The present invention relates generally to systems for determining drug administration profiles, and more specifically to systems for determining basal rate profiles for the administration of one or more diabetes therapy drugs.
Many patients having a diabetic condition are required to receive a diabetes therapy or treatment drug one or more times per day. Some such patients are required to receive several doses of the diabetes therapy or treatment drug periodically throughout the day and night. With such patients, a basal rate profile may be designed that defines a number of sequential doses, or basal rates, of the diabetes treatment drug that are administered to the patient over a period of time. For example, a conventional basal rate profile may consist of 24 separate basal rates, each having a duration of one hour, that are designed to be sequentially administered to the patient over successive 24-hour time periods. It is desirable to design basal rate profiles that are based on patient-specific medical parameters and that have proven to successfully treat diabetic conditions of a significant number of patients.
The present invention may comprise one or more of the features recited in the attached claims, and/or one or more of the following features and combinations thereof. A method is provided for generating a plurality of basal rate models that together model delivery of a corresponding plurality of basal rates of a diabetes treatment drug to a patient over a period of time. The method may comprise collecting information from a plurality of patients that have a diabetic condition and to which the diabetes treatment drug has been delivered. The collected information may include a glycemic control indicator for each of the plurality of patients that is indicative of an efficacy of the diabetes treatment drug in treating the patient's diabetic condition. The method may further comprise filtering the collected information based on the glycemic control indicators to produce a subset of the collected information that includes information only for patients that exhibit acceptable glycemic control, generating the plurality of basal rate models based on the subset of the collected information, and storing the generated plurality of basal rate models in a memory unit.
The collected information may include values of the basal rates of the diabetes treatment drug delivered to each of the plurality of patients over the period of time. Generating the plurality of basal rate models may comprise generating the plurality of basal rate models based, at least in part, on the values of the plurality of basal rates of the diabetes treatment drug delivered to each of the plurality of patients in the subset of the collected information.
The collected information may include a plurality of categorical patient parameters for each of the plurality of patients. Each of the plurality of categorical patient parameters for each of the plurality of patients may have one of two or more possible values or ranges. The method may further comprise partitioning the subset of the collected information into a number of different patient information subgroups each identified by a different combination of the two or more possible values or ranges of at least two of the plurality of categorical patient parameters. Generating the plurality of basal rate models may comprise generating the plurality of basal rate models based on at least one of the number of different patient information subgroups. The at least two of the plurality of categorical patient parameters may be selected from the group of patient gender, diabetes type, pre-dawn phenomenon, patient age, patient height, patient weight, body mass index and diabetes treatment drug delivery mechanism. The one of two or more possible values or ranges of the categorical patient parameter patient gender may be selected from the group of male and female. The one of two or more possible values or ranges of the categorical patient parameter diabetes may be selected from the group of type 1, type 2, gestational, latent autoimmune diabetes in adults (LADA), brittle, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT). The one of two or more possible values or ranges of the categorical patient parameter pre-dawn phenomenon may be selected from the group of patient experiences the pre-dawn phenomenon and the patient does not experience the pre-dawn phenomenon. The one of two or more possible values or ranges of the categorical patient parameter patient age may be selected from a group of non-overlapping age ranges. The one of two or more possible values or ranges of the categorical patient parameter patient height may be selected from a group of non-overlapping height ranges. The one of two or more possible values or ranges of the categorical patient parameter patient weight may be selected from a group of non-overlapping weight ranges. The one of two or more possible values or ranges of the categorical patient parameter body mass index may be selected from a group of non-overlapping body mass index ranges. The one of two or more possible values or ranges of the categorical patient parameter diabetes treatment drug delivery mechanism may be selected from the group of needle, infusion pump, insulin pen and inhalable insulin. The method may further comprise generating a number of sets of basal rate models. Each of the number of sets of basal rate models may comprise a plurality of basal rate models that are generated based on a different one of the number of different patient information subgroups. The method may further comprise storing each of the generated number of sets of basal rate models in the memory unit.
The collected information may comprise a plurality of patient records each for a different one of the plurality of patients. Each of the plurality of patient records may include a reference time within the period of time and a basal rate profile defining a plurality of basal rates of the diabetes treatment drug sequentially delivered to the corresponding patient over the period of time beginning with a first basal rate and ending with a last basal rate. The method may further comprise aligning the basal rate profiles in the plurality of patient records as functions of the reference times such that in each of the plurality of patient records the first basal rate of the corresponding basal rate profile begins at the corresponding reference time. Filtering the collected information may comprise filtering the collected information after aligning the basal rate profiles in the plurality of patient records. Each of the plurality of patient records may further include a start time that corresponds to a time within the period of time that the first basal rate of the corresponding basal rate profile normally begins. Aligning the basal rate profiles may further comprise aligning the basal rate profiles in the plurality of patient records further as functions of the start times such that in each of the patient records the first basal rate of the corresponding basal rate profile begins at the corresponding reference time regardless of the corresponding start time. In any case, the reference time in each of the plurality of patient records may be a time within the period of time that the corresponding patient normally falls asleep.
A method of generating a plurality of basal rate models that together model delivery of a corresponding plurality of basal rates of a diabetes treatment drug to a patient over a period of time may comprise collecting information from a plurality of patients to which the diabetes treatment drug has been delivered. The collected patient information may include a plurality of categorical patient parameters for each of the plurality of patients. Each of the plurality of categorical patient parameters for each of the plurality of patients may have one of two or more possible values or ranges. The method may further comprise partitioning the collected information into a number of different patient information subgroups each identified by a different combination of the two or more possible values or ranges of at least two of the plurality of categorical patient parameters, generating the plurality of basal rate models based on the collected information in at least one of the number of different patient information subgroups, and storing the generated plurality of basal rate models in a memory unit.
