SUBSTANCE MONITORING AND CONTROL IN HUMAN OR ANIMAL BODIES

• US 20110040487A1
• Filed: 10/10/2008
• Published: 02/17/2011
• Est. Priority Date: 10/12/2007
• Status: Active Grant

• Current Assignee: Cambridge Enterprises Limited
• Original Assignee: Cambridge Enterprises Limited
• Inventors: Hovorka, Roman
• Granted Patent: US 8,977,504 B2
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Apparatus for monitoring a substance in human or animal in real time, the apparatus comprising a sensor providing a time series of measurements of substance level, said measurements being indicative of an inferred level of said substance in a part of said human or animal and a processor which applies an interacting multiple model strategy to a system model to provide a combined estimate of the inferred substance level from the substance level measurements. The substance may be glucose. The apparatus may also be adapted to control said substance using said interacting multiple model strategy to a system model to provide a combined estimate of a dose to be applied.

Claims

1-39. -39. (canceled)

40. Apparatus for monitoring glucose in human or animal in real time, the apparatus comprising:

a sensor providing a time series of measurements of glucose level, said measurements being indicative of an inferred level of said glucose in a part of said human or animal and

a processor which is adapted to perform the following steps;

calculate a first estimate of said inferred glucose level from said measured glucose level using a first glucoregulatory system model,

calculate a second estimate of said inferred glucose level from said measured glucose level using a second glucoregulatory system model with said second system model being a variation of said first system model and

predicting a combined estimate of the inferred glucose level based on a combination of the first and second estimates.

41. Apparatus according to claim 40, wherein the processor is adapted to calculate a combined estimate by weighting each estimate according to an associated mixing probability representing a respective probability of each said model correctly predicting said glucose level.

42. Apparatus according to claim 41, wherein the processor is further adapted to update said mixing probability responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor.

43. Apparatus according to claim 40, wherein the processor is a state estimator and is adapted to define a state vector for each model, the state vector comprising a set of variables each having an associated uncertainty, the set of variables including a variable representing an unexplained change in glucose level with each model having a different standard deviation in the unexplained change in glucose level.

44. Apparatus according to claim 40, further comprising a user monitor to receive inputs from the user and/or to display the status of the apparatus.

45. Apparatus according to claim 40, wherein the sensor measures the substance intravenously, subcutaneously and/or intradermally.

46. Apparatus according to claim 40, wherein the glucoregulatory model comprises a sub-model of glucose kinetics in the blood of said human or animal and a sub-model of interstitial glucose kinetics and wherein said inferred substance level comprises a level of said glucose in said blood and said substance level measurements comprises measurements of an interstitial glucose level.

47. Apparatus according to claim 46, further comprising a real-time alarm which is activated when the combined estimate or the combined estimate of future glucose level is below a preset hypoglycaemia threshold or above a preset hyperglycaemia threshold.

50. Apparatus for real time control of glucose in a human or animal, the apparatus comprising:

apparatus for real time monitoring as described in claim 40 or claim 49, and

a dispenser for delivering a specified amount of medication to a user in response to a command from the processor,

wherein the processor is adapted to calculate the specified amount of medication as the amount of medication required to bring the combined predicted glucose level measurement into line with a desired value.

53. Apparatus for controlling glucose in human or animal body, comprising

apparatus for real time monitoring as described in claim 40, and

a user interface for displaying a suggested insulin bolus to be applied

wherein the suggested insulin bolus is calculated by the processor as the amount of medication required to bring the combined estimate into line with a desired glucose value.

55. Apparatus according to claim 53, wherein the processor applies the interacting multiple model strategy to the glucoregulatory model to determine the amount of medication required.

48. Apparatus for monitoring a substance in human or animal in real time, the apparatus comprising:

a sensor providing a time series of measurements of substance level, said measurements being indicative of an inferred level of said substance in a part of said human or animal and

a processor which applies an interacting multiple model strategy to a system model to predict a combined estimate of the inferred substance level from the substance level measurements.

49. Apparatus for monitoring glucose in human or animal in real time, the apparatus comprising:

a sensor providing a time series of measurements of glucose level, said measurements being indicative of an inferred level of said glucose in a part of said human or animal and

a processor which is adapted to

construct at least two state vectors each comprising a set of variables for a respective one of at least two glucose level models, said state vectors representing states of said at least two models, said state vectors also including a variable representing an uncertainty in a change in glucose level with time, each of said variables having an associated probability distribution;

predict a value of a said glucose level using said probability distribution and said state vectors;

update values of said state vectors responsive to a difference between a predicted glucose level measurement for each said model and a glucose level measurement from said sensor; and

update a mixing probability representing a respective probability of each said model correctly predicting said glucose level responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor, and

determine a combined predicted glucose level measurement for said human or animal by combining outputs from said glucose level models according to said updated mixing probability.

