WO2023250076A1

WO2023250076A1 - Systems and methods for identifying medical conditions or treatment issues using optimized diabetes patient management data

Info

Publication number: WO2023250076A1
Application number: PCT/US2023/025961
Authority: WO
Inventors: Israel Hodish; Eran Bashan
Original assignee: Hygieia, Inc.
Priority date: 2022-06-23
Filing date: 2023-06-22
Publication date: 2023-12-28

Abstract

Systems, methods and/or devices for optimizing a patient's insulin dosage regimen over time, comprising at least a first memory for storing data inputs corresponding at least to one or more components in a patient's present insulin dosage regimen, and data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times, and a processor operatively connected to the at least first memory. The processor is programmed at least to determine from the data inputs corresponding to the patient's blood-glucose-level measurements determined at a plurality of times whether and by how much to vary at least one of the one or more components in the patient's present insulin dosage regimen. Also disclosed are systems, devices and/or methods for identifying medical conditions or diabetes treatment issues using systems, devices, and/or methods for optimizing a patient's insulin dosage regimen and/or other diabetes management data.

Description

SYSTEMS AND METHODS FOR IDENTIFYING MEDICAL CONDITIONS OR TREATMENT ISSUES USING OPTIMIZED DIABETES PATIENT MANAGEMENT DATA

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority from U.S. Provisional Application No. 63/354,865, filed June 23, 2022. The entire content of the foregoing related application, in its entirety, is incorporated herein by reference.

[0002] This application further relates to U.S. Application No. 13/868,612, filed April 23, 2013, now U.S. Patent No. 10,272,198, issued April 30, 2019, which is a continuation of U.S. Application No. 12/417,960, filed on April 3, 2009, now U.S. Patent No. 8,457,901, issued June 4, 2013, which claims the benefit of both U.S. Provisional Application No. 61/042,487, filed on April 4, 2008, and U.S. Provisional Application No. 61/060,645, filed on June 11, 2008. The entire contents of each of the foregoing applications is incorporated herein by reference in their entirety.

[0003] In addition, the present application relates to U. S. Application No. 14/067,479, filed on October 30, 2013, now U.S. Patent No. 10,335,546, issued July 2, 2019, U.S. Application No. 14/452,140, filed on August 5, 2014, now U.S. Patent No. 10,624,577, issued April 21, 2020, U.S. Application No. 15/947,448, filed on April 6, 2018, now U.S. Patent No. 10,679,747, issued June 9, 2020, and U.S. Application No. 14/946,259, filed on November 19, 2015, now U.S. Patent No. 10,736,562, issued August 11, 2020. Each of these applications and patents is incorporated by reference in its entirety.

FIELD

[0004] The present disclosure relates to methods for identifying medical conditions and/or treatment issues based on a patient’s diabetes management, by using such systems, methods and/or devices according to which a processor is programmed at least to determine from the data inputs corresponding to the patient's blood-glucose-level measurements determined at a plurality of times in order to identify medical conditions or diabetes treatment issues according to a patient’s diabetes management thereby allowing for future clinical interventions according to new identifications. BACKGROUND

[0005] Diabetes is a chronic disease resulting from deficient insulin secretion by the endocrine pancreas. About 10% of the general population in the Western Hemisphere suffers from diabetes. Of these persons, roughly 95% suffer from Type-2 diabetes while approximately 5% suffer from Type-1. In Type-1 diabetes, patients effectively surrender their endocrine pancreas to autoimmune distraction and so become dependent on daily insulin injections to control blood-glucose-levels. In Type-2 diabetes, on the other hand, the endocrine pancreas gradually fails to satisfy increased insulin demands, thus requiring the patient to compensate with a regime of oral medications or insulin therapy. In the case of either Type-1 or Type-2 diabetes, the failure to properly control glucose levels in the patient may lead to such complications as heart attacks, strokes, blindness, renal failure, and even premature death.

[0006] Uncommon forms of diabetes also exist which are important for clinicians to consider, as these forms can impact the clinical management of a diabetic patient. Some uncommon conditions include latent autoimmune diabetes in adults (LADA), pancreatitis associated diabetes and mature onset diabetes of the young (MODY). However, these atypical forms of diabetes are frequently misdiagnosed as Type-2 diabetes due to lack of special expertise in the field. Failure to properly diagnose these uncommon forms can lead to mismanagement of diabetes as well as failure to identify other ancillary conditions such as autoimmune conditions that often accompany Type-1 diabetes such as hypothyroidism or Celiac disease.

[0007] Diabetes is a metabolic disorder where the individual’s ability to secrete insulin, and therefore to regulate glucose level has been compromised. For a non-diabetic person, normal glucose levels are typically around 85-110 mg/dl, and can spike after meals to typically around 140-200 mg/dl. In individuals with diabetes who are overtly insulin deficient and require insulin therapy, glucose levels can range from hypo- to hyper- glycemia. Low glucose levels or hypoglycemia can drop below life-sustaining level and lead to seizures, consciousness-loss, and even death. Hyperglycemia, particularly over a long period of time, has been associated with far increased chances to develop diabetes related complications such as heart disease, hypertension, kidney disease, and blindness among others.

[0008] Insulin therapy is the mainstay of Type- 1 diabetes management and one of the most widespread treatments in Type-2 diabetes, about 27% of the sufferers of which require insulin. Insulin administration is designed to imitate two different physiological insulin secretions, basal and bolus. During the first few years of insulin therapy for Type-2 diabetes, provision of long-acting insulin is often sufficient to maintain adequate glucose levels since patients are able to secrete their own bolus component from their own pancreases. After about 3 years, however, most patients lose this capability, and they require both basal and bolus insulin components. Two regimens can fulfill both basal and bolus needs. Basal-bolus insulin therapy includes the injection of two insulin classes into the patient’s body: long-acting insulin, which fulfills basal metabolic needs; and short-acting insulin (also known as fast-acting insulin), which compensates for sharp elevations in blood-glucose-levels following patient meals. The long- acting insulin is typically given as one or two injections per day and the rapid acting or fast acting insulin is given as one injection with each meal. A second regimen includes premixed or biphasic insulin which is a mixture of two types of insulin functionalities in the same vial. The first function is a fast acting or rapid acting insulin that covers part of the bolus needs and an intermediate acting insulin that covers the basal needs. A premixed or biphasic regimen is typically given as an injection with breakfast and with dinner. Orchestrating the process of dosing these types of insulin, in whatever form (e.g., basal, bolus or as premixed insulin) involves numerous considerations. Accurate identification of changes in insulin requirements over time, particularly in the presence of acute or subacute medical conditions, the need for concentrated insulins, or the need to change regimens based on individual disease biology, are important to the management of diabetes.

[0009] First, patients measure their blood-glucose-levels (using some form of a glucose meter or a continuous glucose monitor) about 1 to 4 times per day. The number of such measurements and the variations therebetween complicates the interpretation of these data, making it difficult to extrapolate trends therefrom that may be employed to better maintain the disease. Second, the complexity of human physiology continuously imposes changes in insulin needs for which frequent insulin dosage regimen adjustments are warranted. Presently, these considerations are handled by a patient's endocrinologist or other healthcare professional during clinic appointments. Unfortunately, these visits are relatively infrequent— occurring once every 3 to 6 months— and of short duration, so that the physician or other healthcare professional is typically unable to address changes in insulin needs between appointments. In consequence, it has been shown that more than 70% of patients who use insulin to control their diabetes achieve sub-optimal glucose levels, leading to unwanted complications from the disease. Acute and sub-acute complications such as deterioration of kidney functions, infections, or worsening heart failure, can result in considerable alterations in insulin needs and thus uncontrolled diabetes, posing both short-term and long-term problems to patients due to infrequent medical visits for diabetes management. [0010] Indeed, one of the maj or obstacles of diabetes management is the lack of availability of a patient’s healthcare professional and the relative infrequency of clinic appointments. Studies have, in fact, established that more frequent insulin dosage regimen adjustments, for example, every 1 to 2 weeks — improves diabetes control in most patients. Yet as the number of diabetes sufferers continues to expand, it is expected that the possibility of more frequent insulin dosage regimen adjustments via increased clinic visits will, in fact, decrease. And, unfortunately, conventional diabetes treatment solutions do not address this obstacle. Therefore, the ability to identify patients in need of insulin regimen changes in a more convenient and timely manner is needed now more than ever.

[0011] The device most commonly employed in diabetes management is the glucose meter. Such devices come in a variety of forms, although most are characterized by their ability to provide patients near instantaneous readings of their blood-glucose-levels. This additional information can be used to better identify dynamic trends in blood-glucose-levels. However, conventional glucose meters, as well as continuous glucose monitors, are designed to be diagnostic tools rather than therapeutic ones. Therefore, by themselves, even state-of-the-art glucose meters or continuous glucose monitors do not lead to improved glycemic control.

[0012] The most common biomarker used to access glycemic control is hemoglobin A1C (A1C for brevity). The relationship between average glucose levels and A1C has been studied. For healthy individuals A1C is between 4.6% and 5.7%, for people with diabetes the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) recommend maintaining A1C<7% that correlates to an average glucose level below 150 mg/dl. [0013] Studies have demonstrated the relationship between A1C and complication. The

ADA and EASD have set the goal of getting A1C to below 7%. This was chosen as a compromise between lowering the risk for developing complications and the risk of severe (and potentially fatal) hypoglycemia. As a result, diabetes management has developed with its main goal being to bring A1C down as reflected by several consensus statements issued by various authorities. Up until recently, little attention has been devoted to the other side of the equation being prevention of hypoglycemia. It is assumed that hypoglycemia is a side effect of insulin, or some oral anti-diabetes drugs (OAD), therapy as when mean glucose decreases one’s chances of seeing more low glucose levels increases. Since lowering A1C and avoiding hypoglycemia may be considered as inversely related, the standard of care is that clinical studies aim at reducing A1C while reporting the observed rate of hypoglycemia as the unavoidable evil that is part of the therapy. [0014] While the foregoing solutions are beneficial in the diagnosis, management, and treatment of diabetes in some patients, or at least hold the promise of being so, there continues to exist the need for methods for identifying additional medical conditions or issues based on a patient’s diabetes regimen management, that would efficiently improve a patient’s diabetes control as well as discover additional pertinent conditions requiring clinical treatment.

SUMMARY

[0015] Certain embodiments are directed to systems, devices and/or methods for identifying medical conditions or diabetes treatment issues using systems, devices, and/or methods for optimizing a patient’s diabetes regimen. For example, a method for identification of medical conditions or treatment issues based on the optimized diabetes management data, the method comprising: storing one or more components of the patient’s insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting said data of patient’s insulin regimen, historic insulin dosage function, insulin doses, blood glucose readings and/or A1C levels; inputting demographic and clinical information such as the patient’s age, duration of diabetes, duration of insulin treatment, ethnicity, and/or BMI; and detecting identified trends to determine additional medical conditions or diabetes treatment issues based on diabetes management data.

[0016] Certain embodiments are directed to systems, devices and/or methods for identifying uncommon types of diabetes using systems, devices, and/or methods for optimizing a patient’ s diabetes regimen. For example, a method for identifying patients with an uncommon type of adult-onset diabetes, the method comprising: storing one or more components of the patient’s insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose- level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when the reading was obtained; inputting said data of patient’s insulin regimen, historic insulin dosage function, insulin doses, blood glucose readings and/or A1C levels; inputting demographic and clinical information such as the patient’s age, duration of diabetes, duration of insulin treatment, ethnicity, and/or BMI; detecting patients who use unusually low total daily insulin dose and have unusually high frequency of hypoglycemia; wherein the unusually low total daily insulin dose and unusually high frequency of hypoglycemia is unusual compared to the average patient who qualifies for diagnosis of Type-2 diabetes.

[0017] In certain embodiments, the unusual type of adult-onset diabetes may include latent autoimmune diabetes in adults (LADA), pancreatitis associated diabetes or mature onset diabetes of the young (MODY).

[0018] Certain embodiments are directed to systems, devices and/or methods for identifying additional medical conditions in diabetes patients using systems, devices, and/or methods for optimizing a patient’s diabetes regimen. For example, a method for identifying patients with acute or sub-acute medical conditions based on dynamics in insulin doses and glucose levels, the method comprising: storing one or more components of the patient’s insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when the reading was obtained; inputting said data of patient’s insulin regimen, insulin doses, blood glucose readings and A1C levels; and detecting an unusual decrease in total daily insulin in a persistent way or an unusual increase in average glucose levels despite previous stability of glucose levels and/or A1C levels being within the therapeutic range.

[0019] In certain embodiments, the acute or sub-acute medical conditions may include deterioration in kidney functions, severe infections, deterioration of cirrhosis, maldigestion, malabsorption, thyroid dysfunction, or worsening of heart failure.

[0020] In certain embodiments, the acute or sub-acute medical conditions may or may not be diabetes-related.

[0021] Certain embodiments are directed to systems, devices and/or methods for identifying patients with hypoglycemia unawareness using systems, devices, and/or methods for optimizing a patient’s diabetes regimen. For example, a method for identifying patients with hypoglycemia unawareness based on dynamics in insulin doses and glucose levels, the method comprising: storing one or more components of the patient’s insulin dosage regime; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when the reading was obtained; inputting said data of patient’s insulin regimen, insulin doses, blood glucose readings and A1C levels; and detecting abnormal distribution of episodes of hypoglycemia, e.g., during scheduled dosing events rather than between dosing events, i.e. post-injections. In certain embodiments, a normal distribution of episodes of hypoglycemia is a distribution shared by most people with type 2 diabetes. [0022] Certain embodiments are directed to systems, devices and/or methods for changing a patient’s insulin dosage regimen using systems, devices, and/or methods for optimizing a patient’s diabetes regimen. For example, a method for identifying patients in need of insulin regimen changes based on dynamics in insulin doses, glucose levels, and A1C levels, the method comprising: storing one or more components of the patient’s insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when the reading was obtained; inputting said data of patient’s insulin regimen, insulin doses, blood glucose readings and A1C levels; detecting normal fasting glucose levels in patients with Type-2 diabetes who use long-acting insulin and have either A1C levels greater than 8% and/or unusually high average glucose levels; and initiating a change of the insulin regimen from long-acting insulin to premixed or basal bolus insulin. In certain embodiments, unusually high non-fasting glucose levels refer to comparing the patient non-fasting glucose level to those commonly seen in patients treated with basal only insulin.