The at least two of the plurality of categorical patient parameters may be selected from the group of patient gender, diabetes type, pre-dawn phenomenon, patient age, patient height, patient weight, body mass index and diabetes treatment drug delivery mechanism. The one of two or more possible values or ranges of the categorical patient parameter patient gender may be selected from the group of male and female. The one of two or more possible values or ranges of the categorical patient parameter diabetes may be selected from the group of type 1, type 2, gestational, latent autoimmune diabetes in adults (LADA), brittle, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT). The one of two or more possible values or ranges of the categorical patient parameter pre-dawn phenomenon may be selected from the group of patient experiences the pre-dawn phenomenon and the patient does not experience the pre-dawn phenomenon. The one of two or more possible values or ranges of the categorical patient parameter patient age may be selected from a group of non-overlapping age ranges. The one of two or more possible values or ranges of the categorical patient parameter patient height may be selected from a group of non-overlapping height ranges. The one of two or more possible values or ranges of the categorical patient parameter patient weight may be selected from a group of non-overlapping weight ranges. The one of two or more possible values or ranges of the categorical patient parameter body mass index may be selected from a group of non-overlapping body mass index ranges. The one of two or more possible values or ranges of the categorical patient parameter diabetes treatment drug delivery mechanism may be selected from the group of needle, infusion pump, insulin pen and inhalable insulin. The method may further comprise generating a number of sets of the plurality of basal rate models each based on the collected information in a different one of the number of different patient information subgroups. The method may further comprise storing the generated number of sets of the plurality of basal rate models in the memory unit.
The collected information may include a plurality of medical condition indicators each indicative of a medical condition of a different one of the plurality of patients. The method may further comprise filtering the collected information based on the plurality of medical condition indicators to produce a subset of the collected information that includes patient information only for patients for which the corresponding medical condition is acceptable. Partitioning the collected information into a number of different patient information subgroups may comprise partitioning the collected information from the subset of the collected information into the number of different patient subgroups.
The collected information may comprise a plurality of patient records each for a different one of the plurality of patients. Each of the plurality of patient records may include a reference time within the period of time and a basal rate profile defining a plurality of basal rates of the diabetes treatment drug sequentially delivered to the corresponding patient over the period of time beginning with a first basal rate and ending with a last basal rate. The method may further comprise aligning the basal rate profiles in the plurality of patient records as functions of the reference times such that in each of the plurality of patient records the first basal rate of the corresponding basal rate profile begins at the corresponding reference time. Filtering the collected information may comprise filtering the collected information after aligning the basal rate profiles in the plurality of patient records. Each of the plurality of patient records may further include a start time that corresponds to a time within the period of time that the first basal rate of the corresponding basal rate profile normally begins. Aligning the basal rate profiles may further comprise aligning the basal rate profiles in the plurality of patient records further as functions of the start times such that in each of the patient records the first basal rate of the corresponding basal rate profile begins at the corresponding reference time regardless of the corresponding start time. In any case, the reference time in each of the plurality of patient records may be a time within the period of time that the corresponding patient normally falls asleep.
A method of generating a plurality of basal rate models that together model a basal rate profile defining a corresponding plurality of basal rates of a diabetes treatment drug sequentially delivered to a patient over a period of time beginning with a first basal rate and ending with a last basal rate may comprise collecting information in the form of a plurality of patient records each for a different patient to which the diabetes treatment drug has been delivered. Each of the plurality of patient records may include a reference time within the period of time and a basal rate profile that are specific to the corresponding patient. The method may further comprise aligning the basal rate profiles in the plurality of patient records as functions of the reference times such that in each of the plurality of patient records the first basal rate of the corresponding basal rate profile begins at the corresponding reference time, generating the plurality of basal rate models based on the patient records having aligned basal rate profiles, and storing the generated plurality of basal rate models in a memory unit.
The reference time in each of the plurality of patient records may be a time within the period of time that the corresponding patient normally falls asleep. Each of the plurality of patient records may further include a start time that corresponds to a time within the period of time that the first basal rate of the corresponding basal rate profile normally begins. Aligning the basal rate profiles further comprises aligning the basal rate profiles in the plurality of patient records further as functions of the start times such that in each of the patient records the first basal rate of the corresponding basal rate profile begins at the corresponding reference time regardless of the corresponding start time. The period of time may be twenty four hours in duration. The basal rate profile in each of the plurality of patient records may comprise twenty four basal rates each having a time duration of one hour.
The collected information may include a plurality of categorical patient parameters for each of the plurality of patients. Each of the plurality of categorical patient parameters for each of the plurality of patients may have one of two or more possible values or ranges. The method may further comprise partitioning the subset of the collected information into a number of different patient information subgroups each identified by a different combination of the two or more possible values or ranges of at least two of the plurality of categorical patient parameters. Generating the plurality of basal rate models may comprise generating the plurality of basal rate models based on at least one of the number of different patient information subgroups. The at least two of the plurality of categorical patient parameters may be selected from the group of patient gender, diabetes type, pre-dawn phenomenon, patient age, patient height, patient weight, body mass index and diabetes treatment drug delivery mechanism. The one of two or more possible values or ranges of the categorical patient parameter patient gender may be selected from the group of male and female. The one of two or more possible values or ranges of the categorical patient parameter diabetes may be selected from the group of type 1, type 2, gestational, latent autoimmune diabetes in adults (LADA), brittle, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT). The one of two or more possible values or ranges of the categorical patient parameter pre-dawn phenomenon may be selected from the group of patient experiences the pre-dawn phenomenon and the patient does not experience the pre-dawn phenomenon. The one of two or more possible values or ranges of the categorical patient parameter patient age may be selected from a group of non-overlapping age ranges. The one of two or more possible values or ranges of the categorical patient parameter patient height may be selected from a group of non-overlapping height ranges. The one of two or more possible values or ranges of the categorical patient parameter patient weight may be selected from a group of non-overlapping weight ranges. The one of two or more possible values or ranges of the categorical patient parameter body mass index may be selected from a group of non-overlapping body mass index ranges. The one of two or more possible values or ranges of the categorical patient parameter diabetes treatment drug delivery mechanism may be selected from the group of needle, infusion pump, insulin pen and inhalable insulin. The collected information may include a plurality of medical condition indicators each indicative of a medical condition of a different one of the plurality of patients. The method may further comprise filtering the collected information based on the plurality of medical condition indicators to produce a subset of the collected information that includes patient information only for patients for which the corresponding medical condition is acceptable. Aligning the basal rate profiles in the plurality of patient records may comprise aligning the basal rate profiles only in patient records included in the subset of the collected information.
