HK1188106B - Calibration of a handheld diabetes managing device that receives data from a continuous glucose monitor - Google Patents

Description

Calibration of a handheld diabetes management device receiving data from a continuous glucose monitor

Technical Field

The present disclosure relates generally to medical devices and, more particularly, to a process for calibrating a blood glucose meter to estimate a glucose level of a patient based on data received from a continuous glucose monitor and self-test blood glucose measurements obtained from the blood glucose meter.

Background

Medical devices are often used as diagnostic and/or therapeutic devices in diagnosing and/or treating a medical condition of a patient. For example, a blood glucose meter is used as a diagnostic device to measure blood glucose levels of patients with diabetes. An insulin infusion pump is used as a treatment device to administer insulin to patients with diabetes.

Diabetes mellitus (commonly referred to as diabetes) is a chronic condition in which a person has elevated glucose levels caused by a deficiency in the body's ability to produce and/or use insulin. There are three main types of diabetes. Type 1 diabetes can be autoimmune, genetic, and/or environmental and usually affects children and young adults. Type 2 diabetes accounts for 90-95% of diabetes cases and is associated with obesity and physical inactivity. Gestational diabetes is a form of glucose intolerance diagnosed during pregnancy and usually heals naturally after delivery.

In 2009, at least 2 million people worldwide had diabetes according to the world health organization. In 2005, it was estimated that 110 million people died from diabetes. The incidence of diabetes is rapidly increasing and it is estimated that the number of deaths from diabetes doubles between 2005 and 2030. In the united states, nearly 2 thousand 4 million americans have diabetes and an estimated 25% of the elderly aged 60 years and older are affected. Central prediction of disease control and prevention: 1 of 3 americans born after 2000 will develop diabetes during their life. National diabetes information exchange center estimates: diabetes costs in the united states alone at 1320 billion dollars per year. Without treatment, diabetes can lead to serious complications such as heart disease, stroke, blindness, kidney failure, amputation, and death associated with pneumonia and influenza.

Diabetes is managed primarily by controlling the glucose level in the bloodstream. This level is dynamic and complex, and is influenced by a number of factors including the amount and type of food consumed and the amount of insulin in the blood (which mediates transport of glucose across cell membranes). Glucose levels are also sensitive to exercise, sleep, stress, smoking, travel, illness, menses, and other psychological and lifestyle factors unique to individual patients. The dynamic nature of blood glucose and insulin, as well as all other factors affecting blood glucose, generally requires that people with diabetes predict blood glucose levels. Thus, the time of treatment in the form of insulin or oral medication or both may be determined to maintain blood glucose levels within an appropriate range.

Management of diabetes is time consuming for patients due to the need to constantly obtain reliable diagnostic information, follow prescribed therapy, and manage lifestyle on a daily basis. Typically, diagnostic information (e.g., blood glucose) is obtained from a capillary blood sample with a lancing device and then measured with a handheld blood glucose meter. Interstitial glucose levels can be obtained from a continuous glucose sensor worn on the body. The prescribed therapy may include insulin, oral medication, or both. Insulin may be delivered using a syringe, ambulatory infusion pump, or a combination of the two. In the case of insulin therapy, determining the amount of insulin to be injected may require forecasting the dietary composition of fat, carbohydrates and proteins, as well as the effects of exercise or other physiological states. Management of lifestyle factors (e.g., weight, diet, and exercise) can significantly affect the type and effectiveness of treatment.

Management of diabetes involves a large amount of diagnostic and prescription data acquired in a number of ways: from a medical device; from a personal care device; from a patient record log; from laboratory tests; and recommendations from health care professionals. The medical devices include the patient's own bG meter, a continuous glucose monitor, a non-ambulatory insulin infusion pump, diabetes analysis software, and diabetes device configuration software. Each of these systems generates and/or manages a large amount of diagnostic and prescription data. The personal care device includes a weight scale, a blood pressure cuff, an exercise machine, a thermometer, and weight management software. The patient record log includes information related to meals, exercise, and lifestyle. Laboratory test results include HbA1C, cholesterol, triglycerides, and glucose tolerance. The healthcare professional recommendations include prescriptions, diets, test plans, and other information related to the patient's treatment.

There is a need for a handheld device for aggregating, manipulating, managing, presenting and communicating diagnostic and prescription data from medical devices, personal care devices, patient record information, biomarker information and record information in an efficient manner. The handheld device may improve the care and health of a person with diabetes, so that the person with diabetes may live an extended life and reduce the risk of complications from diabetes.

Furthermore, in order to effectively manage the care and health of a patient, there is a need for a handheld device that communicates with and processes information received from other medical devices and systems. The handheld device may receive patient information from many different sources, such as an insulin pump, a continuous glucose monitor, a computer program, user input, etc. To accurately utilize this information, the handheld device may need to calibrate the information received from these sources. For example, a handheld diabetes management device may receive raw data from a continuous glucose monitor relating to a patient's glucose level. To use the raw data, the handheld diabetes management device may need to be calibrated to correlate the received raw data with the measured blood glucose level of the patient. The accuracy of this calibration can affect patient care and treatment. Accordingly, there is a need for a method of calibrating a handheld diabetes management device to determine an accurate estimate of a patient's glucose level from data received from a continuous glucose monitor.

The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.

