Advanced analyte sensor calibration and error detection   

Abstract: Systems and methods for processing sensor data and self-calibration are provided. In some embodiments, systems and methods are provided which are capable of calibrating a continuous analyte sensor based on an initial sensitivity, and then continuously performing self-calibration without using, or with reduced use of, reference measurements. In certain embodiments, a sensitivity of the analyte sensor is determined by applying an estimative algorithm that is a function of certain parameters. Also described herein are systems and methods for determining a property of an analyte sensor using a stimulus signal. The sensor property can be used to compensate sensor data for sensitivity drift, or determine another property associated with the sensor, such as temperature, sensor membrane damage, moisture ingress in sensor electronics, and scaling factors.

This application claims the benefit of U.S. Provisional Application No. 61/476,145 filed Apr. 15, 2011. The aforementioned application is incorporated by reference herein in its entirety, and is hereby expressly made a part of this specification.

TECHNICAL FIELD

The embodiments described herein relate generally to systems and methods for processing sensor data from continuous analyte sensors and for self-calibration.

Diabetes mellitus is a chronic disease, which occurs when the pancreas does not produce enough insulin (Type I), or when the body cannot effectively use the insulin it produces (Type II). This condition typically leads to an increased concentration of glucose in the blood (hyperglycemia), which can cause an array of physiological derangements (e.g., kidney failure, skin ulcers, or bleeding into the vitreous of the eye) associated with the deterioration of small blood vessels. Sometimes, a hypoglycemic reaction (low blood sugar) is induced by an inadvertent overdose of insulin, or after a normal dose of insulin or glucose-lowering agent accompanied by extraordinary exercise or insufficient food intake.

A variety of sensor devices have been developed for continuously measuring blood glucose concentrations. Conventionally, a diabetic person carries a self-monitoring blood glucose (SMBG) monitor, which typically involves uncomfortable finger pricking methods. Due to a lack of comfort and convenience, a diabetic will often only measure his or her glucose levels two to four times per day. Unfortunately, these measurements can be spread far apart, such that a diabetic may sometimes learn too late of a hypoglycemic or hyperglycemic event, thereby potentially incurring dangerous side effects. In fact, not only is it unlikely that a diabetic will take a timely SMBG measurement, but even if the diabetic is able to obtain a timely SMBG value, the diabetic may not know whether his or her blood glucose value is increasing or decreasing, based on the SMBG alone.

Heretofore, a variety of glucose sensors have been developed for continuously measuring glucose values. Many implantable glucose sensors suffer from complications within the body and provide only short-term and less-than-accurate sensing of blood glucose. Similarly, transdermal sensors have run into problems in accurately sensing and reporting back glucose values continuously over extended periods of time. Some efforts have been made to obtain blood glucose data from implantable devices and retrospectively determine blood glucose trends for analysis; however these efforts do not aid the diabetic in determining real-time blood glucose information. Some efforts have also been made to obtain blood glucose data from transdermal devices for prospective data analysis, however similar problems have occurred.

SUMMARY

In a first aspect, a method is provided for calibrating sensor data generated by a continuous analyte sensor, comprising: generating sensor data using a continuous analyte sensor; iteratively determining, with an electronic device, a sensitivity value of the continuous analyte sensor as a function of time by applying a priori information comprising sensor sensitivity information; and calibrating the sensor data based at least in part on the determined sensitivity value.

In an embodiment of the first aspect or any other embodiment thereof, calibrating the sensor data is performed iteratively throughout a substantially entire sensor session.

In an embodiment of the first aspect or any other embodiment thereof, iteratively determining a sensitivity value is performed at regular intervals or performed at irregular intervals, as determined by the a priori information.

In an embodiment of the first aspect or any other embodiment thereof, iteratively determining a sensitivity value is performed throughout a substantially entire sensor session.

In an embodiment of the first aspect or any other embodiment thereof, determining a sensitivity value is performed in substantially real time.

In an embodiment of the first aspect or any other embodiment thereof, the a priori information is associated with at least one predetermined sensitivity value that is associated with a predetermined time after start of a sensor session.

In an embodiment of the first aspect or any other embodiment thereof, at least one predetermined sensitivity value is associated with a correlation between a sensitivity determined from in vitro analyte concentration measurements and a sensitivity determined from in vivo analyte concentration measurements at the predetermined time.

In an embodiment of the first aspect or any other embodiment thereof, the a priori information is associated with a predetermined sensitivity function that uses time as input.

In an embodiment of the first aspect or any other embodiment thereof, time corresponds to time after start of a sensor session.

In an embodiment of the first aspect or any other embodiment thereof, time corresponds to at least one of time of manufacture or time since manufacture.

In an embodiment of the first aspect or any other embodiment thereof, the sensitivity value of the continuous analyte sensor is also a function of at least one other parameter.

In an embodiment of the first aspect or any other embodiment thereof, the at least one other parameter is selected from the group consisting of: temperature, pH, level or duration of hydration, curing condition, an analyte concentration of a fluid surrounding the continuous analyte sensor during startup of the sensor, and combinations thereof.

In an embodiment of the first aspect or any other embodiment thereof, calibrating the sensor data is performed without using reference blood glucose data.

In an embodiment of the first aspect or any other embodiment thereof, the electronic device is configured to provide a level of accuracy corresponding to a mean absolute relative difference of no more than about 10% over a sensor session of at least about 3 days, and wherein reference measurements associated with calculation of the mean absolute relative difference are determined by analysis of blood.

