The disclosure relates to physiological monitoring, and in particular, to a methods and displays for providing estimated and predicted biological values from biological measurements.
For monitoring glycemia, the American Diabetes Association (ADA) recommends the hemoglobin AlC test, hereinafter referred to HbAlC. Health care providers (HCPs) use HbAlC as a surrogate marker to evaluate a patient's glycemia over a previous 2 to 3 month period and as a target parameter by which to treat patients. For example, the HbAlC value, which can be presented as a percentage of glycated hemoglobin or in international standardized units mmol/mol (by International Federation of Clinical Chemistry, IFCC), is needed by the HCP when deciding or recommending a change to a patient's therapy. Therapy modification may include a change to, an addition to, or a switch in insulin therapy, oral medication, nutrition, physical activity, or combinations thereof in order to regulate a patient's glucose with the goal of improving a patient's HbAlC value. For a high quality determination (i.e., coefficient of variation (CV)<3%) of the HbAlC value, HbAlC assays are the norm in which blood samples are tested for the extent of glycation of hemoglobin by use of laboratory devices such as, for example, a D-10 analyzer from Bio-Rad Laboratories, or a G-7 analyzer from Tosoh Bioscience, Inc. For an approximated assessment of glycemia, HCPs alternatively use blood glucose (bG) values to determine an average glucose value, and then interpret the results to derive an estimated HbAlC value from spot monitoring blood glucose (SMBG) values. However, the estimated HbAlC value so obtained by such a method, in general, is poor in quality (i.e., CV>5%).
Other methods of solving a true mean bG value and estimating HbAlC value have been based on both the SMBG data collected during various clinical trials and the relationships derived there from. For example, many such methods use SMBG data to develop prediction models based on statistical methods. Other methods consider weighted bG value schemas with additional predicators, such as a previous HbAlC value, to determine an estimated HbAlC value using a noted study relationship. While still other methods further include transforming a bG value and then using the transformed bG value to determine the estimated HbAlC value. However, such methods have the following potential issues: model parameters typically needs returning, correlation is still generally poor (i.e., CV>5%), the standard errors are typically large, and adjustments to account for lifestyle related variations are not made such that any such reported patient specific solution is not specific enough to account for lifestyle related variations.
It is to be appreciated that one of the key limiting factors to finding a good generic algorithm which provides an accurate HbAlC estimation (i.e., determining the current HbAlC value) or prediction (i.e., determining the future HbAlC value) is the difficulty in obtaining comprehensive and detail (frequently sampled) blood glucose data under various conditions. For instance, studies having data sets based on continuous blood glucose monitoring, although providing dense data are typically conducted on relatively smaller population sizes and with durations that are relatively shorter in time than studies with SMBG data sets. With SMBG, on the other hand, there is a practical limitation of how many measurements can be collected. Since bG varies during the day, due to many factors such as physical activity, meal response, drug response (such as oral drugs or insulin) and stress and so forth, it is not possible to get an accurate picture of a glucose excursion by just a few daily measurements. This means that the SMBG data sets (i.e., time-interval based data sets) often fail to capture true bG variation of the patient with diabetes (PwD). The implication is that the resulting prediction models are normally then very study specific. Such prediction models therefore can neither be extended to account for other variables not addressed by the study(-ies) which they were based on nor used in an alternate situation to make predictions without the need for an additional clinical trial to validate such model extensions. Furthermore, as such methods fail to account for the context associated with bG measurements or in other words, to account for influence(s) of events such as carbohydrate ingestion, physical activity, insulin therapy, oral drug therapy, and so forth, such methods are generally unsuitable for determining an estimated HbAlC value of good quality (i.e., CV<3%) for a patient specific lifestyle. The lack of context associated with measurements can also limit the application of results when studying other glycemic excursion or non glycemic excursion factors (e.g., lipid profiles, insulin concentration profile, heart rate profile assuming availability of spot/continuous monitoring of the respective parameter). Finally, such methods fail to provide the estimated parameter, such as the estimated HbAlC values (or other parameters such as mean glucose, weighted glucose, fructosamine, biomarkers for various lipid levels, etc.) in a manner that can allow one to assess the relative impact of various components on a patient's overall HbAlC in a manner that would provide a quick evaluation of an implemented therapy.
In one embodiment, a method for providing an estimated or predicted biological values in a sectioned display to assess the relative impact of a set of variables is provided. The method includes collecting biological measurements, grouping the biological measurements based on the set of variables, and evaluating the biological measurements to determine grouped estimated biological values or grouped predicted biological values. The method further provides the grouped estimated biological values or grouped predicted biological values within a plurality of sections in the sectioned display, wherein the plurality of sections correspond to the set of variables.
In another embodiment, a sectioned display device for displaying grouped biological values is provided. The sectioned display device includes an input terminal for collecting both biological measurements and associated context of the biological measurements at daily times or events, memory for storing the biological measurements, the associated context of the biological measurements and instructions, and a processor in communication with the memory. The processor is operable to execute the instructions such that the instructions cause the processor to group the biological measurements based on a set of variables, evaluate the biological measurements to determine grouped estimated biological values or grouped predicted biological values, and provide the grouped estimated biological values or grouped predicted biological values within a plurality of sections in the sectioned display, wherein the plurality of sections correspond to the set of variables.
In still another embodiment, a method for selectively displaying a patient's glycated hemoglobin (HbAlC) based on various types of values is provided. The method includes collecting both bG measurements and associated context of the bG measurements at daily times or events, weighting each of the collected bG measurements based on the associated context, determining estimated HbAlC values from the weighted measurements of the collected bG measurements and determining additional types of HbAlC values. The method further includes selecting which types of HbAlC values to display and displaying the selected types of HbAlC values.
These and other advantages and features disclosed herein will be made more apparent from the description, drawings and claims that follow.
The following detailed description of the embodiments of the present invention can be best understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals, and in which:
It is to be appreciated that embodiments of the present disclosure enhance existing software and/or hardware that retrieves and processes biological measurements such as blood glucose (bG) data. The embodiments of the disclosure can be directly incorporated into existing home glucose monitors, or used for the enhancement of software that retrieves and processes biological measurements (e.g., bG data), by introducing a method for delivering estimated biological values (e.g., estimated glycated hemoglobin (HbAlC) values) of good quality from structured spot biological measurements having a coefficient of variation (CV) of less than 5% in one embodiment, and less than 3% in a preferably embodiment.
In the sections to follow, a discussion is made first to the exemplary approach used to derive the equations for providing the estimated true mean blood glucose (bG) value and the estimated glycated hemoglobin (HbAlC) value from structured spot measurements of blood glucose (i.e., bG data) collected, as per a measurement schema according to the present disclosure. It is to be appreciated that the measurement schema according to the present disclosure assumes that the PwD maintains a repeatable average behavior, whereby collection exceptions (i.e., missed testing times) are managed by the algorithm on the estimated HbAlC value. It is further to be appreciated that the utility of providing an estimated HbAlC value that demonstrates continuous blood glucose monitoring will provide a fairly accurate idea of the overall level of glycemia of the PwD, as compared to the uncertainty associated with estimating glycemia and its variation based only on spot monitoring. Additionally, certain HbAlC values have been linked to various disease states, and thus having a good estimated HbAlc value between laboratory based assays values can help identify much earlier a patient's potential risk associated to long term complications, such as micro-vascular (retinopathy, neuropathy, nephropathy) disease complications. Furthermore, providing an assessment of overall glycemia via an estimated HbAlC value of good quality can empower the PwD to manage better his/her diabetes. Alternatively, using a shorter window the algorithm can provide predicted HbAlc which allows PwD and HCP impact of current lifestyle on future HbAlc. A discussion of the methodology used to provide the estimated true mean blood glucose value, estimated glycated hemoglobin (HbAlC) value and other estimated and predicted biological values from collected biological measurements according to the present disclosure now follows.