[0023] Certain embodiments are directed to systems, devices and/or methods for changing components of a patient’s insulin dosage regimen using systems, devices, and/or methods for optimizing a patient’s insulin dosage regimen. For example, a method for identifying patients in need of concentrated insulin, based on dynamics in insulin doses and glucose levels, the method comprising: storing one or more components of the patient’s insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when the reading was obtained; inputting said data of patient’s insulin regimen, insulin doses, and blood glucose readings; detecting patients of which any component of their insulin doses exceeds a specific amount of units of insulin per day.

[0024] Certain embodiments are directed to systems, devices and/or methods for changing components of a patient’s insulin dosage regimen using systems, devices, and/or methods for optimizing a patient’s insulin dosage regimen. For example, a method for identifying patients that mistakenly use a different insulin than prescribed, based on dynamics in insulin doses and glucose levels, the method comprising: storing one or more components of the patient’s insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when the reading was obtained; inputting said data of patient’s insulin regimen, insulin doses, and blood glucose readings; detecting a ratio between doses of each component of a patient’s insulin regimen that is unusually higher or lower than expected.

[0025] In certain embodiments, the system comprises at least a first memory for storing data inputs corresponding at least to one or more components of a patient's present insulin dosage regimen, and data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times; and a processor operatively connected to the at least first memory. The processor is programmed at least to determine from the data inputs corresponding to the patient's blood-glucose-level measurements determined at a plurality of times whether and by how much to vary at least one of the one or more components in the patient's present insulin dosage regimen.

[0026] In certain embodiments, the at least first memory and the processor are resident in a single apparatus. Per one feature, the single apparatus further comprises a glucose meter. The glucose meter may be separate from the single apparatus, further to which the glucose meter is adapted to communicate to the at least first memory of the single apparatus the data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times. Per another feature, the single apparatus may comprise a continuous glucose monitor (e.g., a glucose monitoring sensor(s)). The continuous glucose monitor may be separate from the single apparatus, further to which the continuous glucose monitor is adapted to communicate to the at least first memory of the single apparatus the data inputs corresponding at least to the patient’s blood-glucose-level measurements determined at a plurality of times.

[0027] Per one feature thereof, the single apparatus may further comprises data entry means for entering data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times directly into the at least first memory. In certain aspects, the single apparatus may further comprises a way to enter data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times directly into the at least first memory.

[0028] There may, per other aspects of the disclosure, further be provided data entry means disposed at a location remote from the single apparatus for remotely entering data inputs corresponding at least to the one or more components in the patient's present insulin dosage regimen into the at least first memory. In certain aspects, the data entry may be disposed at a location remote from the single apparatus for remotely entering data inputs corresponding at least to the one or more components in the patient's present insulin dosage regimen into the at least first memory. [0029] Certain embodiments may comprise at least a first data entry means disposed at a location remote from the at least first memory and processor for remotely entering data inputs corresponding at least to the one or more components in the patient's present insulin dosage regimen into the at least first memory, and at least second data entry means, disposed at a location remote from the at least first memory, processor and at least first data entry means, for remotely entering data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times into the at least first memory.

[0030] Certain embodiments may comprise a way to enter a first data set disposed at a location remote from the at least first memory and processor for remotely entering data inputs corresponding at least to the one or more components in the patient's present insulin dosage regimen into the at least first memory, and a way to enter a second data set, disposed at a location remote from the at least first memory, processor and the first data set corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times that is entered into the at least first memory.

[0031] In certain aspects, the data inputs corresponding at least to the patient's blood- glucose-level measurements determined at a plurality of times are each associated with an identifier indicative of when the measurement was taken and when the measurement was input into the memory. Optionally, there may be provided data entry means enabling a user to define the identifier associated with each blood-glucose-level measurement data-input, to confirm the correctness of the identifier associated with each blood-glucose-level measurement data-input, and/or to modify the identifier associated with each blood-glucose-level measurement data- input. Optionally, there may be provided a way to enter data enabling a user to define the identifier associated with each blood-glucose-level measurement data-input, to confirm the correctness of the identifier associated with each blood-glucose-level measurement data-input, and/or to modify the identifier associated with each blood-glucose-level measurement data- input.

[0032] According to other embodiments, the processor is programmed to determine on a predefined schedule whether and by how much to vary at least one of the one or more components in the patient's present insulin dosage regimen.

[0033] In certain aspects, the processor is programmed to determine whether each data input corresponding to the patient's blood-glucose-level measurements represents a (e.g., Level 2) hypoglycemic event, defined as a glucose reading below a certain threshold (e.g., 30, 35, 40, 45, 50, 55, or 60 mg/dl), and to vary at least one of the one or more components in the patient's present insulin dosage regimen in response to a determination that a data input corresponding to the patient's blood-glucose-level measurements represents a (e.g., Level 2) hypoglycemic event.

[0034] According to certain embodiments, the processor is programmed to determine from the data inputs corresponding to the patient's blood-glucose-level measurements determined at a plurality of times if there have been an excessive number of hypoglycemic events over a predefined period of time, and to vary at least one of the one or more components in the patient's present insulin dosage regimen in response to a determination that there have been an excessive number of such hypoglycemic events over a predefined period of time.

[0035] In certain aspects, the processor is programmed to determine from the data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times if the patient's blood-glucose level measurements fall within or outside of a predefined range, and to vary at least one of the one or more components in the patient's present insulin dosage regimen only if the patient's blood-glucose level measurements fall outside of the predefined range. The processor may be further programmed to determine from the data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times whether the patient's blood-glucose-level measurements determined at a plurality of times represent a normal or abnormal distribution. In certain aspects, this determination comprises determining whether the third moment of the distribution of the patient's blood-glucose-level measurements determined at a plurality of times fall within a predefined range.

[0036] In certain embodiments, where the one or more components in the patient's present insulin dosage regimen comprise a long-acting insulin dosage component, the processor is programmed to determine from the identifier indicative of when a measurement was input into the memory at least whether the measurement is a morning or bed-time blood-glucose-level measurement, to determine whether the patient's morning and bed-time blood-glucose-level measurements fall within a predefined range, and to determine by how much to vary the patient's long-acting insulin dosage component only when the patient's morning and bed-time blood-glucose-level measurements are determined to fall outside of the said predefined range. In connection therewith, the processor may further be programmed to factor in an insulin sensitivity correction factor that defines both the percentage by which any of the one or more components of the insulin dosage regimen may be varied and the direction in which any fractional variations in any of the one or more components are rounded to the nearest whole number. Optionally, the at least first memory further stores data inputs corresponding to a patient's present weight, and the insulin sensitivity correction factor is in part determined from the patient's present weight. Per certain aspects, the determination of by how much to vary the long-acting insulin dosage component of a patient's present insulin dosage regimen may be a function of the present long-acting insulin dosage, the insulin sensitivity correction factor, and the patient's blood-glucose-level measurements.

[0037] In certain embodiments, the one or more components in the patient's present insulin dosage regimen comprise a short-acting insulin dosage component defined by a carbohydrate ratio and plasma glucose correction factor, and the processor is programmed to determine whether and by how much to vary the patient's carbohydrate ratio and plasma glucose correction factor. In connection with this determination, the processor may be programmed to factor in an insulin sensitivity correction factor that defines both the percentage by which any one or more components of the insulin dosage regimen may be varied and the direction in which any fractional variations in the one or more components are rounded to the nearest whole number.

[0038] In certain embodiments, the determination of by how much to vary the present plasma glucose correction factor component of a patient's insulin dosage regimen may be a function of a predefined value divided by the mean of the total daily dosage of insulin administered to the patient, the patient's present plasma glucose correction factor, and the insulin sensitivity correction factor. Alternatively, a value representing a division of the patient's daily dosage of long-acting insulin in the present insulin dosage regimen by a certain factor (e.g. 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, or 0.8) may be substituted for the mean of the total daily dosage of insulin administered to the patient as an approximation thereof. Per still another feature hereof, the plasma glucose correction factor component of the patient's insulin dosage regimen may be quantized to predefined steps of mg/dL.

[0039] According to certain embodiments, the determination of by how much to vary the present carbohydrate ratio component of a patient's insulin dosage regimen is a function of a predefined value divided by the mean of the total daily dosage of insulin administered to the patient, the patient's present carbohydrate ratio, and the insulin sensitivity correction factor. Alternatively, a value representing a division of the patient's daily dosage of long-acting insulin in the present insulin dosage regimen by a certain factor (e.g. 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, or 0.8) is substituted for the mean of the total daily dosage of insulin administered to the patient as an approximation thereof. Further hereto, the processor may also be programmed to determine a correction factor that allows variations to the carbohydrate ratio component of a patient's insulin dosage regimen to be altered in order to compensate for a patient's individual response to insulin at different times of the day.

[0040] A further feature of certain embodiments is that the one or more components in the patient's present insulin dosage regimen comprise a long-acting insulin dosage component, and the determination of by how much to vary the long-acting insulin dosage component is constrained to an amount of variation within predefined limits.

[0041] In certain embodiments the one or more components in the patient's present insulin dosage regimen comprise a short-acting insulin dosage component defined by a carbohydrate ratio and plasma glucose correction factor, and the determination of by how much to vary any one or more of each component in the short-acting insulin dosage is constrained to an amount of variation within predefined limits.

[0042] According to certain embodiments, the one or more components in the patient's present insulin dosage regimen comprise a short-acting insulin dosage component taken according to a sliding scale, and the processor is programmed to determine whether and by how much to vary at least one of the components of the sliding scale. The determination of by how much to vary the sliding scale may further be constrained to an amount of variation within predefined limits.

[0043] According to certain embodiments, the one or more components in the patient's present insulin dosage regimen comprise a short-acting insulin dosage component where meal bolus components, whether a carbohydrate to insulin ratio or a fixed dose with a sliding scale, may differ from one meal to the other, and the processor is programmed to determine whether and by how much to vary at least one of the components independent of the other components. The determination of by how much to vary a dosage component may further be constrained to an amount of variation within predefined limits.

[0044] According to certain embodiments, insulin dosage may comprise of a single component representing a daily total of long acting insulin the user has to administer. Such daily total may be administer as a single injection or split between more than one injection, and the processor is programmed to determine whether and by how much to vary the daily total insulin units of the long acting insulin component.

[0045] According to certain embodiments, insulin dosage may comprise of a two component representing a two separate insulin doses to be taken with specific events. Such example may be a breakfast dose and a dinner dose of premixed or biphasic insulin, and the processor is programmed to determine whether and by how much to vary at least one of the two different dosage component. [0046] In certain embodiments, the processor is programmed to calculate glycemic index indicative of the user metabolic state associated with a particular event. In certain embodiments, glycemic index is a single number comprised of the average, median, minimum, maximum, or other metrics of the data set being measured, and the processor is programmed to determine whether and by how much to vary at least one of the one or more insulin dosage components based at least on glycemic index.

[0047] Certain embodiments are methods for determining the amount of insulin needed by a diabetic comprising the steps of: A. taking a plurality of historical blood glucose readings from a patient; B. taking a plurality of historical readings of insulin administered to a patient; C. determining a protocol for providing insulin to a patient based upon the plurality of historical readings and a patient's blood glucose reading at a fixed time; and D. providing insulin to the patient based upon the protocol, historical readings of Steps A and B and the patient’s blood glucose reading of Step C. In certain aspects, the protocol is reevaluated over a fixed time interval. In certain aspects, the fixed time interval is, for example, weekly or every two weeks. In certain aspects, the protocol is reevaluated based on predefined events (e.g., a blood glucose reading indicating a hypo-glycemic event) in an asynchronous manner. In certain embodiments, the plurality of historical readings of insulin administered to a patient includes the number of units and the type of insulin for each time insulin is administered to a patient.

[0048] Certain embodiments are to systems to determine the amount of insulin needed by a diabetic patient comprising: A. means to input blood glucose readings of a patient; B. means to determine a protocol based upon the blood glucose readings; and C. means to modify the protocol over a period of time based upon historical blood glucose readings. In certain aspects, the system is provided within a glucose meter. In certain aspects, the system further comprises means to input quantities of insulin administered by a patient. In certain aspects, the system further comprises an infusion pump to administer insulin to the patient based upon the protocol and the blood glucose readings.

Certain embodiments are systems to determine the amount of insulin needed by a diabetic patient comprising: A. a way to input blood glucose readings of a patient; B. a way to determine a protocol based upon the blood glucose readings; and C. a way to modify the protocol over a period of time based upon historical blood glucose readings. In certain aspects, the system is provided within a glucose meter. In certain aspects, the system further comprises a way to input quantities of insulin administered by a patient. In certain aspects, the system further comprises an infusion pump to administer insulin to the patient based upon the protocol and the blood glucose readings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings and figures facilitate an understanding of the various embodiments of this invention.

[0050] FIG. 1 is a simplified schematic of an apparatus according to exemplary embodiments.

[0051] FIG. 2 is a drawing of a representative display for providing information to a patient.

[0052] FIG. 3 is a drawing of another representative display for providing information to a patient.

[0053] FIG. 4 is a drawing of yet another representative display for providing information to a patient.

[0054] FIG. 5 is a drawing of still another representative display for providing information to a patient.

