / or 20. Display devices 14, 16, 18, and / or 20 may be configured to present (and / or alert) information, such as sensor information, transmitted by sensor electronics 12 for display on display devices 14, 16, 18, and / or 20.

[0094] The display device may include a relatively small key fob-type display device 14, a relatively large handheld display device 16, a mobile phone 18 (e.g., a smartphone, tablet, etc.), a computer 20, and / or any other user device configured to present at least information (e.g., medication delivery information, separate self-monitoring glucose readings, a heart rate monitor, a food intake monitor, etc.).

[0095] In one implementation, the factory calibration algorithms described herein may be performed at least in part by the display device.

[0096] In some example implementations, the relatively small key fob display device 14 may include a wristwatch, a belt, a necklace, a pendant, jewelry, an adhesive patch, a pager, a key fob, a plastic card (e.g., a credit card), an identification (ID) card, and / or the like. This small display device 14 may include a relatively small display (e.g., smaller than the large display device 16) and may be configured to display certain types of displayable sensor information, such as numbers, arrows, or color codes.

[0097] In some example implementations, the relatively large handheld display device 16 may include a handheld receiver device, a palmtop computer, and / or the like. This large display device may include a relatively large display (e.g., larger than the small display device 14) and may be configured to display information such as a graphical representation of continuous sensor data, including current and historical sensor data, output by the sensor system 8.

[0098] In some exemplary implementations, the continuous analyte sensor 10 includes a sensor for detecting and / or measuring an analyte, and the continuous analyte sensor 10 may be configured to continuously detect and / or measure the analyte as a non-invasive, subcutaneous, transcutaneous, and / or intravascular device. In some exemplary implementations, the continuous analyte sensor 10 may analyze multiple intermittent blood samples, although other analytes may be used as well.

[0099] In some exemplary implementations, the continuous analyte sensor 10 may include a glucose sensor configured to measure glucose in blood or interstitial fluid using one or more measurement techniques, such as enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, immunochemical, etc. In implementations in which the continuous analyte sensor 10 includes a glucose sensor, the glucose sensor may include any device capable of measuring the concentration of glucose and may provide data, such as a data stream indicative of the concentration of glucose in a host, using various techniques for measuring glucose, including invasive, minimally invasive, and non-invasive sensing techniques (e.g., fluorescence monitoring). The data stream may be sensor data (raw and / or filtered), which may be converted into a calibrated data stream used to provide glucose values ​​to a host, such as a user, patient, or caregiver (e.g., a parent, relative, guardian, teacher, doctor, nurse, or any other individual interested in the host's health). Furthermore, the continuous analyte sensor 10 may be embedded as at least one of the following types of sensors: Implantable glucose sensors, transcutaneous glucose sensors implanted in the host's vasculature or extracorporeal, subcutaneous sensors, replaceable subcutaneous sensors, intravascular sensors.

[0100] While the disclosure herein refers to some implementations including a continuous analyte sensor 10, including a glucose sensor, the continuous analyte sensor 10 may include other types of analyte sensors as well. Additionally, while some implementations refer to the glucose sensor as an implantable glucose sensor, other types of devices capable of detecting the concentration of glucose and providing an output signal representative of the glucose concentration may also be used. Additionally, while the description herein refers to glucose as the analyte being measured, processed, etc., other analytes may also be used, including, for example, ketone bodies (e.g., acetone, acetoacetate, and β-hydroxybutyrate, lactate, etc.), glucose, acetyl-CoA, triglycerides, fatty acids, intermediates in the citric acid cycle, choline, insulin, cortisol, testosterone, etc.

[0101] 2 shows one example of sensor electronics 12 according to some example implementations. Sensor electronics 12 may include sensor electronics configured to process sensor information, such as sensor data, and generate transformed sensor data and displayable sensor information, for example, via a processor module. For example, the processor module may transform 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 / warning information, calibration information, such as may be determined by factory calibration algorithms disclosed herein, sensor data smoothing and / or filtering algorithms, and / or the like.

[0102] In some embodiments, the processor module 214 is configured to accomplish a significant portion, if not all, of the data processing, including data processing related to factory calibration. The processor module 214 may be integral to the sensor electronics 12 and / or may be located remotely, such as in one or more of the devices 14, 16, 18, and / or 20 and / or in the cloud 490. In some embodiments, the processor module 214 may include multiple smaller subcomponents or submodules. For example, the processor module 214 may include an alert module (not shown), or a prognostic module (not shown), or any other suitable module that may be utilized to efficiently process data. When the processor module 214 is comprised of multiple submodules, the submodules may be located within the processor module 214, including within the sensor electronics 12 or other associated devices (e.g., 14, 16, 18, 20, and / or 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 within the network 406.

[0103] In some example implementations, processor module 214 may be configured to calibrate the sensor data, and data storage 220 may store the calibrated sensor data points as transformed sensor data. Additionally, processor module 214 may be configured in some example implementations to wirelessly receive calibration information from display devices, such as devices 14, 16, 18, and / or 20, to enable calibration of sensor data from sensor 12. Additionally, processor module 214 may be configured to perform additional algorithmic processing on the sensor data (e.g., calibrated and / or filtered data and / or other sensor information), and data storage 220 may be configured to store the transformed sensor data and / or sensor diagnostic information associated with the algorithm. Processor module 214 may further be configured to store and use calibration information determined from factory calibration, as described below.

[0104] In some example implementations, sensor electronics 12 may include an application specific integrated circuit (ASIC) 205 coupled to a user interface 222. ASIC 205 may further include a potentiostat 210, a telemetry module 232 for transmitting data from sensor electronics 12 to one or more devices, such as devices 14, 16, 18, and / or 20, and / or other components for signal processing and data storage (e.g., processor module 214 and data storage device 220). While FIG. 2 shows ASIC 205, other types of circuits may be used as well, including a field programmable gate array (FPGA), one or more microprocessors configured to provide some (if not all) of the processing performed by sensor electronics 12, analog circuitry, digital circuitry, or a combination thereof.