Disclosure of Invention

According to the present disclosure, a method for calibrating a handheld diabetes management device to determine an estimated glucose level of a patient from data generated by a continuous glucose monitor is presented. The method may include the step of sampling a current value of the continuous glucose monitor at a sampling interval over a time period to generate a plurality of current samples over the time period. The current value is related to the glucose level of the patient. The method may also include determining an average of the plurality of current samples, determining a median (mean) of the plurality of current samples, and determining a standard deviation of the plurality of current samples. Further, the method includes measuring a blood glucose level of the patient at a first time. Finally, the method includes determining a calibration equation based on the measured blood glucose level and a plurality of current samples, the calibration equation associating the plurality of current samples with the estimated glucose level of the patient when the standard deviation is less than a first threshold and an absolute value of a difference between the mean and median is less than a second threshold.

According to the present disclosure, a method for calibrating a handheld diabetes management device to determine an estimated glucose level of a patient from data generated by a continuous glucose monitor is presented. The method may include sampling a current value of the continuous glucose monitor at a sampling interval over a time period to generate a plurality of current samples over the time period, wherein the current value is related to a glucose level of the patient. The method may also include determining an average of the plurality of current samples, determining a median of the plurality of current samples, determining a standard deviation of the plurality of current samples, determining a 25% quantile value of the plurality of current samples, determining a 75% quantile value of the plurality of current samples, and determining a trend (trend) metric of the plurality of current samples. The trend metric may correspond to a change in the current value over the time period. The method may also include measuring a blood glucose level of the patient at a first time. Further, the method may include determining a linear calibration equation that associates the plurality of current samples with the estimated glucose level of the patient based on the measured blood glucose level and the plurality of current samples when (i) the standard deviation is less than a first threshold, (ii) an absolute value of a difference between the mean and median is less than a second threshold, (iii) the median minus the 25% quantile is less than a third threshold, (iv) the 75% quantile minus the median is less than a fourth threshold, and (v) an absolute value of the trend metric is less than a fifth threshold.

A diabetes management system that periodically determines an estimated glucose level of a patient may include a continuous glucose monitor and a handheld diabetes management device. The continuous glucose monitor may be configured to: (i) sampling a current value related to a glucose level of a patient at a sampling interval over a time period to generate a plurality of current samples over the time period, (ii) determining a mean of the plurality of current samples, (iii) determining a median of the plurality of current samples, and (iv) determining a standard deviation of the plurality of current samples. The handheld diabetes management device may be in communication with a continuous glucose monitor. Further, the handheld diabetes management device may be configured to: (i) measuring a blood glucose level of the patient at a first time, (ii) receiving a mean, a median, and a standard deviation of the plurality of current samples from the continuous glucose monitor, (iii) determining a calibration equation that associates the plurality of current samples with the estimated glucose level of the patient based on the measured blood glucose level and the plurality of current samples when the standard deviation is less than a first threshold and an absolute value of a difference between the mean and the median is less than a second threshold, and (iv) determining the estimated glucose level of the patient based on the calibration equation and a current value sampled by the continuous glucose monitor.

Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the disclosure.

Drawings

The present disclosure will become more fully understood from the detailed description and the accompanying drawings, wherein:

FIG. 1 shows a patient and a treating clinician;

FIG. 2 shows a patient with a Continuous Glucose Monitor (CGM), ambulatory durable insulin infusion pump, ambulatory non-durable insulin infusion pump and diabetes manager;

FIG. 3 illustrates a diabetes care system of the system used by patients and clinicians to manage diabetes;

FIG. 4 is a functional block diagram of a diabetes manager;

FIG. 5 is a functional block diagram of a continuous glucose monitor;

FIG. 6 shows a flow chart illustrating an exemplary method of calibrating a handheld diabetes management device in accordance with the present disclosure; and

FIG. 7 shows a flowchart illustrating another exemplary method of calibrating a handheld diabetes management device according to the present disclosure.

Detailed Description

Referring now to FIG. 1, a patient 100 with diabetes and a health care professional or clinician 102 are shown in a clinical environment. People with diabetes include people with metabolic syndrome, pre-diabetes, type 1 diabetes, type 2 diabetes, and gestational diabetes, and are collectively referred to as patients. Healthcare providers for diabetes are diverse and include nurses, nurse practitioners, physicians, and endocrinologists, and are commonly referred to as clinicians.

During a healthcare consultation, the patient 100 typically shares a variety of patient data with the clinician 102, including blood glucose measurements, continuous glucose monitor data, the amount of insulin infused, the amount of food and beverages consumed, exercise schedules, and other lifestyle information. Clinician 102 may obtain additional patient data including measurements of HbA1C, cholesterol levels, triglycerides, blood pressure, and body weight of patient 100. The patient data may be recorded manually or electronically on the handheld diabetes management device 104, diabetes analysis software executing on a Personal Computer (PC) 106, and/or a web-based diabetes analysis site (not shown). The clinician 102 may use diabetes analysis software and/or a web-based diabetes analysis site to manually or electronically analyze patient data. After analyzing the patient data and checking the patient 100 for compliance with previously prescribed therapy, the clinician 102 may decide whether to modify the therapy for the patient 100.

