CN117918832A - Real-time blood glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data acquisition - Google Patents
Real-time blood glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data acquisition Download PDFInfo
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Abstract
The invention discloses a real-time blood glucose hexagonal model based on subcutaneous glucose continuous monitoring data acquisition and a propeller model evaluation algorithm, which belong to the technical field of medical information.
Description
Technical Field
The invention relates to the technical field of medical information, in particular to a real-time blood glucose hexagonal model based on subcutaneous glucose continuous monitoring data acquisition and a propeller model evaluation algorithm.
Background
Blood glucose analysis is to measure and evaluate the glucose content in blood to understand the blood glucose condition of individuals or groups, so as to diagnose, monitor and treat diseases such as diabetes and the like related to blood glucose. In conducting a blood glucose analysis, it is often necessary to measure values at two points, fasting blood glucose and postprandial blood glucose. Fasting blood glucose refers to blood glucose measured after 8-12 hours of fasting, while postprandial blood glucose is blood glucose measured 2 hours after feeding. By comparing the blood glucose values at these two time points, the individual's ability to regulate blood glucose can be assessed.
The existing blood sugar management method and system can transmit blood sugar information of patients to a nis system or a background server of a hospital for medical staff to use, store and keep files, and monitor blood sugar level, guide medicine treatment, give diabetes knowledge to announce and teach and prevent acute complications according to medical orders. However, the analysis means is single, the blood sugar display mode and the blood sugar report content are simple, the blood sugar is difficult to comprehensively analyze, and the analysis precision of medical staff is affected.
Therefore, it is necessary to provide a real-time blood glucose hexagonal model based on subcutaneous glucose continuous monitoring data acquisition and a propeller model evaluation algorithm, so as to solve the above problems.
Disclosure of Invention
Aiming at the defects existing in the prior art, the embodiment of the invention aims to provide a real-time blood glucose hexagonal model based on subcutaneous glucose continuous monitoring data acquisition and a propeller model evaluation algorithm so as to solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A real-time blood glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data acquisition comprises the following steps:
step S1, continuously detecting subcutaneous glucose to obtain blood glucose data in real time;
s2, processing blood glucose data and performing visual map display on the data processing result;
Step S3, performing high and low blood sugar alarm according to real-time data, performing blood sugar assessment according to different time periods and scenes, and simultaneously, automatically generating a blood sugar report from all data;
And S4, uploading the obtained data to a cloud system, and storing and processing the data by the system.
As a further aspect of the present invention, the visual map display content in the step S2 includes a blood glucose fluctuation curve, a diet record, a drug/insulin record, a reference blood glucose, and an exercise record.
As a further aspect of the present invention, the blood glucose report in the step S3 includes user information, glucose profile, single-day continuous map, 24-hour overlapping map, glucose fluctuation trend map, glucose index model, daily glucose propeller model, and periodic glucose propeller model glucose analysis table, daily glucose analysis, overall high/low glucose analysis, overall glucose test result, and glucose control standard result.
As a further aspect of the invention, the glucose profile comprises glucose level, glucose range, blood glucose fluctuations, glucose level details, blood glucose fluctuation details.
As a further aspect of the present invention, the glucose indicator model in step S3 includes a glucose hexagonal model, a daily glucose propeller model, and a periodic glucose propeller model.
As a further aspect of the present invention, the indexes in the glucose hexagonal model include time when blood glucose is out of a target range, coefficient of variation, hypoglycemic strength, hyperglycemic strength, average blood glucose value, and glucose management index.
As a further aspect of the present invention, the indexes in the daily glucose propeller model include time when blood glucose exceeds a target range, variation coefficient, low blood glucose intensity, high blood glucose intensity, average blood glucose value, average blood glucose fluctuation amplitude in the day, maximum blood glucose fluctuation amplitude, average standard deviation of blood glucose in the day, blood glucose risk assessment, average daily risk value and standard deviation of multi-point blood glucose in the day.
As a further aspect of the present invention, the indexes in the periodic glucose propeller model include a glucose management index, a coefficient of variation, a hypoglycemic strength, a hyperglycemic strength, an average blood glucose value, a maximum blood glucose fluctuation range, and a blood glucose risk assessment.