In an embodiment of the first aspect or any other embodiment thereof, the sensor session is at least about 4 days.

In an embodiment of the first aspect or any other embodiment thereof, the sensor session is at least about 5 days.

In an embodiment of the first aspect or any other embodiment thereof, the sensor session is at least about 6 days.

In an embodiment of the first aspect or any other embodiment thereof, the sensor session is at least about 7 days.

In an embodiment of the first aspect or any other embodiment thereof, the sensor session is at least about 10 days.

In an embodiment of the first aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 7% over the sensor session.

In an embodiment of the first aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 5% over the sensor session.

In an embodiment of the first aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 3% over the sensor session.

In an embodiment of the first aspect or any other embodiment thereof, the a priori information is associated with a calibration code.

In an embodiment of the first aspect or any other embodiment thereof, the a priori sensitivity information is stored in the sensor electronics prior to use of the sensor.

In a second aspect, a system is provided for implementing the method of the first aspect or any embodiments thereof.

In a third aspect, a method is provided for calibrating sensor data generated by a continuous analyte sensor, the method comprising: generating sensor data using a continuous analyte sensor; determining, with an electronic device, a plurality of different sensitivity values of the continuous analyte sensor as a function of time and of sensitivity information associated with a priori information; and calibrating the sensor data based at least in part on at least one of the plurality of different sensitivity values.

In an embodiment of the third aspect or any other embodiment thereof, calibrating the continuous analyte sensor is performed iteratively throughout a substantially entire sensor session.

In an embodiment of the third aspect or any other embodiment thereof, the plurality of different sensitivity values are stored in a lookup table in computer memory.

In an embodiment of the third aspect or any other embodiment thereof, determining a plurality of different sensitivity values is performed once throughout a substantially entire sensor session.

In an embodiment of the third aspect or any other embodiment thereof, the a priori information is associated with at least one predetermined sensitivity value that is associated with a predetermined time after start of a sensor session.

In an embodiment of the third aspect or any other embodiment thereof, the at least one predetermined sensitivity value is associated with a correlation between a sensitivity determined from in vitro analyte concentration measurements and a sensitivity determined from in vivo analyte concentration measurements at the predetermined time.

In an embodiment of the third aspect or any other embodiment thereof, the a priori information is associated with a predetermined sensitivity function that uses time as input.

In an embodiment of the third aspect or any other embodiment thereof, time corresponds to time after start of a sensor session.

In an embodiment of the third aspect or any other embodiment thereof, time corresponds to time of manufacture or time since manufacture.

In an embodiment of the third aspect or any other embodiment thereof, the plurality of sensitivity values are also a function of at least one parameter other than time.

In an embodiment of the third aspect or any other embodiment thereof, the at least one other parameter is selected from the group consisting of: temperature, pH, level or duration of hydration, curing condition, an analyte concentration of a fluid surrounding the continuous analyte sensor during startup of the sensor, and combinations thereof.

In an embodiment of the third aspect or any other embodiment thereof, calibrating the continuous analyte sensor is performed without using reference blood glucose data.

In an embodiment of the third aspect or any other embodiment thereof, the electronic device is configured to provide a level of accuracy corresponding to a mean absolute relative difference of no more than about 10% over a sensor session of at least about 3 days; and wherein reference measurements associated with calculation of the mean absolute relative difference are determined by analysis of blood.

In an embodiment of the third aspect or any other embodiment thereof, the sensor session is at least about 4 days.

In an embodiment of the third aspect or any other embodiment thereof, the sensor session is at least about 5 days.

In an embodiment of the third aspect or any other embodiment thereof, the sensor session is at least about 6 days.

In an embodiment of the third aspect or any other embodiment thereof, the sensor session is at least about 7 days.

In an embodiment of the third aspect or any other embodiment thereof, the sensor session is at least about 10 days.

In an embodiment of the third aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 7% over the sensor session.

In an embodiment of the third aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 5% over the sensor session.

In an embodiment of the third aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 3% over the sensor session.

In an embodiment of the third aspect or any other embodiment thereof, the a priori information is associated with a calibration code.

In a fourth aspect, a system is provided for implementing the method of the third aspect or any embodiments thereof.

In a fifth aspect, a method is provided for processing data from a continuous analyte sensor, the method comprising: receiving, with an electronic device, sensor data from a continuous analyte sensor, the sensor data comprising at least one sensor data point; iteratively determining a sensitivity value of the continuous analyte sensor as a function of time and of an at least one predetermined sensitivity value associated with a predetermined time after start of a sensor session; forming a conversion function based at least in part on the sensitivity value; and determining an analyte output value by applying the conversion function to the at least one sensor data point.

In an embodiment of the fifth aspect or any other embodiment thereof, the iteratively determining a sensitivity of the continuous analyte sensor is performed continuously.

In an embodiment of the fifth aspect or any other embodiment thereof, iteratively determining a sensitivity is performed in substantially real time.

In an embodiment of the fifth aspect or any other embodiment thereof, the method further comprises determining a baseline of the continuous analyte sensor, and wherein the conversion function is based at least in part on the baseline.

In an embodiment of the fifth aspect or any other embodiment thereof, determining a baseline of the continuous analyte sensor is performed continuously.

