초록 ▼

Embodiments of a testing method for optimizing a therapy to a diabetic patient comprise collecting at least one sampling set of biomarker data, computing a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, wherein

Embodiments of a testing method for optimizing a therapy to a diabetic patient comprise collecting at least one sampling set of biomarker data, computing a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, wherein the probability distribution function is calculated to approximate the probability distribution of the biomarker data, the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of higher risk of complications, the risk function is the product of the probability distribution function and the hazard function, and the risk value is calculated by the integral of the risk function, minimizing the risk value by adjusting the diabetic patient's therapy, and exiting the testing method when the risk value for at least one sampling set is minimized to an optimal risk level.

대표청구항 ▼

1. A testing method for optimizing a therapy to a diabetic patient via a device comprising a processor, the testing method comprising the steps of: querying a diabetic patient via the device to confirm that entry criteria have been satisfied;collecting at least one sampling set of biomarker data if

1. A testing method for optimizing a therapy to a diabetic patient via a device comprising a processor, the testing method comprising the steps of: querying a diabetic patient via the device to confirm that entry criteria have been satisfied;collecting at least one sampling set of biomarker data if entry criteria have been satisfied, wherein each sampling set comprises one or more sampling instances recorded over a collection period and each sampling instance comprises one or more biomarker readings;evaluating whether the collection of the sampling set satisfies adherence criteria;computing a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, the probability distribution function is calculated to approximate the probability distribution of the biomarker data,the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of higher risk of complications, wherein the hazard function H(B) is defined by the equation H(B)=Hhypo(B)+Hhyper(B), wherein B is a biomarker reading in the sampling set, Hhypo(B) is the hazard function associated with hypoglycemic events, Hhyper(B) is the hazard function associated with hyperglycemic events,the risk function is the product of the probability distribution function and the hazard function, andthe risk value J is calculated by the integral of the risk function J=∫B=0∞⁢p⁡(B)⁢H⁡(B)⁢ⅆB, wherein p(B) is a probability distribution; minimizing the risk value by adjusting the diabetic patient's therapy; andinstructing the patient via the device to exit the testing method when the risk value for at least one sampling set is minimized to an optimal risk level,wherein the above steps are performed by the processor. 2. The testing method of claim 1 wherein the diabetic patient's therapy for minimizing the risk value is selected from the group of adjusting an insulin dosage parameter, adjusting diabetic patient behaviors to reduce biomarker variability, adjusting a target biomarker level, or combinations thereof. 3. The testing method of claim 2, wherein the insulin dosage parameter is selected from the group consisting of a basal dosage parameter, an insulin to carbohydrate parameter, an insulin sensitivity parameter, a meal rise parameter, a meal offset parameter, an insulin active parameter, and combinations thereof. 4. The testing method of claim 2 wherein the insulin dosage is variable or constant during the collection period. 5. The testing method of claim 2 wherein the insulin is basal insulin. 6. The testing method of claim 2 wherein the target biomarker level is adjusted according to a biomarker target adjustment regimen, the biomarker target adjustment regimen being defined as an adjustment or a sequence of adjustments to the target biomarker level to the level that minimizes the risk for the current amount of noise. 7. The testing method of claim 2 wherein the risk level is minimized to an optimal level when a mean of the sampling set is at a target biomarker level or adjusted target biomarker level or within a target biomarker range or an adjusted target biomarker range, and a standard deviation of the sampling set falls within a similar noise range of a prior sampling set. 8. The testing method of claim 2 further comprising adjusting the insulin dosage parameter according to an insulin adjustment regimen, which is determined by comparing the mean of the current completed sampling set to the target biomarker level, the insulin adjustment regimen being defined as the number of insulin dosage adjustments required to achieve a target insulin level and the amount of each adjusted insulin dosage, wherein the target insulin level is the amount required to achieve the target biomarker level. 9. The testing method of claim 8 wherein the insulin adjustment regimen is determined by calculating the requisite insulin dosage to achieve the target biomarker level, Dtarget, is calculated by the equation Dtarget=Dk+m·(Btarget−Bk), wherein m is the rate of change from a first biomarker reading Bk−1 to a subsequent second biomarker reading Bk based on the adjustment of insulin from a first dosage Dk−1 to a subsequent second dosage Dk as defined by the equation 1m=Bk-Bk-1Dk-Dk-1; and Btarget is the target biomarker level. 10. The testing method of claim 9 wherein the adjusted insulin dosage cannot exceed a maximum insulin dosage set by a healthcare provider. 