Kinetics of Glycation of Hemoglobin
Glycation is a non-enzymatic chemical reaction wherein the glucose molecules bind with the amino acid groups of the proteins. Of the many glycated proteins hemoglobin AlC is fairly stable and one of the dominant forms of glycohemoglobin. Synthesis of HbAlC is primarily a condensation of hexose with the hemoglobin structure to form an unstable intermediate Schiff base adduct, or aldimine, followed by the Amadori rearrangement to form the stable ketoamine adduct, HbAlc. The kinetics of the glycation of hemoglobin to surrounding glucose concentration can be modeled by three differential equation, Equations (1)-(3), which are disclosed more fully by the publication of Mortensen, H. B.; “Glycated hemoglobin. Reaction and biokinetic studies. Clinical application of hemoglobin Alc in the assessment of metabolic control in children with diabetes mellitus,” Danish medical bulletin (1985), 32(6), pp. 309-328. The model according to Equations (1)-(3), is referred herein as the Mortensen Model.
Mortensen Model
In the Mortensen model, the term HbA represents the sub-pool of erythrocytes of same age, whereby the pool consists of cohorts of erythrocytes of varying age. The behavior of each cohort is represented by a corresponding set of Equations (1)-(3). From the Mortensen publication, the k parameters used in the model are known as follows: k12=5.76 mmol/l/min; k21=0.006/min; k23=0.000852/min; and k32=0.000102/min. Next, to test the utility of the Mortensen model in helping to generate a basic relation between HbAlC and bG, glycation simulations were run on simulated data for meal related bG excursions which is discussed hereafter.
Glycation Simulation Setup
Meal related bG excursions were evaluated first using simple mathematical formulas, whereby simulated data helped to generate a basic relation between HbAlC and bG. It is to be appreciated that the glycation of erythrocytes is a continuous process. However, the erythrocytes have finite lifespan of approximately 120 days. Depending on problem needs one can use other lifespan values such as ranging more or less from 90˜120 days to cover different population groups and/or physiological conditions. This means that in addition to the glycation, erythrocytes are continuously being added and removed from the glycation process. As the aged erythrocytes are replaced, the state of glycation of all the cells has to be managed. From a simulation perspective, instead of using Equations (1)-(3) for each cell, a simplification was done by grouping cells into cohorts of equally aged cells. In particular for the glycation simulation setup, n numbers of cohorts of erythrocytes were considered, whereby each of the cohorts was described by the set of the 3 differential equations (Equations (1)-(3)). Each cohort is assumed to have a life span of n days. When a cohort's maximum age is reached, a new cohort replaces it. The simulation handled this by resetting the 3-states of the oldest cohort (i.e. when the age of the cohort reaches its life-duration of 120 days) to the state of a fresh cohort of erythrocytes with non-glycated hemoglobin. In all, there were n sets of differential equations used in the simulation, whereby each set of equations represented a state of the corresponding cohort.
The 3n states were stored in columns as shown schematically in
where the summation counter i is the column corresponding to the HbAlc state. Using Equations (1)-(4), HbAlC can be simulated for an arbitrary bG profile. The true mean blood glucose value,
where AUC is the area under a continuous bG excursion curve. However, when bG measurements are sparse and non-continuous, as in the case of spot bG measurements, then it is to be appreciated that Equation 5 is no longer valid. Accordingly, a new relationship was derived in order to estimate the true mean bG as follows.
Simulated Cases
Under the above mentioned idealized setup, the relationship between periodic glucose profiles and corresponding HbAlC values was then examined for deriving useful insights and relationships. Specifically, two profiles were examined: (1) Sinusoidal glucose profile with offset (Equation (6)); and (2) Gamma function profile with offset (Equation (8)). These functions can be seen as representative of the meal event with post-prandial glucose behavior for varying levels of control, whereby constant glucose is a special case of both of the functions. For the sinusoidal glucose profile, Equation (6) is defined as:
where, bGconst provides the steady state offset and
is the cosine curve with amplitude A and period T.
The disturbance model (in which the gamma function was used to model disturbance) is shown by
In this simulation embodiment, the Gamma function was used to represent the post meal glucose excursion. Grossly approximated, the model shows a two (2) compartment model of glucose in a post prandial state. It has been used primarily to understand the impact of glucose varying from the aspects of different rates of postprandial rise, decay and magnitude of an excursion. The parameters α and β approximately represent the number of compartments and time to peak. In fact, if α is set to 2, a 2nd order compartment model is considered with a time constant for both compartments equal to β (2nd order system with repeated poles). Therefore, the above function according to Equation (7) simplifies to:
The peak value of the function ƒ(t) is reached when t=β. The peak value is then
Therefore, the glucose excursion used herein can be defined by Equation 8:
where A is the peak bG value with respect to bGconst.
The response of HbAlC to glucose excursions for various time to peak and peak values was then studied both analytically and in simulation. The Gamma functions for the various combinations of the parameters studied are listed in Table 1.
The results obtained from simulation show that the true mean bG is linearly related to HbAlC. This linear relationship is shown by
Lifestyle Aspects and Context Based Measurements
Intensive therapy addresses the occurrences of bG excursion and provides insulin dosing rules for correcting events such as meals, exercise, medication, etc. This leads to the term “lifestyle” which captures the properties/characteristics of the occurrences of meal events, exercise events, medication events, etc., for a PwD. Lifestyle thus has a strong connotation of daily habits. In the following example, the habits are limited to meals, but other embodiments may be extended to include other events captured, for example, physical activity, intake of oral drugs, and other daily activities.
In the following example, one daily lifestyle pattern (habit) examined consisted of an overnight period and a day period consisting of multiple meals and snacks for a patient. The daily lifestyle pattern repeats itself over a period of months whereby the timing of meals varied randomly around expected meal times. The size and composition of the meal was similarly modeled by assigning the parameters of the gamma function values generated from statistical distribution. In general, it was assumed that by considering more or less a 3 month time frame, the persistent average behavior would be observed in HbAlC value even though from meal to meal there could be potentially large variability. Thus, in the given example, a modal day consisted of an overnight period and 3 meal types: breakfast, lunch and supper as is shown in
Further complexity in glucose excursion characteristics is addressed by modeling a range of meal content characterized generally by amount and speed. Meal content is given by meal composition and amount of meal which relates to speed and duration of glucose absorption. It is observed that individuals have repeatability in their meal selection, which from a glycemic excursion perspective can be classified by its speed and amount. In one embodiment, as shown in
Mathematically, then, statistical properties were assigned to each of the categories. As per the above description, meals were further classified by 3 broad categories of meal speed: fast, regular and slow, and meal amount is similarly classified into 3 categories as: small, medium and large. Other terms used were less than normal, normal and more than normal. While specific categories are presented herein, it should be appreciated that other additional or alternative categories may be used to provide a generalization of a different problem. The latter part described better the majority of the cases. For purposes of simplification of the simulation, physical activity was assumed to be fixed. Thus, grouping glucose sections by event type, for instance meals, and further sub-grouping them by characterizing the meal size and speed, allows one to characterize and quantify them, which is illustrated by
It is to be appreciated that in reality the glucose profiles of a PwD are richer in their response and are potentially harder to characterize. The richness is associated with multiple physiological factors influencing the overall glucose state. However, assuming that the meal related glucose push is dominant, the PwD is working towards regulating glucose to a target value by means of medications, diet control and exercise and their combinations Inherently there is an objective of achieving euglycemia at all times. By averaging many such responses, however, the glucose effect on glycemia can be estimated based on the relations derived using the gamma function. The arbitrary meal response curves shown in
Mean Value for Gamma Function
It is to be appreciated that the gamma function ƒ(t) is neither symmetric nor periodic. We define parameter T which is the time duration between consecutive meal. Considering the exponential properties, the decay of a pure exponential curve to 99% of its starting value is equal to 4 times the time constant. Therefore, the gamma function according to Equation 8 is basically a 2nd order differential equation with repeated poles. The time constant for the gamma function is thus 1/β, and the mean value can be determined by considering the waning factor n, which is defined as:
where n=3, 4.