[0055] FIG. 6 is a simplified diagram of an apparatus for employing the disclosed system, according to certain embodiments thereof.

[0056] FIG. 7 is a simplified diagram of an apparatus for employing the disclosed system, according to certain embodiments.

[0057] FIG. 8 is a simplified diagram of an apparatus for employing the disclosed system, according to certain embodiments thereof.

[0058] FIG. 9 is a schematic view of an exemplary arrangement, according to certain embodiments.

[0059] FIG. 10 is a schematic view of an exemplary arrangement for employing, according to certain embodiments.

[0060] FIG. 11 is a generalized diagram of the steps employed in updating a patient's insulin dosage regimen according to certain exemplary embodiments.

[0061] FIG. 12 is a flowchart of an exemplary algorithm employed in updating a patient's insulin dosage regimen according to certain exemplary embodiment.

[0062] FIG. 13 is a generalized diagram of the steps employed in identifying medical conditions or treatment issues according to an exemplary embodiment.

[0063] FIG. 14 is a generalized diagram of the steps employed in identifying a patient with an uncommon adult-onset diabetes according to an exemplary embodiment.

[0064] FIG. 15 illustrates the insulin dosage and average blood glucose level of a subject on insulin therapy over a nine month period where the subject had low total daily insulin dose and a high frequency of hypoglycemia episodes. [0065] FIG. 16 is a generalized diagram of the steps employed in identifying a patient with an acute/sub-acute medical conditions according to an exemplary embodiment.

[0066] FIG. 17 illustrates the insulin dosage and average blood glucose level of a subject on insulin therapy over an eleven month period where the patient’s total daily insulin decreased by more than fifty percent over a four-week period.

[0067] FIG. 18 is a generalized diagram of the steps employed in identifying a patient with hypoglycemia unawareness according to an exemplary embodiment.

[0068] FIG. 19 illustrates the daily blood glucose measurements of a patient over a three week period where the subject displayed multiple low glucose readings (<70 mg/dL) during scheduled dosing events rather than more uniformly distributed throughout the day.

[0069] FIG. 20 is a generalized diagram of the steps employed in identifying a patient that requires an insulin regimen change from long-acting insulin to pre-mixed or basal bolus insulin according to an exemplary embodiment.

[0070] FIG. 21 illustrates the insulin dosage and average blood glucose level of a patient on long-acting once daily insulin therapy over a six month period. The patient’s mean weekly glucose is below 120 mg/dl but A1C above 8% (not showed).

[0071] FIG. 22 is a generalized diagram of the steps employed in identifying a patient that requires concentrated insulin according to an exemplary embodiment.

[0072] FIG. 23 illustrates the insulin dosage and average blood glucose level of a subject on a multi-dose insulin regimen over a five month period where the subject required increased insulin doses in multiple doses of the regimen.

[0073] FIG. 24 is a generalized diagram of the steps employed in identifying a patient that is using the wrong prescribed insulin according to an exemplary embodiment.

[0074] FIG. 25 illustrates the insulin dosage and average blood glucose level of a subject on insulin therapy that mistakenly used a wrong formulation of insulin than prescribed.

[0075] FIG. 26 illustrates the system architecture of the d-Nav System.

[0076] FIG. 27 illustrates the software components and the components’ interaction with the d-Nav system.

[0077] FIG. 28 illustrates the Phone-App interface.

DETAILED DESCRIPTION

[0078] The following description is provided in relation to several embodiments which may share common characteristics and features. It is to be understood that one or more features of one embodiment may be combinable with one or more features of the other embodiments. In addition, any single feature or combination of features in any of the embodiments may constitute additional embodiments. Specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the disclosed embodiments and variations of those embodiments. The accompanying drawings are not necessarily to scale, and some features may be exaggerated or minimized to show details of particular components.

[0079] Referring now to the drawings, wherein like numerals refer to like or corresponding parts throughout the several views, certain embodiments of the present disclosure comprehend methods of identifying treatment issues and/or medical conditions using optimized diabetes management data determined at a plurality of times.

[0080] In this specification, the word “comprising” is to be understood in its “open” sense, that is, in the sense of “including”, and thus not limited to its “closed” sense, that is the sense of “consisting only of’. A corresponding meaning is to be attributed to the corresponding words “comprise”, “comprised” and “comprises” where they appear.

[0081] The term “insulin dosage function” or “IDF” as used herein with respect to certain embodiments refers to a lookup table indicative of an insulin regimen, a protocol, or a combination thereof that a user follows. For example, for a patient following premixed insulin regimen the insulin dosage function may contain two numbers associated with two events reflective of two insulin injection per day, say X insulin units with breakfast and Y insulin units with dinner. The term IDF history as used herein with respect to certain embodiments refers to chronology of insulin dosage functions and external insulin dosage functions viewed as one data set. The first IDF in an IDF history is the active insulin dosage function or the lookup table currently use to recommend the user an appropriate insulin dose per a particular event and event related information. The next record is the second IDF in IDF history the following is the third IDF in IDF history and so forth through the existing records in IDF history.

[0082] The term “adult onset Type-1 diabetes” as used herein with respect to certain embodiments refers to uncommon types of diabetes diagnosed in adults. For example, in certain embodiments the adult onset Type-1 diabetes may be Latent Autoimmune Diabetes of Adults (LADA), pancreatitis-associated diabetes, alcoholism-associated diabetes, Mature Onset Diabetes of the Young (MODY), Cystic Fibrosis-related diabetes, lipodystrophy, or drug- induced diabetes.

[0083] The term “acute or sub-acute medical condition” as used herein with respect to certain embodiments refers to conditions that have sudden or rapid onset that require urgent care. For example, in certain embodiments an acute or sub-acute condition may include deterioration in kidney function, severe infection, deterioration of cirrhosis, maldigestion, malabsorption, thyroid dysfunction, or worsening heart failure.

[0084] The term “concentrated insulin” as used herein with respect to certain embodiments refers to insulin with a concentration greater than 100 units/mL, thereby allowing for higher doses and less injectable volumes. Standard insulin is referred to as U100, and contains 100 units for 1ml of fluid. There are more concentrated versions of insulin like U200, U300, and U500, that contains 200, 300, or 500 units for every 1ml of fluid, respectively.

[0085] The term “diabetes management data” as used herein with respect to certain embodiments refers to clinical and demographic information related to a patient’s diabetes medical condition. For example, in certain embodiments diabetes management data may include insulin regimens; historic insulin dosage functions; insulin doses; blood glucose-levels; A1C levels; age; ethnicity; body mass index; duration of diabetes; or duration of insulin treatment.

[0086] Certain embodiments are directed to a therapeutic device which is a glucose meter or a continuous glucose monitor equipped with artificial intelligence (Al) and capable of optimizing medication dosage of patients treated with various types of insulin, including optimizing combination of insulin types, i.e., both short and long acting insulin. Certain embodiments monitor patient glucose reading and additional parameters and modify insulin dosage as needed in a similar manner to what an endocrinologist, or other qualified health care provider, would do if that person had continuous access to patient’s data. By dynamically modifying medication dosage based on individual lifestyle and changing needs an optimal dosage level is reached. In turn, this leads to superior glycemic control, better patient prognosis, and the ability to identify additional medical conditions and/or treatment issues that require a clinical intervention.

[0087] Certain embodiments are directed to systems, devices and/or methods for identifying medical conditions and/or treatment issues using a system for optimizing a patient’s diabetes regimen. For example, a method for identification of medical conditions and/or treatment issues based on the diabetes management, the method comprising: storing one or more components of the patient’s insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting said data of patient’s insulin regimen, historic insulin dosage function, insulin doses, blood glucose readings and/or A1C levels; inputting demographic and/or clinical information which may include the patient’s age, duration of diabetes, duration of insulin treatment, ethnicity, and/or body mass index (“BMI”); and detecting identified trends to identify medical conditions and/or treatment issues based on the acquired optimized diabetes management data.

[0088] Certain embodiments comprise methods to identify medical conditions and/or treatment issues based on the acquired optimized diabetes management data. FIG. 13 shows a generalized diagram of the steps employed in identifying medical conditions or treatment issues according to an exemplary embodiment. At 600, data acquired via systems, methods, and/or devices for optimizing the insulin dosage regimen in diabetes patients over time, is input into a computing device. At 610, the diabetes regimen data is analyzed based on various parameters dependent on what medical condition or treatment issue is being sought. At 620, a medical condition and/or treatment issue is identified based on the parameters analyzed at 610. [0089] FIG. 14 shows a generalized diagram of the steps employed in identifying a patient with an uncommon adult-onset Type-1 diabetes according to an exemplary embodiment. At 700, data acquired via systems, methods, and/or devices for optimizing the insulin dosage regimen in diabetes patients over time, which may include insulin regimen, historic insulin dosage function, insulin doses, glucose readings, A1C levels, as well as demographic and/or clinical information, which may include age, duration of diabetes, duration of insulin treatment, ethnicity, and BMI, is input into a computing device. At 710, the diabetes management data is analyzed to detect patients that may have an unusually low total daily insulin dose and high frequency of hypoglycemia, in comparison to the average Type-2 diabetes patient. The upper threshold for the total daily insulin dose detected may be 90, 85, 80, 75, 70, 65, 60, 55, 50, 45, or 40 insulin units or other reasonable numbers in that range. The lower threshold for hypoglycemia events detected may be 1, 3, or 5 episodes per day, per week, or per month or other reasonable numbers in that range. If a detection at 710 is made, at 720 an uncommon adult-onset Type-1 diabetes may be identified in the patient. Given a low insulin total daily dose resulted in an unexpected increase in hypoglycemia events, an uncommon adult-onset Type-1 diabetes may be identified as a potential explanation.

[0090] Lipodystrophy, one of the uncommon conditions associated with profound insulin resistance and diabetes, may be identified by detecting the opposite at 710, a high total daily insulin dose and a low frequency of hypoglycemia. Analysis of the historical diabetes management data comprises the lower threshold for the total daily insulin dose detected which may be 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 420, 440, or 450 insulin units or other reasonable numbers in that range. The upper threshold for hypoglycemia events detected may be 2, 4, or 6 episodes per day, per week, or per month, or other reasonable numbers in that range. If a detection at 710 is made for a high total daily insulin dose and a low frequency of hypoglycemia, at 720 lipodystrophy associated diabetes may be identified in the patient.

[0091] FIG. 15 illustrates the insulin dosage (total daily dose) and average blood glucose level of a subject on insulin therapy over a nine month period where the subject had a total daily insulin dose of about 60 units and a frequency of hypoglycemia episodes greater than 2 per month. The patient was a 66-year-old white man with a BMI of 32 kg/m² diagnosed with diabetes at age forty -two who started using insulin twelve years later, therefore was identified as having an uncommon type of adult-onset diabetes.

[0092] FIG. 16 shows a generalized diagram of the steps employed in identifying a patient with an acute/sub-acute medical conditions according to an exemplary embodiment. At 730, data acquired via systems, methods, and/or devices for optimizing the insulin dosage regimen in diabetes patients over time, which may include insulin regimen, historic insulin dosage function, insulin doses, glucose readings, and A1C levels, is input into a computing device. At 740, the diabetes management data is analyzed to detect patients with an unusual decrease in total daily insulin that persists for a period of time or an unusual increase in average glucose levels despite stability in glucose levels previously and/or therapeutic A1C levels. The lower threshold for an unusual decrease of the total daily insulin may be a decrease of more than 20, 25, 30, 35, 40, 45, 50, 55, 60%, or other reasonable numbers in that range. The lower threshold for persistent weeks of low total daily insulin may be 2, 4, 6, 8, or other reasonable numbers in that range. If a detection at 740 is made, at 750 an acute or sub-acute medical condition may be identified in the patient. The unusual decrease in total daily insulin for a persistent time period is indicative of an irregularity such as an acute or sub-acute medical condition.

[0093] FIG. 17 illustrates the insulin dosage (total daily dose) and average blood glucose level of a subject on insulin therapy over an eleven month period where the patient’s total daily insulin decreased by more than fifty percent over a four-week period. The patient was found to have rapid progressive kidney disease that was not diabetes-related.

[0094] FIG. 18 is a generalized diagram of the steps employed in identifying a patient with hypoglycemia unawareness according to an exemplary embodiment. At 760, data acquired via systems, methods, and/or devices for optimizing the insulin dosage regimen in diabetes patients over time, which may include insulin regimen, historic insulin dosage function, insulin doses, glucose readings, and A1C levels, is input into a computing device. At 770, the diabetes management data is analyzed to detect episodes of hypoglycemia during schedule dosing events rather than having a more standard distribution throughout the day. The upper threshold for hypoglycemia episodes may be 50, 55, 60, 65, 70, 75, 80 mg/dL, or other reasonable numbers in that range. In patients who have good hypoglycemia awareness, events of hypoglycemia may occur at any time during the day and the data shows labels that are not necessarily associated with a dosing event. The patient will recognize symptoms of hypoglycemia and initiate glucose measurement testing at non-scheduled times. However, in patients that have hypoglycemia unawareness, hypoglycemia may be detected during scheduled glucose measurements at dosing events which include “breakfast,” “lunch,” “dinner,” or “bedtime.” If a detection at 770 is made, at 780 the patient may be identified as having developed hypoglycemia unawareness. The patient’s failure to perceive or experience any symptoms of hypoglycemia yet still present as hypoglycemic at the time of a scheduled glucose measurement before an scheduled injection, is indicative of hypoglycemia unawareness.

[0095] FIG. 19 illustrates the daily blood glucose measurements of a patient over a three week period where the subject displayed multiple low glucose readings (<70 mg/dL), largely over scheduled dosing events. This patient was unaware of hypoglycemia. There were multiple occurrences where the patient did not perceive symptoms of hypoglycemia to prompt a glucose measurements at a non-scheduled time, and instead hypoglycemia glucose levels were detected at scheduled glucose measurements before injection events.