In summary, compared with the prior art, the embodiment of the invention has the following beneficial effects:
According to the invention, through acquiring blood glucose information in real time and visually displaying an information map and uploading the information map to the cloud in real time, a complete blood glucose analysis report comprising various data and results can be generated, the risk of data loss is reduced, the data difference and fluctuation can be fully observed through a glucose hexagonal model, a daily glucose propeller model and a periodic glucose propeller model, the blood glucose data can be conveniently and intuitively analyzed and managed by medical staff, the blood glucose analysis report visual display device has the effects of visually displaying the data map, comprehensively and reliably analyzing the blood glucose conveniently and intuitively, and the blood glucose control condition, the specific blood glucose fluctuation condition and the high/low blood glucose occurrence condition of diabetics can be comprehensively evaluated.
In order to more clearly illustrate the structural features and efficacy of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a workflow diagram of a real-time blood glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data acquisition in an embodiment of the invention.
FIG. 2 is a schematic diagram of a glucose hexagonal model in an embodiment of the invention.
FIG. 3 is a schematic representation of a daily glucose propeller model in an inventive example.
Fig. 4 is a schematic diagram of a periodic glucose propeller model in an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
In one embodiment of the present invention, referring to fig. 1,2, 3 and 4, the real-time blood glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data acquisition comprises the following steps:
step S1, continuously detecting subcutaneous glucose to obtain blood glucose data in real time;
s2, processing blood glucose data and performing visual map display on the data processing result;
Step S3, performing high and low blood sugar alarm according to real-time data, performing blood sugar assessment according to different time periods and scenes, and simultaneously, automatically generating a blood sugar report from all data;
And S4, uploading the obtained data to a cloud system, and storing and processing the data by the system.
In this embodiment, the visual map display content in the step S2 includes a blood glucose fluctuation curve, a diet record, a drug/insulin record, a reference blood glucose, and an exercise record;
The blood glucose report in the step S3 includes user information, glucose profile, single-day continuous map, 24-hour overlapping map, glucose fluctuation trend map, glucose index model, daily glucose propeller model, glucose analysis table of periodic glucose propeller model, daily glucose analysis, overall high/low blood glucose analysis, overall glucose detection result, and glucose control standard reaching result;
the glucose index model in the step S3 comprises a glucose hexagonal model, a daily glucose propeller model and a periodic glucose propeller model;
The blood glucose information is obtained in real time, the information map is visually displayed, the cloud is uploaded in real time, a complete blood glucose analysis report comprising various data and results can be generated, the risk of data loss is reduced, the data difference and fluctuation can be fully observed through a glucose hexagonal model, a daily glucose propeller model and a periodic glucose propeller model, medical staff can intuitively analyze blood glucose data and manage blood glucose conveniently, and the blood glucose analysis report has the effects of visual display of the data map, comprehensive reliability of the blood glucose analysis report and convenience in intuitively analyzing blood glucose.
Specific:
(1) User information in a blood glucose report includes, but is not limited to: name, gender, age, past diagnosis, hospital, department, care level, hospital number, clinic number, wearing duration, start time, end time, etc.;
(2) The glucose profile includes:
glucose levels, the main content being average glucose value (mmol/L) and eHbA c value (glycosylated hemoglobin value,%);
The glucose range mainly comprises clinical symptoms of obvious hypoglycemia (< 3 mmol/L), hypoglycemia early warning (< 3 mmol/L), TIR (glucose standard reaching time percentage, TIME IN RANGE,3.9-10.0 mmol/L), hyperglycemia early warning (> 10 mmol/L) and clinical symptoms of obvious hyperglycemia (> 13.9 mmol/L);
Blood glucose excursions, including CV (coefficient ofvariation, coefficient of variation,%), SD (standard deviation, mmol/L) and data sufficiency/CGM usage time (%);
glucose level detail, which is the area under the curve ((mmol/L) h) of the daytime, nighttime and throughout the day, respectively, which is the area under the curve per hour of less than 3.9 mmol/L;
The blood sugar fluctuation details comprise IQR (quartile range, mmol/L), MAGE (mean blood sugar fluctuation amplitude in the day), MODD (mean standard deviation of blood sugar in the day), HBGI (high glycemic index) and LBGI (low glycemic index), wherein the IQR is a description mode of blood sugar distribution, all values are arranged from small to large and are divided into four equal parts, the values at the positions of three division points are quartiles, and the lengths of the equal parts are the quartiles range IQR;
(3) The single-day continuous map is displayed as a visual map, and the time sequence is a wearing period (from wearing start to current time period), wherein daily data curves are marked by different colors respectively, and diet recording time points, medicine/insulin use time points, reference blood sugar input time points, exercise time points and the like are marked in the curves at the same time.
