acetaminophen	6.5	1-2
ascorbic acid	3.8	0.8-1.2
dopamine	3.1	0.03-0.104
ibuprofen	40	0.5-2.04
salicylic acid	50	15-30
tolbutamide	70	5.3-10
creatinine	30	1.5
uric acid	9.5	7
ephedrine	0.94	0.005-0.01
L-dopa	1.82	0.02-0.3
methyl dopa	1.735	0.1-0.5
tetracycline	0.44	0.4

The graph ofFIG. 26B represents data as an average of the five sensors tested in each of the interferent solutions at high concentrations (see column entitled, “Solution Concentration Tested” in Table 1). The y-axis represents signal strength in picoAmps after a three-minute immersion time in each solution. The x-axis represents the interferents tested. It is noted that at least three interferents showed substantially no response (Ascorbic acid, Ibuprofen and Tetracycline). Other interferents (Acetaminophen, Creatinine, Ephedrine, Salicylic acid, Tolazamide, Tolbutamide and Uric Acid) showed minimal response, which is believed to provide sufficient interferent blocking to enable functional (useful) sensor data even in the presence of these interferents at the tested concentrations (seeFIG. 26C).

The glucose sensors of the preferred embodiments, constructed with a cellulose acetate butyrate interference domain, and including embodiments with a PVP electrode domain, have been shown to block a broad spectrum of exogeneous and endogeneous interferents in therapeutic concentrations, including, but not limited to, Acetaminophen, Ascorbic acid, Dopamine, Ibuprofen, Salicylic acid, Tolbutamide, Creatinine, Uric acid, Ephedrine, L-dopa, methyl dopa and Tetracycline. Additionally, while some prior art interference domains have been known to create altered sensitivity of the sensor to glucose (e.g., variability and/or unreliability of sensors in manufacture), the preferred glucose sensors built with a cellulose acetate butyrate interference domain, and including embodiments with a PVP electrode domain, provide excellent glucose sensitivity consistency for sensor manufacture. Furthermore, it was observed that the preferred embodiments described herein provide glucose sensors that reduce or eliminate break-through upon prolonged exposure to a solution containing an interferant.

Deposition of Resistance Domain Using Physical Vapor Deposition

Twenty nine (29) transcutaneous sensors were fabricated by providing a platinum wire, vapor-depositing the platinum with Parylene to form an insulating coating, helically winding a silver wire around the insulated platinum wire (to form a “twisted pair”), masking sections of electroactive surface of the silver wire, vapor depositing Parylene on the twisted pair, chloridizing the silver electrode to form a silver chloride reference electrode, and removing a radial window on the insulated platinum wire to expose a circumferential electroactive working electrode surface area thereon, this assembly also referred to as a “parylene-coated twisted pair assembly.” The electroactive surface of the radial window of each Parylene-coated twisted pair was then cleaned by surface treatment.

An electrode domain was formed over the electroactive surface areas of the working and reference electrodes by dip coating the assembly in an electrode solution (comprising BAYHYDROL® 123, an aliphatic polycarbonate urethane resin) and drying. An enzyme domain was formed over the electrode domain by subsequently dip coating the assembly in an enzyme solution (comprising BAYHYDROL® 140AQ, an aliphatic polyester urethane resin, and glucose oxidase) and drying. A resistance domain was formed over the enzyme domain using a physical vapor deposition process as described above; namely, by placing twenty nine of the assemblies coated with the electrode and enzyme domains into a vacuum chamber and using an ultrasonic nozzle to produce a mist of micro-droplets of the resistance domain solution (comprising a blend of CHRONOTHANE®-1020 (a polyetherurethaneurea based on polytetramethylene glycol, methylene diisocyanate and organic amines) and CHRONOTHANE®-H (a polyetherurethaneurea based on polytetramethylene glycol, polyethylene glycol, methylene diisocyanate, and organic amines)) within the vacuum chamber (solution feed rate 1.5 ml/minute; nozzle power 1.5 watts; solution temperature ambient room temp.;chamber temperature 30° C.;nozzle frequency 120 kHz; chamber gas argon; purgegas pressure 3 psi; solvent physical properties: tetrahydrofuran (boiling point 65-67° C., vapor pressure 143 mm Hg @ 20° C.) and dimethylacetamide (boiling point 164.5° C.,vapor pressure 2 mm Hg @ 25° C.)). The contact time with the mist within the vacuum chamber was about 36 minutes in duration and included 12 spray cycles lasting three minutes each. The resistance domain was then dried for 1 hr at 50° C., 26″ vacuum. After drying, the twenty nine sensors were tested in vitro to determine their slope (i.e., glucose sensitivity). The twenty nine sensors had an average slope of 4.97 pA/mg/dL, with a standard deviation of 0.55 pA/mg/dL, showing the use of vapor deposition methods, as described herein, to produce functional sensors with good consistency and uniformity. Specifically, the manufacturing lot consisted of sensors having in vitro sensitivities with a standard deviation of about 11%. Methods for producing manufacturing lots of sensors can be employed to produce sensors typically with a standard deviation of less than about 20%, preferably less than about 18%, more preferably a standard deviation of less than about 16%, more preferably still a standard deviation of less than about 12%, and most preferably a standard deviation of less than about 8%.