[0096] FIG. 20 is a generalized diagram of the steps employed in identifying a patient that requires an insulin regimen change from long-acting insulin to pre-mixed or basal bolus insulin according to an exemplary embodiment. At 800, data acquired via systems, methods, and/or devices for optimizing the insulin dosage regimen in diabetes patients over time, which may include insulin regimen, historic insulin dosage function, insulin doses, glucose readings, and A1C levels, is input into a computing device. At 810, the diabetes management data is analyzed to detect normal fasting glucose in patients with Type-2 diabetes with either an AIC greater than 8%, or average glucose higher than 150, 160, 170, 180, 190, 200 mg/dl, or other reasonable numbers with that range, who use long acting insulin. The average normal fasting glucose in patients with Type-2 diabetes may be 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140 mg/dL, or other reasonable numbers within that range. When glucose levels are higher during the rest of the day, the regimen needs to change to include a short acting insulin component which may include a premixed insulin or a basal bolus insulin. If a detection at 810 is made, at 820 the patient may be identified as requiring a change to premixed or basal bolus insulin. [0097] FIG. 21 illustrates the insulin dosage and average blood glucose level of a patient on long-acting once daily insulin therapy over a six month period. The patient was treated with once a day long-acting insulin. The patient’s average weekly glucose determined by fasting glucose was about 120 mg/dL but the patient’s A1C was higher at 8.7%. The A1C remaining high while the fasting glucose was normal indicated the patient’s insulin regimen required adjustment. This patient was identified as needing a change in insulin regimen to a premixed or basal bolus insulin.

[0098] FIG. 22 is a generalized diagram of the steps employed in identifying a patient that requires concentrated insulin according to an exemplary embodiment. At 830, data acquired via systems, methods, and/or devices for optimizing the insulin dosage regimen in diabetes patients overtime, which may include insulin regimen, historic insulin dosage function, insulin doses, and glucose readings is input into a computing device. At 840, the diabetes management data is analyzed to detect any component of the insulin regimen whose dose exceeds 200, 210, 220, 230, 240, 250, 260, 270, or other reasonable numbers within that range. If a detection at 840 is made, at 850 the patient may be identified as requiring concentrated insulin. A component of an insulin regimen that is increasingly high over a period of time is indicative of requiring a more concentrated insulin to better treat the patient.

[0099] FIG. 23 illustrates the insulin dosage and average blood glucose level of a subject on a multi-dose insulin regimen over a five month period where the subject required increased insulin doses in multiple doses of the regimen. The patient required more than 250 units of insulin for bedtime in February 2013, then more than 250 units at dinner in April 2013, and more than 250 units at lunch in June 2013. This patient was identified as needing concentrated insulin because of the persistent requirement of increased insulin component doses over time. [00100] FIG. 24 is a generalized diagram of the steps employed in identifying a patient that is using the insulin other than the one prescribed or expected according to an exemplary embodiment. At 860, data acquired via systems, methods, and/or devices for optimizing the insulin dosage regimen in diabetes patients over time, which may include insulin regimen, historic insulin dosage function, insulin doses, and glucose readings is input into a computing device. At 870, the diabetes management data is analyzed to detect a ratio between doses of each component of a patient’s insulin regimen which is unusually higher or lower than expected. In an embodiment, if a patient used premixed insulin as opposed to rapid insulin, the optimization of the insulin regimen would result in the long-acting insulin reducing to 5, 10, 15, 20, 25, 30, 35, 40% or some other reasonable number within that range, of the total daily insulin. If a detection at 870 is made, at 880 the patient may be identified as using an insulin other than the one prescribed or expected. In a patient who has mistakenly used only rapid acting insulin both for basal and meal boluses, the data may show an unusual high fraction of the long acting insulin dose. In a patient who has mistakenly and persistently used long actin insulin for both long acting and meal boluses, the data may show an unusual low fraction of long acting insulin dose. The same may apply to a patient who has mistakenly and persistently swapped between their long acting insulin and rapid acting insulin.

[00101] FIG. 25 illustrates the insulin dosage and average blood glucose level of a subject on insulin therapy that mistakenly used a wrong formulation of insulin instead of the one prescribed. The patient was using a premixed insulin, i.e. a combination of rapid acting and intermediate acting insulin, as opposed to the prescribed rapid acting insulin, for breakfast, lunch, and dinner doses. In response, the patient’s long-acting dose was reduced to 10% of the total daily insulin. The compensation in the long-acting insulin component identified the mistaken use of a wrong formulation of insulin, the premixed insulin, because of the decrease in the long-acting insulin dose.

[00102] FIG. 26 illustrates the architecture of the d-Nav® System, according to an exemplary embodiment. The d-Nav® System comprises systems, devices, and/or methods for optimizing a patient’s insulin dosage regimen and/or other diabetes management. The d-Nav® System comprises the hardware components: the System Server 900 and a mobile phone 905. In an exemplary embodiment, the System Server 900 may be implemented on Amazon Web Services. The mobile phone 905 may comprise an iOS or Android mobile phone. The d-Nav® System may receive glucose measurement data manually entered 910 into the patient user software on the mobile phone 905 or blood glucose measurements are automatically obtained via the cloud 915 through a linked glucose to the System Server 900.

[00103] FIG. 27 illustrates the d-Nav® System software components and their interaction, according to an exemplary embodiment. The System Server 900 comprises the following software components: a Website 920; a System Database 925; a blood glucose meter (“BGM”) Connect Webservice 930; a Communication Webservice 935; a SAAS Webservice 940; and a SAAS Database 945. On the mobile phone 905, a Phone App 965 and a Phone App Local Storage 960 are utilized when the System operation is configured for local usage. The d-Nav® System comprises two user-interactive software elements. First, a patient user interface residing on a hand-held device, such as a mobile phone or enabled glucose meter, and is used to enter glucose event data and receive a recommended insulin dose. Second, Health Care Provider (“HCP”) user interface software tool that may be used by HCPs to set up patient software for its intended use. [00104] The Website 920 may be utilized by an authorized Health Care Provider (“HCP”) to add a new user of the Phone App 965 and set up their physician-prescribed, patient-specific insulin dose instructions and treatment plan. The Website 920 may be used by the HCP to enable or disable the Phone App 965, and monitor the user’s insulin and glucose history. The System Database 925 comprises information regarding the system such as Website 920 user login information, Phone App 965 user information, connected BGM information 915 including the unique relation to users and the records of blood glucose readings sent to the Phone App 965, records of insulin instructions sent to the 965 Phone App, records of glucose event data received from the Phone App 965, and insulin instruction updates received from the Phone App 965. The BGM Connect Webservice 930 sends the blood glucose reading to the Phone App 965 on the user’s mobile phone 905. The blood glucose reading may be received by the BGM Connect Webservice 930 either directly from a linked BGM via the cloud or from a BGM manufacturer or other cloud-based infrastructure, or alternatively, from a linked continuous glucose monitor (“CGM”), in other embodiments. The Communication Webservice 935 may serve as an endpoint for the Phone App 965 to communicate with the System Server 900.

[00105] The Server Get-Dose Library 955 and local mobile phone Get-Dose Library 975 calculate new recommended insulin dose or updates current insulin instructions based on glucose readings, user’s current insulin instructions, and history of events. The Application Program Interface (API) may enable the dose calculations and updates either by the Algorithm Integration Library 970 in the Phone App 965 or by the Algorithm Integration Library 950 with the SAAS Webservice 940 on the System Server 900. In the cloud implementation, the SAAS Webservice 940 returns all new data values to the Phone App 960 to sync with the System Server 900. The following configurations are exemplary embodiments of implementation of d- Nav® System: (1) Patient user software resides on a mobile phone 905, uses manual glucose measurements entry 910, and the Get-Dose Library 975 resides locally within the mobile phone 905; (2) Patient user software resides on a mobile phone 905, uses manual glucose measurements entry 910, and the Get-Dose Library 955 resides in the cloud; (3) Patient user software resides on a mobile phone 905, uses automated glucose measurement entry via the cloud 915, and the Get-Dose Library 975 resides locally within the mobile phone 905; and (4) Patient user software resides on a mobile phone 905, uses automated glucose measurement entry via the cloud 915, and the Get-Dose Library 955 resides in the cloud.

[00106] In an exemplary embodiment, the SAAS Database 945 may be a SQL database located on the System Server 900. The SAAS Database 945 is utilized by the SAAS Webservice 940 and comprises information specific to the user’s insulin treatment plan but no user personally identifiable information (“PII”). The SAAS Database 945 comprises information such as Phone App 965 instance unique identification values, insulin dose function history including current insulin instructions and the selected treatment plan, insulin drug, and dose(s), and glucose event records with glucose data, event names, carbohydrates (if applicable), recommended and recorded dose, and timestamp for each record. The Phone App Local Storage 960 may be a SQLite database located on the mobile phone 905. The Phone App Local Storage 960 is utilized by the local Algorithm Integration Library 970 and comprises information specific to the user’s insulin treatment but no PII. The information comprises Phone App 965 instance unique identification values, insulin dose function history including current insulin instructions and the selected treatment plan, insulin drug, and dose(s), and glucose event records with glucose data, event names, carbohydrates (if applicable), recommended and recorded dose, and timestamp for each record.

[00107] When the system operation is configured for local usage, the Phone App 965 passes glucose event data comprising glucose readings, event type, carbohydrates, dose, and time stamp, to the Algorithm Integration Library 970 in the Phone App 965 through a class level function call to request a new recommended dose or update existing instructions. When the system is configured for cloud server usage, the Phone App 965 request to the System Server 900 to pass new glucose event data may be a Representational State Transfer (“REST”) API request, that is sent through the internet via a secure SSL connection which is then received by the SAAS Webservice 940.

[00108] FIG. 28 illustrates the d-Nav® Phone App interface. The Welcome Screen 1001 prompts the user to enter their current glucose test and also provides information on the user’s last glucose test. The Blood Glucose Screen 1002 allows the user to manually enter their blood glucose 1002. The Event Screen 1003 prompts the user to select the event (breakfast, lunch, dinner, bedtime, nighttime, or other). The Dose Screen 1004 provides the user with the d-Nav recommended dose and prompts the user to input a dose modification if they intend to modify the recommended dose. A Summary Screen 1005 is provided with the date, time, event, insulin type inputs the dose currently using. The Insulin Instruction Screen 1006 then displays the current insulin instructions for the user to follow. The Glucose History Screen 1007 displays recent glucose readings and insulin doses. Upon a low glucose measurement of less than 60 mg/dL, an insulin dose recommendation will not be provided and instead the Low Glucose Screen 1008 will appear with instructions to treat the low glucose and retest. The Treatment Plan Screen 1009 summarizes the entered treatment plan by the HCP. [00109] In certain embodiments, the present disclosure comprehends systems, methods, and/or devices for optimizing the insulin dosage regimen in diabetes patients over time— such as in between clinic visits— to thereby enhance diabetes control.

[00110] As used herein with respect to certain embodiments, the term "insulin dose" means and refers to the quantity of insulin taken on any single occasion, while the term "insulin dosage regimen" refers to and means the set of instructions (typically defined by the patient's physician or other healthcare professional) defining when and how much insulin to take in a given period of time and/or under certain conditions. One conventional insulin dosage regimen comprises several components, including a long-acting insulin dosage component, a plasma glucose correction factor component, and a carbohydrate ratio component. Thus, for instance, an exemplary insulin dosage regimen for a patient might be as follows: 25 units of long acting insulin at bedtime; 1 unit of fast-acting insulin for every 10 grams of ingested carbohydrates; and 1 unit of fast-acting insulin for every 20 mg/dL by which a patient's blood glucose reading exceeds 120 mg/dL.

[00111] Referring to FIG. 1, which constitutes a generalized schematic thereof, of certain exemplary embodiments more particularly comprises an apparatus 1 having at least a first memory 10 for storing data inputs corresponding at least to one or more components of a patient's present insulin dosage regimen (whether comprising separate units of long-acting and short-acting insulin, premixed insulin, etc.) and the patient's blood-glucose-level measurements determined at a plurality of times, a processor 20 operatively connected (indicated at line 11) to the at least first memory 10, and a display 30 operatively coupled (indicated at line 31) to the processor and operative to display at least information corresponding to the patient's present insulin dosage regimen. The processor 20 is programmed at least to determine from the data inputs corresponding to the patient's blood-glucose-level measurements determined at a plurality of times whether and by how much to vary at least one or the one or more components of the patient's present insulin dosage regimen. Such variation, if effected, leads to a modification of the patient's present insulin dosage regimen data as stored in the memory 10, as explained further herein. Thus, the data inputs corresponding to the one or more components of the patient's present insulin dosage regimen as stored in the memory device 10 will, at a starting time for employment of the apparatus, constitute an insulin dosage regimen prescribed by a healthcare professional, but those data inputs may subsequently be varied by operation of the apparatus (such as during the time interval between a patient's clinic visits). In the foregoing manner, the apparatus is operative to monitor relevant patient data with each new input of information (such as, at a minimum, the patient's blood-glucose-level measurements), thereby facilitating the optimization of the patient's insulin dosage regimen in between clinic visits. [00112] It is contemplated that the apparatus as generalized herein may be embodied in a variety of forms, including a purpose-built, PDA-like unit, a commercially available device such as a cell-phone, IPHONE, etc. Preferably, though not necessarily, such a device would include data entry means, such as a keypad, touch-screen interface, etc. (indicated generally at the dashed box 40) for the initial input by a healthcare professional of data corresponding at least to a patient's present insulin dosage regimen (and, optionally, such additional data inputs as, for instance, the patient's present weight, defined upper and lower preferred limits for the patient's blood-glucose-level measurements, etc.), as well as the subsequent data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times (and, optionally, such additional data inputs as, for instance, the patient's present weight, the number of insulin units administered by the patient, data corresponding to when the patient eats, the carbohydrate content of the foodstuffs eaten, the meal type (e.g., breakfast, lunch, dinner, snack, etc.). As shown, such data entry means 40 are operatively connected (indicated at line 41) to the memory 10.