A method of determining a set of basal rate models that define delivery of a diabetes treatment drug to a particular patient over a period of time may comprise collecting information from a plurality of patients to which the diabetes treatment drug has been delivered, generating a number of sets of basal rate models based on the information collected from the plurality of patients, collecting information that is specific to the particular patient, determining the set of basal rate models for the particular patient based on the number of sets of basal rate models and on the collected information that is specific to the particular patient, and storing the determined set of basal rate models for the particular patient in a memory unit.
The information collected from the plurality of patients may include a plurality of categorical patient parameters for each of the plurality of patients. The method may further comprise partitioning the information collected from the plurality of patients into a number of different patient information subgroups each identified by a different combination of the plurality of categorical patient parameters. Generating the number of sets of basal rate models may comprise generating each of the number of sets of basal rate models based on a different one of the number of different patient information subgroups. Collecting information that is specific to the particular patient may comprise collecting the plurality of categorical patient parameters for the particular patient. Determining the set of basal rate models for the particular patient may comprise selecting from the number of sets of basal rate models a set of basal rate models that was based on a plurality of the categorical patient parameters that most closely matches the plurality of categorical patient parameters for the particular patient. Generating a number of sets of basal rate models based on the information collected from the plurality of patients may be carried out on a first electronic device or system. Collecting information that is specific to the particular patient and determining the set of basal rate models for the particular patient may be carried out on a second electronic device that is remote from the first electronic device or system. Storing the determined set of basal rate models for the particular patient may comprise storing the determined set of basal rate models for the particular patient in a memory unit of the second electronic device.
The method may further comprise delivering the diabetes treatment drug to the particular patient according to the set of basal rate models for the particular patient over successive time periods each having duration equal to the period of time.
A method is provided for generating a basal rate profile that defines delivery of a plurality of basal rates of a diabetes treatment drug to a particular patient over a period of time. The method may comprise collecting information from a plurality of patients to which the diabetes treatment drug has been delivered, and generating a plurality of basal rate model sets based on the information collected from the plurality of patients. Each of the plurality of basal rate model sets may model delivery of a different plurality of basal rates of the diabetes treatment drug to a patient over the period of time. The method may further comprise collecting a first set of information that is specific to the particular patient, selecting one of the plurality of basal rate model sets based on the first set of information that is specific to the particular patient, collecting a second set of information that is specific to the particular patient, generating the basal rate profile based on the selected one of the plurality of basal rate model sets and on the second set of information that is specific to the particular patient, and storing the generated basal rate profile in a memory unit.
The information collected from the plurality of patients may include a plurality of categorical patient parameters for each of the plurality of patients. The method may further comprise partitioning the information collected from the plurality of patients into a number of different patient information subgroups each identified by a different combination of the plurality of categorical patient parameters. Generating the plurality of basal rate model sets may comprise generating each of the plurality of basal rate model sets based on a different one of the number of different patient information subgroups. Collecting a first set of information that is specific to the particular patient may comprise collecting the plurality of categorical patient parameters for the particular patient. Selecting one of the plurality of basal rate model sets based on the first set of information that is specific to the particular patient may comprise selecting from the plurality of basal rate model sets the one of the plurality of basal rate model sets that was based on a plurality of the categorical patient parameters that most closely matches the plurality of categorical patient parameters for the particular patient. Collecting a second set of information that is specific to the particular patient may comprise collecting a number of independent variables that are specific to the particular patient. Generating the basal rate profile may comprise computing a plurality of basal rates of the diabetes treatment drug to be sequentially delivered to the particular patient over successive time periods each having duration equal to the period of time. Each of the plurality of basal rates of the diabetes treatment drug may be based on a different basal rate model of the selected one of the plurality of basal rate model sets and on the number of independent variables that are specific to the particular patient. The method may further comprise sequentially delivering the plurality of basal rates of the diabetes treatment drug to the particular patient over each of the successive time periods.
Generating a plurality of basal rate model sets based on the information collected from the plurality of patients may be carried out on a first electronic device or system. Collecting the first set of information, selecting the one of the plurality of basal rate model sets, collecting the second set of information and generating the basal rate profile may be carried out on a second electronic device that is remote from the first electronic device or system. Storing the generated basal rate profile may comprise storing the generated basal rate profile in a memory unit of the second electronic device.
Still another method is provided for generating a plurality of basal rate models that together model delivery of a corresponding plurality of basal rates of a diabetes treatment drug to a patient over a period of time. The method may comprise collecting patient information from a plurality of patients to which the diabetes treatment drug has been delivered, partitioning the collected patient information into a calibration data subset and a validation data subset, generating the plurality of basal rate models based on the calibration data subset, determining whether the plurality of basal rate models are valid by processing the validation data subset using the plurality of basal rate models that were generated based on the calibration data subset, and storing the generated plurality of basal rate models in a memory unit if the plurality of basal rate models that were generated based on the calibration data subset are valid.
For the purposes of promoting an understanding of the principles of the invention, reference will now be made to a number of illustrative embodiments shown in the attached drawings and specific language will be used to describe the same.