11. The testing method of claim 9 wherein the calculated insulin dosage to achieve the target biomarker level, Dtarget for the sampling set is assessed by a healthcare provider in comparison to a calculated Dtarget for a prior sampling set. 12. The testing method of claim 9, wherein the calculated maximum dosage to achieve a target biomarker level Dtarget is assessed against a maximum insulin dosage set by a healthcare provider. 13. The testing method of claim 9 wherein an adjusted insulin dosage Dk+1 after adjustment by the insulin adjustment regimen is calculated by the equation Dk+1=Dk+λ·m·(Btarget−Bk) wherein λ is a regimen tuning parameter which ranges from 0 to 1 and corresponds to an aggressiveness of the insulin adjustment regimen. 14. The testing method of claim 13 wherein an aggressive insulin adjustment regimen adjusts an insulin dosage to the maximum level in a first adjustment, and includes a λ value of 1. 15. The testing method of claim 13 wherein a less aggressive insulin adjustment regimen adjusts an insulin dosage to the maximum level incrementally over at least two dosage adjustments, and includes a λ value of 0.5 or less. 16. The testing method of claim 1 wherein the optimal risk level may be set by a physician. 17. The testing method of claim 1 wherein the probability distribution function is calculated from a mean and standard deviation of the sampling set. 18. The testing method of claim 1 wherein the probability distribution function is calculated using a kernel density estimator. 19. The testing method of claim 1 wherein the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of hyperglycemia or hypoglycemia and yields hazard values at or near zero at a target biomarker level or within a target biomarker range. 20. The testing method of claim 1 wherein for minimization of the risk value to the optimal level, there are no hazard values indicative of a hyperglycemic or hypoglycemic event. 21. The testing method of claim 1 wherein the collection period for the sampling set is defined as multiple sampling instances within a day, multiple sampling instances within a week, multiple sampling instances within consecutive weeks, or multiple sampling instances on consecutive days within a week. 22. The testing method of claim 1 wherein each sampling instance comprises the biomarker reading and other contextual data associated with the biomarker reading, wherein the contextual data is selected from the group consisting of the time of collection, the date of collection, the time when the last meal was consumed, stress, exercise, energy level, the time and dose of medications including insulin, the recommended amount of insulin, and combinations thereof. 23. The testing method of claim 1 wherein the biomarker readings are fasting blood glucose readings. 24. The testing method of claim 1 wherein the biomarker reading includes information concerning a biomarker type selected from glucose, triglycerides, low density lipids, and high density lipids. 25. The testing method of claim 1 wherein the hazard function H(B) is defined by the equation H(B)=(1.509(log(B)1.0804−5.381))2, wherein B is a biomarker reading in the sampling set. 26. The testing method of claim 1 wherein the hazard function is an index which correlates a biomarker reading to a corresponding hazard value. 27. The testing method of claim 1 further comprising collecting one or more additional sampling sets of biomarker data when the risk level is not at an optimal level. 28. The testing method of claim 1 further comprising conducting a new testing plan after the risk is minimized to an optimal level. 29. The testing method of claim 1 wherein the adherence criteria requires a fasting period before collection of the sampling set of biomarker data. 30. The testing method of claim 1 further comprising determining whether to adjust a target biomarker level based on a variance of the sampling set, wherein a variance within a similar noise range does not require the adjustment of the target biomarker level, the similar noise range being the variance computed from one or more prior sampling sets; anda variance within an acceptable noise range requires adjustment of the target biomarker level, the acceptable noise range being a maximum tolerable variance for a sampling set. 31. The testing method of claim 30 wherein the similar noise range is less than a 10% change from a previous amount of noise calculated. 32. The testing method of claim 30 further comprising contacting a physician when the variance is greater than the acceptable noise range. 33. A device configured to guide a diabetic patient through a testing plan directed to optimizing an administration dosage of insulin, comprising: a processor coupled to memory, wherein the memory comprises collection procedures; andsoftware having instructions that when executed by the processor causes the processor to: determine whether entry criteria for the diabetic patient to begin the testing plan have been met;instruct the diabetic patient to collect one or more sampling sets of biomarker data in accordance with the collection procedures, wherein each sampling set comprises one or more sampling instances recorded over a collection period, and each sampling instance comprises one or more biomarker readings;evaluate whether the collection of the sampling set satisfies adherence criteria; andcompute a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, the probability distribution function is calculated to approximate the probability distribution of the biomarker data,the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of higher risk of complications, wherein the hazard function H(B) is defined by the equation H(B)=Hhypo(B)+Hhyper(B), wherein B is a biomarker reading in the sampling set, Hhypo(B) is the hazard function associated with hypoglycemic events, Hhyper(B) is the hazard function associated with hyperglycemic events,the risk function is the product of the probability distribution function and the hazard function, andthe risk value J is calculated by the integral of the risk function J=∫B=0∞⁢p⁡(B)⁢H⁡(B)⁢ⅆB, wherein p(B) is a probability distribution; instruct the diabetic patient to minimize the risk value by adjusting the patient's therapy, or exit the testing method if the risk value for at least one sampling set is minimized to an optimal risk level. 