[0105] 2, potentiostat 210 couples to a continuous analyte sensor 10, such as a glucose sensor, through a first input port 211 for sensor data to generate sensor data from the analyte. Potentiostat 210 may also provide a voltage to continuous analyte sensor 10 via data line 212 to bias the sensor for measurement of a value (e.g., a current value) indicative of the analyte concentration in the host (also referred to as the analog portion of the sensor). Potentiostat 210 may have one or more channels depending on the number of working electrodes in continuous analyte sensor 10.

[0106] In some exemplary implementations, potentiostat 210 may include a resistor that converts current values ​​from sensor 10 to voltage values, while in some exemplary implementations a current-to-frequency converter (not shown) may also be configured to continuously incorporate measured current values ​​from sensor 10, 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 10 into so-called "counts" to enable processing by processor module 214. The resulting counts may be directly related to the current measured by potentiostat 210, which may be directly related to an analyte level, such as a glucose level, within the host.

[0107] The telemetry module 232 may be operatively connected to the processor module 214 and may provide hardware, firmware, and / or software that enables wireless communication between the sensor electronics 12 and one or more other devices, such as a display device, a processor, a network access device, etc. Various wireless technologies that may be implemented in the telemetry module 232 include Bluetooth®, Bluetooth® Low-Energy, ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16, cellular radio access technology, radio frequency (RF), infrared (IR), paging network communication, magnetic induction, satellite data communication, spread spectrum communication, frequency hopping communication, near field communication, and / or the like. In some example implementations, the telemetry module 232 includes a Bluetooth® chip, although Bluetooth® technology may also be implemented in a combination of the telemetry module 232 and the processor module 214.

[0108] The processor module 214 may control the processing performed by the sensor electronics 12. For example, the processor module 214 may be configured to process data (e.g., counts) from the sensor, filter the data, calibrate the data, perform fail-safe checks, and / or the like.

[0109] In some exemplary implementations, the processor module 214 may include a digital filter, such as an infinite impulse response (IIR) or finite impulse response (FIR) filter. This digital filter may smooth the raw data stream received from the sensor 10. Generally, the digital filter is programmed to filter data sampled at predetermined time intervals (also referred to as the sampling rate). In some exemplary implementations, such as when the potentiostat 210 is configured to measure an analyte (e.g., glucose and / or the like) at discrete time intervals, these time intervals determine the sampling rate of the digital filter. In some exemplary implementations, the potentiostat 210 may be configured to measure the analyte continuously, for example, using a current-to-frequency converter. In these current-to-frequency converter implementations, the processor module 214 may be programmed to request digital values ​​from the integrator of the current-to-frequency converter at predetermined time intervals (the acquisition time). These digital values ​​obtained by the processor module 214 from the integrator may be averaged over the acquisition time due to the continuity of the current measurements. Therefore, the acquisition time may be determined by the sampling rate of the digital filter.

[0110] Processor module 214 may further include a data generator (not shown) configured to generate data packages for transmission to devices such as display devices 14, 16, 18, and / or 20. Additionally, processor module 214 may generate data packets for transmission to these external sources via telemetry module 232. In some example implementations, the data packages may be customizable for each display device, as described, and / or may include any available data, such as timestamps, displayable sensor information, converted sensor data, identifier codes for the sensors and / or sensor electronics 12, raw data, filtered data, calibrated data, rate of change information, trend information, error detection or correction, and / or the like.

[0111] The processor module 214 may also include program memory 216 and other memory 218. The processor module 214 may be coupled to a communications interface, such as a communications port 238, and a power source, such as a battery 234. Additionally, the battery 234 may be further coupled to a charger and / or regulator 236 to provide power to the sensor electronics 12 and / or charge the battery 234.

[0112] The program memory 216 may be implemented as a pseudo-static memory for storing data such as identifiers (e.g., sensor identifiers (IDs)) for the coupled sensors 10, and for storing code (also referred to as program code) for calibrating the ASIC 205 to perform one or more of the operations / functions described herein. For example, the program code may configure the processor module 214 to process and filter data streams or counts, perform calibration methods described below, perform fail-safe checks, etc.

[0113] The memory 218 may also be used to store information. For example, the processor module 214, including the memory 218, may be used as a cache memory for the system, providing temporary storage for recent sensor data received from the sensors. In some example implementations, the memory may include storage components such as read-only memory (ROM), random access memory (RAM), dynamic RAM, static RAM, non-static RAM, easily erasable programmable read-only memory (EEPROM), rewritable ROM, flash memory, etc.

[0114] The data storage device 220 may be coupled to the processor module 214 and may be configured to store various sensor information. In some example implementations, the data storage device 220 stores one or more days of continuous analyte sensor data. For example, the data storage device may store 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, and / or 30 days (or more) of continuous analyte sensor data received from the sensor 10. The stored sensor information may include one or more of the following: a timestamp, raw sensor data (one or more raw analyte concentration values), calibrated data, filtered data, converted sensor data, and / or any other displayable sensor information, calibration information (e.g., reference BG values ​​and / or previous calibration information, such as calibration information from a factory calibration), sensor diagnostic information, etc.

[0115] The user interface 222 may include various interfaces, such as one or more buttons 224, a liquid crystal display (LCD) 226, a vibrator 228, an audio transducer (e.g., a speaker) 230, a backlight (not shown), and / or the like. The components comprising the user interface 222 may provide controls for interacting with a user (e.g., a host). The one or more buttons 224 may enable, for example, a toggle, a menu selection, an option selection, a state selection, a yes / no response to an on-screen question, a "turn off" function (e.g., for an alarm), an "acknowledged" function (e.g., for an alarm), a reset, and / or the like. The LCD 226 may provide, for example, visual data output to the user. The audio transducer 230 (e.g., a speaker) may provide audible signals in response to the triggering of certain alerts, such as current and / or predicted hyperglycemic and hypoglycemic conditions. In some example implementations, the audible signals may be differentiated by tone, volume, duty cycle, pattern, duration, and / or the like. In some example implementations, the audible signal may be configured to be muted (e.g., acknowledged or turned off) by pressing one or more buttons 224 on the sensor electronics 12 and / or by signaling the sensor electronics 12 using a button or selection on a display device (e.g., a key fob, cell phone, and / or the like).