Referring now to fig. 2, the patient 100 may use a Continuous Glucose Monitor (CGM) 200, a non-ambulatory, durable insulin infusion pump 202 or a non-ambulatory, non-durable insulin infusion pump 204 (collectively referred to as insulin pumps 202 or 204), and a handheld diabetes management device 104 (hereinafter referred to as diabetes manager 104). The CGM200 uses a subcutaneous sensor to sense and monitor the amount of glucose in the subcutaneous fluid of the patient 100 and transmits the corresponding degrees to the handheld diabetes management device 104.

The diabetes manager 104 performs various tasks including measuring and recording blood glucose levels, determining the amount of insulin to be administered to the patient 100 via the insulin pump 202 or 204, receiving patient data via a user interface, archiving patient data, and the like. The diabetes manager 104 periodically receives from the CGM200 a number of degrees indicative of the glucose level of the subcutaneous fluid of the patient 100. The diabetes manager 104 transmits instructions to the insulin pump 202 or 204, and the insulin pump 202 or 204 delivers insulin to the patient 100. Insulin may be delivered in a single dose, which increases the amount of insulin in the blood of the patient 100 by a predetermined amount. Furthermore, insulin may be delivered in a scheduled manner in the form of a basal dose, which maintains a predetermined insulin level in the blood of the patient 100.

Referring now to FIG. 3, a diabetes management system 300 used by the patient 100 and the clinician 102 includes one or more of the following devices: diabetes manager 104, Continuous Glucose Monitor (CGM) 200, insulin pump 202 or 204, mobile device 302, diabetes analysis software on PC 106, and other healthcare devices 304. The diabetes manager 104 is configured as a system hub and communicates with the devices of the diabetes management system 300. Alternatively, the insulin pump 204 or the mobile device 302 may act as a system hub. Communication between the various devices in the diabetes management system 300 can be performed using a wireless interface (e.g., bluetooth) and/or a wired interface (e.g., USB). The communication protocols used by these devices may include protocols conforming to the IEEE11073 standard as extended using the Guidelines provided by Continua Health Alliance Design Guidelines. In addition, the patient 100 and clinician 102 may exchange information using a Health record system (e.g., Microsoft ® Health valve and Google ™ Health).

The diabetes manager 104 can receive glucose readings from one or more sources (e.g., from the CGM 200). The CGM200 continuously measures the glucose level of the patient 100. The CGM200 periodically communicates the glucose level to the diabetes manager 104. The diabetes manager 104 and the CGM200 communicate wirelessly using a proprietary Gazell wireless protocol developed by Nordic Semiconductor, Inc.

In addition, the diabetes manager 104 includes a Blood Glucose Meter (BGM) and a port for communicating with the BGM (both not shown). The port may receive a blood glucose measurement strip 306. The patient 100 places a blood sample or other bodily fluid onto the blood glucose measurement strip 306. BGM analyzes a sample and measures blood glucose levels in the sample. The blood glucose level measured from the sample and/or the blood glucose level read by the CGM200 may be used to determine the amount of insulin to be administered to the patient 100.

The diabetes manager 104 communicates with the insulin pump 202 or 204. The insulin pump 202 or 204 can be configured to receive instructions from the diabetes manager 104 to deliver a predetermined amount of insulin to the patient 100. In addition, the insulin pump 202 or 204 can receive other information, including meal and/or exercise schedules for the patient 100. The insulin pump 202 or 204 can determine the amount of insulin to be administered based on the additional information.

The insulin pump 202 or 204 can also transmit data to the diabetes manager 104. The data may include the amount of insulin delivered to the patient 100, the corresponding delivery time, and the pump status. The diabetes manager 104 and the insulin pump 202 or 204 can communicate using a wireless communication protocol (e.g., bluetooth). Other wireless or wired communication protocols may also be used.

In addition, the diabetes manager 104 can communicate with other healthcare devices 304. For example, other healthcare devices 304 may include a sphygmomanometer, a weight scale, a pedometer, a fingertip pulse oximeter, a thermometer, and the like. The other healthcare device 304 obtains the personal health information of the patient 100 and transmits the personal health information of the patient 100 to the diabetes manager 104 via a wireless, USB, or other interface. Other healthcare devices 304 may use a communication protocol compliant with ISO/IEEE 11073 extended using the guidelines from continuous ® Health Alliance. The diabetes manager 104 can communicate with other healthcare devices 304 using interfaces including bluetooth, USB, and the like. Further, the devices of the diabetes management system 300 can communicate with each other via the diabetes manager 104.

The diabetes manager 104 can communicate with the PC 106 using a Bluetooth, USB, or other interface. The diabetes management software running on the PC 106 includes an analyzer-configurator that stores configuration information for the devices of the diabetes management system 300. The configurator has a database for storing configuration information for the diabetes manager 104 and other devices. The configurator may communicate with the user through a computer screen in a standard web or non-web application. The configurator transmits the user-approved configuration to the devices of the diabetes management system 300. The analyzer retrieves data from the diabetes manager 104, stores the data in a database, and outputs the analysis results through a standard web page or computer screen in a non-web based application.

The diabetes manager 104 can use bluetooth to communicate with the mobile device 302. The mobile device 302 may comprise a cellular telephone, PDA, or pager. The diabetes manager 104 can send the message to an external network through the mobile device 302. The mobile device 302 can transmit the message to an external network based on receiving a request from the diabetes manager 104.