34. The device of claim 33 wherein the diabetic patient's therapy for minimizing the risk value is selected from the group consisting of adjusting the insulin dosage, adjusting diabetic patient behaviors to reduce biomarker variability, adjusting a target biomarker level, or combinations thereof. 35. The device of claim 33 wherein, for minimization of the risk value to the optimal level, there are no hazard values indicative of a hyperglycemic or hypoglycemic event. 36. The device of claim 33 wherein the collection device is a continuous glucose monitor to obtain time-resolved glucose information that is provided as biomarker data to the processor. 37. The device of claim 33 further comprising a therapy device configured to administer insulin to a diabetic patient. 38. The device of claim 33 wherein the therapy device is an insulin pen. 39. The device of claim 33 further comprising a lancet operable to pierce the skin of the diabetic patient to obtain a blood glucose biomarker. 40. The device of claim 33 further comprising a meter configured to measure one or more selected biomarkers. 41. The device of claim 30 wherein the software instructions when executed causes the processor to determine whether to adjust a target biomarker level based on a variance of the sampling set, wherein a variance within a similar noise range does not require the adjustment of the target biomarker level, the similar noise range being the variance computed from one or more prior sampling sets; anda variance within an acceptable noise range requires adjustment of the target biomarker level, the acceptable noise range being the maximum tolerable variance for a sampling set. 42. The testing method of claim 41 wherein a physician is contacted when the variance is greater than the acceptable noise range. 43. A testing method for optimizing a therapy to a diabetic patient via a device comprising a processor, the testing method comprising the steps of: querying a diabetic patient via the device to confirm that entry criteria have been satisfied;collecting at least one sampling set of biomarker data if entry criteria have been satisfied, wherein each sampling set comprises one or more sampling instances recorded over a collection period and each sampling instance comprises one or more biomarker readings;evaluating whether the collection of the sampling set satisfies adherence criteria;computing a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, the probability distribution function is calculated to approximate the probability distribution of the biomarker data,the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of higher risk of complications, wherein the hazard function H(B) is defined by the equation H(B)=Hhypo(B)+Hhyper(B), wherein B is a biomarker reading in the sampling set, Hhypo(B) is the hazard function associated with hypoglycemic events, Hhyper(B) is the hazard function associated with hyperglycemic events, wherein Hhypo⁡(B)=50⁢(1-11+e-(B-55)/5)+50⁢ⅇ12·302⁢B2,andHhyper⁡(B)⁢⁢is⁢⁢Hhyper⁡(B)=ⅇ1100⁢B,the risk function is the product of the probability distribution function and the hazard function, andthe risk value is calculated by the integral of the risk function minimizing the risk value by adjusting the diabetic patient's therapy; andinstructing the patient via the device to exit the testing method when the risk value for at least one sampling set is minimized to an optimal risk level,wherein the above steps are performed by the processor. 44. The testing method of claim 43 wherein each sampling instance comprises the biomarker reading and other contextual data associated with the biomarker reading, wherein the contextual data is selected from the group consisting of the time of collection, the date of collection, the time when the last meal was consumed, stress, exercise, energy level, the time and dose of medications including insulin, the recommended amount of insulin, and combinations thereof. 45. The testing method of claim 43 further comprising determining whether to adjust a target biomarker level based on a variance of the sampling set, wherein a variance within a similar noise range does not require the adjustment of the target biomarker level, the similar noise range being the variance computed from one or more prior sampling sets; anda variance within an acceptable noise range requires adjustment of the target biomarker level, the acceptable noise range being a maximum tolerable variance for a sampling set. 46. The testing method of claim 45 further comprising contacting a physician when the variance is greater than the acceptable noise range. 47. The testing method of claim 43 wherein the diabetic patient's therapy for minimizing the risk value is selected from the group of adjusting an insulin dosage parameter, adjusting diabetic patient behaviors to reduce biomarker variability, adjusting a target biomarker level, or combinations thereof. 48. The testing method of claim 47, wherein the insulin dosage parameter is selected from the group consisting of a basal dosage parameter, an insulin to carbohydrate parameter, an insulin sensitivity parameter, a meal rise parameter, a meal offset parameter, an insulin active parameter, and combinations thereof. 