The mean bG value for
(from Equation 8) is now derived. If we consider,
and integrate g(t) by parts, the following Equations (9)-(13) are provided:
where T=nβ. Note, however, the mean value is a function of β, but if T is expressed in terms of β, then β falls out, which further simplifies to
and finally,
In this manner, when the waning factor n equals 3, the mean value
Thus, if a peak bG value is measured, then the true mean value
So, for a given gamma function one could simply state that for the mean value,
HbAlC=K
From the above derivation, it is also clear that both meal size and meal duration (associated with speed) influences the degree of glycation. Next, a discussion of the process used to characterize a PwD's lifestyle is provided. Equation (15) is central to derivations presented in latter paragraphs. It is to be appreciated that the relationship between estimated mean bG and the parameters are dependent on context and sampling assumptions. Accordingly the parameters in equation (15) (K, constant) can have potentially different values in other situations. Another example to which the above approach can be applied is the estimation of fructosamine based of the biological measurement blood glucose.
Lifestyle (Meal Only)
As discussed above, the day, as per the lifestyle, is divided into appropriate sections where the bG traces for each day are sectioned and each like sections grouped (e.g.,
For a meal related section, the gamma function is described by the peak value A with respect to the basal or fasting bG and time to peak, β for bG. The parameters are summarized in Table 2.
In terms of analysis then, the meals are then characterized to cover a time period, such as for example, a 2-4 month period between HCP visitations, in the following manner. For breakfast type meals, the total number of breakfasts is represented by the term mBF, and the ratio of the number of small breakfast meals, medium breakfast meals, large breakfast meals and no breakfast meals are represented by αSMALLBF, αMEDBF, αLARGEBF and αΦBF, respectively. Total breakfasts mBF can then be defined according to Equation (17) as:
αSMALLBFmBF+αMEDBFmBF+αLARGEBF+αΦBFmBF=mBF (17).
Similarly, meal speeds for fast, regular, and slow meals are represented by the terms: λFASTBF, λREGBF, and λSLOWBF, respectively. Therefore, total breakfasts mBF can also be defined according to Equation (18) as:
λFASTBFmBF+λREGBFmBF+λSLOWmBF+αΦBFmBF=mBF (18).
It is assumed that on average for each meal amount category there is a breakdown for meal speed with the same ratios. In other words, for example, small breakfast meals mSMALLBF can be defined according to Equation (19) as:
λFASTBFαSMALLBFmBF+λREGBFαSMALLBFmBF+λSLOWBFαSMALLBFmBF=mBF (19).
Equation (16) the bGi terms on the right hand side are grouped as per
where TON covers time duration for overnight part as illustrated in
What can constitute fasting bG values requires more specifics. For example, pre-meal bG measurements could be grouped as fasting bG values under certain conditions, overnight bG measurements, early morning bG measurements. Average of such measurements approximately represents the mean bG for the overnight period. Then the component required for
ON=
Next, given the first predictor term
FASTING (22),
where A is the peak disturbance with respect to
Determination of “A” for the case when various glucose excursions due to different meals is now explained. As explained earlier and summarized by
which covers all small breakfast meals. The term TBF is the time duration between start of breakfast to start of lunch. Similar equations can be written for medium and large breakfasts, which when combined results in Equation (24), which is defined as:
The term AφBF is of course zero. The number of meals considered in the equation covers a time window of interest. Such a window may range from 2 months to 4 months, or may be as few as 7 day to 30 days, if an estimated prediction is desired as explained in a later section.
If Term-1 is considered, then the term KiBF, meal speed, can now be factored out as a constant. The result is shown by Equation (25).
It is to be appreciated that the PwD categorizes and provides the size of meals as small, medium large meal amounts, as well as the meal speed. For instance, all small meals can be simply represented by an average value ĀSMALLBF. Thus, for example, all fast small meals may be represented by Equation (26) as:
λFASTBFαSMALLBFmBFĀSMALLBF (26).
Collecting all the terms together, Equation (25) then can be rewritten as Equation (27) as:
Now considering all the meal types we get the following relation shown by Equation (28) is follows:
On further simplification, Equation (28) becomes:
The last group of terms on the right-hand side are the weighted amplitude terms which is the average amplitude. Thus, equation (28) can be further rewritten as:
The mean values
HbAlCBreakfast+HbAlCLunch+HbAlCSupper+HbAlCFasting=HbAlC (30).
While the above equation breaks down the estimated HbAlC values into time-based components (i.e., breakfast, lunch, supper and fasting) which combine into a complete day, the estimated HbAlC values may alternatively or additionally be broken into other variable-based components (e.g., event-based components or context-based components) as will become appreciated herein. For example, Equation (29) can be rewritten as Equations (29A) and (29B):
Likewise, similar to Equations (29A) and (29B), Equation (30) can be rewritten as Equation (30A) in terms of HbAlC:
ΔHbAlCBreakfast+ΔHbAlCLunch+ΔHbAlCSupper+ΔHbAlCFasting=HbAlC (30A).
Another fundamental aspect to the algorithm is temporal weighting schema. The affect of past breakfast on current HbAlC is not equally weighted. Such weight schema is theoretically derived based on the assumption of lifespan of the erythrocytes. As discussed above, this approach can similarly extended to lipid profile, insulin profile, fructosamine and other metabolites
Temporal Weighting
Temporal weighting of bG values becomes relevant when the prediction model is derived between SMBG values and HbAlC. As mentioned previously above, each cohort has a finite life span of approximately 120 days. Thus, for this example, a lifespan of 120 days is considered. The aged cells are constantly being replaced by young erythrocytes. So at any given time each of the cohort's age will range from 0 to 119 days. Each cohort thus is exposed to a subset of corresponding bG data. Considering the glycated hemoglobin at current time and all the bG values over the last 120 days, then the bG value that is 120 days old influences only 1 out of 120 cohorts and none of the other cohorts with ages less than 120 days. On the other hand, the current bG value affects all ages of the surviving cohorts i.e. the last 120 cohorts. In context of constant bG for ith day and considering the physiological aspect, this suggests that an appropriate weighted mean bG value can help improve HbAlC prediction.
In a simulation exercise, a lifespan L was set to 120 days and a number of cohorts N was made equal to 120 cohorts, where cohort # 120 is the oldest cohort, and cohort #1 is the newest. For a cohort aged L days (considering the oldest cohort), then the impact of bG, on HbAlC can be approximated according to Equation (31) as:
where bGi is glucose value on ith day, where index i is 1, 2, 3, . . . L, from the latest glucose measurement to oldest glucose measurement. Similarly, for a cohort aged L-1 day, mean bG can be defined according to Equation (32) as:
And so on. Collecting weights for same bG, the weights may be defined according to Equation (33) as:
It is to be appreciated that the above weighting scheme corresponds to a harmonic series. As such, the weights will be referred to herein as harmonic weighting.
Additional results showed that harmonic temporal weighting is a relevant scheme in the determination of the HbAlC estimate based on SMBG measurements, and that the period over which SMBG data contributes significantly to estimating HbAlC is about 60 days (considering in this case life of erythrocytes as 120 days. Similar reasoning can be used when considering erythrocytes for other ages). Analysis results also supports that collecting bG values collected over a visitation period of about approximately 60 days provides the best estimate on HbAlC. In one embodiment, the collecting of both bG measurements and associated context of the bG measurement at daily times specified by the structured sampling schema is over a period of about 2 to about 4 months. In another embodiment, a small time window such as ranging from 1 week to 4 weeks can be used as a representative of glucose behavior covering a 3 to 4-month period. This allows the HCP and patient to revise the current therapy or behavior to try achieving prescribed targeted goals. The resulting predicted HbAlC then represents a future HbAlC which provides the patient and/or HCP the future glycemic level is assuming the current glucose behavior is maintained. The principles behind the process used to derive a correlation coefficient for meal sections according to the present disclosure is now discussed hereafter.
Correlation Coefficient for Meal Sections
Glucose data collected during two independent clinical studies in 2003 and 2006 were used to determine a correlation coefficient for meal sections from which to devise a sampling schema for use with the HbAlC prediction model. The clinical trials studied the post prandial glucose control for meals with different meal composition. The key aspects of each of the two studies are summarized below.