Referring to
The one or more patient data source (PDS) electronic devices or systems 301-30N may likewise be conventional. Examples include, but are not limited to, one or more personal computers (PCs), laptop computers, notebook computers, hand-held electronic devices such as a personal data assistant (PDA), smart-phones or the like. Illustratively, each of the one or more PDS electronic devices/systems 301-30N is configured to wirelessly communicate with the BRMD electronic device/system 12 via the internet, e.g., world-wide-web (WWW) 35, although each of the one or more PDS electronic devices/systems 301-30N may alternatively be configured to wirelessly communicate with the BRMD electronic device/system 12 via one or more other wireless communication mediums such as cellular telephone or telephone modem. Alternatively still, each of the one or more PDS electronic devices/systems 301-30N may be configured to communicate with the BRMD electronic device/system 12 via one or more corresponding hardwire signal paths 361-36N.
Each of the one or more health care professional (HCP) electronic devices 401-40M includes a conventional processor 42 that is operatively coupled to a conventional display 44, a conventional memory 46, a conventional keypad or keyboard 48 and at least two conventional communication ports 501 and 502. Illustratively, each of the one or more HCP electronic devices 401-40M may be co-located with a different health care professional or different health care professional facility. Examples of the one or more HCP electronic devices 401-40M include, but are not limited to, one or more personal computers (PCs), laptop computers, notebook computers, hand-held electronic devices such as a personal data assistant (PDA) or the like. Illustratively, each of the one or more HCP electronic devices 401-40M is configured to wirelessly communicate with the BRMD electronic device/system 12 via the internet, e.g., world-wide-web (WWW) 45, although each of the one or more HCP electronic devices 401-40M may alternatively be configured to wirelessly communicate with the BRMD electronic device/system 12 via one or more other wireless communication mediums, such as cellular telephone or telephone modem. Alternatively still, each of the one or more HCP electronic devices 401-40M may be configured to communicate with the BRMD electronic device/system 12 via one or more corresponding hardwire signal paths 461-46M. Each of the HCP electronic devices 401-40M is further configured to wirelessly communicate with a conventional programmable medication delivery device 60 via a conventional wireless communication protocol, e.g., radio frequency (RF), inductive coupling, infrared (IR), or the like. Alternatively, each of the one or more HCP electronic devices 401-40M may be configured to communicate with a programmable medication delivery device 60 via a hardwire signal paths 60. The programmable medication delivery device may be any conventional electronically controlled medication delivery device, and examples include, but are not limited to, an implantable drug infusion pump, an externally worn drug infusion pump, or the like.
As will be described in greater detail hereinafter, the system 10 illustrated in
Once collected in the database 16 of the BRMD electronic device/system 12, the patient information is processed by the BRMD electronic device/system 12 to create the plurality of sets of basal rate model sets. Thereafter, health care professionals may access the BRMD electronic device/system 12 via the HCP electronic devices 401-40M, and use appropriate ones of the plurality of basal rate model sets to generate patient-specific basal rate profiles.
Referring now to
The process 100 may have multiple entry points, and one such entry point is an entry point A that leads to step 102 of the process 100. At step 102, information is collected from a plurality of patients that have a diabetic condition and to which a diabetes treatment drug has been delivered. Illustratively, the plurality of patients from which information is collected at step 102 comprises a large population of patients to which a diabetes treatment drug has been delivered. The diabetes treatment drug may be any conventional drug that is effective to modify, e.g., raise or lower, blood glucose levels, and that may be delivered using any conventional drug delivery structures and/or techniques. Examples of conventional diabetes treatment drugs may include, but should not be limited to, insulin and the like, and examples of conventional drug delivery structures and/or techniques include, but should not be limited to, subcutaneous drug delivery mechanisms including hypodermic needles, drug dosing pens, implanted or externally worn electronically or electromechanically controlled drug delivery mechanisms such as drug infusion pumps, and the like, transcutaneous drug delivery mechanisms including drug patches or the like, inhalable drugs or the like.
In one illustrative embodiment, the information or data is collected at step 102 in the form of individual patient records for each of the plurality of patients, wherein each of the plurality of individual patient records contains disease-related information, drug-related information, personal information and/or other information that is specific to the particular patient. For example, each patient record illustratively contains information that relates to the delivery of a diabetes treatment drug over a predefined time interval via a plurality of individual and sequentially delivered basal rates of the drug that span the predefined time interval. Illustratively, the predefined time interval may be 24 hours, and the plurality of basal rates may be one hour (60 minutes) in duration, although this disclosure contemplates other embodiments having different predefined time intervals and/or basal rate durations. It will be understood that while several embodiments and accompanying formulae will be described in this document, such embodiments and formulae are provided only by way of example. Modifications to these example embodiments and formulae to provide for time interval durations other than 24 hours and/or basal rate durations other than 60 minutes may be required, although such modifications will generally be a mechanical step or steps for someone skilled in the art. In any case, the patient information collection step 102 can be carried out in any format, e.g., xml, html, or the like, using any conventional data collection device, machine or system.
Illustratively, the information collected at step 102 comprises categorical patient information, i.e., one or more categorical patient parameters, each of which places a patient in one of a number of categories, and further comprises drug delivery-related information, medical condition indication information, e.g., one or more indicators of the patient's general or specific health, and one or more patient-specific independent variables. Illustratively, the one or more of the categorical patient parameters may place a patient in either of two categories, and others of the categorical patient parameters may place a patient in one of more than two categories. In other words, each of the categorical patient parameters for each patient will have one of two or more possible values or ranges. Examples of categorical patient parameters that may be collected at step 102 and that place a patient in one of two categories may include, but should not be limited to, patient gender, e.g., male or female, diabetes type, e.g., type 1 or type 2, whether or not a patient experiences the so-called pre-dawn or dawn phenomenon, e.g., yes or no, and the like. For purposes of this disclosure, the pre-dawn or dawn phenomenon is defined as being characterized by an early morning elevated blood glucose resulting from changes in glucose metabolism during sleep. It is generally known that some diabetic patients experience this phenomenon while others do not. Examples of categorical patient parameters that may be collected at step 102 and that place a patient in one of more than two categories may include, but should not be limited to, patient age, e.g., grouped by a number of non-overlapping age ranges, diabetes type, e.g., type 1, type 2, gestational, latent autoimmune diabetes in adults (LADA), brittle, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) or other, patient height, e.g., grouped by a number of non-overlapping height ranges, patient weight, e.g., grouped by a number of non-overlapping weight ranges, body mass index (BMI), e.g., grouped by a number of non-overlapping BMI ranges, diabetes treatment drug delivery mechanism, e.g., needle, infusion pump, insulin pen, inhalable insulin, and the like.