(4) The 24-hour continuous map is displayed by a visual map, the time sequence is 24 hours, and the blood glucose curves of each day in the wearing period are drawn by different color curves at the same time;
(5) The glucose fluctuation trend graph is a daily glucose fluctuation trend graph, the glucose value unit is (mmol/L), a target range interval (3.9-10) is marked in the graph, and the graph is subjected to color deepening treatment, and the average blood sugar value is given at the same time;
(6) The main contents of the glucose hexagonal model diagram comprise TOR (time when blood sugar exceeds a target range, time out ofrange min/d), variation coefficient CV (%), low blood sugar intensity (MG/dl min), high blood sugar intensity (MG/dl min), average blood sugar value MG (mmol/L) and glucose management index GMI (%), and the reference value ranges of the indexes are displayed and marked by colors at the same time of drawing the indexes;
(7) Daily glucose propeller model map, the main content includes: TOR (time when blood glucose exceeds a target range, time out ofrange, min/d), coefficient of variation CV (%), low blood glucose level (MG/dl×min), high blood glucose level (MG/dl×min), average blood glucose level MG (mmol/L), MAGE (mean daily blood glucose excursion), MAGE (maximum blood glucose excursion), MODD (mean daily blood glucose standard deviation), GRADE (blood glucose risk assessment), ADRR (mean daily risk value), SDBG (standard deviation of daily multipoint blood glucose);
(8) The periodic glucose propeller model diagram mainly comprises the following components: seven indexes of glucose management index GMI (%), variation coefficient CV (%), low blood sugar strength (MG/dl min), high blood sugar strength (MG/dl min), average blood sugar value MG (mmol/L), LAGE (maximum blood sugar fluctuation amplitude) and GRADE (blood sugar risk assessment);
Wherein a) time to glucose out of target range (TOR): the parameter is complementary with the Time (TIR) of the blood sugar in the target range, and the formula and the quantitative formula are as follows:
tor=1440-TIR in minutes;
TOR axis (mm) = (tor× 0.00614) ×1.581+14;
b) Coefficient of Variation (CV): the ratio of Standard Deviation (SD) to mean glucose value (MG) is 100% and the CV axis quantization formula is:
CV axis= (CV-17) ×0.92+14;
c) Hypoglycemic intensity (IntHyper): calculated by AUC (area under curve) and time (T) with blood sugar less than 70mg/dl, the calculation formula and the quantification formula are as follows:
IntHyper axis= (IntHyper × 0.000115) 1.51 +14;
d) Hyperglycemia strength (IntHypo): the AUC and time calculation of the blood sugar of more than 160mg/dl are used, and the calculation formula and the quantification formula are as follows:
IntHypo axis = e (IntHypo×0.00057) +13;
e) Average glucose (MG): the average value of the glucose measurement values and the MG axis quantization formula are:
MG axis= [ (MG-90) ×0.0217] 2.63 +14;
f) Glucose Management Index (GMI): the blood glucose control level is evaluated by the following calculation formula:
GMI(％)＝3.31+0.02392×[mean glucose in mg/dL]，
wherein "mean glucose in mg/dL" is the average blood glucose value of at least 10 days of CGM monitoring data;
g) Mean daily blood glucose fluctuation amplitude (MAGE): screening glucose fluctuation with 24h fluctuation range of >1 SD, counting the glucose fluctuation range according to the first effective fluctuation direction, wherein MAGE is the average value of all glucose fluctuation ranges, and the calculation formula is as follows:
wherein lambda is the difference between the maximum value and the minimum value of each effective glucose fluctuation, x is the number of effective fluctuation times, v is 1 SD of average glucose for 24 hours;
h) Maximum blood glucose excursion (range): the difference between the maximum glucose value and the minimum glucose value is calculated by the following formula:
LAGE＝Gmax-Gmin，
Wherein G max is the measured maximum glucose value and G min is the measured minimum glucose value;
i) Mean standard deviation of daily blood glucose (MODD): the average value of the absolute differences between the glucose matched with 2 continuous glucose monitoring patterns for 24 hours is calculated as follows:
Where k is the number of matched glucose, G t is the glucose monitor, G t-1440 is the glucose monitor before 1d matched to G t;