In an embodiment of the fifth aspect or any other embodiment thereof, determining a sensitivity of the continuous analyte sensor and determining a baseline of the analyte sensor are performed at substantially the same time.

In an embodiment of the fifth aspect or any other embodiment thereof, the at least one predetermined sensitivity value is set at a manufacturing facility for the continuous analyte sensor.

In an embodiment of the fifth aspect or any other embodiment thereof, the method further comprises receiving at least one calibration code; and applying the at least one calibration code to the electronic device at a predetermined time after start of the sensor session.

In an embodiment of the fifth aspect or any other embodiment thereof, iteratively determining a sensitivity is performed at regular intervals or performed at irregular intervals, as determined by the at least one calibration code.

In an embodiment of the fifth aspect or any other embodiment thereof, the at least one calibration code is associated with the at least one predetermined sensitivity.

In an embodiment of the fifth aspect or any other embodiment thereof, the at least one calibration code is associated with a predetermined sensitivity function that uses time of the function of time as input.

In an embodiment of the fifth aspect or any other embodiment thereof, time corresponds to time after start of the sensor session.

In an embodiment of the fifth aspect or any other embodiment thereof, time corresponds to time of manufacture or time since manufacture.

In an embodiment of the fifth aspect or any other embodiment thereof, the sensitivity value of the continuous analyte sensor is also a function of at least one other parameter.

In an embodiment of the fifth aspect or any other embodiment thereof, the at least one other parameter is selected from the group consisting of: temperature, pH, level or duration of hydration, curing condition, an analyte concentration of a fluid surrounding the continuous analyte sensor during startup of the sensor, and combinations thereof.

In a sixth aspect, a system is provided for implementing the method of the fifth aspect or any embodiments thereof.

In a seventh aspect, a method is provided for calibrating a continuous analyte sensor, the method comprising: receiving sensor data from a continuous analyte sensor; forming or receiving a predetermined sensitivity profile corresponding to a change in sensor sensitivity to an analyte over a substantially entire sensor session, wherein the predetermined sensitivity profile is a function of at least one predetermined sensitivity value associated with a predetermined time after start of the sensor session; and applying, with an electronic device, the sensitivity profile in real-time calibrations.

In an embodiment of the seventh aspect or any other embodiment thereof, the at least one predetermined sensitivity value, the predetermined sensitivity profile, or both are set at a manufacturing facility for the continuous analyte sensor.

In an embodiment of the seventh aspect or any other embodiment thereof, the method further comprises receiving at least one calibration code; and applying the at least one calibration code to the electronic device at a predetermined time after start of the sensor session.

In an embodiment of the seventh aspect or any other embodiment thereof, the at least one calibration code is associated with the at least one predetermined sensitivity.

In an embodiment of the seventh aspect or any other embodiment thereof, the at least one calibration code is associated with a predetermined sensitivity function that uses time as input.

In an embodiment of the seventh aspect or any other embodiment thereof, the sensitivity profile is a function of time.

In an embodiment of the seventh aspect or any other embodiment thereof, time corresponds to time after start of the sensor session.

In an embodiment of the seventh aspect or any other embodiment thereof, time corresponds to time of manufacture or time since manufacture.

In an embodiment of the seventh aspect or any other embodiment thereof, the sensitivity value is a function of time, the predetermined sensitivity value, and at least one parameter selected from the group consisting of: temperature, pH, level or duration of hydration, curing condition, an analyte concentration of a fluid surrounding the continuous analyte sensor during startup of the sensor, and combinations thereof.

In an eighth aspect, a system is provided for implementing the method of the seventh aspect or any embodiments thereof.

In a ninth aspect, a method is provided for processing data from a continuous analyte sensor, the method comprising: receiving, with an electronic device, sensor data from a continuous analyte sensor, the sensor data comprising at least one sensor data point; receiving or forming a sensitivity profile corresponding to a change in sensor sensitivity over a substantially entire sensor session; forming a conversion function based at least in part on the sensitivity profile; and determining an analyte output value by applying the conversion function to the at least one sensor data point.

In an embodiment of the ninth aspect or any other embodiment thereof, the sensitivity profile is set at a manufacturing facility for the continuous analyte sensor.

In an embodiment of the ninth aspect or any other embodiment thereof, the method comprises receiving at least one calibration code; and applying the at least one calibration code to the electronic device at a predetermined time after start of the sensor session.

In an embodiment of the ninth aspect or any other embodiment thereof, the at least one calibration code is associated with the at least one predetermined sensitivity.

In an embodiment of the ninth aspect or any other embodiment thereof, the at least one calibration code is associated with the sensitivity profile.

In an embodiment of the ninth aspect or any other embodiment thereof, the sensitivity profile is a function of time.

In an embodiment of the ninth aspect or any other embodiment thereof, time corresponds to time after start of the sensor session.

In an embodiment of the ninth aspect or any other embodiment thereof, time corresponds to time of manufacture or time since manufacture.

In an embodiment of the ninth aspect or any other embodiment thereof, the sensitivity is a function of time and at least one parameter is selected from the group consisting of: temperature, pH, level or duration of hydration, curing condition, an analyte concentration of a fluid surrounding the continuous analyte sensor during startup of the sensor, and combinations thereof.

In a tenth aspect, a system is provided for implementing the method of the ninth aspect or any embodiments thereof.

