PREDICTIVE TREATMENT OF DYSGLYCEMIC EXCURSIONS ASSOCIATED WITH DIABETES MELLITUS
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Abstract
A predictive technique for treating diabetes mellitus is described whereby a patient'"'"'s blood glucose levels are monitored “continuously” over an extended period of time and a life-event diary is maintained records all significant life-events (e.g., food intake, medication, exercise, mood/emotions, etc.). This information is analyzed to derive a mathematical model that closely matches the patient'"'"'s glucose level variations for the period of monitoring. Specific daily time periods of dysglycemic vulnerability are determined by calculating when the mathematical model predicts that crossings of predetermined hyperglycemic and hypoglycemic threshold levels will occur. These predicted periods of vulnerability are then used to devise a therapeutic plan that administers treatment in anticipation of predicted dysglycemic excursions, thereby limiting the extent of those excursions or eliminating them altogether.
11 Citations
21 Claims
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1. (canceled)
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10. A system for predicting dysglycemic excursions in diabetes mellitus patients, comprising:
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a continuous glucose monitoring system for recording a patient'"'"'s glucose levels over an extended period of time; a life-event diary system for recording life-event information during continuous glucose monitoring; and means for analyzing recorded glucose level information in the context of life-event information recorded by the life-event diary system to determine a model sinusoidal function that closely approximates glucose levels observed during monitoring; and means monitoring and recording the patient'"'"'s blood glucose levels repeatedly over short intervals of time for an extended period of time of at least 72 hours to provide continuously generated glucose data with a continuous glucose monitoring system; - View Dependent Claims (11, 12, 13, 14)
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18. A system for predicting dysglycemic excursions in diabetes mellitus patients, comprising:
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a continuous glucose monitoring system for recording a patient'"'"'s glucose levels over an extended period of time; a computing device for recording life-event information during continuous glucose monitoring; computing means for analyzing recorded glucose level information in the context of life-event information recorded by the life-event diary system to determine a model sinusoidal function that closely approximates glucose levels observed during monitoring; and computing means for determining anticipated glucose threshold crossing times by determining times when said model sinusoidal function crosses those threshold levels. - View Dependent Claims (19)
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20. A method for predicting time-dependent glycemic patterns in a diabetes mellitus patient, comprising:
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monitoring and recording the patient'"'"'s blood glucose levels repeatedly over short intervals of time for an extended period of time of at least 72 hours to provide continuously generated glucose data with a continuous glucose monitoring system; recording life-event data about the patient, including food intake, work stress, emotional stress, exercise, pain, gastroparesis, and sleep, arousal, and menses, for the extended period of time over which the patient'"'"'s blood glucose levels are repeatedly monitored with a programmed system for recording the life-event data; analyzing the life-event data occurring more frequently than every 24 hours in the context of the continuously generated glucose data with the automated analysis system to define periods of vulnerability to unacceptable dysglycemic excursions of the patient when high risk life-events occur; and identifying significant periods of dysglycemic excursion risk for the patient based upon anticipated unacceptable dysglycemic excursions of the patient based on the analysis of the identified correlations between the life-event data occurring more frequently than every 24 hours in the context of the continuously generated glucose data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 15, 16, 17, 21)
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Specification