[00113] Display 30 is operative to provide a visual display to the patient, healthcare professional, etc. of pertinent information, including, by way of non-limiting example, information corresponding to the present insulin dosage regimen for the patient, the current insulin dose (i.e., number of insulin units the patient needs to administer on the basis of the latest blood-glucose-level measurement and current insulin dosage regimen), etc. To that end, display 30 is operatively connected to the processor 20, as indicated by the dashed line 31. [00114] As noted, the data entry means 40 may take the form of a touch-screen, in which case the data entry means 40 and display 30 may be combined (such as exemplified by the commercially available IPHONE (Apple, Inc., California)).

[00115] Referring then to FIGS. 2 through 5, there are depicted representative images for a display 30 and a touch-screen type, combined display 30/data entry means 40 exemplifying both the patient information that may be provided via the display, as well as the manner of data entry.

[00116] More particularly, FIG. 2 shows a display 30 providing current date/time information 32 as well as the patient's current blood-glucose-level measurement 33 based upon a concurrent entry of that data. FIG. 2 further depicts a pair of scrolling arrows 42 by which the patient is able to scroll through a list 34 of predefined choices representing the time of the patient's said current blood-glucose-level measurement. In an alternate embodiment, the user may touch a predefined choice directly from the list 34 to select that choice without using the scrolling arrows 42. As explained further herein in association with a description of an exemplary algorithm for implementing certain embodiments, selection of one of these choices will permit the processor to associate the measurement data with the appropriate measurement time for more precise control of the patient's insulin dosage regimen.

[00117] FIG. 3 shows a display 30 providing current date/time information 32, as well as the presently recommended dose of short-acting insulin units 35— based upon the presently defined insulin dosage regimen— for the patient to take at lunchtime.

[00118] FIG. 4 shows a display 30 providing current date/time information 32, as well as, according to a conventional "carbohydrate-counting" therapy, the presently recommended base (3 IUS) and additional doses (1 IU per every 8 grams of carbohydrates ingested) of short-acting insulin units 36 for the patient to take at lunchtime— all based upon the presently defined insulin dosage regimen.

[00119] In FIG. 5, there is shown a display 30 providing current date/time information 32, as well as the presently recommended dose of short-acting insulin units 37— based upon the presently defined insulin dosage regimen— for the patient to take at lunchtime according to a designated amount of carbohydrates to be ingested. As further depicted in FIG. 5, a pair of scrolling arrows 42 are displayed, by which the patient is able to scroll through a list of predefined meal choices 38, each of which will have associated therewith in the memory a number (e.g., grams) of carbohydrates. In an alternate embodiment, the user may touch a predefined meal choices directly from the list 34 to select that choice without using the scrolling arrows 42. When the patient selects a meal choice, the processor is able to determine from the number of carbohydrates associated with that meal, and the presently defined insulin dosage regimen, a recommended dose of short-acting insulin for the patient to take (in this example, 22 IUs of short-acting insulin for a lunch of steak and pasta).

[00120] In one embodiment thereof, shown in FIG. 6, the apparatus as described herein in respect of FIG. 1 optionally includes a glucose meter (indicated by the dashed box 50) operatively connected (as indicated at line 51) to memory 10 to facilitate the automatic input of data corresponding to the patient's blood-glucose-level measurements directly to the memory 10.

[00121] Alternatively, it is contemplated that the glucose meter 50' could be provided as a separate unit that is capable of communicating (such as via a cable, wirelessly, or through the cloud via network connectivity, represented at line 51') with the device 1' so as to download to the memory 10' the patient' s blood-glucose-level measurements, such as shown in FIG. 7. [00122] According to another embodiment, shown in FIG. 8, the apparatus 1" may be combined with a smart insulin pen or pump 60" and, optionally, a glucose meter 50" as well. According to this embodiment, the processor 20" is operative to determine from at least the patient's blood-glucose-level measurement data (which may be automatically transferred to the memory 10" where the apparatus is provided with a glucose meter 50", as shown, is connectable to a glucose meter so that these data may be automatically downloaded to the memory 10", or is provided with data entry means 40" so that these data may be input by the patient) whether and by how much to vary the patient's present insulin dosage regimen. The processor 20", which is operatively connected to the insulin pen or pump 60" (indicated at line 61"), is operative to employ the insulin dosage regimen information to control the insulin units provided to the patient via the pen or pump 60". Therefore, the processor 20" and the pen or pump 60" form a semi-automatic, closed-loop system operative to automatically adjust the pen or pump dose recommendation based on at least the patient's blood-glucose-level measurements. It will be appreciated that, further to this embodiment, the insulin pen or pump 60" may be operative to transfer to the memory 10" data corresponding to the dose of insulin delivered to the patient by the pen or pump according to the patient's present insulin dosage regimen. These data may be accessed by the processor 20" to calculate, for example, the amount of insulin units delivered by the pen or pump to the patient over a predefined period of time (e.g., 24 hours). Such data may thus be employed in certain embodiments to more accurately determine a patient's insulin sensitivity, plasma glucose correction factor and carbohydrate ratio, for instance.

[00123] Also further to this embodiment, the apparatus 1" may optionally be provided with data entry means, such as a keypad, touch-screen interface, etc. (indicated generally at the dashed box 40") for entry of various data, including, for instance, the initial input by a healthcare professional of data corresponding at least to a patient's present insulin dosage regimen (and, optionally, such additional data inputs as, for instance, the patient's present weight, defined upper and lower preferred limits for the patient's blood-glucose-level measurements, etc.), as well as the subsequent data inputs corresponding at least to the patient's blood-glucose-level measurements determined at a plurality of times (to the extent that this information is not automatically transferred to the memory 10" from the blood glucose meter 50") and, optionally, such additional data inputs as, for instance, the patient's present weight, the number of insulin units administered by the patient, data corresponding to when the patient eats, the carbohydrate content of the foodstuffs eaten, the meal type (e.g., breakfast, lunch, dinner, snack), etc. [00124] It is also contemplated that certain embodiments may be effected through the input of data by persons (e.g., patient and healthcare professional) at disparate locations, such as illustrated in FIG. 9. For instance, it is contemplated that the data inputs pertaining to at least the patient's initial insulin dosage regimen may be entered by the healthcare professional at a first location, in the form of a general purpose computer, cell phone, IPHONE, or other device 100 (a general purpose computer is depicted), while the subsequent data inputs (e.g., patient blood-glucose-level readings) may be entered by the patient at a second location, also in the form of a general purpose computer, cell phone, IPHONE, or other device 200 (a general purpose computer is depicted), and these data communicated to a third location, in the form of a computer 300 comprising the at least first memory and the processor. According to this embodiment, the computers 100, 200, 300 may be networked in any known manner (including, for instance, via the internet). Such networking is shown diagrammatically via lines 101 and 201. Thus, for instance, the system may be implemented via a healthcare professional/patient accessible website through which relevant data are input and information respecting any updates to the predefined treatment plan are communicated to the patient and healthcare professional.

[00125] Alternatively, it is contemplated that certain embodiments may be effected through the input of data via persons (e.g., patient and healthcare professional) at disparate locations, and wherein further one of the persons, such as, in the illustrated example, the patient, is in possession of a single device 200' comprising the processor and memory components, that device 200' being adapted to receive data inputs from a person at a disparate location. FIG. 10. This device 200' could take any form, including a general-purpose computer (such as illustrated), a PDA, cell-phone, purpose-built device such as heretofore described, etc. According to this embodiment, it is contemplated that the data inputs pertaining to at least the patient's initial insulin dosage may be entered (for instance by the healthcare professional) at another location, such as via a general purpose computer, cell phone, or other device 100' (a general purpose computer is depicted) operative to transmit data to the device 200', while the subsequent data inputs (e.g., patient blood-glucose-level measurements) may be entered directly into the device 200'. According to this embodiment, a healthcare professional could remotely input the patient's initial insulin dosage at a first location via the device 100', and that data could then be transmitted to the patient's device 200' where it would be received and stored in the memory thereof. According to a further permutation of this embodiment, the afore described arrangement could also be reversed, such that the patient data inputs (e.g., patient blood-glucose-level measurements) may be entered remotely, such as via a cell phone, computer, etc., at a first location and then transmitted to a remotely situated device comprising the processor and memory components operative to determine whether and by how much to vary the patient's present insulin dosage regimen. According to this further permutation, modifications to the patient's insulin dosage effected by operation of certain embodiments could be transmitted back to the patient via the same, or alternate, means.

[00126] Referring again to FIG. 9, it is further contemplated that there may be provided a glucose meter 50'" (including, for instance, in the form of the device as described above in reference to FIG. 6) that can interface 51'" (wirelessly, through the internet, via a hard-wire connection such as a USB cable, FIREWIRE cable, etc.) with a general purpose computer 200 at the patient's location to download blood-glucose-level measurements for transmission to the computer 300 at the third location. Referring also to FIG. 10, it is further contemplated that this glucose meter 50'" may be adapted to interface 51'" (wirelessly, through the internet, via a hardwire connection such as a USB cable, FIREWIRE cable, etc.) with the single device 200', thereby downloading blood-glucose-level measurement data to that device directly.

[00127] Turning now to FIG. 11 , there is shown a diagram generalizing the manner in which the certain embodiments may be implemented to optimize a diabetes patient's insulin dosage regimen.

[00128] In certain embodiments, there is initially specified, such as by a healthcare professional, a patient insulin dosage regimen (comprised of, for instance, a carbohydrate ratio ("CHR"), a long-acting insulin dose, and a plasma glucose correction factor). Alternatively, the initial insulin dosage regimen can be specified using published protocols for the initiation of insulin therapy, such as, for example, the protocols published by the American Diabetes Association on Oct. 22, 2008. However specified, this insulin dosage regimen data is entered in the memory of an apparatus (including according to several of the embodiments described herein), such as by a healthcare professional, in the first instance and before the patient has made any use of the apparatus.

[00129] Thereafter, the patient will input, or there will otherwise automatically be input (such as by the glucose meter) into the memory at least data corresponding to each successive one of the patient's blood-glucose-level measurements. Upon the input of such data, the processor determines, such as via the algorithm described herein, whether and by how much to vary the patient's present insulin dosage regimen. Information corresponding to this present insulin dosage regimen is then provided to the patient so that he/she may adjust the amount of insulin they administer. [00130] According to certain exemplary embodiments, determination of whether and by how much to vary a patient's present insulin dosage regimen is undertaken both on the basis of evaluations conducted at predefined time intervals (every 7 days, for example) as well as asynchronously to such intervals. The asynchronous determinations will evaluate the patient's blood-glucose-level data for safety each time a new blood-glucose-level measurement is received to determine whether any urgent action, including any urgent variation to the patient's present insulin dosage, is necessary.

[00131] More particularly, each time a new patient blood-glucose-level measurement is received 300 into the memory it is accessed by the processor and sorted and tagged according to the time of day the measurement was received and whether or not it is associated with a certain event, e.g., pre-breakfast, bedtime, nighttime, etc. 310. Once so sorted and tagged, the new and/or previously recorded blood-glucose-level measurements are subjected to evaluation for the need to update on the basis of the passage of a predefined period of time 320 measured by a counter, as well as the need to update asynchronously for safety 330. For instance, a very low blood glucose measurement (e.g., below 50 mg/dL) representing a level 2 hypoglycemic event or the accumulation of several low measurements in the past few days may lead to an update in the patient's insulin dosage regimen according to the step 330, while an update to that regimen may otherwise be warranted according to the step 320 if a predefined period of time (e.g., 7 days) has elapsed since the patient's insulin dosage regimen was last updated. In either case, the patient will be provided with information 340 corresponding to the present insulin dosage regimen (whether or not it has been changed) to be used in administering his/her insulin. [00132] Referring next to FIG. 12, there is shown a flowchart that still more particularly sets forth an exemplary algorithm by which certain embodiments may be implemented to optimize a diabetes patient's insulin dosage regimen. According to the exemplary algorithm, the insulin dosage modification contemplates separate units of long-acting and short-acting insulin. However, it will be appreciated that certain embodiments are equally applicable to optimize the insulin dosage regimen of a patient where that dosage is in another conventional form (such as pre-mixed insulin). It will also be understood from this specification that certain embodiments may be implemented otherwise than as particularly described herein below.

[00133] According to a first step 400, data corresponding to a patient's new blood-glucose- level measurement is input, such as, for instance, by any of the exemplary means mentioned above, into the at least first memory (not shown in FIG. 12). This data is accessed and evaluated (by the processor) at step 410 of the exemplary algorithm and sorted according to the time it was input. [00134] More particularly according to this step 410, the blood-glucose-level measurement data input is "tagged" with an identifier reflective of when the reading was input; specifically, whether it is a morning (i.e., "fast") measurement (herein "MPG"), a pre-lunch measurement (herein "LPG"), a pre-dinner measurement (herein "DPG"), a bedtime measurement (herein "BTPG"), or a nighttime measurement (herein "NPG").