In an eleventh aspect, a system is provided for monitoring analyte concentration in a host, the system comprising: a continuous analyte sensor configured to measure analyte concentration in a host and to provide factory-calibrated sensor data, the factory-calibrated sensor data being calibrated without reference blood glucose data; wherein the system is configured to provide a level of accuracy corresponding to a mean absolute relative difference of no more than about 10% over a sensor session of at least about 3 days, wherein reference measurements associated with calculation of the mean absolute relative difference are determined by analysis of blood.

In an embodiment of the eleventh aspect or any other embodiment thereof, the sensor session is at least about 4 days.

In an embodiment of the eleventh aspect or any other embodiment thereof, the sensor session is at least about 5 days.

In an embodiment of the eleventh aspect or any other embodiment thereof, the sensor session is at least about 6 days.

In an embodiment of the eleventh aspect or any other embodiment thereof, the sensor session is at least about 7 days.

In an embodiment of the eleventh aspect or any other embodiment thereof, the sensor session is at least about 10 days.

In an embodiment of the eleventh aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 7% over the sensor session.

In an embodiment of the eleventh aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 5% over the sensor session.

In an embodiment of the eleventh aspect or any other embodiment thereof, the mean absolute relative difference is no more than about 3% over the sensor session.

In a twelfth aspect, a method for determining a property of a continuous analyte sensor, the method comprising: applying a bias voltage to an analyte sensor; applying a voltage step above the bias voltage to the analyte sensor; measuring, using sensor electronics, a signal response of the voltage step; determining, using the sensor electronics, a peak current of the signal response; determining, using the sensor electronics, a property of the sensor by correlating the peak current to a predetermined relationship.

In an embodiment of the twelfth aspect or any other embodiment thereof, correlating the peak current to the predetermined relationship comprises calculating an impedance of the sensor based on the peak current and correlating the sensor impedance to the predetermined relationship.

In an embodiment of the twelfth aspect or any other embodiment thereof, the property of the sensor is a sensitivity of the sensor or a temperature of the sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof, the peak current is a difference between a magnitude of the response prior to the voltage step and a magnitude of the largest measured response resulting from the voltage step.

In an embodiment of the twelfth aspect or any other embodiment thereof, the predetermined relationship is an impedance-to-sensor sensitivity relationship, and wherein the property of the sensor is a sensitivity of the sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof, the method further comprises compensating sensor data using the determined property of the sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof, the compensating comprises correlating a predetermined relationship of the peak current to sensor sensitivity or change in sensor sensitivity and modifying a value or values of the sensor data responsive to the correlated sensor sensitivity or change in sensor sensitivity.

In an embodiment of the twelfth aspect or any other embodiment thereof, predetermined relationship is a linear relationship over time of use of the analyte sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof, the predetermined relationship is a non-linear relationship over time of use of the analyte sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof, wherein the predetermined relationship is determined by prior testing of sensors similar to the analyte sensor.

In an embodiment of the thirteenth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processor of the sensor system, cause the sensor system to implement the method of the twelfth aspect or any embodiment thereof.

In a fourteenth aspect, a method is provided for calibrating an analyte sensor, the method comprising: applying a time-varying signal to the analyte sensor; measuring a signal response to the applied signal; determining, using sensor electronics, a sensitivity of the analyte sensor, the determining comprising correlating at least one property of the signal response to a predetermined sensor sensitivity profile; and generating, using sensor electronics, estimated analyte concentration values using the determined sensitivity and sensor data generated by the analyte sensor.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the sensitivity profile comprises varying sensitivity values over time since implantation of the sensor.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the predetermined sensitivity profile comprises a plurality of sensitivity values.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the predetermined sensitivity profile is based on sensor sensitivity data generated from studying sensitivity changes of analyte sensors similar to the analyte sensor.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the method further comprises applying a bias voltage to the sensor, wherein the time-varying signal comprises a step voltage above the bias voltage or a sine wave overlaying a voltage bias voltage.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the determining further comprises calculating an impedance value based on the measured signal response and correlating the impedance value to a sensitivity value of the predetermined sensitivity profile.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the method further comprises applying a DC bias voltage to the sensor to generate sensor data, wherein the estimating analyte concentration values includes generating corrected sensor data using the determined sensitivity.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the method further comprises applying a conversion function to the corrected sensor data to generate the estimated analyte concentration values.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the method further comprises forming a conversion function based at least in part of the determined sensitivity, and wherein the conversion function is applied to the sensor data to generate the estimated analyte concentration values.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the property is a peak current value of the signal response.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the determining further comprises using at least one of performing a Fast Fourier Transform on the signal response data, integrating at least a portion of a curve of the signal response, and determining a peak current of the signal response.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the determining further comprises selecting the predetermined sensitivity profile based on the determined sensor property from a plurality of different predetermined sensitivity profiles.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the selecting includes performing a data association analysis to determine a correlation between the determined sensor property and each of the plurality of different predetermined sensitivity profiles and wherein the selected predetermined sensitivity profile has the highest correlation.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the method further comprises generating estimated analyte concentration values using the selected sensitivity profile.

In an embodiment of the fourteenth aspect or any other embodiment thereof, the method further comprises determining a second sensitivity value using the selected sensitivity profile, wherein a first set of estimated analyte concentration values is generated using the determined sensitivity value and sensor data associated with a first time period, and wherein a second set of concentration values is generated using the second sensitivity value and sensor data associated with a second time period.