49. The testing method of claim 47 wherein for minimization of the risk value to the optimal level, there are no hazard values indicative of a hyperglycemic or hypoglycemic event. 50. The testing method of claim 47 further comprising adjusting the insulin dosage parameter according to an insulin adjustment regimen, which is determined by comparing the mean of the current completed sampling set to the target biomarker level, the insulin adjustment regimen being defined as the number of insulin dosage adjustments required to achieve a target insulin level and the amount of each adjusted insulin dosage, wherein the target insulin level is the amount required to achieve the target biomarker level. 51. The testing method of claim 50 wherein the insulin adjustment regimen is determined by calculating the requisite insulin dosage to achieve the target biomarker level, Dtarget, is calculated by the equation Dtarget=Dk+m·(Btarget−Bk), wherein m is the rate of change from a first biomarker reading Bk−1 to a subsequent second biomarker reading Bk based on the adjustment of insulin from a first dosage Dk−1 to a subsequent second dosage Dk as defined by the equation 1m=Bk-Bk-1Dk-Dk-1; and Btarget is the target biomarker level. 52. The testing method of claim 51 wherein an adjusted insulin dosage Dk+1 after adjustment by the insulin adjustment regimen is calculated by the equation Dk+1=Dk+λ·m·(Btarget−Bk) wherein λ is a regimen tuning parameter which ranges from 0 to 1 and corresponds to an aggressiveness of the insulin adjustment regimen. 53. The testing method of claim 52 wherein an aggressive insulin adjustment regimen adjusts an insulin dosage to the maximum level in a first adjustment, and includes a λ value of 1. 54. The testing method of claim 52 wherein a less aggressive insulin adjustment regimen adjusts an insulin dosage to the maximum level incrementally over at least two dosage adjustments, and includes a λ value of 0.5 or less. 55. The testing method of claim 52 wherein the adjusted insulin dosage cannot exceed a maximum insulin dosage set by a healthcare provider. 56. The testing method of claim 52 wherein the calculated insulin dosage to achieve the target biomarker level, Dtarget for the sampling set is assessed by a healthcare provider in comparison to a calculated Dtarget for a prior sampling set. 57. A testing method for optimizing a therapy to a diabetic patient via a device comprising a processor, the testing method comprising the steps of: querying a diabetic patient via the device to confirm that entry criteria have been satisfied;collecting at least one sampling set of biomarker data if entry criteria have been satisfied, wherein each sampling set comprises one or more sampling instances recorded over a collection period and each sampling instance comprises one or more biomarker readings;evaluating whether the collection of the sampling set satisfies adherence criteria;computing a probability distribution function, a hazard function, a risk function, and a risk value for the sampling set of biomarker data wherein, the probability distribution function is calculated to approximate the probability distribution of the biomarker data,the hazard function is a function which yields higher hazard values for biomarker readings in the sampling set indicative of higher risk of complications,the risk function is the product of the probability distribution function and the hazard function, andthe risk value is calculated by the integral of the risk function,minimizing the risk value by adjusting the diabetic patient's insulin dosage according to an insulin adjustment regimen, which is determined by comparing the mean of the current completed sampling set to the target biomarker level, the insulin adjustment regimen being defined as the number of insulin dosage adjustments required to achieve a target insulin level and the amount of each adjusted insulin dosage, wherein the target insulin level is the amount required to achieve the target biomarker level, wherein the insulin adjustment regimen is determined by calculating the requisite insulin dosage to achieve the target biomarker level, Dtarget, is calculated by the equation Dtarget=Dk+m·(Btarget−Bk), wherein m is the rate of change from a first biomarker reading Bk−1 to a subsequent second biomarker reading Bk based on the adjustment of insulin from a first dosage Dk−1 to a subsequent second dosage Dk as defined by the equation 1m=Bk-Bk-1Dk-Dk-1, and Btarget is the target biomarker level, wherein an adjusted insulin dosage Dk+1 after adjustment by the insulin adjustment regimen is calculated by the equation Dk+1=Dk+λ·m·(Btarget−Bk) wherein λ is a regimen tuning parameter which ranges from 0 to 1 and corresponds to an aggressiveness of the insulin adjustment regimen; and instructing the patient via the device to exit the testing method when the risk value for at least one sampling set is minimized to an optimal risk level,wherein the above steps are performed by the processor. 58. The testing method of claim 57 wherein an aggressive insulin adjustment regimen adjusts an insulin dosage to the maximum level in a first adjustment, and includes a λ value of 1. 59. The testing method of claim 57 wherein a less aggressive insulin adjustment regimen adjusts an insulin dosage to the maximum level incrementally over at least two dosage adjustments, and includes a λ value of 0.5 or less. 60. The testing method of claim 57 wherein the adjusted insulin dosage cannot exceed a maximum insulin dosage set by a healthcare provider. 61. The testing method of claim 57 wherein the calculated insulin dosage to achieve the target biomarker level, Dtarget for the sampling set is assessed by a healthcare provider in comparison to a calculated Dtarget for a prior sampling set.