Meal Study 2003
1. Study was conducted during the year 2006-2007
2. Demographics of the subjects:
3. Each visit (block) is 4 days long:
4. Number of study blocks is 4. A study block is the re-visitation of the subject for performing the meal study with a different test meal and/or insulin therapy algorithm.
5. Meal sections were extracted from Meal Study 2006. The sections were of duration:
The above test meal labels A-F describe the meal speed. Meals labeled A and B are fast meals, meals labeled C and D are regular, and meals labeled E and F are slowly absorbing meals. The meals were classified by a professional dietician. The meal study data set provided discrete frequently measured bG data, where the sampling rates for the time window covering the test meals were 10 minutes. Sampling rates at other times range from 1 minute to as rarely as hourly measurement, such as overnight. Also available in the bG data is specific insulin, ingested mixed meal information and interventions. It is to be appreciated that the clinical bG data set did not include HbAlC values. HbAlC values were then generated artificially by using the Mortensen model (Equations (1)-(3)) with the bG data.
It is clear from earlier analysis that there exists a linear relationship between true mean bG and HbAlC. It is then clear that one could simply focus on the question of determining either true mean bG or HbAlC. Given the continuous and/or frequent bG measurements in the bG data, the bG curves were then sectioned into relevant groups and correlation between various parameters such as minimum, maximum, glucose value at specified time and so forth were correlated to true mean bG as well as HbAlC.
In regards to HbAlC, this value was determined by inputting the glucose curve to the Mortensen model Equations (1)-(3). In this regard then, the meal data was first divided into meal sections. Each of the meal sections was curve fitted and then the resulting signal was repeated to create as input a bG input signal of duration 150 days. The resulting profile was then passed through the Mortensen model. (Equations (1)-(3)) to generate the HbAlC values. In this way, HbAlC for each of the meal sections was generated.
Next, several predictors were examined to correlate with HbAlC. The most meaningful single-point predictor discovered by the inventors was a bG measurement taken at a particular post-prandial time point. For this predictor, a Pearson correlation coefficient was used as a function of bG(t) which is shown plotted in
Although the correlation coefficients may differ for different clinical studies, in general the trends are expected to be similar. As shown by
The variations in meal behavior are due to main factors such as physiology, meal content variation, inaccuracies in physiological parameter estimates, basal setting. The correlation coefficients indicate that meals correlate to HbAlC very strongly when bG measurements are conducted postprandially in time range around 3 hours. It is also clear from simulation that the transient bG has comparatively less impact than the steady state behavior of the meal that is the relatively slow and steady push. The variability in the early transients is clearly indicative of lack of specific knowledge of day to day physiological variability and imprecise knowledge of meal but the general control strategy on the latter post prandial state is important in achieving low HbAlC. While this method was described with respect to HbAlC, the general method also provides in detail how one can extend the approach to cover other biological values (e.g., metabolites and biomarkers) such as fructosamine (a biomarker for glycemia over past 3˜4 weeks, where fructosamine is glycated albumin). Furthermore, solutions for problems under different assumptions can be redone to derive estimation relations and/or the parameters.
The following section hereafter focuses on deriving an optimal sampling schema for determination of true mean bG and HbAlC. Sampling schema is determined by using the equations developed in earlier sections, such as lifestyle related time weighting addressing a modal day, and glucose weighting addressing the data covering a visitation period (i.e., period between visitations).
Structured Sampling Schema
Using clinical data from Meal study 2003, bG profiles are generated by combining various meal sections by randomly selecting bG profile sections from different meal bins and concatenating the sections. The various meal bins are listed in Table 4.
The bins in Table 4 represent meal sections and are first of all grouped by collecting the sections obtained from breakfast, lunch and supper and overnight time periods. The meal sections were further ranked and sorted in ascending order in terms of corresponding HbAlC values from simulation. The breakfast meal pool was then divided into 3 equal groups by selecting the first one-third breakfast meals and labeled as low—HbAlC, then the second one third of breakfast meals labeled as Medium—HbAlC and the remaining breakfast meals as High—HbAlC. In a similar fashion, lunch and supper are also binned. In all, 9 meal bins were created. To create lifestyle based bG sequence, lifestyle is described as the modal day consisting of breakfast starting at 8 am with one of the HbAlC group (Low, Medium or High); lunch at noon with one of the HbAlC group (Low, Medium or High) and supper at 6 pm with one of the HbAlC group (Low, Medium or High). In this manner, 174 bG sequences were generated covering various combinations.
As mentioned in the previous section, if bG measurements are conducted postprandially around the time interval when the correlation coefficient is high (e.g., t>150 minutes,
As per lifestyle (primarily done for meal in the illustrated embodiment) the sampling schema was setup according to Table 5 as follows:
Linear regression was then carried out to predict HbAlC from SMBG measurements, whereby SMBG values were processed by various lifestyle weighting and averaging strategies.
In
of 0.55 was obtained as shown by
Regression Model for Estimating HbAlC
As per the sampling schema, the sampled bG data were then regressed and plotted, which are shown by
The Estimated HbAlc in Table 7 comprises virtual HbAlc determined based of the Mortenson model in which patient specific relationships and estimates can be addressed by calibration as discussed later herein. Alternatively, the weighted component based bG estimates presented herein can be used in HbAlc estimate relations such as the Nathan's relation (Nathan, D. M.; Schoenfeld, D.; Kuenen, J.; Heine, R., J.; Borg, R.; Zheng, H.; “Translating the AlC Assay into estimated average glucose values,” Diabetes Care, Vol 31, Nos 8, August 2008, pp. 1-6.), which estimate patient HbAlc, or Abensour's relation (U.S. Patent Publication No. US 2007/0010950 A1).
Validation
To validate the results obtained above in Table 7, which were derived using the meal sections extracted from the Meal Study 2003, the Meal Study 2006 was then used. Similar to Meal Study 2003, all the meal sections from Meal Study 2006 were extracted. Overall, 286 meal sections were obtained from the 2006 study. All the meal sections were then fitted by a polynomial curve, and ordered in an ascending order by their individual HbAlC values (obtained by using the Mortensen Model). The meal sections were then binned into groupings done in the manner explained for Meal Study 2003. Using the meal sections, a bG sequence covering a duration of 300 days was generated as per the previous lifestyle used in the 2003 meal study. The simulation duration was also set to 300 days. The bG profiles and HbAlC were then stored for sampling and HbAlC prediction. In all 108 simulations were generated.
The bG values were then sampled as per the Sampling Schema listed in Table 7. Using the sampled bG values for each of the 108 simulation cases, mean bG was determined. The relation between the mean bG and HbAlC, as determined by simulation, is plotted in
From the above results, if a slightly lower R2 of 0.85 is used, then the number of measurements/event can be reduced to 45 over 80 days. With 3 meal events per day plus a nighttime measurement, then the number of measurements equal 180 measurements. This implies approximately 2.25 measurements/day are needed as per sampling schema described above to achieve an estimated HbAlC value that has a precision within 3% CV.
It should be appreciated that the stated derived results are one of many ways of using the approach. The estimated value could alternatively or additionally be plugged into pre-defined models, for instance Nathan's relation as described in Nathan, D. M.; Schoenfeld, D.; Kuenen, J.; Heine, R., J.; Borg, R.; Zheng, H.; “Translating the AlC Assay into estimated average glucose values,” Diabetes Care, Vol 31, Nos 8, August 2008, pp. 1-6., where the mean bG value based of the analysis presented herein is used in the other model. Nathan's relation, for example, can provide an estimate through a relationship that that is accepted in the medical community while using a better estimate of the mean bG (which should ideally improve the estimate as well as provide better acceptance of the result by the medical community).