Examples of drug delivery-related information that may be collected and included in the individual patient records may include, but should not be limited to, diabetes treatment drug delivery mechanism, e.g., needle, infusion pump, continuous subcutaneous insulin infusion, insulin pen, inhalable insulin, elapsed time on current diabetes treatment drug mechanism, e.g., days and/or years since started, current insulin pump configuration times and/insulin type, e.g., fast-acting or slow-acting, total daily dose (TDD) of diabetes treatment drug, current basal rate profile, basal rate philosophy, basal rate profile start time, insulin pump type, elapsed time on current basal rate, e.g., date started or elapsed time since last basal rate change, or the like.
Examples of medical condition indication information that may be collected and included in the individual patient records may include, but should not be limited to, Hb1AC or other measure of glycemic control or other measure of the efficacy of the current diabetes therapy, average daily, weekly or monthly blood glucose level, or the like. For purposes of this disclosure, the term HbA1C is defined as a measure of glycated hemoglobin, which is typically used as a long-running average of blood glucose levels. Examples of patient-specific independent variables that may be collected and included in the individual patient records may include, but should not be limited to, patient age, patient height, patient weight, body mass index (BMI), country of residence, elapsed time having diabetes, e.g., age diagnosed, years since onset of diabetes, etc., pregnancy, e.g., whether currently pregnant or not currently pregnant, date of conception, dates of previous pregnancy or pregnancies, number of pregnancies, etc., regular time of falling asleep, regular time of waking, patient exercise schedule and/or frequency of exercise and/or duration and/or classification of exercise, e.g., light, medium or extended, and the like.
From step 102, the process 100 advances to step 104 where the plurality of patient records collected at step 102 are processed to align the basal rate profiles in each of the individual patient information records. Generally, a basal rate profile is made up of a number of sequential delivery rates of a diabetes treatment drug that begin with a first basal rate and end with a last basal rate. At step 104, the plurality of patient records are processed in a manner that aligns the basal rate profiles of each of the plurality of patients so that the first basal rate in each patient record begins at a reference time that is specific to that patient.
As described by example above, conventional programmable diabetes treatment drug delivery mechanisms, such as implantable or externally worn insulin pumps and the like, allow for the basal rate profile to be defined in the form of 24 individually programmable diabetes treatment drug infusion rates each having a duration of one hour. Many, but not all, such programmable drug delivery mechanisms define the first of the 24 one-hour intervals as beginning at midnight. In any case, the use of such a 24-hour basal rate profile reflects an assumption of an underlying circadian rhythm in the body's basal insulin needs. In any given patient record, the basal rate profile should therefore be expected to show a phase shift that is determined by the patient's sleep/wake rhythm relative to the starting time of the first basal rate in the patient's basal rate profile. At step 104, the plurality of patient information records are processed to remove this phase shift so that the basal rate profiles of the plurality of patients from which information was collected at step 102 are aligned and can therefore be piecewise modeled as a function of macroscopic parameters.
Illustratively, because the circadian rhythm is closely tied to a patient's sleep/wake cycle, the patient records are processed at step 104 by aligning the basal rate profiles in each patient record so that the first basal rate in each of the patient records begins at either the patient's normal sleep time or wake time. Alternatively, the patient records may be processes at step 104 by aligning the basal rate profiles in each patient record so that the first basal rate in each of the patient records begins at another reference time that is patient specific and that may differ between patients. In either case, aligning of the basal rate profiles relative to a reference time may further take into account the timing of other patient-related events or activities, examples of which may include, but should not be limited to, timing of a female menstrual cycle, seasonal or other timing of exercise types and/or durations, e.g., fall/winter indoor exercise activity types and durations vs. spring/summer outdoor exercise activity types and durations, seasonal or other timing of physiological conditions, e.g., seasonal allergies, seasonal asthmatic conditions, etc., or the like.
In the example that follows, step 104 will be described as aligning the basal rate profiles in each of the plurality of patient records so that the first basal rate in each patient records begins at the patient's normal sleep time, although it will be understood that step 104 may alternatively be configured to align the basal rate profiles in each patient record so that the first basal rate in each patient record begins at the patient's normal wake time or, alternatively still, at another reference time that is specific to each patient and which may be different for some patients as compared with others and/or which may take into account other patient-relating timing factors.
Referring now to
T
K,SLEEP=[(TK,STH*60)+TK,STM] (1),
where TK,STH represents the “hours” portion of the Kth patient's normal sleep time in the Kth patient record and TK,STM represents the “minutes” portion of the Kth patient's normal sleep time in the Kth patient record. Using the same example above, TK,SLEEP for the Kth patient having a normal sleep time of 22:30 would thus be TK,SLEEP=[(22*60)+45]=1365 minutes.
The process illustrated in
MT
K,SLEEP=ROUND(TK,SLEEP/60) (2).