In a fifteenth aspect, a sensor system is provided for implementing the method of the fourteenth aspect or any embodiments thereof.

In an embodiment of the fifteenth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the fourteenth aspect or any embodiment thereof.

In a sixteenth aspect, a method is provided for determining whether an analyte sensor system is functioning properly, the method comprising: applying a stimulus signal to the analyte sensor; measuring a response to the stimulus signal; estimating a value of a sensor property based on the signal response; correlating the sensor property value with a predetermined relationship of the sensor property and a predetermined sensor sensitivity profile; and initiating an error routine if the correlation does not exceed a predetermined correlation threshold.

In an embodiment of the sixteenth aspect or any other embodiment thereof, correlating includes performing a data association analysis.

In an embodiment of the sixteenth aspect or any other embodiment thereof, the error routine comprises displaying a message to a user indicating that the analyte sensor is not functioning properly.

In an embodiment of the sixteenth aspect or any other embodiment thereof, the sensor property is an impedance of the sensor membrane.

In a seventeenth aspect, a sensor system is provided configured to implement the method of the sixteenth aspect or any embodiment thereof.

In an embodiment of the seventeenth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the sixteenth aspect or any embodiment thereof.

In an eighteenth aspect, a method is provided for determining a temperature associated with an analyte sensor, the method comprising: applying a stimulus signal to the analyte sensor; measuring a signal response of the signal; and determining a temperature associated with of the analyte sensor, the determining comprising correlating at least one property of the signal response to a predetermined relationship of the sensor property to temperature.

In an embodiment of the eighteenth aspect or any other embodiment thereof, the method further comprises generating estimated analyte concentration values using the determined temperature and sensor data generated from the analyst sensor.

In an embodiment of the eighteenth aspect or any other embodiment thereof, the generating includes compensating the sensor data using the determined temperature and converting the compensated sensor data to the generated estimated analyte values using a conversion function.

In an embodiment of the eighteenth aspect or any other embodiment thereof, the generating includes forming or modifying a conversion function using the determined temperature and converting the sensor data to the generated estimated analyte values using the formed or modified conversion function.

In an embodiment of the eighteenth aspect or any other embodiment thereof, the method further comprises measuring a temperature using a second sensor, wherein the determining further comprises using the measured temperature to determine the temperature associated with the analyte sensor.

In an embodiment of the eighteenth aspect or any other embodiment thereof, the second sensor is a thermistor.

In a nineteenth aspect, a sensor system is provided configured to implement the methods of the eighteenth aspect or any embodiment thereof.

In an embodiment of the nineteenth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the eighteenth aspect or any embodiment thereof.

In a twentieth aspect, a method is provided for determining moisture ingress in an electronic sensor system, comprising: applying a stimulus signal having a particular frequency or a signal comprising a spectrum of frequencies to an analyte sensor; measuring a response to the stimulus signal; calculating, using sensor electronics, an impedance based on the measured signal response; determining, using sensor electronics, whether the impedance falls within a predefined level corresponding to moisture ingress; initiating, using sensor electronics, an error routine if the impedance exceeds one or both of the respective predefined levels

In an embodiment of the twentieth aspect or any other embodiment thereof, the method further comprises the error routine includes one or more of triggering an audible alarm and a visual alarm on a display screen to alert a user that the sensor system may not be functioning properly.

In an embodiment of the twentieth aspect or any other embodiment thereof, the stimulus signal has a predetermined frequency.

In an embodiment of the twentieth aspect or any other embodiment thereof, the stimulus signal comprises a spectrum of frequencies.

In an embodiment of the twentieth aspect or any other embodiment thereof, the calculated impedance comprises a magnitude value and a phase value, and wherein the determination comprises comparing the impedance magnitude value to a predefined impedance magnitude threshold and the phase value to a predefined phase threshold.

In an embodiment of the twentieth aspect or any other embodiment thereof, the calculated impedance is a complex impedance value.

In a twenty-first aspect, a sensor system is provided configured to implement the methods of the twentieth aspect or any embodiment thereof.

In an embodiment of the twenty-first aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the twentieth aspect or any embodiment thereof.

In an twenty-second aspect, a method is provided for determining membrane damage of an analyte sensor using a sensor system, comprising: applying a stimulus signal to an analyte sensor; measuring a response to the stimulus signal; calculating, using sensor electronics, an impedance based on the signal response; determining, using the sensor electronics, whether the impedance falls within a predefined level corresponding to membrane damage; and initiating, using the sensor electronics, an error routine if the impedance exceeds the predefined level.

In an embodiment of the twenty-second aspect or any other embodiment thereof, the error routine includes triggering one or more of an audible alarm and a visual alarm on a display screen.

In an embodiment of the twenty-second aspect or any other embodiment thereof, the stimulus signal has a predetermined frequency.

In an embodiment of the twenty-second aspect or any other embodiment thereof, the stimulus signal comprises a spectrum of frequencies.

In an embodiment of the twenty-second aspect or any other embodiment thereof, the calculated impedance comprises a magnitude value and a phase value, and wherein the determination comprises comparing the impedance magnitude value to a predefined impedance magnitude threshold and the phase value to a predefined phase threshold.

In an embodiment of the twenty-second aspect or any other embodiment thereof, the calculated impedance is a complex impedance value.

In a twenty-third aspect, a sensor system is provided configured to implement the methods of the twenty-second aspect or any embodiment thereof.