[0116] While audible and vibratory alarms are described with respect to Figure 2, other alarm mechanisms may be used as well. For example, in some exemplary implementations, a tactile alarm is provided that includes a poking mechanism configured to "poke" or make physical contact with the patient in response to one or more alarm conditions.

[0117] Another battery 234 may be operably connected to the processor module 214 (and possibly other components of the sensor electronics 12) and provide the necessary power for the sensor electronics 12. In some exemplary implementations, the battery is a lithium manganese dioxide battery, although any appropriately sized and powered battery may be used (e.g., AAA, nickel-cadmium, zinc-carbon, alkaline, lithium, nickel-metal hydride, lithium-ion, zinc-air, zinc-mercury oxide, silver-zinc, or sealed). In some exemplary implementations, the battery is rechargeable. In some exemplary implementations, multiple batteries may be used to power the system. In still other implementations, the receiver may be powered transcutaneously, for example, via inductive coupling.

[0118] Battery charger and / or balancer 236 may be configured to accept energy from an internal and / or external charger. In some exemplary implementations, battery conditioner (or balancer) 236 regulates the recharging process by bleed-off excess charge current to allow all cells or batteries in sensor electronics 12 to be fully charged without overcharging other cells or batteries. In some exemplary implementations, battery(ies) 234 are configured to be charged via an inductive and / or wireless charging pad, although any other charging and / or power mechanism may be used as well.

[0119] One or more communication ports 238, also referred to as external connector(s), may be provided to enable communication with other devices; for example, a PC communication (com) port may be provided to enable communication with systems separate from or integral to the sensor electronics 12. For example, the communication port may include a serial (e.g., universal serial bus or "USB") communication port to enable communication with another computer system (e.g., a PC, a personal digital assistant or "PDA," a server, etc.). In some example implementations, the sensor electronics 12 can transmit historical data to a PC or other computer device (e.g., an analyte processor disclosed herein) for retrospective analysis by the patient and / or physician. As another example of data transmission, factory information may also be transmitted to the algorithm from the sensor or from a cloud data source.

[0120] The one or more communication ports 238 may further include a second input port 237 through which calibration data may be received and an output port 239 that may be used to transmit calibration data or data to be calibrated to a receiver or mobile device. Figure 2 illustrates these aspects schematically. While the ports may be physically separate, it will be appreciated that in alternative implementations, a single communication port may provide the functionality of both the second input port and the output port.

[0121] In some continuous analyte sensor systems, the on-skin portion of the sensor electronics may be simplified to minimize the complexity and / or size of the on-skin electronics, for example, providing only raw, calibrated, and / or filtered data to a display device configured to perform calibration and other algorithms needed to display the sensor data. However, the sensor electronics 12 (e.g., via the processor module 214) may also be implemented to execute predictive algorithms used to generate the converted sensor data and / or displayable sensor information, including, for example, algorithms to evaluate the clinical acceptability of the reference and / or sensor data, evaluate the calibration data for best calibration based on inclusion criteria, evaluate the quality of the calibration, compare predicted analyte values ​​to time-corresponding estimated analyte values, analyze variability 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 in the sensor data, perform dynamic and intelligent analyte value estimation, perform diagnostics on the sensor and / or sensor data, set the mode of operation, evaluate the data with respect to the above, and / or the like.

[0122] Although separate data storage and program memory are shown in Figure 2, various configurations may be used as well. For example, one or more memories may be used to provide storage space to support data processing and storage requirements in sensor electronics 12.

[0123] proofreading In some cases, calibration of an analyte sensor may use a priori calibration information. As used herein, a priori information includes information obtained prior to a particular calibration, such as from a previous calibration of a particular sensor session (e.g., feedback from previous calibration(s)), information obtained prior to sensor insertion (e.g., factory information from in vitro testing or data obtained from already implanted analyte concentration sensors, e.g., sensors from the same manufacturing lot of sensors and / or sensors from one or more different lots), previous in vivo testing of a similar sensor on the same host, and / or previous in vivo testing of a similar sensor on a different host. Calibration information includes information useful for calibrating a continuous glucose sensor, such as sensitivity (m), change in sensitivity (dm / dt) (which may also be referred to as drift in sensitivity), acceleration of change in sensitivity (d2m / dt2), baseline / intercept (b), change in baseline (db / dt), rate of change of baseline (d2b / dt2), baseline and / or sensitivity profile (i.e., change over time) associated with the sensor, linearity, response time, relationships between sensor characteristics (e.g., the relationship between sensitivity and baseline), or a particular stimulus signal output (e.g., impedance of the sensor), as described in U.S. Patent Publication No. 2012 / 0265035-A1, which is incorporated herein by reference in its entirety. The distribution information may include, but is not limited to, a relationship between the sensor sensitivity or temperature (e.g., determined from previous in vivo and / or in vitro studies), sensor data obtained from an already implanted analyte concentration sensor, a calibration code(s) associated with a calibrated sensor, a patient-specific relationship between the sensor and sensitivity, baseline, drift, impedance, impedance / temperature relationship (e.g., determined from previous studies of the patient or other patients with characteristics in common with the patient), sensor implantation site (abdomen, arm, etc.), and / or a specific relationship (different sites may have different vascular densities). Distribution information includes a range, distribution function, distribution parameters (e.g., mean, standard deviation, skewness, etc.), a general function, a statistical distribution, a profile, or the like representing multiple possible values ​​for the calibration information.In summary, a priori calibration distribution information includes range(s) or distribution(s) of values ​​(e.g., describing their associated probabilities, probability density functions, likelihoods, or frequencies of occurrence) provided prior to a particular calibration process that are useful for calibrating a sensor (e.g., sensor data).

[0124] For example, in some embodiments, the a priori calibration distribution information includes a probability distribution for sensitivity (m) or sensitivity-related information, and a baseline (b) or baseline-related information, e.g., based on sensor type. As noted above, the prior distributions of sensitivity and / or baseline may be obtained from a factory (e.g., from in vitro or in vivo testing of representative sensors) or may be obtained from prior calibrations.