Using the above example, of a normal sleep time for the Kth patient of 1365 minutes since midnight, equation (2) would yield MTK,SLEEP=ROUND(1365/60)=ROUND(22.75)=23, which corresponds to 2300 hours or 11:00 p.m. It will be understood that this disclosure contemplates embodiments wherein a patient's normal sleep time is recorded in that patient's record in a format other than military time, and in such embodiments step 122 and/or 124 of the process illustrated in
As described above, many, but not all, programmable drug delivery mechanisms define the first of the 24 one-hour intervals of basal rate drug delivery as beginning at midnight. The process illustrated in
T
K,START=[(TK,BRSH*60)+TK,BRSM] (3),
where TK,BRSH represents the “hours” portion of the drug delivery mechanism starting time for delivering the first basal rate of the drug to the Kth patient, and TK,BRSM represents the “minutes” portion of the drug delivery mechanism starting time for delivering the first basal rate of the drug to the Kth patient. Illustratively, TK,BRSH and TK,BRSM are stored in the Kth patient record in units of standard military time as described above, although this disclosure contemplates storing TK,BRSH and TK,BRSM in the patient records using other formats, and modifying either or both of steps 126 and 128 to accommodate any such other formats, as also described above.
Following step 128, the process illustrated in
MBR
K(J)=BRK(1+MOD [(J+MTK,SLEEP−MTK,START−1),Q] (4),
where MBRK(J) is the Jth modified basal rate number for the Kth patient, BRK( ) is the corresponding original basal rate number for the Kth patient, Q is the total number of basal rate profiles, e.g., 24, and MOD is the well-known modulo function. Following step 132, the value of J, corresponding to the basal rate number for the Kth patient that is currently being processed, is compared at step 134 to Q, corresponding to the total number of basal rates, e.g., 24. If J is not equal to Q at step 134, the process advances to step 138 where the value of J is incremented by one, and the process then loops from step 138 back to step 132 to process the next basal rate in the Kth patient record. If, at step 134, J=Q, the process advances to step 136 where K, corresponding to the current patient record being processed, is compared with L, corresponding to the total number of patient records. If K is not equal to L at step 136, the process advances to step 140 where the value of K is incremented by one, and the process then loops from step 140 back to step 122. If, at step 136, K=L, the process illustrated in
As another numerical example of the process illustrated in
Referring again to
From step 106, the process 100 advances to step 108 where the subset of collected patient information is partitioned into a number, N, of different patient information subgroups each containing only patient records that are identified by different combinations of categorical patient parameters forming part of the patient records, wherein N may be any positive integer greater than 1. As described hereinabove with respect to step 102, the collected patient information may include categorical patient information, i.e., one or more categorical patient parameters, each of which places a patient in one of a number of categories. Illustratively, one or more of the categorical patient parameters may place a patient in either of two categories, and others of the categorical patient parameters may place a patient in one of more than two categories. In any case, the patient records are processed at step 108 to partition the patient records into a number, N, of different patient information subgroups, wherein N is determined by the number, M, of categorical patient parameters used and also by the number of categories defined by each of the categorical patient parameters. As an illustrative example of step 108, assume that M=3 and the categorical patient parameters include patient gender, diabetes type (e.g., 1 or 2) and whether or not the patient experiences the dawn or pre-dawn effect (e.g., yes or no). Each of these categorical patient parameters define two categories (e.g., male or female, type 1 or type 2, and yes or no), and the number of different patient information subgroups formed at step 108 is therefore 2M=23=8. The eight different patient information subgroups formed at step 108 in this example each include only patient records containing a different combination of the outcome of the three categorical patient parameters, as summarized in Table I below.
Patient information subgroup 1 contains only patient records for females having type 1 diabetes that experience the dawn/pre-dawn phenomenon, patient information subgroup 2 contains only patient records for females having type 1 diabetes that do not experience the dawn/pre-dawn phenomenon, patient information subgroup 3 contains only patient records for females having type 2 diabetes that experience the dawn/pre-dawn phenomenon, and so forth. In cases where one or more of the categorical patient parameters define more that two categories, e.g., such as more than two weight ranges for a categorical patient parameter “weight,” the number, N, of patient information subgroups will accordingly be more than 2M, where M is the number of categorical patient parameters.
Following step 108, the process 100 advances to step 110 where each of the N different patient information subgroups is processed to generate a corresponding plurality of basal rate models. Each of the plurality of basal rate models for each patient information subgroup is configured to model a corresponding one of the plurality of individual basal rates for that patient information subgroup. Using the example set forth in Table I above, a plurality of basal rate models, e.g., 24, are generated at step 110 for each of the eight different patient information subgroups. Each set of basal rate models is determined using the information contained in the patient records for the corresponding patient information subgroup.
Referring now to
From step 152, the process illustrated in
At step 188, the mean (M) and standard deviation (SD) of each independent and intermediate variable in each patient record of the Mth calibration data subset, CDSM, are determined using conventional techniques. Thus, in the example given above in which each patient record in the Mth calibration data subset, CDSM, has X independent variables and Y intermediate variables, the mean values of the independent variables are represented as MIND1-MINDX. the standard deviation values of the independent variables are represented as SDIND1-SDINDX, the mean values of the intermediate variables are represented as MINT1-MINTY and the standard deviation values of the intermediate variables are represented as SDINT1-SDINTY. Each of the mean and standard deviation values are stored in memory Illustratively, the mean and standard deviation values may be appended to corresponding patient information records.
Following step 188, the counter value Z is again set to one at step 190. Thereafter at step 192, each of the independent variables of the Zth patient record of the Mth calibration data subset, CDSM, is standardized with respect to its mean and standard deviation values. Illustratively, each of the independent variables of the Zth patient record of the Mth calibration data subset, CDSM, is standardized at step 192 using the formula SINDV=(INDV-MINDV)/SDINDV, where V ranges from 1 to X and where SINDV is the Vth standardized independent variable of the Zth patient record of the Mth calibration data subset, CDSM. Alternatively or additionally, each of the independent variables of the Zth patient record of the Mth calibration data subset, CDSM, may be standardized at step 192 using one more other conventional data standardization techniques.