Implementation Examples
The above described sampling schema and prediction algorithm for providing both an estimated true mean blood glucose value and an estimated glycated hemoglobin (HbAlC) value from structured spot measurements of blood glucose may be implemented using hardware, software or a combination thereof. For example, the above described sampling schema and prediction algorithm may be implemented in one or more microprocessor based systems, such as a portable computer or other processing systems, such as personal digital assistants (PDAs), or directly in self-monitoring glucose devices or meters (bG meters) equipped with adequate memory and processing capabilities to process a chronological sequence of measurements of a time dependent parameter measured in or on the human body, namely of the glucose level (e.g. the glucose (bG) level). In some embodiments, remote servers may process the measurements to determine the estimated and/or predicted values and provide these determined values to a personal glucose meter, PDA or the like. In these embodiments, the personal glucose meter, PDA or the like may thereby be operable with a relatively smaller processor that could not as quickly determine the values compared to an application running on the remote server.
In an example embodiment, the sampling schema and prediction algorithm are implemented in software running on a self-monitoring blood glucose (bG) meter 100 as illustrated in
In the illustrated embodiment, the bG meter 100 includes one or more microprocessors, such as processor 102, which is connected to a communication bus 104, which may include data, memory, and/or address buses. The bG meter 100 may include a display interface 106 providing graphics, text, and other data from the bus 104 (or from a frame buffer not shown) for display on a display 108. The display interface 106 may be a display driver of an integrated graphics solution that utilizes a portion of main memory 110 of the bG meter 100, such as random access memory (RAM) and processing from the processor 102 or may be a dedicated graphics card. In another embodiment, the display interface 106 and display 108 additionally provide a touch screen interface for providing data to the bG meter 100 in a well-known manner.
Main memory 110 in one embodiment is random access memory (RAM), and in other embodiments may include other memory such as a ROM, PROM, EPROM or EEPROM, and combinations thereof. In one embodiment, the bG meter 100 includes secondary memory 112 which may include, for example, a hard disk drive 114 and/or a removable storage drive 116, representing a floppy disk drive, a magnetic tape drive, an optical disk drive, a flash memory, etc. The removable storage drive 116 reads from and/or writes to a removable storage unit 118 in a well-known manner. Removable storage unit 118, represents a floppy disk, magnetic tape, optical disk, flash drive, etc. which is read by and written to by the removable storage drive 116. As will be appreciated, the removable storage unit 118 includes a computer usable storage medium having stored therein computer software and/or data.
In alternative embodiments, secondary memory 112 may include other means for allowing computer programs or other instructions to be loaded into the bG meter 100. Such means may include, for example, a removable storage unit 120 and an interface 122. Examples of such removable storage units/interfaces include a program cartridge and cartridge interface, a removable memory chip (such as a ROM, PROM, EPROM or EEPROM) and associated socket, and other removable storage units 120 and interfaces 122 which allow software and data to be transferred from the removable storage unit 120 to the bG meter 100.
The bG meter 100 in one embodiment includes a communications interface 124. The communications interface 124 allows software and data to be transferred between the bG meter 100 and an external device(s) 132. Examples of communications interface 124 may include one or more of a modem, a network interface (such as an Ethernet card), a communications port (e.g., USB, firewire, serial or parallel, etc.), a PCMCIA slot and card, a wireless transceiver, and combinations thereof. In one embodiment, the external device 132 is a personal computer (PC), and in another embodiment is a personal digital assistance (PDA). In still another embodiment, the external device 132 is a docking station wherein the communication interface 124 is a docket station interface. In such an embodiment, the docking station may be provided and/or connect to one or more of a modem, a network interface (such as an Ethernet card), a communications port (e.g., USB, firewire, serial or parallel, etc.), a PCMCIA slot and card, a wireless transceiver, and combinations thereof. Software and data transferred via communications interface 124 are in the form of wired or wireless signals 128 which may be electronic, electromagnetic, optical, or other signals capable of being sent and received by communications interface 124. For example, as is known, signals 128 may be sent between communication interface 124 and the external device(s) 132 using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, an infrared link, other communications channels, and combinations thereof. In some embodiments, the bG meter 100 comprises remote server connection 125 to send data to an external server such that the external server processes the requested information and sends the results back to the bG meter 100 as discussed herein.
In one embodiment, the external device 132 is used for establishing a communication link 130 between the bG meter 100 and still further electronic devices such as a remote Personal Computer (PC) of the patient, and/or a health care provider (HCP) computer 134, or an external server 135 directly or indirectly, such as through a communication network 136, such as the Internet and/or other communication networks. The communication interface 124 and/or external device(s) 132 may also be used to communicate with further data gathering and/or storage devices such as insulin delivering devices, cellular phones, personal digital assistants (PDA), etc. Specific techniques for connecting electronic devices through wired and/or wireless connections (e.g. USB and Bluetooth, respectively) are well known in the art.
In the illustrative embodiment, the bG meter 100 provides a strip reader 138 for receiving a glucose test strip 140. The test strip 140 is for receiving a sample from a patient 142, which is read by the strip reader 138. Data, representing the information provided by the test strip, is provided by the strip reader 138 to the processor 102 which executes a computer program, e.g., provided in main memory 110, to perform various calculations as discussed in great detail below on the data. The results of the processor 102 from using the data is displayed on the display 108 and/or recorded in secondary memory 112 by the processor 102, which is herein referred to as self-monitored glucose (bG) data. The bG data may include, but not limited thereto, the glucose values of the patient 142, the insulin dose values, the insulin types, and the parameter values used by processor 102 to calculate future glucose values, supplemental insulin doses, and carbohydrate supplements. Each glucose value and insulin dose value is stored in memory 112 by the processor 102 with a corresponding date and time. An included clock 144 of the bG meter 100 supplies the current date and time to processor 102. The bG meter 100 further provides a user input device(s) 146 such as keys, touchpad, touch screen, etc. for data entry, program control, information requests, and the likes. A speaker 148 is also connected to processor 102, and operates under the control of processor 102 to emit audible and/or visual alerts/reminders to the patient of daily times for bG measurements and events, such as for example, to take a meal, of possible future hypoglycemia, and the likes. A suitable power supply 150 is also provided to power the bG meter 100 as is well known to make the meter portable.
The terms “computer program medium” and “computer usable medium” are used to generally refer to media such as removable storage drive 116, a hard disk installed in hard disk drive 114, signals 128, etc. These computer program products are means for providing software to bG meter 100. Embodiments of this disclosure include such computer program products.
Computer programs (also called computer control logic) are stored in main memory 110 and/or secondary memory 112. Computer programs may also be received via the communications interface 124. Such computer programs, when executed, enable the bG meter 100 to perform the features of the present disclosure as discussed herein. In particular, the computer programs, when executed, enable processor 102 to perform the functions of the present disclosure. Accordingly, such computer programs represent controllers of bG meter 100. Alternatively or additionally, the computer programs may be stored and/or run on remote servers with input and output data communicated via wired or wireless communication networks.
In an embodiment where the disclosure is implemented using software, the software may be stored in a computer program product and loaded into bG meter 100 using removable storage drive 116, removable storage unit 120, hard disk drive 114, or communications interface 124. The control logic (software), when executed by the processor 102, causes the processor 102 to perform the functions of the disclosure as described herein.
In another embodiment, the disclosure is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine to perform the functions described herein will be apparent to persons skilled in the relevant art(s).
In yet another embodiment, the disclosure is implemented using a combination of both hardware and software.
In an example software embodiment of the disclosure, the methods described hereafter are implemented in the C++ programming language, but could be implemented in other programs such as, but not limited to, Visual Basic, C, C#, Java or other programs available to those skilled in the art (or alternatively using script language or other proprietary interpretable language used in conjunction with an interpreter).