[00135] The "tagging" process may be facilitated using a clock internal to the processor (such as, for instance, the clock of a general purpose computer) that provides an input time that can be associated with the blood-glucose-level measurement data synchronous to its entry. Alternatively, time data (i.e., " 10:00 AM," "6:00 PM," etc.) or event-identifying information (i.e., "lunchtime," "dinnertime," "bedtime," etc.) may be input by the patient reflecting when the blood-glucose-level measurement data was taken, and such information used to tag the blood-glucose-level measurement data. As a further alternative, and according to the embodiment where the blood-glucose-level measurement data are provided directly to the memory by a glucose monitor, time data may be automatically associated with the blood- glucose-level measurement data by such glucose monitor (for instance, by using a clock internal to that glucose monitor). It is also contemplated that, optionally, the user/patient may be queried (for instance at a display) for input to confirm or modify any time-tag automatically assigned a blood-glucose-level measurement data-input. Thus, for instance, a patient may be asked to confirm (via data entry means such as, for example, one or more buttons or keys, a touch-screen display, etc.) that the most recently input blood-glucose-level measurement data reflects a pre-lunch (LPG) measurement based on the time stamp associated with the input of the data. If the patient confirms, then the LPG designation would remain associated with the measurement. Otherwise, further queries of the patient may be made to determine the appropriate time designation to associate with the measurement.

[00136] It will be understood that any internal clock used to tag the blood-glucose-level measurement data may, as desired, be user adjustable so as to define the correct time for the time zone where the patient is located.

[00137] Further according to the exemplary embodiment, the various categories (e.g., DPG, MPG, LPG, etc.) into which the blood-glucose-level measurement data are more particularly sorted by the foregoing "tagging" process are as follows:

NPG— The data are assigned this designation when the time stamp is between 2 AM and 4 AM.

MPG— The data are assigned this designation when the time stamp is between 4 AM and 10 AM. LPG— The data are assigned this designation when the time stamp is between 10 AM and 3 PM.

DPG— The data are assigned this designation when the time stamp is between 3 PM and 9 PM.

BTPG— The data are assigned this designation when the time stamp is between 9 PM and 2 AM. If the BTPG data reflect a time more than three hours after the patient's presumed dinnertime (according to a predefined time window), then these data are further categorized as a dinner compensation blood-glucose-level.

[00138] According to the employment of a time stamp alone to "tag" the blood-glucose- level data inputs, it will be understood that there exists an underlying assumption that these data were in fact obtained by the patient within the time-stamp windows specified above.

[00139] Per the exemplary embodiment, if the time stamp of a blood-glucose-level measurement data-input is less than 3 hours from the measurement that preceded the last meal the patient had, it is considered biased and may be omitted unless it represents a hypoglycemic event.

[00140] According to the next step 420, the newly input blood-glucose-level measurement is accessed and evaluated (by the processor) to determine if the input reflects a present, level 2 hypoglycemic event. This evaluation may be characterized by the exemplary formula PG(t)<w, where PG(t) represents the patient's blood-glucose-level data in mg/dL, and w represents a predefined threshold value defining a level 2 hypoglycemic event (such as, by way of nonlimiting example, 50 mg/dL).

[00141] If a level 2 hypoglycemic event is indicated at step 420 then, according to the step 430, the patient's present insulin dosage regimen data (in the memory 10 [not shown in FIG. 12]) is updated as warranted and independent of the periodic update evaluation described further below. More particularly, the algorithm will in this step 430 asynchronously (that is, independent of the periodic update evaluation) determine whether or not to update the patient's insulin dosage regimen on the basis of whether the patient's input blood-glucose-level data reflect the accumulation of several low glucose values over a short period of time. According to the exemplary embodiment, the dosage associated with the newly input blood-glucose-level measurement is immediately decreased. More specifically, for a level 2 hypoglycemic event at MPG, the long-acting insulin dosage is decreased by 20%; and for a level 2 hypoglycemic event at LPG the breakfast short-acting insulin dose is decreased by 20%.

[00142] The algorithm also at this step 430 updates a counter of hypoglycemic events to reflect the newly-input (at step 400) blood-glucose-level measurement. Notably, modifications to the patient's insulin dosage regimen according to this step 430 do not reset the timer counting to the next periodic update evaluation. Thus, variation in the patient's insulin dosage regimen according to this step 430 will not prevent the algorithm from undertaking the next periodic update evaluation.

[00143] Any such blood-glucose-level measurement is also entered into a hypoglycemic events database in the memory. In the exemplary embodiment, this is a rolling database that is not reset. Instead, the recorded hypoglycemic events expire from the database after a predefined period of time has elapsed; essentially, once these data become irrelevant to the patient's insulin dosage regime. Thus, by way of example only, this database may contain a record of a hypoglycemic event for 7 days.

[00144] Further according to the step 430, one or more warnings may be generated for display to the patient (such as via a display 30 [not shown in FIG. 12]). It is contemplated that such one or more warnings would alert a patient to the fact that his/her blood-glucose-level is dangerously low so that appropriate corrective steps (e.g., ingesting a glucose tablet) could be taken promptly. Additionally, and without limitation, such one or more warnings may also correspond to any one or more of the following determinations:

[00145] That the patient's blood-glucose-level measurement data reflect that there have been more than two hypoglycemic events during a predetermined period of time (such as, by way of example only, in the past 7 days); that more than two drops in the patient's blood-glucose-level measurements between the nighttime measurement and the morning measurement are greater than a predetermined amount in mg/dL (70 mg/dL, for instance); and/or that more than two drops in the patient's blood-glucose-level measurement between the nighttime measurement and the morning measurement are greater than a predetermined percentage (such as, for instance, 30%).

[00146] If a severe hypoglycemic event is not indicated at step 420, the recorded (in the memory 10) data inputs corresponding to the number of patient hypoglycemic events over a predetermined period of days are accessed and evaluated by the processor (20, not shown) at step 440 to determine if there have been an excessive number of regular hypoglycemic events (e.g., a blood-glucose-level measurement between 50 mg/dL and 75 mg/dL) over that predetermined period. This evaluation is directed to determining whether the patient has experienced an excessive number of such regular hypoglycemic events in absolute time and independent of the periodic update operation as described elsewhere herein. This assessment, made at step 440, may be described by the following, exemplary formula: Is(#{of events at HG}>Q) or is (#{of hypoglycemic events in the last W days}=Q)? where HG represents the recorded number of hypoglycemic events, W is a predefined period of time (e.g., 3 days), and Q is a predefined number defining an excessive number of hypoglycemic events (e.g., 3). By way of example, Q may equal 3 and W may also equal 3, in which case if it is determined in step 440 that there were either 4 recorded hypoglycemic events or there were 3 recorded hypoglycemic events in the last 3 days, the algorithm proceeds to step 430.

[00147] Notably, if step 440 leads to step 430, then a binary (" 1 " or "0") hypoglycemic event correction "flag" is set to " 1," meaning that no increase in the patient's insulin dosage regimen is allowed and the algorithm jumps to step 490 (the periodic dosage update evaluation routine). Potentially, the periodic update evaluation may concur that any or all the parts of the insulin dosage regimen require an increase due to the nature of blood-glucose-levels currently stored in the memory 10 and the execution of the different formulas described hereafter. However, by setting the hypoglycemic event correction flag to " 1," the algorithm will ignore any such required increase and would leave the suggested part of the dosage unchanged. Therefore, this will lead to a potential reduction in any or all the components of the insulin dosage regimen to thus address the occurrence of the excessive number of hypoglycemic events. Further according to this step, the timer counting to the next periodic update evaluation is reset.

[00148] In the next step 450, the recorded, time-sorted/tagged blood-glucose-level measurement data corresponding to the number of patient hypoglycemic events over a predetermined period of days (for example, 7 days) are accessed and evaluated by the processor to determine if there have been an excessive number of such hypoglycemic events at any one or more of breakfast, lunch, dinner and/or in the morning over the predetermined period. This evaluation may be characterized by the exemplary formula: #{HG(m)(b)(l)(d) in XX[d]}=Y?; where #HG(m)(b)(l)(d) represents the number of hypoglycemic events at any of the assigned (by the preceding step) measurement times of morning, bedtime, lunch or dinner over a period of XX (in the instant example, 7) days ("[d]"), and Y represents a number of hypoglycemic events that is predetermined to constitute a threshold sufficient to merit adjustment of the patient's insulin dosage regimen (in the present example, 2 hypoglycemic events). It will be appreciated that the employment of this step in the algorithm permits identification with greater specificity of possible deficiencies in the patient's present insulin dosage regimen. Moreover, the further particularization of when hypoglycemic events have occurred facilitates timespecific (e.g., after lunch, at bedtime, etc.) insulin dosage regimen modifications. [00149] If an excessive number of such hypoglycemic events is not indicated at step 450, then the algorithm queries at step 460 whether or not it is time to update the patient's insulin dosage regimen irrespective of the non-occurrence of hypoglycemic events, and based instead upon the passage of a predefined interval of time (e.g., 7 days) since the need to update the patient's insulin dosage regimen was last assessed. If such an update is not indicated— i.e., because an insufficient time interval has passed— then no action is taken with respect to the patient's insulin dosage and the algorithm ends (indicated by the arrow labeled "NO") until the next blood-glucose-level measurement data are input.

[00150] If, however, an update is indicated by the fact that it has been 7 days (or other predefined interval) since the need to update the patient's insulin dosage was last evaluated, then before such update is effected the algorithm first determines, in step 470, if the patient's general condition falls within a predetermined "normal" range. This determination operation may be characterized by the exemplary formula: xxx < E{PG} < yyy; where xxx represents a lower bound for a desired blood-glucose-level range for the patient, yyy represents an upper bound for a desired blood-glucose-level range for the patient, and E{PG} represents the mean of the patient's recorded blood-glucose-level measurements. According to the exemplary embodiment, the lower bound xxx may be predefined as 80 mg/dL, and the upper bound yyy may be predefined as 135 mg/dL.

[00151] It will be understood that the foregoing values may be other than as so specified, being defined, for instance, according to the particular country in which the patient resides. Furthermore, it is contemplated that the upper (yyy) and lower (xxx) bounds may be defined by the patient's healthcare professional, being entered, for instance, via data entry means such as described elsewhere herein.

[00152] Where the patient's general condition is outside of the predetermined "normal" range, the algorithm proceeds to step 490 where the data are evaluated to determine whether it is necessary to correct the patient's long-acting insulin dosage regimen.

[00153] Where, however, the patient's general condition is within the predetermined "normal" range, the algorithm next (step 480) queries whether the patient's recorded blood- glucose-level measurement data represent a normal (e.g., Gaussian) or abnormal distribution. This may be characterized by the exemplary formula: -X < E{PG^A3} < X; where E{PG^A3} represents the third moment of the distribution of the recorded (in the memory) blood-glucose- level measurement data— i.e., the third root of the average of the cubed deviations in these data around the mean of the recorded blood-glucose-levels, and X represents a predefined limit (e.g., 5). It is contemplated that the predefined limit X should be reasonably close to 0, thus reflecting that the data (E{ PG 3 }) are well balanced around the mean.

[00154] Thus, for example, where X is 5, the data are considered to be normal when the third root of the average of the cubed deviations thereof around the mean of the recorded blood- glucose-level s is greater than -5 but less than 5. Otherwise, the data are considered to be abnormal.

[00155] Where the data are determined to be normal in step 480 (indicated by the arrow labeled "YES"), then no action is taken to update the patient's insulin dosage regimen.

[00156] However, if in step 470 the mean of all of a patient's recorded blood-glucose-level measurement data are determined to fall outside of the predetermined "normal" range, then in step 490 the algorithm evaluates whether it is necessary to correct the patient's long-acting insulin dosage regimen. This is done by evaluating whether the patient's recorded MPG and BTPG data fall within an acceptable range or, alternatively, if there is an indication that the patient's long-acting insulin dosage should be corrected due to low MPG blood-glucose-level measurements. The determination of whether the patient's MPG and BTPG data fall within a predetermined range may be characterized by the exemplary formula: xxy < E{MPG}, E{BTPG} < yyx; where xxy is a lower bound for a desired blood-glucose-level range for the patient, yyx is an upper bound for a desired blood-glucose-level range for the patient, E{MPG} represents the mean of the patient's recorded MPG blood-glucose-level measurements, and E{BTPG} represents the mean of the patient's recorded BTPG measurements. According to the exemplary embodiments, xxy may be predefined as 80 mg/dL, while yyx may be predefined as 200 mg/dL. However, it will be understood that these values may be otherwise predefined, including, as desired, by the patient's healthcare provider (being entered into the memory via data entry means, for instance).

[00157] If the determination in step 490 is positive, then update of the patient's long-acting insulin dosage (step 510) is bypassed and the algorithm proceeds to step 500, according to which the patient's short-acting insulin dosage (in the form of the carbohydrate ratio ("CHR"), a correction factor A, and the plasma glucose correction factor are each updated and the hypoglycemic correction "flag" reset to 0 (thus permitting subsequent modification of the insulin dosage regimen at the next evaluation thereof).

[00158] If, on the other hand, the determination in step 490 is negative, then the patient's long-acting insulin dosage is updated at step 510, along with performance of the updates specified at step 500. In either case, the process ends following such updates until new patient blood-glucose-level measurement data are input. [00159] Updates of the long-acting insulin dosage regimen data may be characterized by the following, exemplary formulas:

Ba

Ba where a(l) represents a percentage by which the patient's present long-acting insulin dosage regimen is to be varied, a(2) represents a corresponding binary value (due to the need to quantize the dosage), LD(k) represents the patient's present dosage of long-acting insulin, LD(k+l) represents the new long-acting insulin dosage, bi, b2, and bs represent predetermined blood-glucose-level threshold parameters in mg/dL, and E{MPG} is the mean of the patient's recorded MPG blood-glucose-level measurements.

[00160] Since a patient's insulin dosage regimen is expressed in integers (i.e., units of insulin), it is necessary to decide if a percent change (increase or decrease) in the present dosage regimen of long-acting insulin that does not equate to an integer value should be the nearest higher or lower integer. Thus, for instance, if it is necessary to increase by 20% a patient's long- acting insulin dosage regimen from a present regimen of 18 units, it is necessary to decide if the new dosage should be 21 units or 22 units. In the exemplary algorithm, this decision is made on the basis of the patient's insulin sensitivity.