Following step 192, each of the intermediate variables of the Zth patient record of the Mth calibration data subset, CDSM, is standardized with respect to its mean and standard deviation values. Illustratively, each of the intermediate variables of the Zth patient record of the Mth calibration data subset, CDSM, is standardized at step 194 using the formula SINTU=(INTU-MINTU)/SDINTU, where U ranges from 1 to Y and where SINTU is the Uth standardized intermediate variable of the Zth patient record of the Mth calibration data subset, CDSM. Alternatively or additionally, each of the intermediate variables of the Zth patient record of the Mth calibration data subset, CDSM, may be standardized at step 194 using one more other conventional data standardization techniques. In any case each of the standardized independent variables, SINDV, and the standardized intermediate variables, SINTU, are stored in memory. Illustratively, the standardized independent variables, SINDV, and the standardized intermediate variables, SINTU, may be appended to corresponding patient information records.
Following step 194, the count value Z is compared to LC at step 196, where LC is the record length of the Mth calibration data set, i.e., the total number of patient records in the Mth calibration data subset. If, at step 196, Z is not yet equal to LC, the count value Z is incremented by one at step 198 and execution of the process illustrated in
It should be apparent that the process illustrated in
Referring again to
Referring now to
Following step 206, the count value Z is compared to LC at step 208, where LC is the record length of the Mth calibration data set, i.e., the total number of patient records in the Mth calibration data subset. If, at step 208, Z is not yet equal to LC, the count value Z is incremented by one at step 210 and execution of the process illustrated in
SBR
P
=a
0
+a
1
*SIND
1
+ . . . +a
X
*SINT
X
+b
1
*SINT
1
+ . . . +b
Y
*SINT
Y+ε (5),
where SBRP is the Pth standardized basal rate, SIND1-SINTX are the standardized independent variables, SINT1-SINTY are the standardized intermediate variables, a0-aX and b1-bY are the model coefficients determined by the regression technique, and ε is a conventional error term. Following step 214 the count value Z is compared to LC at step 216, where LC is the record length of the Mth calibration data set. If, at step 216, Z is not yet equal to LC, the count value Z is incremented by one at step 218 and execution of the process illustrated in
SBRM
P
=a
0
+a
F
*SIND
1
+ . . . +a
G
*SIND
G
+b
H
*SINT
H
+ . . . +b
I
*SINT
I+ε (6),
where F and G are elements of the set [1, X], and H and I are elements of the set [1, Y]. The corresponding mean values of the independent and intermediate variables, MINDF-MINDH and MINTH-MINTI, the corresponding standard deviation values of the independent and intermediate variables, SDINDF-SDINDG and SDINTH-SDINTI, the mean value of the Pth basal rate, MNBRP and the standard deviation of the Pth basal rate, SDBRP, are also shown in step 220. The mean and standard deviation values will be used to compute a modified intercept value when the Pth basal rate model is subsequently validated since the variables in the Mth validation data set, VDSM, are per se not standardized. In any case, from step 220, the process of
Referring again to
Following step 232, the intermediate variables in the Zth patient record of the Mth validation data subset, VDSM, that correspond to the intermediate variables remaining in the Pth basal rate model, i.e., independent variables H-I (e.g., see step 220 of
Following step 234, the standardized independent and intermediate variables for the Zth record in the Mth validation data subset, VDSM, are plugged into equation (6) above, corresponding to the Pth basal rate model, at step 236 to compute a standardized basal rate estimate, SBRMPE. Thereafter at step 238, the standardized basal rate estimate, SBRMPE, is converted to a non-standardized basal rate estimate, EBRP. In embodiments in which the formula of step 206 (in the process of
At step 242, the error value, ERR, is compared with an error threshold, ERRTH, which has been pre-selected to achieve a desired level of model certainty, and which is stored in memory. If, at step 242, the error value, ERR, exceeds the threshold error value, ERRTH, the illustrated process advances to step 244 where the message BRMP VALIDATION UNSUCCESSFUL is generated to indicate that validation of the Pth basal rate model has failed. Thereafter, the illustrated process is returned to step 160 of the process illustrated in
Referring again to
Referring again to
If at step 110, it is determined that all of the created basal rate models were validated, the process 100 advances to step 112 where the plurality of basal rate models for each of the N patient information subgroups, along with basal rate mean and standard deviation values, are stored in memory. Thereafter, the process 100 ends.
Referring now to
Illustratively, the process 300 may be carried out by a health care professional using one of the HCP electronic devices 401-40M, although this disclosure contemplates that the process 300 may alternatively be carried out by other persons and/or using one or more other electronic devices. The plurality of sets of basal rate models may be or include any of the one or more sets of basal rate models generated as described herein, although this disclosure contemplates that the process 300 may alternatively determine the set of patient-specific basal rates using other basal rate model sets. In either case, the plurality of basal rate model sets are stored in the BRMD electronic device/system 12 or in any of the electronic devices/systems 301-30N, and are accessible by the HCP electronic device 401-40M or other electronic device/system using any of the data transfer structures and/or techniques described hereinabove with respect to
The process 300 begins at step 302 where communications is established between the BRMD electronic device/system 12 and the HCP electronic device 401. Illustratively, the HCP electronic device 401 is operable to initiate the communications with the BRMD electronic device/system 12, although this disclosure contemplates alternate embodiments in which the BRMD electronic device/system 12 is operable at step 302 to establish communications with the HCP electronic device 401. In any case, communications between the BRMD electronic device/system 12 and the HCP electronic device 401 may be established via any communication medium illustrated and described hereinabove with respect to
The process 300 advances from step 302 to step 304 where a plurality of data subset identifiers are transferred from the BRMD electronic device/system 12 to the HCP electronic device 401, and thereafter at step 306 the plurality of data subset identifiers are displayed on the HCP electronic device 401. Illustratively, the HCP electronic device 401 is operable at step 304 to request transmission of the data subset identifiers from the BRMD electronic device/system 12, although this disclosure contemplates alternate embodiments in which the BRMD electronic device/system 12 is operable at step 304 to transfer unprompted the data subset identifiers after communications with the HCP electronic device 401 is established. In any case, the plurality of data subset identifiers transferred to, and displayed on, the HCP electronic device 401 at steps 304 and 306 correspond to identifiers of the categorical patient parameters that were used to partition the subset of the collected patient information into the N different patient information subgroups at step 108 of the process 100 of
The process 300 advances from step 306 to step 308 where the health care professional enters, e.g., via the keypad 48, patient-specific data that correspond to the displayed data subset identifiers, i.e., values of the displayed data subset identifiers that are specific to the patient for whom diabetes treatment is currently being designed, into the HCP electronic device 401. The HCP electronic device 401 then transfers the entered patient-specific values of the displayed data subset identifiers to the BRMD electronic device/system 12, e.g., in response to a user prompt to do so. Thereafter at step 310, the BRMD electronic device/system 12 transfers the basal rate model set and corresponding mean and standard deviation data (e.g., see step 220 of the process of
As an example of steps 304-312 of the process 300 that is consistent with Table I above, assume that the patient for whom the diabetes treatment is being designed is a male that has type 2 diabetes and that does experience the dawn/pre-dawn effect. At step 304, the BRMD electronic device/system 12 transfers the data identifiers Patient Gender, Diabetes Type and Dawn/Pre-dawn Effect to the HCP electronic device 401, and at step 308 the health care professional enters “male” for Patient Gender, “2” for Diabetes Type and “yes” for Dawn/Pre-dawn Effect. At step 310, the BRMD electronic device/system 12 matches the entered patient-specific values of male, 2 and yes to patient information subgroup 7, and then transfers the basal rate model set and mean and standard deviation data that corresponds to the patient information subgroup 7 to the HCP electronic device 401 where the model set and corresponding mean and standard deviation data are stored in the memory 46.