[0125] As previously mentioned, an analyte sensor generally includes electrodes for monitoring changes in current in either a coreactant or product to determine the analyte concentration, e.g., glucose concentration. In one example, sensor data includes digital data in "counts" (e.g., voltage or amperage) converted from an analog signal by an A / D converter. Calibration is the process of determining the relationship between the measured sensor signal in counts and the analyte concentration in clinical units. For example, calibration allows a given sensor measurement in counts to be related to a measured analyte concentration value, e.g., in milligrams per deciliter. Referring to graph 310 of FIG. 3, this relationship is generally a linear relationship of the form y=mx+b, where "y" is the sensor signal in counts or picoamperes (y-axis 312), "x" is the clinical value of the analyte concentration (axis 314), and "m" is the sensor sensitivity, having units of [counts / (mg / dL)] or [pA / (mg / dL)]. A line 316 is shown, the slope of which is referred to as the sensor sensitivity. "b" (see line segment 315) is the baseline sensor signal, which can be considered or, for advanced sensors, can generally be reduced to zero or near zero. In some implementations, a constant background signal is seen, and such a signal is modeled by y = m(x + d), where d is the glucose offset between the sensor site and blood glucose. In either case, once line 316 is determined, the system can convert the measured counts (or amperes, e.g., picoamps, as described above) to a clinical value of analyte concentration.

[0126] A more generalized form of a linear equation is provided below, although it is understood that non-linear relationships may be used in factory calibration in accordance with the present principles as well.

[0127] In one particular situation, the values ​​of m and b will vary between sensors and require determination. Additionally, the gradient value m is not necessarily constant. For example, and with reference to FIG. 5, the value m may vary over time within a session from an initial sensitivity value m0 to a final sensitivity value m FThe rate of change is seen to be greatest during the first few days of use, and this rate of change is m R It is called.

[0128] The gradient is a function of time in vivo for many reasons. In particular, with regard to initial changes in calibration, such changes are often due to the sensor membrane "settling" into the in vivo environment and achieving equilibrium with that environment. Sensors are generally initially calibrated in vitro or on the bench, and efforts are made to make the in vitro environment as close as possible to the in vivo environment, but differences are still apparent, and the in vivo environment itself varies between users. Additionally, sensors may differ due to differences in the microenvironment each sensor faces during manufacturing and testing, as well as sterilization or shelf-life / storage conditions. Calibration changes that occur later in a session are often due to changes in the tissue surrounding the sensor, such as biofilm buildup on the sensor.

[0129] Whatever the cause, certain effects of variability have been measured and determined. For example, the ultimate sensitivity m F It is known that variability in m is the largest contributor to overall sensor inaccuracy. Similarly, it is known that variability in initial sensitivity m and physiology are the largest contributors to sensor inaccuracy on the first day.

[0130] Therefore, while calibration is essential for the effective use of such sensors, it is desirable to minimize the user effort required to perform such calibration. The present systems and methods according to present principles are directed, in part, to methods that reduce or eliminate such required calibration.

[0131] Specifically, and as described above, one exemplary factory calibration workflow attempts to parameterize a number of sensor operating parameters (e.g., initial / final sensitivity, drift performance, baseline shift, etc.) and compartmental effects to prospectively model future sensor behavior. This factory calibration workflow, in one implementation, includes two models: a sensitivity profile model, which can be single-parameter or dual-parameter, and a baseline model. These two models have several operating parameter inputs, such as initial sensitivity, final sensitivity, exponential drift rate, drift rate due to membrane degradation, drift rate due to electrochemical break-in, initial and final amounts of compartment bias, and drift rate of vanishing compartment bias. Overall, most of these operating parameters are determined based on sensor / membrane calibration, and data is largely culled from clinical trials.

[0132] To obtain "key" or "index" values ​​for a particular sensor, and particularly to advantageously obtain the relationship between measurable initial sensor parameters and desired "end result" in vivo sensitivity, "calibration check" tests and long-term drift tests may be performed, in which calibration check test data, generally corresponding to the sensor's signal output as a function of multiple input analyte concentrations, is nondestructively obtained. That is, in a calibration check test, the sensor is placed in analyte test solutions with varying analyte concentrations, and the corresponding output signals are measured. A "calibration check" or "cal check" may be performed to demonstrate sensor linearity for high glucose concentration values ​​(e.g., 400 mg / dL, 500 mg / dL, or 600 mg / dL). This is done by placing the sensor in buffer solutions with increasing glucose concentrations (e.g., in 100 mg / dL increments). Calibration checks are also performed to demonstrate sensor linearity at low oxygen concentrations (e.g., pO2 of 0.25 mg / L) and to demonstrate a 95% response time that is within acceptable limits (e.g., within 5 minutes).

[0133] A "calibration check" test can be used to determine the in vitro initial sensitivity or slope by measuring several such outputs as a function of analyte concentration and performing a linear regression. However, any test can be used so long as it determines a sensor characteristic or other such indicator, or initial measurable parameter of the sensor, which can then be used as an independent variable to determine forward values ​​of one or more in vivo operating parameters, e.g., initial and / or final values ​​of sensitivity, as described in more detail below. While calibration check sensor sensitivity or slope is often the preferred such initial measurable parameter, other initial measurable parameters, such as initial film thickness, can be used as well. Generally, but not necessarily, suitable initial measurable parameters include those that are measurable in vitro.