j) Blood Glucose Risk Assessment (GRADE): by calculating the weights of the high glucose, the low glucose and the normal glucose on the equation, if the weights deviate from the normal glucose more, the calculation formula is as follows:
GRADE＝median(425×{log[log(Gn)]+0.16}2)，
Wherein G is a measured glucose value (mmol/L);
k) Average daily risk value (ADRR): the glucose monitoring result is statistically converted into a high glucose or low glucose risk value, and then the average value of the risk values is calculated, wherein the calculation formula is as follows:
xi＝1.509×{[ln(Gi)]1.084-5.381}，
wherein N is the total number of glucose measurements, LR is a low glucose risk value, HR is a high glucose risk value, x i is the converted glucose value;
l) standard deviation of multipoint blood glucose within day (SDBG): the degree of deviation of all blood sugar measured values of a patient from the average blood sugar level is represented, the discrete feature of blood sugar is reflected, the practicability is high, the blood sugar measuring device is an important simple parameter for evaluating blood sugar fluctuation, and the calculation formula is as follows:
Where x i is the glucose measurement and μ is the mean blood glucose level;
(9) The glucose analysis table comprises the measurement times, average value, standard deviation, variation coefficient, highest glucose value, lowest glucose value, maximum fluctuation amplitude of the scholars, average field of blood glucose with previous day, average fasting value, average postprandial value (1 h, 2h and 3 h), each interval level of glucose values, TIR, time area of high/low glucose curve, average fluctuation amplitude of glucose, estimated glycosylated hemoglobin and high/low glucose time proportion of each stage, etc. in each time period (each day or each period) in the wearing period, and the reference values, total measurement times of blood glucose, average blood glucose value, standard deviation, variation coefficient, highest/low blood glucose value and blood glucose time ratio of each interval are given.
(10) Daily glucose analysis mainly comprises a daily glucose map, blood glucose fluctuations, estimated glycation values, details of blood glucose fluctuations and the like, wherein the blood glucose fluctuations comprise CV (%) and SD (mmol/L); details of glycemic fluctuation, including IQR (interquartile range, mmol/L), MAGE (mean glycemic fluctuation amplitude over the day), MODD (mean standard deviation of daily blood glucose), HBGI (high glycemic index), LBGI (low glycemic index);
(11) The overall hyperglycemic/hypoglycemic analysis mainly analyzes the hyperglycemic/hypoglycemic time details, including average time (min/day), average onset (times/day) and average duration (min) of hyperglycemia and hypoglycemia, wherein the hypo-glycemic is in the interval of <3.0 and <3.9mmol/L, and the hyperglycemic is in the interval of >7.8, >10.0 and >13.9 mmol/L;
(12) The overall glucose test results mainly comprise glucose level, glucose fluctuation, hypoglycemia result, hyperglycemia result and preliminary analysis summary and suggestion;
Wherein the glucose level mainly comprises an average glucose value and a reference value, an estimated glycosylated hemoglobin and a reference value, a TIR standard reaching rate (3.9-10 mmol/L) and a reference value, and a TIR standard reaching rate (3.9-7.8 mmol/L) and a reference value;
Glucose excursion comprises a coefficient of variation (CV,%), an average glucose excursion (mmol/L), a standard deviation (SD, mmol/L), a quartile range (IQR, mmol/L) and respective reference values;
the high/low blood sugar results mainly comprise time proportion of each level of high/low blood sugar, a reference value, a high/low blood sugar index and a reference value;
The glucose control standard condition mainly comprises TIR (at target Time proportion, TIME INRANGE,%) TBR (lower than target Time proportion, time below Range,%) TAR (higher than target Time proportion, time above Range,%) and respective reference values;
the preliminary analysis summary mainly comprises relevant analysis results such as whether the user confirms diabetes mellitus, abnormal blood sugar fluctuation assessment, blood sugar management advice and the like.