In some embodiments, the CGM200 measures the glucose level in the interstitial fluid of the patient 100 by sampling the current value. The glucose level in the interstitial fluid, and thus the sampled current value, is correlated to the glucose level of the patient 100. In order to accurately estimate the glucose level of the patient 100 based on the interstitial fluid glucose level measured by the CGM200, the diabetes manager 104 can be periodically calibrated.

The diabetes manager 104 can be calibrated by determining a calibration equation based on the at least one current sample and the at least one blood glucose measurement. The current value sampled by the CGM200 and the blood glucose level of the patient 100 can be assumed to have a linear relationship within a normal measurement region of about 40 to 400 milligrams per deciliter. Based on the assumed linear relationship, the calibration equation may be a linear equation that associates one or more current samples with the estimated glucose level of the patient. After calibration, the diabetes manager 104 can determine an estimated glucose level of the patient 100 based on the calibration equation and the current value sampled by the CGM 200.

Referring now to FIG. 4, the example diabetes manager 104 includes a Blood Glucose Measurement (BGM) module 400, a communication module 402, a user interface module 404, a user interface 406, a processing module 408, a memory 410, and a power module 412. The user interface module 404 and the processing module 408 may be implemented by an application processing module 409. The BGM module 400 includes a blood glucose measurement engine that analyzes samples provided by the patient 100 on the blood glucose measurement strip 306 and measures the amount of blood glucose in the samples. The communication module 402 may include a plurality of radios that communicate with different devices of the diabetes management system 300. The user interface module 404 connects the diabetes manager 104 to various user interfaces 406 that the patient 100 can use to interact with the diabetes manager 104. For example, the user interface 406 may include keys, switches, a display, a speaker, a microphone, a Secure Digital (SD) card port, and/or a USB port, among others (all not shown).

The processing module 408 processes data received from the BGM module 400, the communication module 402, and the user interface module 404. The processing module 408 uses a memory 410 for processing and storing data. The memory 410 may include volatile and non-volatile memory. The processing module 408 outputs data to the user interface 406 and receives data from the user interface 406 via the user interface module 404. The processing module 408 outputs data to and receives data from the devices of the diabetes management system 300 via the communication module 402. The power module 412 supplies power to the components of the diabetes manager 104. The power module 412 may include a rechargeable battery or other power source. The battery may be recharged, for example, by using an adapter plugged into a wall outlet and/or via a USB port on the diabetes manager 104.

Referring now to fig. 5, an exemplary Continuous Glucose Monitor (CGM) 200 includes a sensor 421, an additional communication module 423, an additional processing module 425, an additional memory 427, and an additional power module 429. The sensor 421 may monitor the condition of the patient 100 in relation to the glucose level of the patient 100. For example, the sensor 421 may periodically sample (alone or in combination with the further processing module 425) a current value corresponding to a glucose level in the interstitial fluid of the patient 100. The additional communication module 423 may include one or more radios that communicate with different devices of the diabetes management system 300.

The further processing module 425 processes data received from the sensor 421 and the further communication module 423. The further processing module 425 processes and stores data using a further memory 427. The additional memory 427 may include volatile and non-volatile memory. The further processing module 425 outputs data to and receives data from a device of the diabetes management system 300 (e.g., the diabetes manager 104) via the further communication module 423. The additional power module 429 supplies power to the components of the CGM 200. In some embodiments, the additional power module 429 includes a battery and other power source. The power source may include a battery that can be recharged, for example, through the use of an adapter that plugs into a wall outlet.

Referring now to fig. 6, an exemplary method 500 of calibrating a handheld diabetes management device 104 to determine an estimated glucose level of a patient 100 from data generated by a Continuous Glucose Monitor (CGM) 200 in accordance with the present disclosure is illustrated. The method 500 begins at step 501 where the CGM200 samples the current value associated with the glucose level of the patient 100 at a sampling interval in step 501. As described above, this current value may be a measurement of the glucose level of the interstitial fluid of the patient 100, which in turn is related to the glucose level of the patient. For example only, the sampling interval may be one second, i.e., the CGM200 may measure the current value once per second. At step 503, the CGM200 may generate a plurality of current samples over a period of time. In one example, if the sampling interval is one second and the time period is one minute, the CGM200 will generate sixty current samples per time period.

To reduce the amount of information stored by the CGM200 and/or transmitted to the diabetes manager 104, multiple current samples can be pre-processed. The CGM200 may pre-process the plurality of current samples of the time period by determining one or more statistics from the plurality of current samples. The statistical value may represent a plurality of current samples. Examples of statistics include, but are not limited to, a mean, a median, a standard deviation, a 25% quantile, and a 75% quantile of the plurality of current samples. Additional statistical values may also be utilized by the calibration method 500, such as a trend metric corresponding to the change in the current sample over the time period. The trend metric, discussed more fully below, may be used to indicate the direction and rate of change of multiple current samples. In this manner, the CGM200 and/or the diabetes manager 104 can store statistical value(s) representing a number of current samples over the time period, which can reduce the amount of data to be stored and transferred. Further, the statistical value(s) can be utilized by the CGM200 and/or the diabetes manager 104 for calibration purposes.