In an embodiment of the twenty-third aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the twenty-second aspect or any embodiment thereof.

In a twenty-fourth aspect, a method for determining reuse of an analyte sensor, comprising, applying a stimulus signal to an analyte sensor; measuring a response of the stimulus signal; calculating an impedance response based on the response; comparing the calculated impedance to a predetermined threshold; initiating a sensor reuse routine if it is determined that the impedance exceeds the threshold.

In an embodiment of the twenty-fourth aspect or any other embodiment thereof, the sensor reuse routine includes triggering an audible and/or visual alarm notifying the user of improper sensor reuse.

In an embodiment of the twenty-fourth aspect or any other embodiment thereof, the sensor reuse routing includes causing a sensor system to fully or partially shut down and/or cease display of sensor data on a user interface of the sensor system.

In a twenty-fifth aspect, a sensor system is provided configured to implement the methods of the twenty-fourth aspect or any embodiments thereof.

In an embodiment of the twenty-fifth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the twenty-fourth aspect or any embodiments thereof.

In a twenty-sixth aspect, a system is provided for determining reuse of an analyte sensor, comprising, applying a stimulus signal to an analyte sensor; measuring a response of the stimulus signal; calculating an impedance based on the response; using a data association function to determine a correlation of the calculated impedance to one or more recorded impedance values; and initiating a sensor reuse routine if it is determined that the correlation is above a predetermined threshold.

In an embodiment of the twenty-sixth aspect or any other embodiment thereof, the sensor reuse routine includes triggering an audible and/or visual alarm notifying the user of improper sensor reuse.

In an embodiment of the twenty-sixth aspect or any other embodiment thereof, the sensor reuse routing includes causing a sensor system to fully or partially shut down and/or cease display of sensor data on a user interface of the sensor system.

In an embodiment of the twenty-sixth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the twenty-fifth aspect.

In a twenty-seventh aspect, a method is provided for applying an overpotential to an analyte sensor, comprising, applying a stimulus signal to an analyte sensor; measuring a response of the stimulus signal; determining a sensor sensitivity or change in sensor sensitivity based on the response; and applying an over potential to the sensor based on the determined sensitivity or sensitivity change.

In an embodiment of the twenty-seventh aspect or any other embodiment thereof, the determining further comprises calculating an impedance based on the response and determining the sensitivity or sensitivity change based on the impedance.

In an embodiment of the twenty-seventh aspect or any other embodiment thereof, the determining the sensitivity or sensitivity change further comprises correlating the impedance to a predetermined impedance to sensitivity relationship.

In an embodiment of the twenty-seventh aspect or any other embodiment thereof, the applying comprises determining or modifying a length of time the over potential is applied to the sensor.

In an embodiment of the twenty-seventh aspect or any other embodiment thereof, the applying comprises determining or modifying a magnitude of the over potential applied to the sensor.

In a twenty-eighth aspect, a sensor system is provided configured to implement the methods of the twenty-seventh aspect or any embodiments thereof.

In an embodiment of the twenty-eighth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the twenty-seventh aspect or any embodiment thereof.

In a twenty-ninth aspect, a method is provided for determining a property of a continuous analyte sensor, the method comprising: applying a stimulus signal to a first analyte sensor having a first working electrode and a first reference electrode; measuring a signal response of the stimulus signal using a second analyte sensor having a second working electrode and a second reference electrode; and determining a property of the first sensor by correlating the response to a predetermined relationship.

In an embodiment of the twenty-ninth aspect or any other embodiment thereof, the method further comprises generating sensor data by applying a bias voltage to the first working electrode and measuring a response to the bias voltage.

In an embodiment of the twenty-ninth aspect or any other embodiment thereof, the method further comprises calibrating the sensor data using the determined property.

In an embodiment of the twenty-ninth aspect or any other embodiment thereof, the determined property is one of an impedance and a temperature.

In an embodiment of the twenty-ninth aspect or any other embodiment thereof, the method further comprises determining sensor membrane damage using the determined property.

In an embodiment of the twenty-ninth aspect or any other embodiment thereof, the method further comprises determining moisture ingress in a sensor system encompassing the first and second analyte sensors using the determined property.

In a thirtieth aspect, a sensor system is provided configured to implement the methods of one of the twenty-ninth aspect or any embodiments thereof.

In an embodiment of the thirtieth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the twenty-ninth aspect or any embodiments thereof.

In a thirty-first aspect, a method is provided for determining a scaling used in a continuous analyte sensor system, the method comprising: applying a first stimulus signal to a first working electrode of an analyte sensor; measuring a response to the first stimulus signal; applying a second stimulus signal to a second working electrode of the analyte sensor; measuring a response to the first stimulus signal; determining, using sensor electronics, a scaling factor based on the measured responses to the first and second stimulus signals; and using the scaling factor to generate estimated analyte values based on sensor data generated by the analyte sensor.

In an embodiment of the thirty-first aspect or any other embodiment thereof, the method further comprises the method is performed periodically.

In an embodiment of the thirty-first aspect or any other embodiment thereof, the determining comprises calculating a first impedance using the response to the first stimulus signal and calculating a second impedance using the response to the second stimulus signal, and wherein the scaling factor is a ratio of the first impedance and the second impedance.