As mentioned above, the bG meter 100 is used by the patient 142 for recording, inter alia, insulin dosage readings and spot measured glucose levels. Such bG data obtained by the bG meter 100 in one embodiment is transferable via the communication interface 124 to another electronic device, such the external device 132 (PC, PDA, or cellular telephone), or via the network 136 to the remote PC and/or HCP computer 134. Examples of such bG meters include but are not limited to, the Accu-Chek Active meter and the Accu-Chek Aviva system both by Roche Diagnostics, Inc., which are compatible with the Accu-Chek 360° Diabetes management software to download test results to a personal computer or the Accu-Chek Pocket Compass Software for downloading and communication with a PDA. The program may run on a remote server and generate result. The result is available by one or more communication mode(s) stated earlier. The program device is also functional with 3rd party devices which communicate with 132, 134. Examples of communicating and exchanging information between various devices is further provided in more detail in commonly owned U.S. application Ser. No. 12/119,143, which is herein incorporated by reference.
Accordingly, it is to be appreciated that the bG meter 100 includes the software and hardware necessary to process, analyze and interpret the self-recorded diabetes patient (i.e., bG) data in accordance with predefined flow sequences (as described below in detail) and generate an appropriate data interpretation output. In one embodiment, the results of the data analysis and interpretation performed upon the stored patient data by the bG meter 100 are displayed in the form of a report, trend-monitoring graphs, and charts to help patients manage their physiological condition and support patient-doctor communications. In other embodiments, the bG data from the bG meter 100 may be used to generated reports (hardcopy or electronic) via the external device 132 and/or personal computer (PC) and/or HCP computer 134.
The bG meter 100 further provides the user and/or his or her HCP with the possibilities of a) editing data descriptions, e.g., the title and description of a record; b) saving records at a specified location, in particular in user-definable directories as described above; c) recalling records for display; d) searching records according to different criteria (date, time, title, description, context information, data indexing, time range, etc.); e) sorting records according to different criteria (values of the bG level, date, time, duration, title, description etc.); f) deleting records; g) exporting records; and/or h) performing data comparisons, modifying records, excluding records as is well known. Alternatively or additionally, these functions may be performed on an external device 132, a HCP computer 134 or an external server 135.
As used herein, lifestyle is described in general as a pattern in an individual's habits such as meals, exercise, and work schedule. The individual additionally may be on medications such as insulin therapy or orals that they are required to take in a periodic fashion. Influence of such action on glucose is implicitly considered by the present disclosure.
Estimating True Mean bG and HbAlC
With reference made also to
In one embodiment and generally, as mentioned above the bG meter 100 stores the results of the glucose (bG) measurements in its memory 112 together with a date-time stamp and associated event information (i.e., information regarding the context in which the measurement was obtained) to create a chronological sequence or set G of bG spot measurements, such as measurements bG1k, bG2k, bG3k, bG4k, and bG5k, where k is the day. The measurement set G is sorted by increasing time and may span several days. In one embodiment, the date stored in memory with the measurement consists of some representation of day/month/year, and the time consists of some representation of the time of day (e.g. hh:mm:ss). In other embodiments, other date and time recording methods may be used, such as for example, using a Julian calendar and an alternative count interval for time.
Along with each bG measurement, the patient is requested to input event information concerning the patient's lifestyle. In one embodiment, the meter 100 has enough memory to maintain bG data for at least 40-80 days with the associated event information concerning the patient's lifestyle. In another embodiment, the meter 100 has enough memory to maintain bG data for at least weeks with the associated event information concerning the patient's lifestyle. In one embodiment, lifestyle is classified by information concerning the following events: breakfast, lunch, supper, snack, exercise, physical activity, stress, alternate state, medication and optionally any other relevant event that is custom set into the meter. As with the bG measurements, such events are time stamped and associated with a description of event such as, for example, magnitude, intensity, duration, etc. Other such event characterizations are described more fully in commonly owned U.S. application Ser. Nos. 11/297,733 and 12/119,201, which are herein incorporated by reference. Manual input of the event description by the patient in one embodiment is driven by a questionnaire presented to the patient on the meter 100. In one embodiment, the questionnaire is provided by an HCP or designed to be set up by the patient according to provided instructions contained on the meter 100. In another embodiment, the meter 100 is provided with scheduled reminders (e.g., alarms for taking medication) which are provided at particular times in order to record such event information, e.g., via the questionnaire, within the compliance window according to the sampling schema of Table 7. An event scheduler 300 (
In step 310, if the processor 102 fails to detect an entry via the user interface 146, after expiration of another count down timer e.g., 300 seconds (or in other embodiments the timer can range from few minutes to half an hour, and preferably 5 to 10 minutes), then the processor 102 in step 312 resets the alarm for a future time T which is still within the compliancy window of Table 7 for collecting the event information. If an entry was made and detected in step 310, such as placed into temporary memory, such as main memory 110 via the processor accepting input from the user interface 146, then in step 314 the processor 102 stores the entry in secondary memory 112 in a manner discussed previously above.
If in step 304, the structured sampling schema in memory 110 or 112 does not have an alarm for the processor 102, then the processor 102 in step 316 will check for any triggered events, e.g. auto initiated via another running process of the meter 100 or patient initiated via the user interface 146. If none is detected, then the processor 102 loops back to step 302 and the processes of the event scheduler 300 repeat. If a trigger event is detected, then in step 318 the processor 102 checks whether an entry is needed for the triggered event, such as by doing a lookup in the profile file. If an entry is needed then the process goes to step 308, and if not, the process loops back to step 302 and repeats. It is to be appreciated that the scheduler 300 when executed by the processor 102 of the meter 100 indicates and consequently records a SMBG measurement and the associated event information (e.g., via running the questionnaire) in compliance with the measurement schema provided according to Table 7. Any unforeseen event is also enterable into memory 112 of the meter 100 at any time by the patient 142 via manually running the questionnaire on the meter 100 e.g. a triggered event in step 312. For example, non-prompted entry may occur in step 309 whenever the user decides to submit an entry that was not directly prompted by an alarm.
Returning to
In another embodiment, an associated penalty in the precision of the estimated value may be flagged in step 208 such that a bias in time of bG measurement warning message is indicated with the provided results in step 214. After step 204, and optional step 206, if the bG data is compliant then the estimation processes Steps 210 and 212 are evoked. In step 210, the estimation process as mentioned previously above in reference to
In another embodiment, an enhancement to the above model in Table 7 is to obtain an HbAlC value from an HbAlC assay, which can then be used as the patient specific intercept value c, instead of the given value of 0.5702. Such an embodiment is considered an estimated HbAlC with a one-point calibration. In still a further embodiment, another enhancement to the above model in Table 7, would be to obtain HbAlC using an HbAlC assay at two different points in time. These HbAlC values can then be used to determine a patient specific intercept value c and slope m. In such an embodiment, the two HbAlC values from the HbAlC assay not vary by more than +0.5% HbAlC to provide a good reliable slope m, assuming the assays are high quality (i.e., CV<2%). In another embodiment, the process 200 may then request whether the protocol file used for collection by the scheduler 300 needs updating in step 216. If so the protocol file is updated in step 218 via e.g., accepting user input via the user interface 146, e.g., from the processor 102 re-running a setup questionnaire on the display 108, receiving protocol changes from the HCP computer 134 when connected to the external device 132 such as, e.g., provided as a docking station, and combinations thereof. Afterwards, the process loops to step 202 and repeats. In another embodiment, Nathan's relation may be used for estimating HbAlc, wherein the estimated mean bG as described herein is used with Nathan's relation.
In still other embodiments, collection step 202, along with the scheduler 300, is solely performed on the meter 100, wherein process steps 204-218 are performed on the HCP computer 134. In such an embodiment, the HCP computer 134 may also provide additional capabilities, such as using the collected bG data with other models to perform comparisons with the model results according to the present disclosure. For example, in one embodiment, the HCP could run the collected bG data through a HbAlC population based model derived from continuously monitored glucose data.