The plurality of current samples may contain erroneous or faulty measurements. For example, the current value measured by the CGM200 may contain sensor "noise" that causes the measured current sample to be derived from the actual glucose level of the patient 100. Such "noise" may be caused in particular by physical movement of the CGM200 in relation to the patient 100 and/or electrical noise inherent within the CGM 200. Furthermore, the CGM200 may sometimes malfunction such that one or more current samples substantially differ from the actual glucose level of the patient 100, for example due to internal problems in the electronics of the CGM200 or sensor "missing information". The sensor "missing information" may be due to physiological issues of the attachment of the CGM200 to the patient 100, such as physical movement of the CGM200 relative to the patient 100, such that one or more current samples "drop" to near zero, even if the actual glucose level of the patient 100 is high.

The method proceeds to step 505 where the diabetes manager 104, either alone or in combination with the CGM200, determines whether the plurality of current samples are suitable for use in calibrating the diabetes manager 104 at step 505. In some embodiments, the suitability of calibrating multiple current samples may be determined by the absence of sensor "noise" and/or "missing information" from the current samples. Sensor "noise" and/or "missing information" can be detected in a number of ways. Merely by way of example, variability in the high rate of current samples over a period of time may indicate sensor "noise" and/or "missing information. Thus, different methods of determining the variability of the high rate of the current sample may be used to determine the suitability of the current sample for calibration, as described below.

One method for determining whether the plurality of current samples are suitable for calibration is to compare an absolute value of a difference between a mean and a median of the plurality of current samples to a threshold. The plurality of current samples may be considered suitable for calibration in the event that an absolute value of a difference between a mean and a median of the plurality of current samples is less than a threshold. Similarly, the plurality of current samples may be deemed unsuitable for calibration in the event that the absolute value of the difference between the mean and median values of the plurality of current samples is greater than the threshold. The threshold may be set, for example, based on empirical data.

Another method of determining whether a plurality of current samples are suitable for calibration is to compare the standard deviation of the plurality of current samples to a threshold. In the event that the standard deviation of the plurality of current samples is less than a threshold, the plurality of current samples may be deemed suitable for calibration. Similarly, where the standard deviation of the plurality of current samples is greater than the threshold, the plurality of current samples may be deemed unsuitable for calibration. The threshold may be set, for example, based on empirical data.

Another method of determining whether a plurality of current samples are suitable for calibration is to compare the median of the plurality of current samples minus the 25% quantile of the plurality of current samples to a threshold. The plurality of current samples may be considered suitable for calibration in the event that a median value of the plurality of current samples minus a 25% quantile value of the plurality of current samples is less than a threshold value. Similarly, the plurality of current samples may be deemed unsuitable for calibration if the median of the plurality of current samples minus the 25% quantile of the plurality of current samples is greater than the threshold. The threshold may be set, for example, based on empirical data.

Another method of determining whether a plurality of current samples are suitable for calibration is to compare a value of a 75% quantile of the plurality of current samples minus a median of the plurality of current samples to a threshold. The plurality of current samples may be considered suitable for calibration in the case that a value of 75% quantile of the plurality of current samples minus a median of the plurality of current samples is less than a threshold. Similarly, the plurality of current samples may be deemed unsuitable for calibration if the value of the 75% quantile value of the plurality of current samples minus the median value of the plurality of current samples is greater than the threshold. The threshold may be set, for example, based on empirical data.

An additional method of determining whether a plurality of current samples are suitable for calibration is to compare the absolute value of the trend metric for the plurality of current samples to a threshold. The trend metric may correspond to a change in the current sample over the time period, and may be a measure of a direction and rate of change of the plurality of current samples. A large trend metric may indicate variability in the high rate of the plurality of current samples. The trend metric may be determined by the following equation:

，

wherein= 1, 2, … n, where n is the number of samples in the time period;is the current value at time i;is the average over the time period;is the time at time i, andis the average of the time period. The plurality of current samples may be considered suitable for calibration in the event that the absolute value of the trend metric for the plurality of current samples is less than a threshold. Similarly, the plurality of current samples may be deemed unsuitable for calibration in the event that the absolute value of the trend metric for the plurality of current samples is greater than the threshold. The threshold may be set, for example, based on empirical data.

Although each of the methods discussed above have been described as determining whether multiple current samples are suitable for calibration individually, it should be appreciated that these methods may also be utilized in combination with each other. For example only, the suitability of the plurality of current samples for calibration may be determined by comparing the standard deviation of the plurality of current samples to a first threshold and by comparing the absolute value of the difference between the mean and median of the plurality of current samples to a second threshold. In the case where the standard deviation of the plurality of current samples is less than a first threshold and the absolute value of the difference between the mean and median values of the plurality of current samples is less than a second threshold, the plurality of current samples may be considered suitable for calibration. Similarly, the plurality of current samples may be deemed unsuitable for calibration in the event that the standard deviation of the plurality of current samples is greater than a threshold or the absolute value of the difference between the mean and median of the plurality of current samples is greater than a second threshold.