[0134] Thus, in a first step, and with reference to FIG. 4 , a lot of sensors is manufactured (step 402). In a second step, sensors from that lot are assigned an identification code, and sensor characteristics or measurable initial parameters, e.g., the initial in vitro calibration check slope, are measured and stored via the calibration check procedure described above (step 404). A subset of the lot of sensors is removed from the lot (step 408) for the purpose of testing operational parameters, specifically long-term drift characteristics (step 412), e.g., to track and trend variations in sensitivity drift profiles. Because this testing is largely destructive, sensors in the subset are generally not reused in patients. In one exemplary implementation, the test is run for 15 days. For example, in the first reservoir (0 mg / dL analyte concentration, e.g., glucose), a lot median baseline is calculated and subtracted from the signal of each sensor in the second reservoir (e.g., 250 mg / dL glucose). For each raw count, a sensitivity value is calculated. In this manner, the test determines the parameters

number

[0135] Operating parameters for each sensor

number

[0136] As a precursor to deriving the aforementioned linear coefficients relating calibration check sensitivity to in vivo operating parameters, the next step is to empirically determine one or more in vivo operating parameters using retrospective analysis (step 414), e.g., through the use of a developed model and optimization / fitting practices, to determine sensitivity parameters and baseline coefficients (step 415). This step includes the substeps of determining appropriate sensitivity and baseline models and optimizing a set of operating parameters for such models, appropriately initialized, based on a cost function.

[0137] The following additional terms are used and are defined below for use in systems and methods according to the present principles:

[0138] [Table 1]

[0139] These coefficients are referred to as operating parameters, and the first step in factory calibration is to determine one or more of these operating parameters for a particular type or design of sensor, which determination is based on retrospective data, for example, from previous bench tests or clinical data (step 414). The coefficients are generally developed using numerical analysis and have starting or initial values ​​based on bench tests or clinical trials, as will now be described. Specifically, the basic assumptions about the physical process of the sensor that generates a signal according to analyte concentration are as follows:

[0140]

number

number

[0141] The sensitivity profile model using a single exponential function (see Figure 5) is given below in units (pA / (mg / dL)).

number

[0142] An alternative sensitivity model implemented in the Joint Probability Algorithm (JPA) described in the application incorporated by reference above is a convex combination of two exponential components, one fast and one slow (see Figure 6).

number

number

number

number

[0143] The above describes the model for sensitivity. In contrast, the baseline intercept model is split into two components, the non-enzyme break-in (in pA) is:

number

number

[0144] An illustration of the model for the non-enzymatic break-in baseline B1(t) can be seen in one implementation in Figure 7, and the physiological bias B2(t) can be seen in Figure 8. In an exemplary study, the model

number

number

[0145] The above relationships and models relating the slopes or sensitivity values ​​to each other and to the baseline may then be used to determine values ​​for a given sensor. More specifically, numerical analysis, specifically backward model fitting, may then be used to determine the optimal value for each given sensor, where c is a single vector concatenating all the model parameters.

number

[0146] Initial values ​​used to start the retrospective model fitting may be used, for example, for one type of sensor, initial values ​​from previous bench and clinical data were found as follows:

number

[0147] For other types of sensors, initial values ​​were found as follows:

number

[0148] In this analysis, the first 11 digits of c0 were estimated from non-enzymatic sensor studies and other previous studies. The last three elements of c0 were lot averages from long-term drift studies in the previous studies. The values ​​of these parameters may vary for different sensors, or even as additional details are determined for the same sensor. These values ​​may also be replaced with values ​​measured from long-term drift studies for a specific subset of sensors from a specific lot. In this case, baseline values ​​may be determined from previous studies; in this regard, it should be noted that baseline values ​​tend to be more stable over time than sensitivity values. A double exponential sensitivity model was used, and the last term in c is the λ parameter of the model.

[0149] Given the raw sensor current y(t) and c, the EGV is calculated and combined with analyte readings from a representative external standard (e.g., YSI, SMBG, HPLC, mass spectrometry, etc.), referred to as "matched pairs," to assess the accuracy of the EGV. The number of matched pairs can vary depending on the length of the session and the degree of host compliance, but often 5-10 matched pairs per day can be used. Using the mean absolute relative difference (MARD) as a cost function, numerical analysis can be performed to find the optimal parameters c*, initialized by c0, that minimize MARD. For example, in one implementation, the Matlab® function "fminsearch" was used to find the optimal parameters c*. The cost function can usually be referenced to the more readily available SMBG to accommodate longer test periods.

[0150] If MARD itself is minimized, then the parameter fit is not constrained.

[0151] In another implementation, the parameter fit can be constrained using prior knowledge of the sensors, in which case the minimization is of MARD and the deviation of the measured parameter c from the expected c. For example, for N matched pairs of all sensors, a constrained cost function cost(c, c) can be defined based on the absolute relative difference (ARD).

number

[0152] The rest of the fitting is the same as in the unconstrained scenario above, e.g., using fminsearch in Matlab®. The result of the fitting is the optimized c* vector.

[0153] However, it should be noted that the above is one possible definition of the cost function, and in practice there are many different ways to define the constrained cost function that will strike a different balance between minimizing the error from the EGV to the true reference and minimizing the deviation of the measured parameter c from the typical sensor behavior c for a particular sensor.

[0154] In summary, the above describes one implementation of performing optimization / cost function analysis to determine an optimized c vector for a given sensor using in vivo clinical data. Several equations describing various relationships were used to find the optimized c vector. This c vector is then used to find the optimized b1-b for the particular given sensor. 11 , as well as m0, m R , and m F In this way, the optimized B1 and B2 are also determined as b1 to b8 and b9 to b 11 From in vitro long-term drift (destructive) tests, m 0,LTD , m R,LTD , and m F,LTD was also decided in the same way.

[0155] For in vitro long-term drift (LTD) studies, the intercept model and retrospective analysis differ from their in vivo counterparts. In bench LTD, intercepts are measured from the same lot using different sensor subsets (or subsamplings) and tested in 0 mg / dL analyte solution. Because the in vitro intercept model is empirically determined, initial parameter estimates for LTD are based on m 0,LTD , m F,LTD , and m R,LTD (or

number

number

[0156] Using these initial (seed) parameters, we find the optimal parameter set, c, that minimizes the following in vitro cost function for N raw data points per sensor: * Initialize the search.

number

[0157] In one implementation, the next step is to determine the relationship between the initial measurable parameters of the sensor and the in vivo and in vitro operating parameters of the sensor, e.g., between the sensor's calibration check sensitivity and the in vivo and / or in vitro sensitivity values ​​(step 416). As will be described, this generally involves determining one or more sets of coefficients and producing a prospective, ongoing in vivo sensitivity for the sensor after it has been used in a patient using it.

