Automated Algorithm Of Parameters Based On Continuous Glucose Monitoring Data For Evaluating Glycemic Excursion

Diabetes have become the third chronic diseases that is serious threat to human health,which have great impact to both personal health and socio-economic.Diabetic chronic complications gradually become the main threat factors to life and health.Studies show that glycemic excursion is an independent risk factor for the occurrence and development of diabetic chronic complications.If long-term glycemic excursion is too big,it will certainly increase the risk of diabetic complications,so diabetics monitoring and control of glycemic excursion is the effective measure to reduce diabetic chronic complications.For better controlling of glycemic excursion,it needs accurately quantified evaluation.So there are many parameters that have been put forward for it.Includes:SDBG(Standard Deviation of Blood Glucose),MGB(Mean of Blood Glucose),IQR,M-value,%CV,J-index,LAGE,LBGI(Low Blood Glucose Index),HBGI(High Blood Glucose Index),GRADE(Glycemic Risk Assessment Diabetes Equation),ARR(Average Daily Risk Range),MAGE(Mean Amplitude of Glycemic Excursions),MODD(Mean of Daily Difference),COANGAn=24(Continuous Overall Net Glycemic Action).But so far there is no universally recognized best parameter.With the rapid development of Continuous Glucose Monitoring(CGM)technology and its improved reliability,and MAGE has unique advantage in reflecting glycemic excursion with oxidative stress,it has been recognized by more and more clinicians,and considered the "golden standard" of evaluating glycemic excursion.CGM brings a lot of blood glucose data for evaluating glycemic excursion.However the CGM system itself does not provide the function of computing all the parameters mentioned above,so when using the CGM data to calculate all the parameters,the workload will be great,especially calculating the MAGE,because in clinical it can only calculate by applying manual method.If patient's data increase,the parameter calculation workload will greatly increase,it will consume a lot of time.In addition,the clinical researchers must go through professional training that can ensure the accuracy of data filtering,or it will lead to error result.Now at home and aboard has put forward several automated calculation method for MAGE,but they did not get clinical widely recognized,it mainly because these method are lacking the support of mathematical theory that makes the accuracy of calculation results questioned by clinical;Also they don't include all the other evaluated parameters,making it not comprehensively at all for evaluating glycemic excursion.Therefore,in order to solve these problems,this study proposed an automated algorithm for the calculation of all parameters for glycemic excursion,especially for MAGE calculation,combining with a sound standard to determine effective glycemic excursion,this algorithm established a mathematical model based on integer nonlinear programming method and apply Differential Evolution(DE)algorithm to solve it.To further make it applied to clinical,using C#language develop the software which can be installed and used in Windows system.The interface of the software is convenient,easy to operate and flexible,someone without related professional training can also import the CGM data to calculate the value of parameters.In order to overcome the problem that there is no test standard for MAGE calculation result,this study compare the results of a large number of CGM clinical data of different type diabetes respectively calculated using the algorithm and the traditional method.It shows they have highly relevant correlation and consistency that verifies the reliability of the algorithm in computing MAGE.The proposed algorithm makes it easier to obtain the value of evaluated parameters,greatly shorten the calculation time and improve the efficiency of the clinical.With this,it will further promote the evaluation researches of glycemic excursion.