If the plurality of current samples are deemed unsuitable for calibration at step 505, the method 500 cannot determine a calibration equation based on the plurality of current samples and returns to step 501. However, if at step 505 the plurality of current samples are determined to be suitable for calibration, the method 500 proceeds to step 507 where the blood glucose level of the patient 100 is measured, for example by the diabetes manager 104. The diabetes manager 104 can provide an indication to the patient 100 that: blood glucose measurements are desirably used for calibration, such as by visual, tactile, and/or audible alarms. Typically, the patient 100 then measures his or her blood glucose level by depositing a sample of blood or other bodily fluid on the blood glucose measurement strip 306 to be analyzed by the BGM module 400 associated with the diabetes manager 104, although other methods of blood glucose level measurement may be utilized.

After measuring the blood glucose level of the patient 100 at step 507, the diabetes manager 104 at step 509 may determine a calibration equation based on the measured blood glucose level of the patient 100 and the plurality of current samples. To increase the accuracy of the calibration equation, the time at which the measurement of the blood glucose level of the patient 100 is performed (measurement time) may correspond to a time period during which a plurality of current samples are to be sampled. It should be noted, however, that the time of measurement may not fall within a time period because of a delay in the physiological response of the patient 100, the unsuitability of the current sample over the time period, and so forth.

The calibration equation may be determined in a variety of ways. For example, if a linear relationship between the current value sampled by the CGM200 and the glucose level of the patient 100 is employed, the calibration equation may be a linear equation determined by applying a linear regression algorithm to the various data samples (i.e., the set of measured blood glucose level/measured current value pairs). The diabetes manager 104 can determine the calibration equation based on one measured blood glucose level/measured current value correlation pair by utilizing a predetermined reference pair (such as [0,0] for the measured blood glucose level/measured current value). Further, as the diabetes manager 104/CGM 200 accumulates multiple calibration fiducial points (i.e., measured blood glucose levels/measured current value pairs), these additional fiducial points may be utilized in conjunction with or in place of the predetermined fiducial points in order to more accurately calibrate the diabetes manager 104. However, those skilled in the art will recognize that the diabetes manager 104 can use this alternative technique to determine the calibration equation.

Referring now to fig. 7, another exemplary method 550 of calibrating a handheld diabetes management device 104 to determine an estimated glucose level of a patient 100 from data generated by a Continuous Glucose Monitor (CGM) 200 in accordance with the present disclosure is illustrated. The method 550 is similar to the method 500 discussed above. The method 550 begins at step 551, where the CGM200 samples the current value associated with the glucose level of the patient 100 at a sampling interval in step 551. As described above, this current value may be a measurement of the glucose level of the interstitial fluid of the patient 100, which in turn is related to the glucose level of the patient. For example only, the sampling interval may be one second, i.e., the CGM200 may measure the current value once per second. At step 552, the CGM200 may generate a plurality of current samples over a period of time. In one example, if the sampling interval is one second and the time period is one minute, the CGM200 will generate sixty current samples per time period.

To reduce the amount of information stored by the CGM200 and/or transmitted to the diabetes manager 104, multiple current samples can be pre-processed. The CGM200 may pre-process the plurality of current samples over the time period by determining one or more statistical values from the plurality of current samples. The statistical value may represent a plurality of current samples and may be used to determine the suitability of the plurality of current samples for calibration, as discussed below. Accordingly, at step 553, the CGM200 may determine the mean, median, and standard deviation of the plurality of current samples. At step 554, the diabetes manager 104 (either alone or in conjunction with the CGM 200) determines whether the standard deviation of the plurality of current samples is below a first threshold. The first threshold may be set, for example, based on empirical data. If the standard deviation is greater than the first threshold at step 554, the method 500 does not determine a calibration equation based on the plurality of current samples and returns to step 551. However, if the standard deviation is less than the first threshold, then the method 500 proceeds to step 555.

At step 555, the diabetes manager 104 (either alone or in conjunction with the CGM 200) determines whether the absolute value of the difference between the mean and median values of the plurality of current samples is below a second threshold. The second threshold may be set, for example, based on empirical data. If the absolute value of the difference between the mean and median values is greater than the second threshold at step 555, the method 500 does not determine a calibration equation based on the plurality of current samples and returns to step 551. However, if the absolute value of the difference between the mean and median values is less than the second threshold, then the method 500 proceeds to step 556. In the exemplary method 550 shown in fig. 7, the step of determining whether the plurality of current samples are suitable for calibration (step 505 in fig. 6) in method 500 has been replaced by steps 555 and 556, however, additional or alternative steps may be utilized, as discussed above.

At step 556, the blood glucose level of the patient 100 is measured, for example, by the diabetes manager 104. The diabetes manager 104 can provide an indication to the patient 100 that: blood glucose measurements are desirably used for calibration, such as by visual, tactile, and/or audible alarms. Typically, the patient 100 then measures his or her blood glucose level by depositing a sample of blood or other bodily fluid on the blood glucose measurement strip 306 to be analyzed by the BGM module 400 associated with the diabetes manager 104, although other methods of blood glucose level measurement may be utilized.

After measuring the blood glucose level of the patient 100 at step 556, the diabetes manager 104 at step 557 can determine a calibration equation based on the measured blood glucose level of the patient 100 and the plurality of current samples. The calibration equation may be determined as described above with respect to method 500.

The broad teachings of the disclosure can be implemented in a variety of forms. Therefore, while this disclosure includes particular examples, the true scope of the disclosure should not be so limited since other modifications will become apparent to the skilled practitioner upon a study of the drawings, the specification, and the following claims.