In an embodiment of the thirty-first aspect or any other embodiment thereof, the first working electrode has a membrane comprising an enzyme configured to react with the analyte and the second working electrode has a membrane does not have the enzyme.

In an embodiment of the thirty-first aspect or any other embodiment thereof, determining the scaling factor comprises updating a previous scaling factor based on the measured responses to the first and second stimulus signals.

In an embodiment of the thirty-first aspect or any other embodiment thereof, scaling factor is an acetaminophen scaling factor, wherein the method further comprises updating a further scaling factor based on the acetaminophen scaling factor, and wherein the further scaling factor applied to the sensor data to generate the estimated analyte values.

In a thirty-second aspect, a sensor system is provided configured to implement the methods of the thirty-first aspect or any embodiments thereof.

In an embodiment of the thirty-second aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the thirty-first aspect or any embodiment thereof.

In a thirty-third aspect, a method is provided for calibrating an analyte sensor, the method comprising: applying a predetermined signal to an analyte sensor; measuring a response to the applied signal; determining, using sensor electronics, a change in impedance associated with a membrane of the analyte sensor based on the measured response; calculating a sensitivity change of the analyte sensor based on the determined impedance; calculating a corrected sensitivity based on the calculated sensitivity change and a previously used sensitivity of the analyte sensor; and generating estimated analyte values using the corrected sensitivity.

In an embodiment of the thirty-third aspect or any other embodiment thereof, calculating the sensitivity change comprises applying a non-linear compensation function.

In an embodiment of the thirty-third aspect or any other embodiment thereof, the non-linear compensation function is expressed as the equation ΔS=(a*log(t)+b)*ΔI, where ΔS is the change in sensitivity, t is a time since calibration of the analyte sensor, ΔI is the determined change in impedance, and a and b are a predetermined coefficients.

In an embodiment of the thirty-third aspect or any other embodiment thereof, a and b are determined by prior testing of similar analyte sensors.

In a thirty-fourth aspect, a sensor system is provided configured to implement the methods of the thirty-third aspect or any embodiments thereof.

In an embodiment of the thirty-fourth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the thirty-third aspect or any embodiments thereof.

In a thirty-fifth aspect, a method is provided for calibrating an analyte sensor, comprising: generating sensor data using a subcutaneous analyte sensor; forming or modifying a conversion function, wherein pre-implant information, internal diagnostic information, and/or external reference information are used as inputs to form or modify the conversion function; and calibrating the sensor data using the conversion function.

In an embodiment of the thirty-fifth aspect or any other embodiment thereof, the pre-implant information comprises information selected from the group consisting of: a predetermined sensitivity profile associated with the analyte sensor, a previously determined relationship between a measured sensor property and sensor sensitivity, one or more previously determined relationships between a measured sensor property and sensor temperature; sensor data obtained from previously used analyte sensors, a calibration code associated with the analyte sensor, patient specific relationships between the analyte sensor and one or more of sensitivity, baseline, drift and impedance, information indicative of a site of sensor implantation; time since manufacture of the analyte sensor, and information indicative of the analyte being exposed to temperature or humidity.

In an embodiment of the thirty-fifth aspect or any other embodiment thereof, the internal diagnostic information comprises information selected from the group consisting of: stimulus signal output; sensor data indicative of an analyte concentration measured by the sensor; temperature measurements using the sensor or a separate sensor; sensor data generated by a redundant sensor, where the redundant sensor is designed to be substantially the same as analyte sensor; sensor data generated by an auxiliary sensors, where the auxiliary sensor is having a different modality as the analyte sensor; a time since the sensor was implanted or connected to sensor electronics coupled to the sensor; data representative of a pressure on the sensor or sensor system generated by a pressure sensor; data generated by an accelerometer; a measure of moisture ingress; and a measure of noise in an analyte concentration signal.

In an embodiment of the thirty-fifth aspect or any other embodiment thereof, the reference information comprises information selected from the group consisting of: real-time and/or prior analyte concentration information obtained from a reference monitor, information relating to a type/brand of reference monitor used to provide reference data; information relating to an amount of carbohydrate consumed by a user; information received from a medicament delivery device, glucagon sensitivity information, and information gathered from population based data.

In a thirty-sixth aspect, a sensor system is provided configured to implement the methods of the thirty-fifth aspect or any embodiments thereof.

In an embodiment of the thirty-sixth aspect or any other embodiment thereof, the sensor system comprises instructions stored in computer memory, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the thirty-fifth aspect or any embodiment thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages will be appreciated, as they become better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:

FIG. 1A illustrates a schematic diagram of sensor sensitivity as a function of time during a sensor session, in accordance with one embodiment; FIG. 1B illustrates schematic diagrams of conversion functions at different time periods of a sensor session, in accordance with the embodiment of FIG. 1A.

FIGS. 2A-2B and FIG. 52C-2D collectively illustrate different embodiments of processes for generating a sensor sensitivity profile.

FIG. 3A is a Bland-Altman plot illustrating differences between YSI reference measurements and certain in vivo continuous analyte sensors that were factory calibrated, in accordance with one embodiment; FIG. 3B is a Clarke error grid associated with data from the continuous analyte sensors associated with FIG. 3A.

FIG. 4 illustrates data from one study that examined the accuracy level of continuous analyte sensors that accepted one reference measurement about one hour after insertion into patients, in accordance with one embodiment.