In the above-described method, the morphology of the resistance domain was controlled by adjusting the solvent evaporation rate of the coating liquid through chamber temperature and chamber vacuum control to yield a surface with the preferred morphology (e.g., non-smoothness). Namely, in contrast to prior art devices which are fabricated using methods specifically adapted to depositing a smooth surface (see, for example, WO/2003-072269-A1 to Leiby et al.), devices of the preferred embodiment fabricated using the above-described method have a resistance domain including a substantially non-smooth surface (e.g., a roughness on the surface that varies in appearance under magnification from a super-positioning of disc shaped objects, such as coins, to a beadlike surface) A variety of parameters of the vapor deposition apparatus/process can be adjusted to produce the desired non-smooth membrane surface; namely, parameters including: feed rate, nozzle power, chamber vacuum, liquid solution temperature (to be sprayed), chamber temperature, purge gas pressure or flow rate, identity of the purge gas, total cycle time, number of cycles, and solvent physical properties can be altered, and are dependent on one another, to produce desired membrane surface properties.

FIG. 27 is a photomicrograph obtained by Scanning Electron Microscopy (SEM) at 350× magnification of a sensor formed as described in the example above, including vapor depositing the resistance domain onto the sensor.FIG. 27 shows the substantially non-smooth surface of the sensor after deposition of the resistance domain; namely,FIG. 27 shows a plurality of super-positioned disc-shaped objects, wherein the disc-shaped objects are a result of the deposition of the resistance domain. Preferably, the disc-shaped objects are rounded, for example, circular, oval or tear drop-shaped. In this example, the average diameter of the disc-shaped objects (by either the shortest or longest dimension) is preferably from about 5 to about 250 microns, more preferably from about 10 to about 100 microns, and more preferably still from about 30 to about 80 microns; however, larger or smaller average diameters can be desirable in certain embodiments. For example, in one alternative embodiment, certain parameters of the vapor deposition process were modified (e.g., temperature), which resulted in smaller diameter beads (e.g., a speckled appearance) than shown inFIG. 27.

Rate of Change Calculation Example

FIG. 28 is a graph that shows calibrated glucose sensor data from a glucose sensor built according to the preferred embodiments and implanted in a human. The x-axis represents time; the y-axis represents calibrated glucose sensor data in mg/dL. In general, the graph shows the glucose sensor tracking the host's glucose concentration over time, including increasing and decreasing trends of the host's glucose concentration.

In this example, sensor data was smoothed using an IIR filter. Subsequently, a change rate was calculated based on the smoothed, calibrated data described above as follows: (smooth data point (i)−(smooth data point (i−1))/(time (i)−time (i−1)). The current data point was then defined as an increasing point if the current and previous data points both had a positive rate of change or as a decreasing point if the current and previous data points both had a negative rate of change. After detecting five consecutive increasing data points or decreasing data points, the change rate of the calibrated glucose signal was calculated as follows: (calibrated glucose value (i)−calibrated glucose value (i−1))/(time (i)−time (i−1)). A smoothed calibrated glucose change rate was calculated by employing a 3-point moving average window; namely, mean (calibrated glucose change rate (i−2) to calibrated glucose change rate (i)). Alarms/alerts were set at 2 mg/dL/min, whereby when the smoothed calibrated glucose change rate was negative and its absolute value was greater than a certain threshold (2 mg/dL/min), then an alarm was activated as illustrated by the triangle on the graph.

Advantageously, as can be seen in this example, the alarm was not triggered in circumstances where the rate of change may have exceeded the 2 mg/dL/min threshold using a point-to-point comparison ((data point (i)−data point (−1))/(time (i)−time (i−1))) or the like, which can result in a false positives (e.g., triggering an alarm unnecessarily, which can be annoying to the host wearing the device). Conventional rate of change calculations can be overly sensitive to triggering of alarms, for example, during clinically safe (acceptable) short increases in rate of change and/or during noisy data periods, wherein the data may be subjected to erroneous data points (e.g., non-glucose related signal change). By using some or all of the smoothing techniques and/or other logic described in the example above, and as described in more detail elsewhere herein, patient usability and compliance can be increased due to increased intelligence in the calculation of rate of change and/or triggering of alarms.

Although one example of calculating rate of change and triggering an alarm if the rate of change exceeds a threshold is shown above, alarms can be triggered responsive to combinations of rate of change exceeding a threshold (e.g., 2 mg/dL/min) and actual analyte (glucose) values greater or less than a certain threshold (e.g., less than 180 mg/dL). Advantageously, any combination of these thresholds is settable, e.g., by the patient. This combination can be useful, for example in a glucose sensor, wherein a rate of change greater than 2 mg/dl/min can be acceptable when the patient's glucose concentration is around 200 mg/dL and dropping, which may not pose an immediate risk to the patient; in contrast, the same rate of change (2 mg/dL/min) when the patient's glucose concentration is around 100 mg/dL and dropping can be of considerable risk to the patient.