As previously mentioned above, in one embodiment the alerting and collecting by the processing of bG measurements and associated context of the bG measurement at the daily times and the events can be specified by the structured sampling schema that is stored in memory. In one embodiment, the daily times specified by the structured sampling schema are post-prandial times. In another embodiment, the daily times specified by the structured sampling schema are three post-prandial times and another time. In still another embodiment, the events specified by the structured sampling schema is a specific time with respect to start of a meal. In yet another embodiment, one of the events specified by the structured sampling schema is an aspect of glucose behavior related to the estimated true mean bG value, which in one embodiment, the aspect is a bG mean to peak value. In still another embodiment, the daily times specified by the structured sampling schema are at about 140 to about 240 minutes after a meal time. In a further embodiment, the daily times and the events specified by a structured sampling schema are tailored to a daily lifestyle pattern of the patient. In yet another embodiment, the daily times specified by the structured sampling schema range from about 140 to about 240 minutes after a meal time in accordance with the daily lifestyle pattern of the patient. In even yet another embodiment, such as that which can be used with the Type 2 patient population, a seven point measurement per day may be performed for three days. Such sets of measurements can be taken at regular time periods such as between every two or six weeks (such as every four weeks).
In another embodiment, the processor 102 is further programmed to weigh a bG measurement if collection of the bG measurement was within a time interval from the daily times specified by the structured sampling schema, whereby in one embodiment the time interval is at most ±50 minutes. It is to be appreciated that the time interval also captures the information of whether the measurements, which are typically performed by the patient at random times around a recommended time, are falling within or outside the time interval. Such information may be used by the processor 102 to evaluate whether the patient's lifestyle has been captured appropriately as reflected in the structured sampling schema and/or whether the patient requires training such as, for example, if a threshold number of measurement within the time interval is not meet over a period of time. For example, in one embodiment, if such a threshold number of measurements is not achieved, the processor 102 provides a message on the display 108 indicating a collection problem and can provide a recommendation, such as ways to improve collection compliancy. In still another embodiment, the processor 102 is further programmed to determine the estimated true mean bG value and the estimated HbAlC value from the weighted measurements of the collected bG measurements if a predetermined amount of the bG measurements per each of the daily times and the events has been collected. In one preferred embodiment, the predetermined amount is at least 80 days, and in another embodiment at least 60 days. In still another embodiment, the processor is further programmed to determine the estimated true mean bG value and the estimated HbAlC value from the weighted measurements of the collected bG measurements if the predetermined amount of the bG measurements per each of the daily times and the events has been collected, and if the collection of the bG measurements occurred within a predetermined period of at least 2 weeks.
Displaying Grouped Estimated Biological Values or Grouped Predicted Biological Values
As discussed above, while specific examples are presented herein of weighting bG measurements to determine mean bG values to then determine HbAlC values, other biological measurements can similarly be incorporated to estimate a patient's estimated (current) or predicted (future) condition based on the weighting of current and previous biological measurements. As used herein, “biological measurements” includes any type of measurement that provides insight into the patient's health with respect to diabetes. For example, biological measurements include, but are not limited to, bG measurements, HbAlC measurements, fructosamine, lipids, triglycerides, insulin concentration, etc. Accordingly, one or more of these biological measurements can be measured, weighted and used to determine an estimated or predicted biological measurement (either the same type of biological measurement that was obtained, or a different type of biological measurement, but one that can be determined from the type of biological measurement that was obtained). Moreover, the biological measurements can be grouped by one or more variables, such that the estimated or predicted biological values can be determined for each group in order to compare the effect each variable has on the patient's health.
For example, referring now to
The informational delivery method 400 begins in step 410 by collecting biological measurements (e.g., bG measurements), and potentially other information such as associated context, in any manner discussed above. For example, the collection of bG measurements in step 410 can occur manually (e.g., using testing strips) or automatically (e.g., using a continuous bG meter) or simply comprise a transfer of previously obtained biological measurements, and potentially associated context, from a database (e.g., downloading the information from a bG meter to a physician's computer). In some embodiments, the collection of biological measurements may further be performed continuously or in accordance with a structured sampling schema wherein bG measurements were obtained at regulated times of the day such as within a prescribed period of time before and/or after meals, activities or other events. Such embodiments may help ensure the estimated or predicted biological values are obtained from a sufficient sample to reduce the effect of inconsistencies such as those that may occur when obtaining biological measurements at inconsistent times following meals or obtaining biological measurements arbitrarily throughout the day. In some embodiments, the structured sampling schema may comprise collecting measurements around a structured medication schema (i.e., collecting measurements as the patient takes a prescribed medication according to a structured medication schema). Furthermore, biological measurements may be collected in step 410 by a patient (e.g., self-testing), by a physician (e.g., laboratory testing) or by any third party.
Furthermore, the number of samples and the length of time in which the samples are obtained may be adjusted based on the patient. For example, in some embodiments, such as for patients having Type-2 diabetes, bG measurements may be obtained for 7-14 days before determining an estimated HbAlC value. In some embodiments, such as for patients having Type-1 diabetes, bG measurements may be obtained for 40 to 80 days. Other time period and measurement frequencies may alternatively be utilized to address the specific patients' lifestyle and pharmacological aspects or the specific problem being investigated.
In some embodiments, after or while the bG measurements (or other biological measurements) are collected in step 410, the data is analyzed for adherence in step 411 and an interpretation of the adherence is optionally provided. Specifically, the data collected in step 410 can be analyzed to determine whether the measurements were collected in a manner that complied with the testing protocol (e.g., whether measurements were taken at the proper times around an event within specified duration, whether measurements were taken for the proper number of days, whether the patient underwent any additional lifestyle changes that would influence the measurements, etc.). If the collected measurements were determined that they adhered to the testing protocol in step 412, then the bG measurements are evaluated in step 420.
If the measurements collected in step 410 are determined to not adhere in step 412 (such as missed measurements or measurements collected at incorrect times), then lack of adherence can be flagged in step 413. Specifically, the reason for lack of adherence can be presented to the patient, health care provider or any other relevant party so that any estimated values, if still determined, are reviewed with the lack of adherence in mind. In some exemplary embodiments, the bias from the measurements is provided in step 414 to the patient, health care provider, or other relevant party so that future measurements may be obtained to offset the bias from the measurements already collected. For example, if the patient routinely obtains measurements 30 minutes after they're supposed to, future measurements may be obtained 30 minutes before they were originally supposed to so that the bias of delayed measurements is counter balanced. Depending on the severity of the lack of adherence, the measurements may either still be used to determine estimated values with a revised level of confidence (i.e. accuracy) in step 415 and the collected data can be analyzed and interpreted.
Still referring to
Still referring to
In some embodiments, grouping the biological measurements in step 450 comprises grouping the biological measurements (e.g., bG measurements) by associated context in step 453. As discussed above, associated context can comprise variables on a patient's routine such as the size of a meal or the speed in which consumed food is digested. In such embodiments, not only are the biological measurements weighted by the associated context in step 420 to determine a more accurate estimated HbAlC value in step 430 (as will be discussed later herein), but the biological measurements collected in step 410 can be grouped by the same associated context such that the relative impact of variables within the associated context can be better appreciated.
The biological measurements may be grouped in step 450 automatically based on predetermined parameters (e.g., where a bG meter is programmed by default to group by time), or may be grouped based on the command of an operator. In some embodiments, the method may comprise prompting the operator to select the set of variables in which to group the biological measurements. For example, the operator may be prompted with multiple options such as time, events, associated context, or other variables which are available based on the known variables in which biological measurements were obtained. While specific examples have been provided of how biological measurements may be grouped in step 450 of informational delivery method 400, it should also be appreciated that the biological measurements may further be grouped by any other set of variables which may allow insight into their impact on the patient's estimated or predicted biological values.
Still referring to
In some embodiments, the estimated/predicted HbAlC values (or other estimated/predicted biological values) determined in step 430 are compared to the last measured actual HbAlC value (or other last measured biological value) in step 431. Comparing the estimated biological value with the actual biological value can provide the patient with the predicted increase or decrease in the HbAlC value as compared to their last actual measured HbAlC value. In these embodiments, the patient may then appreciate the effect their medication, lifestyle choices, etc. are having on their health. In some embodiments, the estimated biological value is compared to a target/reference biological value to appreciate the progress of the therapy. In some embodiments, the estimated/predicted biological value determined from one group of biological measurements is compared to the estimated/predicted value(s) determined from one or more other group(s) of biological measurements. In some embodiments, the estimated biological value determined for one time period can be compared to another biological value (either estimated or actual) from another time period. The reference values can be manually entered or can be retrieved from a storage device. For example, in some embodiments, the reference values are stored in a system such us a local system (e.g., the patient's bG monitor) or a remote system (e.g., a computer, server, or other storage device that can be accessed).