FIG. 5 illustrates a diagram showing different types of information that can be input into the sensor system to define the sensor sensitivity profile over time

FIG. 6 illustrates a schematic diagram of sensor sensitivity as a function of time between completion of sensor fabrication and the start of the sensor session, in accordance with one embodiment.

FIG. 7A is a schematic diagram depicting distribution curves of sensor sensitivity corresponding to the Bayesian learning process, in accordance with one embodiment; FIG. 7B is a schematic diagram depicting confidence levels, associated with the sensor sensitivity profile, that correspond with the distribution curves shown in FIG. 7A.

FIG. 8 illustrates a graph that provides a comparison between an estimated glucose equivalent baseline and detected glucose equivalent baseline, in accordance with one study.

FIG. 9 is a schematic of a model sensor circuit in accordance with one embodiment.

FIG. 10 is a Bode plot of an analyte sensor in accordance with one embodiment.

FIG. 11 is a flowchart describing a process for determining an impedance of a sensor in accordance with one embodiment.

FIG. 12 is a flowchart describing a process for determining an impedance of a sensor based on a derivative response in accordance with one embodiment.

FIG. 13 is a flowchart describing a process for determining an impedance of a sensor based on a peak current response in accordance with one embodiment.

FIG. 14A illustrates a step voltage applied to a sensor and FIG. 14B illustrates a response to the step voltage in accordance with one embodiment.

FIG. 15 is a flowchart describing a process for estimating analyte concentration values using a corrected signal based on an impedance measurement in accordance with one embodiment.

FIG. 16 is a flowchart describing a process for estimating analyte concentration values using a predetermined sensitivity profile selected based on an impedance measurement in accordance with one embodiment.

FIG. 17 is a flowchart describing a process for determining an error based whether a sensitivity determined using an impedance measurement sufficiently corresponds to a predetermined sensitivity in accordance with one embodiment.

FIG. 18 is a flowchart describing a process for determining a temperature associated with a sensor by correlating an impedance measurement to a predetermined temperature-to-impedance relationship in accordance with one embodiment.

FIG. 19 is a flowchart describing a process for determining moisture egress in sensor electronics associates with an analyte sensor accordance with one embodiment.

FIG. 20 is a flowchart describing a process for determining membrane damage associated with an analyte sensor in accordance with one embodiment.

FIG. 21 is a flowchart describing a first process for determining sensor reuse associated in accordance with one embodiment.

FIG. 22 is a flowchart describing a second process for determining sensor reuse associated in accordance with one embodiment.

FIG. 23 is a schematic of a dual-electrode configuration used to determine sensor properties in accordance with one embodiment.

FIG. 24 is a diagram of a calibration process that uses various inputs to form a conversion function in accordance with one embodiment.

FIGS. 25-49 collectively illustrate results of studies using stimulus signals to determine sensor properties.

FIG. 50 is a flowchart describing a process for generating estimated analyte values using a compensation algorithm based on a measured change in impedance in accordance with one embodiment.

FIGS. 51-53 are graphs of studies comparing use of compensation algorithms based on measured changes in impedance.

DETAILED DESCRIPTION

In order to facilitate an understanding of the embodiments described herein, a number of terms are defined below.

The term “analyte,” as used herein, is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is are not to be limited to a special or customized meaning), and refers without limitation to a substance or chemical constituent in a biological fluid (for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid or urine) that can be analyzed. Analytes may include naturally occurring substances, artificial substances, metabolites, and/or reaction products. In some embodiments, the analyte for measurement by the sensor heads, devices, and methods disclosed herein is glucose. However, other analytes are contemplated as well, including but not limited to acarboxyprothrombin; acylcarnitine; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine/urocanic acid, homocysteine, phenylalanine/tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobinopathies, A, S, C, E, D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids/acylglycines; free β-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose/gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphate dehydrogenase; glutathione; glutathione perioxidase; glycocholic acid; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17 alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A−1, β); lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin; phytanic/pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3); selenium; serum pancreatic lipase; sissomicin; somatomedin C; specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky\'s disease virus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, leptospira, measles/mumps/rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosoma cruzi/rangeli, vesicular stomatis 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; white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat, vitamins and hormones naturally occurring in blood or interstitial fluids may also constitute analytes in certain embodiments. The analyte may be naturally present in the biological fluid, for example, a metabolic product, a hormone, an antigen, an antibody, and the like. Alternatively, the analyte may be introduced into the body, for example, a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including but not limited to 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 (barbituates, methaqualone, tranquilizers such as 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 (analogs of fentanyl, meperidine, amphetamines, methamphetamines, and phencyclidine, for example, Ecstasy); anabolic steroids; and nicotine. The metabolic products of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals generated within the body may also be analyzed, such as, for example, ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC), Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and 5-Hydroxyindoleacetic acid (FHIAA).

The terms “continuous analyte sensor,” and “continuous glucose sensor,” as used herein, are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to a device that continuously or continually measures a concentration of an analyte/glucose and/or calibrates the device (e.g., by continuously or continually adjusting or determining the sensor\'s sensitivity and background), for example, at time intervals ranging from fractions of a second up to, for example, 1, 2, or 5 minutes, or longer.

The term “biological sample,” as used herein, is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to sample derived from the body or tissue of a host, such as, for example, blood, interstitial fluid, spinal fluid, saliva, urine, tears, sweat, or other like fluids.

The term “host,” as used herein, is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to animals, including humans.