Methods and devices that are suitable for use in conjunction with aspects of the preferred embodiments are disclosed in U.S. Pat. No. 4,994,167; U.S. Pat. No. 4,757,022; U.S. Pat. No. 6,001,067; U.S. Pat. No. 6,741,877; U.S. Pat. No. 6,702,857; U.S. Pat. No. 6,558,321; U.S. Pat. No. 6,931,327; U.S. Pat. No. 6,862,465; U.S. Pat. No. 7,074,307; and U.S. Pat. No. 7,081,195.

Methods and devices that are suitable for use in conjunction with aspects of the preferred embodiments are disclosed in U.S. application Ser. No. 09/447,227 filed Nov. 22, 1999 and entitled “DEVICE AND METHOD FOR DETERMINING ANALYTE LEVELS”; U.S. application Ser. No. 11/335,879 filed Jan. 18, 2006 and entitled “CELLULOSIC-BASED INTERFERENCE DOMAIN FOR AN ANALYTE SENSOR”; U.S. application Ser. No. 11/334,876 filed Jan. 18, 2006 and entitled “TRANSCUTANEOUS ANALYTE SENSOR”; U.S. application Ser. No. 11/333,837 filed Jan. 17, 2006 and entitled “LOW OXYGEN IN VIVO ANALYTE SENSOR”.

All references cited herein, including but not limited to published and unpublished applications, patents, and literature reference, are incorporated herein by reference in their entirety and are hereby made a part of this specification. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.

The term “comprising” as used herein is synonymous with “including,” “containing,” or “characterized by,” and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.

All numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification are to be understood as being modified in all instances 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 the present application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

The above description discloses several methods and materials of the present invention. This invention is susceptible to modifications in the methods and materials, as well as alterations in the fabrication methods and equipment. Such modifications will become apparent to those skilled in the art from a consideration of this disclosure or practice of the invention disclosed herein. Consequently, it is not intended that this invention be limited to the specific embodiments disclosed herein, but that it cover all modifications and alternatives coming within the true scope and spirit of the invention.

Claims

1-20. (canceled)

21. A system for analyzing data from an analyte sensor, comprising:

a receiver configured to receive sensor data from an analyte sensor, the sensor data comprising at least two consecutive sensor data points; and

a processor configured to perform one or more calculations to determine a reliability of the sensor data and to calculate a rate of change of the sensor data from at least two consecutive reliable sensor data points, wherein the processor is configured to control processing of sensor data based upon the reliability.

22. The system ofclaim 21, wherein the processor is configured to detect noise in the sensor data during the rate of change calculation.

23. The system ofclaim 21, wherein the rate of change calculation is substantially without error caused by noise in the sensor data.

24. The system ofclaim 21, wherein the rate of change calculation comprises a smoothed rate of change calculation.

25. The system ofclaim 21, wherein the processing of the sensor data based upon the reliability comprises outputting of sensor data to the user interface.

26. The system ofclaim 21, wherein the processing of the sensor data based upon the reliability comprises outputting to an insulin delivery device.

27. The system ofclaim 21, wherein the consecutive sensor data points comprise a data gap between the points.

28. The system ofclaim 27, wherein the data gap comprises a time period when data does not exist.

29. The system ofclaim 27, wherein the data gap comprises a time period wherein the data not substantially reliable.

30. The system ofclaim 21, wherein the reliability is based on a level of confidence in the data.

31. The system ofclaim 30, wherein the level of confidence comprises a rating.

32. A method of analyzing data from an analyte sensor, comprising:

receiving sensor data from an analyte sensor, the data comprising at least two consecutive sensor data points;

performing one or more calculations to determine a reliability of the sensor data;

calculating a rate of change of the sensor data from at least two consecutive reliable sensor data points; and

controlling processing of sensor data based upon the reliability.

33. The method ofclaim 32, further comprising detecting noise in the sensor data during the rate of change calculation.

34. The method ofclaim 32, wherein the rate of change calculation is substantially without error caused by noise in the sensor data.

35. The method ofclaim 32, wherein the calculating the rate of change comprises performing a smoothing of a rate of change of the sensor data.

36. The method ofclaim 32, wherein the controlling processing of the sensor data based upon the reliability comprises controlling the output of sensor data to the user interface.

37. The method ofclaim 32, wherein controlling processing of the sensor data based upon the reliability comprises controlling the output to an insulin delivery device.

38. The method ofclaim 32, wherein the consecutive sensor data points comprise a data gap between the points.

39. The method ofclaim 38, wherein the data gap comprises a time period when data does not exist.

40. The method ofclaim 38, wherein the data gap comprises a time period wherein the data not substantially reliable.

41. The method ofclaim 32, wherein the reliability is based on a level of confidence in the data.

42. The method ofclaim 41, wherein the level of confidence comprises a rating.