[00161] Insulin sensitivity is generally defined as the average total number of insulin units a patient administer per day divided by the patient weight in kilograms. More particularly, insulin sensitivity (IS(k)) according to the exemplary algorithm may be defined as a function of twice the patient's total daily dosage of long-acting insulin (which may be derived from the recorded data corresponding to the patient's present insulin dosage regimen) divided by the patient's weight in kilograms. This is expressed in the following exemplary formula:

where KK is the patient weight in kilograms.

[00162] A patient's insulin sensitivity factor may of course be approximated by other conventional means, including without reliance on entry of data corresponding to the patient's weight.

[00163] More particularly, the exemplary algorithm employs an insulin sensitivity correction factor (a(2xi)(IS)) )), a 2 entries vector, to determine the percentage at which the dosage will be corrected and to effect an appropriate rounding to the closest whole number for updates in the patient's CHR, PGR and LD. When the patient's weight is known, this determination may be characterized by the following, exemplary formula:

where a(l) is a percentage value of adjustment from the present to a new insulin dosage value, and a(2) is a binary value (i.e., 0 or 1). The value of a(2) is defined by the value of IS(k) in relation to a predefined percent change value (e.g., yi, y2, ys, y4) for a(l). Thus, in the exemplary embodiment of the algorithm: Where, for example, IS(k)<0.3, the value of a(l) is 5 and the value of a(2) is 0; where 0.3<IS(k)<0.5, the value of a(l) is 10 and the value of a(2) is 0; where 0.5<IS(k)<0.7, the value of a(l) is 20 and the value of a(2) is 0; and where 0.7<IS(k), the value of a(l) is 20 and the value of a(2) is 1.

[00164] When the patient weight is unknown, the algorithm will determine a using the following alternative: a(2) is set to "1" if the patient long acting insulin dosage is greater than X units (where, for example X may equal 50 insulin units), and the percentage by which we adjust the dosage will be determined according to the mean of the blood-glucose-level measurements currently in memory (i.e., E{PG}) by:

where wi, W2 and W3 each represent a predefined blood-glucose-level expressed in mg/dL (thus, for example, wi may equal 135 mg/dL, W2 may equal 200 mg/dL, and W3 may equal 280 mg/dL).

[00165] Returning to the exemplary formulas for updating the patient's long-acting insulin dosage, in the exemplary algorithm the decision of whether and by how much to decrease or increase a patient's long-acting insulin dosage regimen is based on the predetermined threshold parameters bi, b2, and b3; where, by way of example only, bi=80 mg/dL, b2=120 mg/dL, and b3=200 mg/dL. More particularly, where the mean of the patient's MPG blood-glucose-level data is less than 80 mg/dL, the new long-acting insulin dosage (LD(k+l)) is the present long- acting insulin dosage (LD(k)) minus the value of Adown (which, as shown above, is a function of the insulin sensitivity correction factors a(l) and a(2), and the patient's long-acting insulin dosage (LD(k)) and may equal half of A. sub. up). Otherwise, if the mean of the patient's MPG blood-glucose-level data is greater than 200 mg/dL, the new long-acting insulin dosage (LD(k+l)) is the present long-acting insulin dosage (LD(k)) plus the value of the A_Up (which, as shown above, is a function of the insulin sensitivity correction factors a(l) and a(2), and the patient's long-acting insulin dosage (LD(k)). Finally, if the mean of the patient's MPG blood- glucose-level data is greater than 150 but less than 200, the new long-acting insulin dosage (LD(k+l)) is the present long-acting insulin dosage (LD(k)) plus the value of the Adown.

[00166] The corrective amount A is calculated as a percentage of the current long-acting insulin dosage rounded according to a(2). In a particular example, if a(l)=20, a(2)=0, and the current long acting insulin dosage LD(k)=58, then A.sub.up equals 20% of 58, which is 11.6, rounded down to A_Up=l l. Accordingly, the long-acting insulin dosage would be updated to LD(k+l)=58+11=69.

[00167] It will be appreciated by reference to the foregoing that in certain embodiments no "ping-pong" effect is allowed; in other words, the patient's long-acting insulin dosage may not be adjustable so that any two successive such adjusted dosages fall below and above the dosage which they immediately succeed. Thus, it is not permitted to have the outcome where the latest LD update (LD(2)) is greater than the initial LD set by the healthcare professional (LD(0)), and the preceding LD update (LD(1)) is less than LD(O). Thus, the outcome LD(2)>LD(0)>LD(l) is not permitted in certain embodiments.

[00168] Returning to the step 450, if an excessive number of hypoglycemic events at any of the time-tagged blood-glucose-level measurement data for breakfast, lunch, dinner or in the morning over the predetermined period (for instance, 7 days) are indicated from the patient's data, then at step 520 the algorithm identifies from the recorded, time-tagged data of hypoglycemic events when those events occurred in order to affect any subsequently undertaken variation to the patient's insulin dosage regimen, and also sets the binary hypoglycemic correction "flag" (e.g., "1" or "0", where 1 represents the occurrence of too many hypoglycemic events, and 0 represents the nonoccurrence of too many hypoglycemic events) to 1. The presence of this "flag" in the algorithm at this juncture prevents subsequent increases in the patient's insulin dosage regimen in the presence of too many hypoglycemic events.

[00169] Further according to this step 520, where the blood-glucose-level measurement data reflects hypoglycemic events in the morning or during the night, the algorithm identifies the appropriate modification required to any subsequent variation of the patient's insulin dosage regimen. This may be characterized by the following, exemplary formula: If #HG events in {MPG+NTPG}=X, then reduce LD by a(l)/2; where #HG is the number of recorded patient hypoglycemic events at the MPG and NTPG-designated blood-glucose-level measurements, X is a predefined value (such as, for example, 2), LD refers to the long-acting insulin dosage, and a(l) represents the afore described insulin sensitivity correction factor, expressed as a percentage. Thus, a(l)/2 reflects that the patient's long-acting insulin dosage is to be reduced only by 1/2 of the value of a(l), if at all, where the recorded hypoglycemic events occur in the morning or overnight.

[00170] Further according to this step 520, where the blood-glucose-level measurement data reflects hypoglycemic events during the day, the algorithm identifies the appropriate modification required to any subsequent variation of the patient's insulin dosage regimen. This may be characterized by the following formula: If #HG events in {LPG or DPG or NTPG}=X, then see update 6; where #HG is the number of recorded patient hypoglycemic events at any of the LPG, DPG or NTPG time-tagged measurements, X is a predefined value (for instance, 2), and "see update A" refers to short-acting insulin dosage correction factor A incorporated into the exemplary form of the algorithm, as described herein.

[00171] Following step 520, the algorithm queries 530 whether it is time to update the patient's insulin dosage regimen irrespective of the occurrence of hypoglycemic events and based upon the passage of a predefined interval of time (by way of non-limiting example, 7 days) since the need to update the patient's insulin dosage regimen was last assessed. Thus, it is possible that a patient's insulin dosage regimen will not be updated even though the HG correction flag has been "tripped" (indicating the occurrence of too many hypoglycemic events) if an insufficient period of time has passed since the regimen was last updated.

[00172] If an insufficient period of time has passed, the process is at an end (indicated by the arrow labeled "NO") until new blood-glucose-level measurement data are input. If, on the other hand, the predefined period of time has passed, then the algorithm proceeds to the step 490 to determine if the long-acting insulin dosage has to be updated as described before followed by the update step 500, according to which the patient's short-acting insulin dosage (in the form of the carbohydrate ratio ("CHR")), the correction factor A, and plasma glucose correction factor are each updated and the hypoglycemic correction flag reset to 0.

[00173] According to the step 500, an update to the patient's plasma glucose correction factor ("PGR") is undertaken. This may be characterized by the following, exemplary formulas:

[00174] More particularly, the new PGR ("NPGR") is a function of a predefined value (e.g., 1700) divided by twice the patient's total daily dosage of long-acting insulin in the present insulin dosage regimen. In the foregoing formulas, the value of this divisor is represented by E{DT}, since the value representing twice the patient's daily dosage of long-acting insulin in the present insulin dosage regimen is substituted as an approximation for the mean of the total daily dosage of insulin administered to the patient (which data may, optionally, be employed if they are input into the memory by an insulin pump, such as in the exemplary apparatus described above, or by the patient using data entry means). A value representing a division of the patient’s daily dosage of long-acting insulin in the present insulin dosage regimen by a certain factor (e.g. 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, or 0.8) may be substituted for the mean of the total daily dosage of insulin administered to the patient as an approximation thereof. The resultant value is subtracted from the present patient PGR ("PGR(k)") to define a difference ("A"). If the A divided by the present PGR(k) is less than or equal to the value of a(l) divided by 100, then the integer value of A (by which new PGR (i.e., PGR(k+l)) is updated) is a function of the formula A=(l-a(2))floor{A}+a(2)ceil{A}, where a(2) is the insulin sensitivity correction factor (1 or 0), "floor" is value of A rounded down to the next integer, and "ceil" is the value of A rounded up to the next integer. If, on the other hand, the A divided by the present PGR(k) is greater than the value of a(l) divided by 100, then the integer value of A is a function of the formula

where a(2) is the insulin sensitivity correction factor (1 or 0), a(l) is the percent value of the insulin sensitivity correction factor, PGR(k) is the present PGR, "floor" is value of A rounded down to the next integer, and "ceil" is the value of A rounded up to the next integer. According to either outcome, the new PGR (PGR(k+l)) is equal to the present PGR (PGR(k)) plus A times the sign of the difference, positive or negative, of NPGR minus PGR(k).

[00175] Furthermore, it is contemplated that the new PGR will be quantized to predefined steps of mg/dL. This is represented by the exemplary formula: PGR(k+l)=quant(PGR(k+l), ZZ) PGR(k+l)=quant(PGR(k+l), ZZ); where, by way of a non-limiting example, ZZ may equal 5.

[00176] Also according to the update step 500, updates to the patient's short-acting insulin dosage regimen are undertaken by modifying the carbohydrate ratio (CHR). CHR represents the average carbohydrate to insulin ratio that a patient needs to determine the correct dose of insulin to inject before each meal. This process may be characterized by the following, exemplary formulas: Qtate Wf C ( m> «B - -~™r

W1

[00177] More particularly, the new CHR ("NCHR") is a function of a predefined value (e.g., 500) divided by twice the patient's total daily dosage of long-acting insulin in the present insulin dosage regimen. In the foregoing formulas, the value of this divisor is represented by E{DT}, since the value representing twice the patient's daily dosage of long-acting insulin in the present insulin dosage regimen is substituted as an approximation for the mean of the total daily dosage of insulin administered to the patient (which data may, optionally, be employed if they are input into the memory by an insulin pump, such as in the exemplary apparatus described above, or by the patient using data entry means). The resultant value is subtracted from the present patient CHR ("CHR(k)") to define a difference ("A"). If the A divided by the present CHR(k) is less than or equal to the value of a(l) divided by 100, then the integer value of A (by which new CHR (i.e., CHR(k+l)) is updated) is a function of the formula A=(l- a(2))floor{A}+a(2)ceil{A}, where a(2) is the insulin sensitivity correction factor (1 or 0), "floor" is value of A rounded down to the next integer, and "ceil" is the value of A rounded up to the next integer. If, on the other hand, the A divided by the present CHR(k) is greater than the value of a(l) divided by 100, then the integer value of A is a function of the formula

[00178] where a(2) is the insulin sensitivity correction factor (1 or 0), a(l) is the percent value of the insulin sensitivity correction factor, CHR(k) is the present CHR, "floor" is value of A rounded down to the next integer, and "ceil" is the value of A rounded up to the next integer. According to either outcome, the new CHR (CHR(k+l)) is equal to the present CHR (CHR(k)) plus A times the sign of the difference, positive or negative, of NCHR minus CHR(k). [00179] As patients may respond differently to doses of short-acting insulin depending upon the time of day the injection is made, a different dose of insulin may be required to compensate for a similar amount of carbohydrates consumed for breakfast, lunch, or dinner. For example, one may administer ' F insulin unit for every ' 10' grams of carbohydrates consumed at lunch while administering ' 1' insulin unit for every ' 8' grams of carbohydrates consumed at dinner. In the exemplary embodiment of the algorithm, this flexibility is achieved by the parameter Delta, 6, which is also updated in the step 500. It will be understood that the carbohydrate to insulin ratio (CHR) as calculated above is the same for all meals. However, the actual dosage differs among meals (i.e., breakfast, lunch, dinner) and equals CHR-6. Therefore, the exemplary algorithm allows the dosage to be made more effective by slightly altering the CHR with 6 to compensate for a patient's individual response to insulin at different times of the day. [00180] Delta 6 is a set of integers representing grams of carbohydrates, and is more specifically defined as the set of values [6b, 61, 6d], where "b" represents breakfast, "1" represents lunch, and "d" represents dinner. Delta, 6, may be either positive— thus reflecting that before a certain meal it is desired to increase the insulin dose— or negative— thus reflecting that due to hypoglycemic events during the day it is desired to decrease the insulin dose for a given meal.

[00181] Initially, it is contemplated that each 8 in the set [6b, 61, 3d] may be defined by the patient's healthcare professional or constitute a predefined value (e.g., 6=[0, 0, 0] for each of [b, 1, d], or [6b, 61, 3d], thus reflecting that the patient's CHR is used with no alteration for breakfast, lunch, or dinner).

[00182] The range of 6 ("R6") is defined as the maximum of three differences, expressed as max(|6b-6I|, |6b-6I|, |6d-6I|). In addition the algorithm defines the minimal entry ("6min") of the set [6b, 61, 3d], expressed as min(6b, 61, 3d).