[0158] First, in one implementation, a measurable initial value of the calibration check slope is correlated with the determined LTD parameters, step 418. For example, in one implementation, this correlation is parameterized by the α, β, μ, and ν coefficients shown below in equation (I).

[0159] The calibration check to the LTD equation is:

[0160]

number

[0161] To derive this empirical correlation for subsequent use in prospective factory calibration, the sensor calibration check and long-term drift data can be inserted into the set of equations (I) and a data fit analysis performed to determine α, β, μ, and ν. In one implementation, a least-squares linear regression is performed between the LTD sensitivity and the calibration check sensitivity. In this regression, any matched pair can be a (calibration check, in vitro sensitivity) pair for one sensor, and for a sensor lot, a linear regression can be performed to obtain, for example, α, β, μ, and ν.

[0162] The results of this regression are then used in conjunction with the set of equations (II) to predict the in vivo operating parameters for each individual sensor.

[0163] That is, the calibration check for in vivo sensitivity is the coefficient in equation (II) below: It is parameterized with an additional set of γ, δ, ε, and σ.

[0164]

number

[0165] In either case, the above equations assume that there is a multiplicative scaling between the calibration check versus bench and the calibration check versus in vivo relationship.

[0166] More specifically, to establish the values ​​of these coefficients in (II), a retrospective analysis was performed based on clinical and longitudinal drift data from previous studies and from the determined c*, as described above, and an analysis, e.g., ordinary least squares (OLS) regression, was performed to determine the calibration check slope and m 0,VIVO and m F,VIVO In this regard, a linear relationship may be derived between the determined c* using the techniques above, and in particular the determined m 0,VIVO , m F,VIVO , and m R,VIVO (or

number

[0167] In this manner, once the initial values ​​of the sensor's operating variables, e.g., the calibration check slope, have been determined for a given sensor, the desired in vivo sensitivity value can be determined using at least the initial values ​​of the operating variables and the determined relationship (step 427).

[0168] One or more of several schemes of factory calibration encoding may then be implemented. Factory Calibration Coding I

[0169] When a single exponential sensitivity model, e.g., equation (2), is used for parameter fitting, the drift rate is, e.g., m R = 0.9. In this case, the value of the OLS linear coefficient may be determined to be:

number

[0170] 9(A) and 9(B) show an exemplary backward OLS fit using the single exponential sensitivity model described above.

[0171] When using the double exponential sensitivity model (Equation (3)), the ratio between the fast and slow components is

number

number

[0172] 10(A) and 10(B) show an exemplary backward OLS fit using the double exponential sensitivity model described above.

[0173] If long-term drift tests from a new subsequent sensor lot deviate significantly, e.g., by more than 5%, more than 10%, more than 20%, or some other amount, from the estimated linear relationship (see, e.g., the ellipses in Figures 11(A) and 11(B)), a rescue strategy may be used to estimate a new linear predictor of the in vivo sensitivity profile for the factory calibration for the given sensor lot (step 425). Two rescue strategies are described below.

[0174] Referring to the figure, line 504 shows the expected linear relationship estimated using the above method. Line 502 shows a ±10% range around the expected linear relationship. Ellipses 508 show long-term drift results that deviate from the expected relationship by more than a predetermined amount, e.g., ±10%. Circles show intercepts that are maintained in the new linear relationship to handle exceptions. Lines 506 and 506 show the new linear regression.

[0175] In the first strategy, the x-axis intercept is maintained, so circle 512 is used with new linear relationship 506. In the second strategy, the y-axis intercept is maintained, so circle 514 is used with new linear relationship 506'.

[0176] Once a suitable relationship is determined between an initial measurable parameter, e.g., calibration check sensitivity, and in vivo sensitivity, the same relationship can be used to prospectively determine in vivo sensitivity for later sensors in the lot.

[0177] 12, a first device 602 may be used to manufacture a lot of sensors, after which a second device 604 may be used subsequently on the assembly line to develop a calibration for the manufactured sensors. The second device 604 may include a sensor calibration module 612 that performs the steps described above to measure initial measurable parameters and determine in vitro sensitivity therefrom. A coding module 616 may be used to develop a calibration indicator that may be associated with the manufactured sensors, which a user can use to calibrate the sensors upon receiving.

[0178] Additionally, with reference to FIG. 13 , the calibration indicator may be in the form of a code 619 that is entered into either the sensor electronics 12 or the mobile device / receiver 18 to provide calibration information. A simple alphabetic code, e.g., “A,” “B,” etc., may be linked to values ​​in a lookup table (21 or 21′) stored in the mobile device or sensor electronics, respectively. In some cases, the code may include calibration information, such as a sensitivity value. In these implementations, the code may be embodied by an RFID tag or NFC tag, which may be swiped by the sensor electronics or mobile device to transmit the code. In a similar manner, the code may be embodied by a barcode or other electronically or machine-readable medium. Given this teaching, other methods of transmitting calibration information will be understood.

[0179] Referring again to Figure 12, a packaging module 614 may form part of the second device 604, which serves to package the sensor and calibration indicator, either individually or together with the sensor electronics device. Referring to Figure 14, a kit 606 may include the sensor 10 as described above and the calibration indicator 608 described above. In an alternative implementation, and referring to Figure 15, a kit 607 may include the sensor 10, the calibration indicator 608 described above, and the sensor electronics device 12. In many implementations, the sensor electronics device 12 forms a transmitter that is mechanically calibrated to physically couple with the sensor.

[0180] A system and method have been described for achieving factory calibration of a sensor using the measured initial value of the sensor and the relationship between the measured initial value and one or more in vivo sensitivity parameters.