Still referring to
Likewise, in embodiments where the estimated or predicted biological values are grouped by events in step 452, the plurality of sections in the sectioned display can, for example, comprise a first type of medication section, a second type of medication section, a no medication section, or the like. Additionally or alternatively, in some embodiments, the plurality of sections may comprise sections based on the amount or concentration of the medication. In even other embodiments, when the estimated or predicted biological values are grouped by other associated context variables in step 453, the plurality of sections of the sectioned display may be based off the different context variables (e.g., a large meal size section for estimated HbAlC values based on bG measurements taken after meals of a large size, a normal meal size section for estimated HbAlC values based on bG measurements taken after meals of a normal size, and a small meal size section for estimated HbAlC values based on bG measurements taken after meals of a small size). As such, the plurality of sections allows for the component-based display of grouped estimated or predicted biological values so that the effect of each component can be assessed. Optionally, an interpretation may be provided based on the grouped estimated or predicted values provided in the display conveying the relevant information such as the relative impact each component (i.e., variable) has on the patient, potential lifestyle changes, potential medication changes, etc.
Therefore, by grouping estimated or predicted biological values by events, one can quickly assess the relative success a prescribed therapy is having on the patient. For example, a patient, physician or other third-party can assess the relative impact attributed to the grouped estimated HbAlC values from each section of the sectioned display. Thus, when one section provides a greater impact to HbAlC values (such as when large meals account for the greatest impact on HbAlC values), its impact can quickly be visualized. This can be utilized to offer quick insight into the effectiveness of a therapy treatment (such as drug administration or exercise regimen) so that progress can be monitored. In addition, by allowing the quick assessment of the impact on the patient's HbAlC through the estimated HbAlC values, therapies can be quickly adjusted or discontinued (or lifestyles can be modified when possible) if they are not producing the expected or necessary results such as elevated glycemia or causing hypo glycemia. For example, in some embodiments, such as where the estimated HbAlC values are grouped based on events in which a new drug was and was not administered, the sectioned display can provide the effect on the patient's HbAlC when the drug was and was not administered. If administration of the drug produced little or no effect, the patient may adjust the drug amount, change the type of drug or stop the therapy to reduce unnecessary costs. The informational delivery method 400 allows this adjustment to occur in real time without waiting to collect actual HbAlC values for the patient during the next clinical visit.
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While specific exemplary displays have been presented herein, it should be appreciated that other additional or alternative features may also be included. For example, such displays may selectively display additional information (such as the date range of measurements, labels/icons corresponding to the groups/events, etc), may be interactive (wherein the user can selectively change the data range, events, or other parameters that are displayed), may display actual values within each section of the sectioned display, may dynamically only display sections that are outside of target ranges, may be in color, gray scale or black and white, and/or contain any other relevant features for displaying grouped estimated biological values or grouped predicted biological values.
Referring now to
In some embodiments, such as that illustrated in
Furthermore, while specific examples have been presented in grouping estimated biological values or predicted biological values (such as estimated HbAlC values according to step 450 of informational delivery method 400) and providing the grouped estimated biological values or grouped predicted biological values in a sectioned display (according to step 460 of informational delivery method 400), it should be appreciated that grouping may alternatively or additionally be performed in any other component-based methodology and be provided in any plurality of sections in the sectioned display that allows for the interpretation of the impact of each component.
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Additional HbAlC values determined in step 531 can comprise any other HbAlC value actually measured from a patient or otherwise calculated from a patient based on other measurements such as bG measurements. For example, in some embodiments, an additional HbAlC value determined in step 531 can comprise a virtual HbAlC value determined in step 532. Virtual HbAlC values are those determined purely as a function of glucose concentration in which it as assumed that the glycation process is the same for each patient (and wherein bG values are not weighted based on the specific context of the patient). As such, while the patient specific physiological variability is not directly addressed in determining the value, glycemic control within patients can nonetheless be compared. Virtual HbAlC values may be determined in step 532 based on bG measurements collected in step 510. In some embodiments, an additional HbAlC value determined in step 531 can comprise the patient's actual HbAlC value as previously or currently measured in a clinical setting. In such embodiments, determining an additional type of HbAlc values may thereby simply comprise collecting actual HbAlC measurements in step 533 such that collecting the actual HbAlC measurements can comprise downloading a set of measurements or continuously updating a set of measurements as new values are determined.
After various HbAlC values are determined or collected through steps 530 and 531 of the selective display method 500, the types of HbAlC values to display are selected in step 540. Specifically, one can select any or all HbAlC values to display such that they can compare the different HbAlC values of the patient. For example, in some embodiments, the method may be incorporated in an electronic device such as a bG meter, PDA or computer. The operator may then be prompted to select which values to compare based on the different values determined/obtained in steps 530 and 531. For example, the operator may be able to select estimated HbAlC values (determined in step 530 which predicts the patient's HbAlC values based on previous bG measurements), actual HbAlC measurements (collected in step 533 which contains recent values actually measured from the patient) and/or virtual HbAlC values (determined in step 532 which estimates the patient's HbAlC value based on bG measurements, but relies on a population based model as opposed to weighting the individual bG measurements based on context). Furthermore, the user may also select the date range, events, or other parameter for which the HbAlC values are to be displayed.
After the types of HbAlC values are selected in step 540, the selected types of HbAlC values are displayed in step 550 of the selective display method 500. Specifically, the selected types of HbAlC values are displayed such that a user can see and/or compare the various HbAlC values. In some embodiments, the various values may be plotted on a common graph. For example, where estimated HbAlC values and actual HbAlC values are selected, then both sets of values may be plotted so a patient can visualize how his or her estimated HbAlC compares to his or her previously measured actual HbAlC results. In some embodiments, the value of the selected types of HbAlC values are displayed such that the patient can see how new values (such as estimated HbAlC values or virtual HbAlC values compare to previously measured actual HbAlC values, such as by including the percent change. Such embodiments may allow the patient to more quickly assess the effect of new therapeutic regimens and determine how such lifestyle changes are influencing his or her health or what actions they can take to improve upon current trends.
In summary, the embodiments of the present disclosure address the ability to provide grouped estimated biological values or grouped predicted biological values (such as estimated or predicted HbAlC values) in a sectioned display to patients, physicians and/or any other party so that the effect of new treatment regimens or other variations in a patient's life can be more quickly assessed without waiting for clinical measurements of actual values. For example, by weighting obtained bG measurements, estimated HbAlC values may be determined through calculating true mean bG values. The bG measurements can be grouped on a set of variables so that the estimated HbAlC values may be displayed so that the relative impact of different times, events or other context can be examined. The grouped HbAlC values may thus be delivered in a sectioned display to quickly asses the effect of each variable-based component towards the patient's HbAlC. Additionally, estimated HbAlC values may be determined along with other types of HbAlC values so that a user may select different types of HbAlC values for comparison. Such embodiments can allow for, among other things, a patient's previously measured actual HbAlC values to be compared with newly determined estimated HbAlC values to study the effect of recent therapeutic and/or lifestyle changes.
Having described the disclosure in detail and by reference to specific embodiments thereof, it will be apparent that modifications and variations are possible without departing from the scope of the disclosure defined in the appended claims. More specifically, although some aspects of the present disclosure are identified herein as preferred or particularly advantageous, it is contemplated that the present disclosure is not necessarily limited to these preferred aspects of the disclosure.
This application is a continuation-in-part of U.S. patent application Ser. No. 12/492,667 filed Jun. 26, 2009 which is incorporated by reference herein.
Number | Date | Country | |
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Parent | 12492667 | Jun 2009 | US |
Child | 13017526 | US |