The process 300 advances from step 312 to step 314 where the HCP electronic device 401 is controlled to display the independent patient variables required by the basal rate model set that was provided to the HCP electronic device 401 at step 310. Illustratively, the HCP electronic device 401 is operable at step 314 to process each term in the transferred basal rate model set to determine the independent patient variables that are required by the model set. Alternatively, the basal rate model set that is transferred to the HCP electronic device 401 by the BRMD electronic device/system 12 at step 310 may be accompanied by a list of such independent patient variables, in which case the HCP electronic device 401 is operable to execute step 314 by reading the independent patient variables from the list provided by the BRMD electronic device/system 12. In any case, the HCP electronic device 401 is operable at step 314 to display the independent patient variables via the display 44 or other suitable visual and/or audible display device.
The process 300 advances from step 314 to step 316 where the health care professional enters into the HCP electronic device 401 e.g., via the keypad 48, patient-specific values of the displayed independent patient variables (PSIND), i.e., values of the displayed independent patient variables that are specific to the patient for whom diabetes treatment is currently being designed. Thereafter at step 318, the HCP electronic device 401 computes and stores in the memory 46 a patient-specific basal rate profile based on the basal rate model set that was transferred to the HCP electronic device 401 at step 310 and further based on the patient-specific independent variables, PSIND, that were entered into the HCP electronic device 401 at step 316. The patient-specific basal rate profile computed at step 318 consists of a plurality of basal rates of a diabetes treatment drug to be sequentially delivered to the patient over a period of time, e.g., 24 one-hour duration basal rates to be sequentially delivered to the patient during every 24-hour cycle. When the patient-specific basal rate profile is computed at step 318, it is thereafter displayed at step 320, e.g., via the display 44 or other suitable visual and/or audible display device. The health care professional and/or patient may then manually program an automatic diabetes treatment drug delivery device to deliver the diabetes treatment drug to the patient according to the patient-specific basal rate profile, or the health care professional may otherwise instruct the patient to self-administer the diabetes treatment drug according to the patient-specific basal rate profile via an alternate drug delivery device or technique. Alternatively or additionally, the HCP electronic device 401 and/or the programmable medication delivery device 60 (see
Referring now to
Following step 330, the patient-specific independent and intermediate variable values, PSIND and PSINT, are standardized at step 332 to the mean and standard deviation values of the corresponding independent and intermediate variables that accompanied the basal rate model set, e.g., see step 220 of
Following step 332, a counter value, J, is set equal to one at step 334. Thereafter at step 336, the Jth standardized basal rate value is computed from the corresponding Jth basal rate model, SBRMJ, forming part of the basal rate model set that was transferred to the HCP electronic device 401 at step 310 of the process 300, as a function of the standardized patient-specific independent and intermediate variables, SPSIND and SPSINT. More specifically, the standardized patient-specific independent and intermediate variables, SPSIND and SPSINT, are plugged into equation (6) above at step 336 to produce the Jth standardized basal rate, SBRJ, where the model coefficients a0, aF-aG and bH-bI are provided by the Jth basal rate model.
Following step 336, the standardized basal rate value, SBRJ, is converted or transformed to a non-standardized basal rate value, BRJ. Illustratively, the non-standardized basal rate estimate is computed at step 338 according to the equation BRJ=(SBRJ*SDBRJ)+MNBRJ, where MNBRJ and SDBRJ are the mean and standard deviations respectively of the Jth basal rate values that accompanied the basal rate model set that was transferred to the HCP electronic device 401 at step 310 of the process 300. From step 338, the illustrated process advances to step 340 where the Jth non-standardized basal rate value, BRJ is stored in the memory 46. Thereafter at step 342, the counter value, J, is compared to the total number, Q, of basal rates that comprise the basal rate profile. If, at step 342, J does not yet equal Q, the count value, J, is incremented by one at step 344 and the illustrated process then loops back to step 336 to compute another basal rate value. If, at step 342, J=Q, the illustrated process advances to step 346 where the illustrated process is returned to the process 300 of
While the invention has been illustrated and described in detail in the foregoing drawings and description, the same is to be considered as illustrative and not restrictive in character, it being understood that only illustrative embodiments thereof have been shown and described and that all changes and modifications that come within the spirit of the invention are desired to be protected.