[0181] In one preferred embodiment, the analyte sensor is an implantable glucose sensor, such as those described with reference to U.S. Patent No. 6,001,067 and U.S. Patent Publication No. US-2005 / 0027463-A1. In another preferred embodiment, the analyte sensor is a transcutaneous glucose sensor, such as those described with reference to U.S. Patent Publication No. US-2006 / 0020187-A1. In yet other embodiments, the sensor is configured to be implanted within a host's blood vessel or outside the body, as described in U.S. Patent Publication No. US-2007 / 0027385-A1, co-pending U.S. patent application Ser. No. 11 / 543,396, filed October 4, 2006, co-pending U.S. patent application Ser. No. 11 / 691,426, filed March 26, 2007, and co-pending U.S. patent application Ser. No. 11 / 675,063, filed February 14, 2007. In one alternative embodiment, the continuous glucose sensor includes a transcutaneous sensor, such as those described in U.S. Pat. No. 6,565,509 to Say et al. In another alternative embodiment, the continuous glucose sensor includes a subcutaneous sensor, such as those described with reference to U.S. Pat. No. 6,579,690 to Bonnecaze et al. and U.S. Pat. No. 6,484,046 to Say et al. In another alternative embodiment, the continuous glucose sensor includes a replaceable subcutaneous sensor, such as those described with reference to U.S. Pat. No. 6,512,939 to Colvin et al. In another alternative embodiment, the continuous glucose sensor includes an intravascular sensor, such as those described with reference to U.S. Pat. No. 6,477,395 to Schulman et al. In another alternative embodiment, the continuous glucose sensor includes an intravascular sensor, such as those described with reference to U.S. Pat. No. 6,424,847 to Mastrototaro et al.

[0182] The connections between elements shown in the figures represent exemplary communication paths. Additional communication paths, either direct or through intermediaries, may be included to further facilitate the exchange of information between elements. The communication paths may be two-way communication paths that allow elements to exchange information.

[0183] The various operations of the methods described above may be performed by any suitable means capable of performing the operations, such as various hardware and / or software component(s), circuits, and / or module(s). Generally, any operation illustrated in the figures may be performed by a corresponding functional means capable of performing the operation.

[0184] The various illustrative logic blocks, modules, and circuits described in connection with this disclosure (such as the blocks of FIGS. 2 and 4 ) may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof, designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be a commercially available processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0185] In one or more aspects, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media may include various types of RAM, ROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, WiFi, and Bluetooth® RFID, NFC, and microwave are included within the definition of medium. Disk and disc, as used herein, include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray® discs, where disks typically reproduce data magnetically while discs reproduce data optically with a laser. Thus, in some aspects, computer-readable medium may include non-transitory computer-readable medium (e.g., tangible media). Additionally, in some aspects, computer-readable medium may include transitory computer-readable medium (e.g., a signal). Combinations of the above should also be included within the scope of computer-readable medium.

[0186] The methods disclosed herein comprise one or more steps or actions for achieving the described method. Method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0187] Certain aspects may include computer program products for performing the operations presented herein. For example, such computer program products may include a computer-readable medium having instructions stored (and / or encoded) thereon, the instructions being executable by one or more processors to perform the operations described herein. For certain aspects, the computer program product may include packaging materials.

[0188] Software or instructions may also be transmitted over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included within the definition of transmission media.

[0189] Furthermore, it should be understood that modules and / or other suitable means for implementing the methods and techniques described herein may be downloaded and / or otherwise obtained by a user terminal and / or base station, where applicable. For example, such devices may be coupled to a server to facilitate the transfer of means for implementing the methods described herein. Alternatively, the various methods described herein may be provided via storage means (e.g., RAM, ROM, physical storage media such as a compact disc (CD) or floppy disk, etc.), such that the user terminal and / or base station may obtain the various methods upon coupling or providing storage means to the device. Furthermore, any other suitable technique for providing the methods and techniques described herein to a device may be utilized.

[0190] It is to be understood that the claims are not limited to the precise configuration and components illustrated above. Various modifications, changes and variations can be made in the arrangement, operation and details of the methods and apparatus described above without departing from the scope of the claims.

[0191] Unless otherwise defined, all terms (including technical and scientific terms) are to be construed as having their ordinary and customary meanings indicated to those skilled in the art, and are not to be limited to any special or customized meaning unless expressly defined as such herein. It should be noted that the use of a particular term when describing a particular feature or aspect of the present disclosure should not be construed as implying that the term is being redefined herein to be limited to include any particular characteristic of the feature or aspect of the present disclosure to which the term pertains. Particularly in the appended claims, terms and phrases used in this application, and variations thereof, should be construed as open-ended as opposed to limiting, unless expressly stated otherwise. As examples of the above, the term "including" should be interpreted to mean "including without limitation," "including but not limited to," etc.; the term "comprising," when used herein, is synonymous with "including," "containing," or "featuring," is inclusive or open-ended, and does not exclude additional, unrecited elements or method steps; the term "having" should be interpreted as "having at least," the term "including" should be interpreted as "including but not limited to," the term "embodiment" is used to provide illustrative examples of the items under discussion rather than an exhaustive or exclusive list of them, and does not refer to "known," "conventional," "ordinary," "ordinary," "ordinary" or "examples." Adjectives such as "standard," and terms of similar import should not be construed to limit the matter described to that available in a given period or at a given time, but rather should be construed to encompass known, conventional, or standard technology that may be available or known at any time now or in the future; and the use of terms such as "preferably," "preferred," "desired," or "desirable," and terms of similar import, should not be understood to imply that a particular feature is critical, essential, or even important to the structure or function of the invention, but rather should be intended merely to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the invention.Similarly, a group of items joined by the conjunction "and" should not be construed as requiring the presence of every single one of the items in the group, but rather should be construed as "and / or" unless otherwise stated. Similarly, a group of items joined by the conjunction "or" should not be construed as requiring mutual exclusivity between the groups, but rather should be construed as "and / or" unless otherwise stated.

[0192] When a range of values ​​is provided, it is understood that the upper and lower limits, and each intervening value between the upper and lower limits of that range, are encompassed within an embodiment.

[0193] With respect to virtually any plural and / or singular term herein, those skilled in the art can convert from plural to singular and / or from singular to plural as appropriate to the context and / or application. Various singular / plural permutations may be expressly set forth herein for clarity. The indefinite articles "a" or "an" do not exclude plurals. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain criteria are recited in mutually distinct independent claims does not indicate that a combination of these criteria cannot be used to advantage. Reference signs in the claims should not be construed as limiting the scope.