51. Apparatus for real time control of a substance in a human or animal, the apparatus comprising

a sensor providing a time series of measurements of substance level, said measurements being indicative of an inferred level of said substance in a part of said human or animal,

a processor which is adapted to calculate an estimate of said inferred level,

a dispenser for delivering a specified amount of medication to a user in response to a command from the processor,

wherein the processor is adapted to calculate the specified amount of medication as the amount of medication required to bring the estimate of said inferred level in line with a desired value by

calculating a first estimate of said specified amount of medication using a first system model,

calculate a second estimate of said specified amount of medication using a second system model with said second system model being a variation of said first system model, and

calculate a combined estimate of said specified amount of medication based on a combination of the first and second estimates.

52. Apparatus according to claim 51, wherein the substance is glucose and the dispenser delivers insulin, glucagon or a similar medication or combinations thereof.

54. Apparatus according to claim 52 or 53, wherein the desired glucose value varies with time to define a trajectory of values.

56. A method of real time glucose monitoring in a living human or animal, the method comprising:

inputting a time series of glucose level measurements from a sensor, said glucose level measurements being indicative of a inferred level of said glucose in a part of said human or animal,

calculating a first estimate of said inferred glucose level from said measured substance level using a first glucoregulatory system model,

calculating a second estimate of said inferred glucose level from said measured substance level using a second glucoregulatory system model with said second system model being a variation of said first system model and

predicting a combined estimate of the inferred glucose level based on a combination of the first and second estimates.

57. A method according to claim 56, comprising applying a Kalman filter.

58. A method according to claim 57, wherein each estimate is weighted according to a mixing probability representing a respective probability of each said model correctly predicting said glucose level when combining the multiple estimates.

59. A method according to claim 58, comprising updating said mixing probability responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor.

60. A method according to claim 58, comprising determining the mixing probability from the model probability.

61. A method according to claim 56, comprising defining a state vector for each model, the state vector comprising a set of variables each having an associated uncertainty, the set of variables including a variable representing an unexplained change in substance level with each model having a different standard deviation in the unexplained change in substance level.

62. A method according to claim 56, wherein the glucoregulatory model comprises a sub-model of glucose kinetics in the blood of said human or animal and a sub-model of interstitial glucose kinetics, wherein said inferred substance level comprises a level of said glucose in said blood and said substance level measurements comprises measurements of an interstitial glucose level.

63. A method according to claim 62, wherein the glucoregulatory model further comprises a sub-model of insulin absorption, a sub-model of insulin action and a sub-model of gut absorption.

64. A method according to claim 61 comprising measuring the glucose level intravenously, subcutaneously and/or intradermally.

65. A method according to claim 61, comprising defining a state vector for each model, the state vector comprising a set of variables each having an associated uncertainty, the set of variables including a variable representing an unexplained change in glucose level with each model having a different standard deviation in the unexplained change in glucose level.

70. A method for controlling glucose in human or animal body, comprising

calculating a combined estimate using real time monitoring as described in claim 56, and

displaying a suggested insulin bolus to be applied on a user interface,

wherein the suggested insulin bolus is calculated by the processor as the amount of medication required to bring the combined estimate into line with a desired glucose value.

71. A carrier carrying computer program code to, when running, implementing the method of claim 56.

66. A method of real time glucose monitoring in a living human or animal, the method comprising

inputting a time series of glucose level measurements from a glucose sensor;

constructing at least two state vectors each comprising a set of variables for a respective one of at least two glucose level models, said state vectors representing states of said at least two models, said state vectors also including a variable representing an uncertainty in a change in glucose level with time, each of said variables having an associated probability distribution;

predicting a value of a said glucose level using said probability distribution and said state vectors;

updating values of said state vectors responsive to a difference between a predicted glucose level measurement for each said model and a glucose level measurement from said sensor; and

updating a mixing probability representing a respective probability of each said model correctly predicting said glucose level responsive to a difference between said predicted glucose level measurement of each respective said model and said glucose level measurement from said sensor, and

determining a combined predicted glucose level measurement for said human or animal by combining outputs from said glucose level models according to said updated mixing probability.

67. A method according to claim 66, further comprising using the value of a first of state vectors to modify a second of said state vectors to thereby represent a transition between said models.

68. A method for real time control of a substance in a human or animal, the method comprising

providing a time series of measurements of substance level, said measurements being indicative of an inferred level of said substance in a part of said human or animal,

calculating an estimate of said inferred level,

calculating a specified amount of medication to be the amount of medication required to bring the estimate of said inferred level in line with a desired value by

calculating a first estimate of said specified amount of medication using a first system model,

calculate a second estimate of said specified amount of medication using a second system model with said second system model being a variation of said first system model,

calculating said specified amount of medication based on a combination of said estimates and

delivering said specified amount of medication to a user.

69. A method according to claim 68, comprising calculating said estimate of said inferred level using real time monitoring comprising:

inputting a time series of glucose level measurements from a sensor, said glucose level measurements being indicative of a inferred level of said glucose in a part of said human or animal,

calculating a first estimate of said inferred glucose level from said measured substance level using a first glucoregulatory system model,

calculating a second estimate of said inferred glucose level from said measured substance level using a second glucoregulatory system model with said second system model being a variation of said first system model and

predicting a combined estimate of the inferred glucose level based on a combination of the first and second estimates.