PREDICTIVE TREATMENT OF DYSGLYCEMIC EXCURSIONS ASSOCIATED WITH DIABETES MELLITUS
First Claim
1. A method for predicting dysglycemic excursions in diabetes mellitus patients, comprising:
- monitoring and recording a patient'"'"'s blood glucose levels continuously over an extended period of time;
recording life-event information for the extended period of time over which continuous monitoring is performed;
analyzing continuous blood glucose monitor data in the context of recorded life-event information to identify correlations between specific life events and periodicities in monitored blood glucose level variations; and
determining a predictive sinusoidal function from said analysis to closely match periodic variations of blood glucose levels.
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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.
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Citations
19 Claims
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1. A method for predicting dysglycemic excursions in diabetes mellitus patients, comprising:
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monitoring and recording a patient'"'"'s blood glucose levels continuously over an extended period of time;
recording life-event information for the extended period of time over which continuous monitoring is performed;
analyzing continuous blood glucose monitor data in the context of recorded life-event information to identify correlations between specific life events and periodicities in monitored blood glucose level variations; and
determining a predictive sinusoidal function from said analysis to closely match periodic variations of blood glucose levels. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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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. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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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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Specification