[0194] When a specific number is intended in an introduced claim recitation, such intention will be clearly stated in the claim, and it will be further understood by those skilled in the art that, in the absence of such a statement, no such intention exists. For example, to aid in understanding, the following appended claims may include the use of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed as implying that introducing a claim recitation with the indefinite article "a" or "an" limits a particular claim that includes such an introduced claim recitation to embodiments containing only one such recitation (e.g., "a" and / or "an" should typically be construed to mean "at least one" or "one or more"). This also applies to the use of definite articles used to introduce claim recitations, even when the same claim includes the introductory phrases "one or more" or "at least one" and an indefinite article such as "a" or "an." Additionally, even when a specific number of recitations in an introduced claim is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., a mere recitation of "two recitations" without other modifiers typically means at least two recitations, or more than two recitations). Furthermore, when a conventional expression similar to "at least one of A, B, and C, etc." is used, such structure is generally intended to include any combination of the recited items, including, for example, a single member, in the sense that a person skilled in the art would understand the conventional expression (e.g., "a system having at least one of A, B, and C" includes, but is not limited to, systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.).When a conventional expression similar to "at least one of A, B, or C, etc." is used, generally, such a structure is intended in the sense that one of ordinary skill in the art would understand the conventional expression (e.g., "a system having at least one of A, B, or C" includes, but is not limited to, a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.). It will be further understood by those skilled in the art that virtually any disjunction word and / or phrase indicating two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibility of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" is understood to include the possibilities of "A" or "B" or "A and B."

[0195] It is to be understood that all numbers expressing quantities of ingredients, reaction conditions, and so forth used herein are generally modified by the term "about." Accordingly, unless indicated to the contrary, the numerical parameters set forth herein are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of any claims in any application claiming priority to this application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

[0196] All references cited herein are incorporated herein by reference in their entirety. To the extent that publications and patents or patent applications incorporated by reference conflict with the disclosure contained herein, the present specification is intended to supersede and / or take precedence over any such conflicting material.

[0197] Headings are included herein for reference and to aid in locating the various sections. These headings are not intended to limit the scope of the concepts described therein. Such concepts may have applicability throughout the entire specification.

[0198] Moreover, while the foregoing has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be apparent to those skilled in the art that certain changes and modifications may be practiced. Therefore, the descriptions and examples should not be construed as limiting the scope of the invention to the specific embodiments and examples described herein, but rather as embracing all modifications and alternatives that fall within the true scope and spirit of the invention.

[0199] The present system and method may be fully implemented on any number of computing devices. Typically, instructions are deployed on a computer-readable medium, generally non-transitory, sufficient to enable a processor in the computing device to implement the methods of the present invention. The computer-readable medium may be a hard drive or solid-state storage device with instructions loaded into random access memory at runtime. Input to the application, for example from multiple users or any single user, may be via any number of suitable computer input devices. For example, a user may enter data related to a computation using a keyboard, mouse, touchscreen, joystick, trackpad, other pointing device, or any other such computer input device. Data may also be entered via an inserted memory chip, hard drive, flash drive, flash memory, optical media, magnetic media, or any other type of file storage medium. Output may be communicated to the user via a video graphics card or integrated graphics chipset coupled to a display viewable by the user. Alternatively, a printer may be used to output a hard copy of the results. Given the present teachings, it will be understood that any number of other tangible outputs are contemplated by the present invention. For example, the output may be stored on a memory chip, a hard drive, a flash drive, flash memory, optical media, magnetic media, or any other type of output. It should also be noted that the present invention may be implemented in any number of different types of computing devices, such as personal computers, laptop computers, notebook computers, netbook computers, handheld computers, personal digital assistants, mobile phones, smartphones, tablet computers, and devices designed specifically for these purposes. In one implementation, a user of a smartphone or wi-fi connected device downloads a copy of the application to their device from a server using a wireless internet connection.Appropriate authentication procedures and secure transaction processes may be provided for payments to be made to the seller. Applications may be downloaded via a mobile connection or via WiFi or other wireless network connection so that the application can be executed by the user. Such networked systems may provide a suitable computing environment for implementation in which multiple users provide separate inputs to the system and method. In the following systems where a factory calibration scheme is contemplated, multiple inputs may allow multiple users to enter relevant data simultaneously. [Explanation of symbols]

[0200] 2. Drug delivery pumps 4. Glucose meter 8. Continuous Analyte Sensor System 10 Continuous Analyte Sensors 12 Sensor Electronics 14 Display Devices 16 Display Devices 18 devices 20 Computer 100 systems 205 Application Specific Integrated Circuits (ASIC) 210 Potentiostat 211 input port 212 data line 214 Processor Module 216 program memory 218 memory 220 Data storage device 222 User Interface 224 Button 226 Liquid Crystal Display (LCD) 228 Vibrator 230 Audio Converter 232 Telemetry Module 234 Batteries 236 Battery Charger / Regulator 237 input port 238 communication port 239 output port 602 devices 604 devices 606, 607 Kit 612 Sensor Calibration Module 614 Packaging Module 616 Coding Module

Claims

[Claim 1] 1. A method of calibrating an analyte concentration sensor, the sensor being part of a manufactured lot of sensors, the lot being of a given type of sensor, one or more in vivo operating parameters corresponding to the sensitivity of sensors of the type of sensor in the lot being determined, the determination of the in vivo operating parameters being based on retrospective data, the method comprising: measuring values ​​of initial measurable parameters for a target sensor at a factory calibration; correlating the measured values ​​of the initial measurable parameters of the target sensor with one or more long-term drift characteristics measured at factory calibration and the one or more in vivo operating parameters by at least one coefficient, the long-term drift characteristics corresponding to the sensitivity of a subset of sensors in the manufactured sensor lot; and using the coefficients to prospectively determine an estimate of at least a final in vivo sensitivity of the target sensor, wherein an in vivo sensitivity value for the target sensor can be estimated given measured values ​​of the initial measurable parameters of the target sensor.

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