[00183] Any correction to the patient's CHR can only result in a new R6 ("R6 (k+1)") that is less than or equal to the greatest of the range of the present set of 6 (R6 (k)) or a predefined limit (D), which may, for instance, be 2, as in the exemplary embodiment.

[00184] Against the foregoing, if the number of hypoglycemic events (HG) in a given meal (b, 1 or d) over a predefined period (for example, 7 days) is equal to a predefined value (for instance, 2), and if the corresponding 6b, 61, or 6d is not equal to the 6min or the range is 0 (R5=0), then the decrease in that 6 (6b, 61, or 6d) is equal to the present value for that 6 minus a predefined value ("d"), which may, for instance, be 1; thus, 6{t}=6{i}-d.

[00185] Otherwise, if the corresponding 8 b, 6 1, or 8 d is equal to the 6. sub. min and the range is other than 0, then the decrease in that 8 (e.g., 8 b, 8 1, or 8 d) is effected by decreasing each 8 in the set (i.e., [8 b, 81, or 8 d]) by the predefined value "d" (e.g., 1); thus, 8 = 8 -d (where 8 refers to the entire set [8 b, 81, or 8 d]).

[00186] If, on the other hand, the number of hypoglycemic events stored in the memory is insignificant, it may be necessary to increase A in one or more of the set (i.e., [8 b, 8 1, or 8 d]). To determine if an increase is due, the algorithm looks for an unbalanced response to insulin between the three meals (b, 1, d). A patient's response to his/her recent short-acting insulin dosage is considered unbalanced if the mean blood-glucose-level measurements associated with two of the three meals falls within a predefined acceptable range (e.g., >ai but <012; where, for instance, ai=80 and a2=120), while the mean of the blood-glucose-level measurements associated with the third meal falls above the predefined acceptable range.

[00187] If the mean for two meals falls within [ai, 012], while the mean of the third meal is >012, then the 8 values for the updated set [8 b, 8 1, or 8 d] are defined by the following, exemplary formulas:

[00188] According to the foregoing, a test set of [6 b, 61, or 6 d], designated 6tmp, is defined, wherein the value of each of 6 b, 6 1, and 6 d equals the present value of each corresponding 6 b, 61, and 6 d. The 6 value in the test set corresponding to the meal (b, 1, or d) where the blood- glucose-level measurement was determined to exceed the predefined acceptable range (e.g., >012) is then increased by the value "d" (e.g., 1), and the new set is accepted if it complies with one of the statements: R5 -tmp<=R 6 (i.e., is the range R 5 of the test set ("R 5 -tmp") less than or equal to the range (R 5) of the present set; or R 5-tmp <=D (i.e., is the range R.sub.A of the test set ("R 5-tmp ") less than or equal to the predefined value "D" (e.g., 2). [00189] The foregoing will thus yield an increase in the insulin dosage for a particular meal if the patient's mean blood-glucose-level measurement data are outside of a predetermined range, such as, by way of example only, between ai=80 and a2=120.

[00190] Further according to this step 500, the binary hypoglycemic correction-flag is reset to 0, reflecting that the patient's insulin dosage regimen has been updated (and thus may be updated again at the next evaluation).

[00191] It will be appreciated that the PGR and CHR values determined at step 500 may optionally be employed by the processor to calculate, per conventional formulas, a "sliding scale"-type insulin dosage regimen. Such calculations may employ as a basis therefore a predefined average number of carbohydrates for each meal. Alternatively, data corresponding to such information may be input into the memory by the patient using data entry means.

[00192] Per the exemplary algorithm as described above, it will be appreciated that if a hypoglycemic event causes some dosage reduction, no other dosage can go up at the next update cycle, with respect to certain embodiments.

[00193] It should be noted that, according to certain exemplary embodiments of the algorithm herein described, any time a periodic evaluation of the patient insulin dosage regimen is undertaken, the algorithm treats the insulin dosage regimen as having been updated even if there has been no change made to the immediately preceding insulin dosage regimen. And, moreover, any time the insulin dosage regimen is updated, whether in consequence of a periodic update evaluation or an asynchronous update, the timer counting to the next periodic update evaluation will be reset to zero.

[00194] As noted, in operation of certain embodiments, there is initially specified by a healthcare professional a patient insulin dosage regimen comprised of, for example, a long- acting insulin dose component, a carbohydrate ratio component and a plasma-glucose correction factor component. This insulin dosage regimen data is entered in the memory of an apparatus, such as by a healthcare professional, in the first instance and before the patient has made any use of the apparatus. Optionally, and as necessary, the internal clock of the apparatus is set for the correct time for the time zone where the patient resides so that the time tags assigned to patient's blood-glucose-level measurements as they are subsequently input into the apparatus are accurate in relation to when, in fact, the data are input (whether automatically, manually, or a combination of both). Thereafter, the patient will input, or there will otherwise automatically be input (such as by the glucose meter) into the memory at least data corresponding to each successive one of the patient's blood-glucose-level measurements. Upon the input of such data, the processor determines, such as via the algorithm described hereinabove, whether and by how much to vary the patient's present insulin dosage regimen. Information corresponding to this present insulin dosage regimen is then provided to the patient so that he/she may adjust the amount of insulin they administer.

Claims

WHAT IS CLAIMED IS:

1. A method for identifying medical conditions, the method comprising: storing one or more components of the patient’s current and historic insulin dosage regimen; obtaining data corresponding to the patient’ s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting diabetes management data into a computing device; detecting trends in the data based on parameters; and identifying a medical condition.

2. The method of claim 1, wherein the diabetes management data further comprises any combination of one or more of: an insulin regimen; a historic insulin dosage function; insulin doses; blood glucose-levels;

A1C levels; an age; an ethnicity; a body mass index; a duration of diabetes; and a duration of insulin treatment.

3. The method of claim 1, wherein the medical condition is an uncommon adultonset diabetes.

4. The method of claim 3, wherein the parameters are an unusually low total daily insulin dose and a high frequency of hypoglycemia when compared to the average Type-2 diabetes patient.

5. The method of claim 4, wherein the upper threshold for the total daily insulin dose is between 90 and 40 units of insulin and the lower threshold for hypoglycemia events is 1, 2, 3, 4 or 5 episodes per a predetermined amount of time (e.g. day, week, or month).

6. The method of claim 3, wherein the parameters are an unusually high total daily insulin dose and low frequency of hypoglycemia compared to the average Type-2 diabetes patient.

7. The method of claim 6, wherein the lower threshold for the total daily insulin dose is between 290 and 450 units of insulin and the upper threshold for hypoglycemia events is 2 to 6 episodes per a predetermined amount of time (e.g. day, week, or month).

8. The method of claim 1, wherein the medical condition is an acute and/or subacute condition.

9. The method of claim 8, wherein the parameter is an unusual decrease in total daily insulin that persists for a period of time.

10. The method of claim 8, wherein the parameters are an unusual increase in average glucose levels despite stability in glucose levels previously or therapeutic A1C levels.

11. The method of claim 9, wherein the lower threshold for an unusual decrease of the total daily insulin is a decrease of more than 20 to 60% and the lower threshold for persistent weeks of low total daily insulin is 2 to 8 weeks.

12. The method of claim 1, wherein the medical condition is hypoglycemia unawareness.

13. The method of claim 12, wherein the parameter is episodes of hypoglycemia during scheduled dosing events.

14. The method of claim 13, wherein the upper threshold for hypoglycemia episodes is 50 to 80 mg/dL.

15. A method for identifying diabetes treatment issues, the method comprising: storing one or more components of the patient’s current and historic insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting diabetes management data into a computing device; detecting trends in the data based on parameters; and identifying a diabetes treatment issue.

16. The method of claim 15, wherein the diabetes management data further comprises any combination of one or more of: an insulin regimen; a historic insulin dosage function; insulin doses; blood glucose-levels; and

A1C levels.

17. The method of claim 15, wherein the treatment issue is a required change in the insulin regimen.

18. The method of claim 17, wherein the parameters are Type-2 diabetes patients using long-acting insulin that have a normal fasting glucose and either an A1C greater than 8% or high average glucose levels.

19. The method of claim 18, wherein the average normal fasting glucose in patients with Type-2 diabetes is between 80 and 140 mg/dL.

20. The method of claim 18, wherein the high average glucose levels are greater than a predefined threshold (e.g., 150 mg/dL).

21. The method of claim 15, wherein the treatment issue is a concentrated insulin is needed for the regimen.

22. The method of claim 21, wherein the parameter is a component insulin dose in the regimen exceeding 220 units.

23. The method of claim 15, wherein the treatment issue is a wrong type of insulin is used by the patient.

24. The method of claim 15, wherein the type of insulin can be rapid, intermediate, premixed, or long-acting.

25. The method of claim 23, wherein the parameter is a ratio between doses of each component of the insulin regimen which is unusually higher or lower than expected.

26. The method of claim 23, wherein the long-acting insulin component reduces by 5 to 40% of the total daily insulin when premixed insulin is used instead of rapid insulin.

27. A method for identifying an uncommon adult-onset diabetes, the method comprising any combination of one or more of: storing one or more components of the patient’s current and historic insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting diabetes management data into a computing device; detecting trends in the data based on parameters; and identifying the uncommon adult-onset diabetes.

28. The method of claim 27, wherein the diabetes management data further comprises: an insulin regimen; a historic insulin dosage function; insulin doses; blood glucose-levels; A1C levels; an age; an ethnicity; a body mass index; a duration of diabetes; and a duration of insulin treatment.

29. The method of claim 27, wherein the parameters are an unusually high or low total daily insulin dose and unusually high or low frequency of hypoglycemia when compared to the average Type-2 diabetes patient.

30. The method of claim 29, wherein the upper threshold for the total daily insulin dose is between 90 and 40 units of insulin and the lower threshold for hypoglycemia events is 1, 2, 3, 4, or 5 episodes per a predetermined amount of time (e.g. day, week, or month).

31. The method of claim 29, wherein the lower threshold for the total daily insulin dose is between 290 and 450 units of insulin and the upper threshold for hypoglycemia is 2 to 6 episodes per a predetermined amount of time (e.g. day, week, or month).

32. A method for identifying an acute or subacute medical condition, the method comprising: storing one or more components of the patient’s current and historic insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting diabetes management data into a computing device; detecting trends in the data based on parameters; and identifying the acute or subacute medical condition.

33. The method of claim 32, wherein the diabetes management data further comprises any combination of one or more of: an insulin regimen; a historic insulin dosage function; insulin doses; blood glucose-levels; and

A1C levels.

34. The method of claim 32, wherein the parameter is an unusual decrease in total daily insulin that persists for a period of time.

35. The method of claim 32, wherein the parameters are an unusual increase in average glucose levels despite stability in glucose levels previously or therapeutic A1C levels.

36. The method of claim 34, wherein the lower threshold for an unusual decrease of the total daily insulin is a decrease of more than 20 to 60% and the lower threshold for persistent weeks of low total daily insulin is 2 to 8 weeks.

37. A method for identifying a condition of hypoglycemia unawareness, the method comprising: storing one or more components of the patient’s insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting diabetes management data into a computing device; detecting trends in the data based on parameters; and identifying the hypoglycemia unawareness condition.

38. The method of claim 37, wherein the diabetes management data further comprises any combination of one or more of: an insulin regimen; a historic insulin function; insulin doses; blood glucose-levels; and

A1C levels.

39. The method of claim 37, wherein the parameter is episodes of hypoglycemia during scheduled dosing events.

40. The method of claim 39, wherein the upper threshold for hypoglycemia episodes is 50 to 80 mg/dL.

40. A method for identifying a need for a component change in the insulin regimen, the method comprising: storing one or more components of the patient’s current and historic insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting diabetes management data into a computing device; detecting trends in the data based on parameters; and identifying a need for a component change in the insulin regimen.

42. The method of claim 41, wherein the diabetes management data further comprises any combination of one or more of: an insulin regimen; a historic insulin function; insulin doses; blood glucose-levels; and

A1C levels.

43. The method of claim 41, wherein the parameters are Type-2 diabetes patients using long-acting insulin that have a normal fasting glucose and either an A1C greater than 8% or high average glucose levels.

44. The method of claim 43, wherein the average normal fasting glucose in patients with Type-2 diabetes is between 80 and 140 mg/dL.

45. The method of claim 43, wherein the high average glucose levels is greater than a predefined threshold (e.g., 150 mg/dL).

46. A method for identifying a need for a concentrated insulin in the insulin regimen, the method comprising: storing one or more components of the patient’s current and historic insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting diabetes management data into a computing device; detecting trends in the data based on parameters; and identifying the need for a concentrated insulin in the insulin regimen.

47. The method of claim 46, wherein the diabetes management data further comprises any combination of one or more of: an insulin regimen; a historic insulin dosage function; insulin doses; blood glucose-levels; and

A1C levels.

48. The method of claim 46, wherein the parameter is a component insulin dose in the regimen exceeding 220 units.

49. A method for identifying a wrong type of insulin used by the patient, the method comprising:

Storing one or more components of the patient’s current and historic insulin dosage regimen; obtaining data corresponding to the patient’s blood glucose-level measurements determined at a plurality of times; tagging each of the blood glucose-level measurements with an identifier reflective of when or why the reading was obtained; inputting diabetes management data into a computing device; detecting trends in the data based on parameters; and identifying the wrong type of insulin.

50. The method of claim 49, wherein the diabetes management data further comprises any combination of one or more of: an insulin regimen; a historic insulin dosage function; insulin doses; blood glucose-levels; and

A1C levels.

51. The method of claim 49, wherein the type of insulin can be rapid, intermediate, premixed, or long-acting.

52. The method of claim 49, wherein the parameter is a ratio between doses of each component of the insulin regimen which is unusually higher or lower than expected.

53. The method of claim 52, wherein the long-acting insulin reduces by 5 to 40% of the total daily insulin when premixed insulin is used instead of rapid insulin.