In conclusion, through obtaining blood glucose information in real time, and visually displaying an information map, uploading the information map to the cloud in real time, a complete blood glucose analysis report comprising various data and results can be generated, the risk of data loss is reduced, data differences and fluctuation can be fully observed through a glucose hexagonal model, a daily glucose propeller model and a periodic glucose propeller model, medical staff can intuitively analyze and evaluate blood glucose data and manage blood glucose conveniently, and the blood glucose analysis report monitoring system has the effects of visually displaying the data map, comprehensively and reliably reporting the blood glucose analysis report, and intuitively analyzing and evaluating blood glucose conveniently.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (8)
1. The real-time blood glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data acquisition is characterized by comprising the following steps of:
step S1, continuously detecting subcutaneous glucose to obtain blood glucose data in real time;
s2, processing blood glucose data and performing visual map display on the data processing result;
Step S3, performing high and low blood sugar alarm according to real-time data, performing blood sugar assessment according to different time periods and scenes, and simultaneously, automatically generating a blood sugar report from all data;
And S4, uploading the obtained data to a cloud system, and storing and processing the data by the system.
2. The real-time blood glucose hexagonal model and propeller model assessment algorithm based on continuous subcutaneous glucose monitoring data acquisition according to claim 1, wherein the visual map presentation in step S2 comprises blood glucose wave curves, diet records, drug/insulin records, reference blood glucose and exercise records.
3. The real-time blood glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data collection according to claim 1, wherein the blood glucose report in step S3 includes user information, glucose profile, single day continuous map, 24 hour overlay map, glucose fluctuation trend map, glucose index model, daily glucose propeller model and periodic glucose propeller model glucose analysis table, daily glucose analysis, overall high/low blood glucose analysis, overall glucose test results and glucose control standard results.
4. The real time glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data collection of claim 3, wherein the glucose profile comprises glucose level, glucose range, glucose fluctuations, glucose level details, glucose fluctuation details.
5. The evaluation algorithm of the real-time glucose hexagonal model and the propeller model based on subcutaneous glucose continuous monitoring data acquisition according to claim 3, wherein the glucose indicator model in step S3 comprises a glucose hexagonal model, a daily glucose propeller model and a periodic glucose propeller model.
6. The real-time glucose hexagonal model and propeller model evaluation algorithm of claim 5 wherein the indices in the glucose hexagonal model include time to glucose out of target range, coefficient of variation, hypoglycemic intensity, hyperglycemic intensity, mean glucose value, and glucose management index.
7. The real-time glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data acquisition according to claim 5, wherein the indicators in the daily glucose propeller model include time when blood glucose is out of a target range, coefficient of variation, hypoglycemic strength, hyperglycemic strength, average blood glucose level, daily average blood glucose fluctuation amplitude, maximum blood glucose fluctuation amplitude, daily average standard deviation, blood glucose risk assessment, daily average risk value, and daily multi-point blood glucose standard deviation.
8. The real-time glucose hexagonal model and propeller model evaluation algorithm based on subcutaneous glucose continuous monitoring data acquisition of claim 5, wherein the indices in the periodic glucose propeller model include glucose management index, coefficient of variation, hypoglycemic intensity, hyperglycemic intensity, average blood glucose value, maximum blood glucose fluctuation amplitude, and blood glucose risk assessment.
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| US20100106000A1 (en) * | 2008-10-27 | 2010-04-29 | Medtronic Minimed, Inc. | Methods and Systems for Evaluating Glycemic Control |
| CN105160199A (en) * | 2015-09-30 | 2015-12-16 | 刘毅 | Continuous blood sugar monitoring based method for processing and displaying diabetes management information with intervention information |
| CN110786865A (en) * | 2018-08-02 | 2020-02-14 | 西诺嘉医药有限公司 | System and method for controlling blood glucose using personalized histograms |
| CN115762782A (en) * | 2022-11-22 | 2023-03-07 | 南京晶捷生物科技有限公司 | Blood glucose analysis method based on CGM dynamic graph and edge calculation device |
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- 2024-01-31 CN CN202410135069.9A patent/CN117918832A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100106000A1 (en) * | 2008-10-27 | 2010-04-29 | Medtronic Minimed, Inc. | Methods and Systems for Evaluating Glycemic Control |
| CN105160199A (en) * | 2015-09-30 | 2015-12-16 | 刘毅 | Continuous blood sugar monitoring based method for processing and displaying diabetes management information with intervention information |
| CN110786865A (en) * | 2018-08-02 | 2020-02-14 | 西诺嘉医药有限公司 | System and method for controlling blood glucose using personalized histograms |
| CN115762782A (en) * | 2022-11-22 | 2023-03-07 | 南京晶捷生物科技有限公司 | Blood glucose analysis method based on CGM dynamic graph and edge calculation device |
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