The detailed description is merely exemplary in nature and is in no way intended to limit the disclosure, its application, or uses. For purposes of clarity, the same reference numbers will be used in the drawings to identify similar elements. As used herein, at least one of the phrases A, B and C should be understood to mean logic (a or B or C) using a non-exclusive logical or. It should be understood that the steps within a method may be performed in a different order without altering the principles of the present disclosure.

As used herein, the term module may refer to, be part of, or include the following: an Application Specific Integrated Circuit (ASIC); an electronic circuit; a combinational logic circuit; a Field Programmable Gate Array (FPGA); a processor (shared, dedicated, or group) that executes code; other suitable components that provide the described functionality; or a combination of some or all of the above, such as in a system on a chip. The term module may include memory (shared, dedicated, or group) that stores code executed by the processor.

As used above, the term code may include software, firmware, and/or microcode, and may refer to programs, routines, functions, classes, and/or objects. As used above, the term shared means: a single (shared) processor may be used to execute some or all code from multiple modules. Further, some or all code from multiple modules may be stored by a single (shared) memory. As used above, the term group means: some or all code from a single module may be executed using a processor complex. In addition, memory banks may be used to store some or all code from a single module.

The apparatus and methods described herein may be implemented by one or more computer programs or applications executed by one or more processors. Computer programs and applications include processor-executable instructions stored on a non-transitory tangible computer-readable medium. The computer program may also include stored data. Non-limiting examples of the non-transitory tangible computer readable medium are nonvolatile memory, magnetic storage, and optical storage.

Some embodiments are described below for illustrative purposes only. Furthermore, the separate features of the different independent embodiments may be combined separately from the dependent embodiments.

1. A method for calibrating a handheld diabetes management device to determine an estimated glucose level of a patient from data generated by a continuous glucose monitor, comprising: sampling a current value of the continuous glucose monitor at a sampling interval over a time period to generate a plurality of current samples over the time period, the current value being related to a glucose level of the patient; determining an average of the plurality of current samples; determining median values of the plurality of current samples; determining a standard deviation of the plurality of current samples; measuring a blood glucose level of the patient at a first time; and determining a calibration equation that associates the plurality of current samples with the estimated glucose level of the patient based on the measured blood glucose level and the plurality of current samples when the standard deviation is less than a first threshold and an absolute value of a difference between the mean and median is less than a second threshold.

2. The method of embodiment 1, wherein the calibration equation is a linear equation.

3. The method of embodiment 1, further comprising determining a 25% quantile value for the plurality of current samples, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is performed when the median minus the 25% quantile value is less than a third threshold.

4. The method of embodiment 3, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when the median minus the 25% quantile value is greater than a third threshold.

5. The method of embodiment 1, wherein the sampling interval is once per second.

6. The method of embodiment 5, wherein the period of time is one minute.

7. The method of embodiment 1, wherein the first time corresponds to the period of time.

8. The method of embodiment 1, further comprising determining a 75% quantile value for the plurality of current samples, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is performed when the 75% quantile value minus the median is less than a fourth threshold.

9. The method of embodiment 8, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when the 75% quantile value minus the median is greater than a fourth threshold.

10. The method of embodiment 1, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when the standard deviation is greater than a first threshold.

11. The method of embodiment 1, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when an absolute value of a difference between the mean and median is greater than a second threshold.

12. The method of embodiment 1, further comprising determining a trend metric for the plurality of current samples, the trend metric corresponding to a change in a current value over the time period, wherein the step of determining the calibration equation that associates the plurality of current samples with the estimated glucose level is performed when an absolute value of the trend metric is less than a fifth threshold.

13. The method of embodiment 12, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when the absolute value of the trend metric is greater than a fifth threshold.

14. The method of embodiment 12 wherein the trend metric is determined by the following equation:

，

wherein= 1, 2, … n, where n is the number of samples in the time period;is the current value at time i;is the average over the time period;is the time at time i, andis the average of the time period.

15. A method for calibrating a handheld diabetes management device to determine an estimated glucose level of a patient from data generated by a continuous glucose monitor, comprising:

sampling a current value of the continuous glucose monitor at a sampling interval over a time period to generate a plurality of current samples over the time period, the current value being related to a glucose level of the patient; determining an average of the plurality of current samples; determining median values of the plurality of current samples; determining a standard deviation of the plurality of current samples; determining a 25% quantile value for the plurality of current samples; determining a 75% quantile value of the plurality of current samples; determining a trend metric for the plurality of current samples, the trend metric corresponding to a change in the current value over the time period; measuring a blood glucose level of the patient at a first time; and determining a linear calibration equation that associates the plurality of current samples with the estimated glucose level of the patient based on the measured blood glucose level and the plurality of current samples when (i) the standard deviation is less than a first threshold, (ii) an absolute value of a difference between the mean and median is less than a second threshold, (iii) the median minus the 25% quantile is less than a third threshold, (iv) the 75% quantile minus the median is less than a fourth threshold, and (v) an absolute value of the trend metric is less than a fifth threshold.

16. The method of embodiment 15, wherein the step of determining the calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when (i) the standard deviation is greater than a first threshold, (ii) an absolute value of a difference between the mean and median is greater than a second threshold, (iii) the median minus the 25% quantile is greater than a third threshold, (iv) the 75% quantile minus the median is greater than a fourth threshold, or (v) the absolute value of the trend metric is greater than a fifth threshold.

17. The method of embodiment 16, wherein the calibration equation is a linear equation.

18. The method of example 17, the trend metric determined by the following equation:

，

wherein= 1, 2, … n, in this casen is the number of samples in the time period;is the current value at time i;is the average over the time period;is the time at time i, andis the average of the time period.

19. A diabetes management system that periodically determines an estimated glucose level of a patient, comprising: a continuous glucose monitor configured to: (i) sampling a current value related to a glucose level of a patient at a sampling interval over a time period to generate a plurality of current samples over the time period, (ii) determining a mean of the plurality of current samples, (iii) determining a median of the plurality of current samples, and (iv) determining a standard deviation of the plurality of current samples; and a handheld diabetes management device in communication with the continuous glucose monitor and configured to: (i) measuring a blood glucose level of the patient at a first time, (ii) receiving a mean, a median, and a standard deviation of the plurality of current samples from the continuous glucose monitor, (iii) determining a calibration equation that associates the plurality of current samples with the estimated glucose level of the patient based on the measured blood glucose level and the plurality of current samples when the standard deviation is less than a first threshold and an absolute value of a difference between the mean and the median is less than a second threshold, and (iv) determining the estimated glucose level of the patient based on the calibration equation and a current value sampled by the continuous glucose monitor.

20. The diabetes care system of embodiment 17, wherein the handheld diabetes management device is further configured to: the plurality of current samples are ignored in determining the calibration equation when the standard deviation is greater than a first threshold and an absolute value of a difference between the mean and median is greater than a second threshold.

Claims (16)

1. A method for calibrating a handheld diabetes management device to determine an estimated glucose level of a patient from data generated by a continuous glucose monitor, comprising:

sampling a current value of the continuous glucose monitor at a sampling interval over a time period to generate a plurality of current samples over the time period, the current value being related to a glucose level of the patient;

determining an average of the plurality of current samples;

determining median values of the plurality of current samples;

determining a standard deviation of the plurality of current samples;

measuring a blood glucose level of the patient at a first time; and

when the standard deviation is less than a first threshold and an absolute value of a difference between the mean and median is less than a second threshold, a calibration equation that associates the plurality of current samples with the estimated glucose level of the patient is determined based on the measured blood glucose level and the plurality of current samples.

2. The method of claim 1, wherein the calibration equation is a linear equation.

3. The method of claim 1 or 2, further comprising determining a 25% quantile value of the plurality of current samples, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is performed when the median minus the 25% quantile value is less than a third threshold.

4. The method of claim 3, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when the median minus 25% quantile value is greater than a third threshold.

5. A method according to claim 1 or 2, wherein the sampling interval is once per second.

6. The method of claim 5, wherein the period of time is one minute.

7. The method of claim 1 or 2, wherein the first time corresponds to the period of time.

8. The method of claim 1 or 2, further comprising determining a 75% quantile value of the plurality of current samples, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is performed when the 75% quantile value minus the median is less than a fourth threshold.

9. The method of claim 8, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when the 75% quantile value minus the median is greater than a fourth threshold.

10. The method according to claim 1 or 2, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when the standard deviation is greater than a first threshold.

11. The method of claim 1 or 2, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when an absolute value of a difference between the mean and median is greater than a second threshold.

12. The method according to claim 1 or 2, further comprising determining a trend measure for the plurality of current samples, the trend measure corresponding to a change in a current value over the time period, wherein the step of determining the calibration equation that associates the plurality of current samples with the estimated glucose level is performed when an absolute value of the trend measure is less than a fifth threshold.

13. The method of claim 12, wherein the step of determining a calibration equation that associates the plurality of current samples with the estimated glucose level is not performed when the absolute value of the trend metric is greater than a fifth threshold.

14. The method of claim 12, wherein the trend metric is determined by the following equation:

，

wherein= 1, 2, … N, where N is the number of samples in the time period;is the current value at time i;is the average over the time period;is the time at time i, andis the average of the time period.

15. A diabetes management system that periodically determines an estimated glucose level of a patient, comprising: a continuous glucose monitor configured to: (i) sampling a current value related to a glucose level of a patient at a sampling interval over a period of time to generate a plurality of current samples over the period of time, (ii) determining an average of the plurality of current samples, (iii) determining a median of the plurality of current samples, and (iv) determining a standard deviation of the plurality of current samples; and a handheld diabetes management device in communication with the continuous glucose monitor and configured to: (i) measuring a blood glucose level of the patient at a first time, (ii) receiving a mean, a median, and a standard deviation of the plurality of current samples from the continuous glucose monitor, (iii) determining a calibration equation that associates the plurality of current samples with the estimated glucose level of the patient based on the measured blood glucose level and the plurality of current samples when the standard deviation is less than a first threshold and an absolute value of a difference between the mean and the median is less than a second threshold, and (iv) determining the estimated glucose level of the patient based on the calibration equation and a current value sampled by the continuous glucose monitor.

16. The diabetes management system of claim 15, wherein the handheld diabetes management device is further configured to: the plurality of current samples are ignored in determining the calibration equation when the standard deviation is greater than a first threshold and an absolute value of a difference between the mean and median is greater than a second threshold.