scholarly journals Patient Controlled, Off-label Use of Continuous Glucose Monitoring: Real-World Medical Costs and Effects of Patient Controlled Sensor Augmented Pump Therapy in Adult Patients Type 1 Diabetes

2020 ◽  
pp. 193229682092090
Author(s):  
Patrik Hidefjäll ◽  
Lars Berg
Keyword(s):  
Type 1 Diabetes ◽  
Data Analysis ◽  
Cost Effectiveness ◽  
Continuous Glucose Monitoring ◽  
Real World ◽  
Glucose Monitoring ◽  
Off Label Use ◽  
Pump Therapy ◽  
Off Label

Background: Continuous glucose monitoring (CGM) has shown promise to reduce glycated hemoglobin (HbA1c) levels, but its cost-effectiveness is seen as uncertain by reimbursement agencies. The aim of this study was to explore the impact of real-world, off-label, patient controlled CGM use in combination with continuous subcutaneous insulin infusion (CSII) on costs and effects in patients with type 1 diabetes in a Swedish clinic. Methods: A real-world, retrospective study with questionnaire on CGM use by adult patients with type 1 diabetes on CSII (Animas Vibe) were offered sensor augmented pump therapy (SAPT) (Dexcom G4) as part of hospital innovation funding program. Direct medical costs, HbA1c, and complications following switch from CSII with self-monitoring of blood glucose (SMBG) to SAPT were calculated. Results: Questionnaire data showed that CGM sensors were on average used 92% of the time for 22 days. One hundred and thirty-nine (95%) of 146 respondents used each sensor for longer than one week. Data analysis showed a statistically significant HbA1c decrease of 0.56% (6.1 mmol/mol) after change to SAPT. In patients using the sensor 100%, the decrease was 0.89% (9.8 mmol/mol). The analysis showed that SAPT led to higher costs (5500 USD/year) than CSII + SMBG (3680 USD/year), with incremental costs being 1815 USD per year to achieve an HbA1c decrease of 0.56% (6.1 mmol/mol). The incidence of all complications declined after switch to SAPT. Conclusion: The primary data analysis showed a decrease in HbA1c values following switch to SAPT, corresponding to previous cost-effectiveness studies, but at substantially lower costs due to longer sensor off-label use.

Continuous Glucose Monitoring in Adults with Type 1 Diabetes: Real-World Data from the German/Austrian Prospective Diabetes Follow-Up Registry

2020 ◽  
Vol 22 (8) ◽  
pp. 602-612
Author(s):  
Dirk Sandig ◽  
Julia Grimsmann ◽  
Christina Reinauer ◽  
Andreas Melmer ◽  
Stefan Zimny ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Continuous Glucose Monitoring ◽  
Real World ◽  
Glucose Monitoring ◽  
Real World Data ◽  
World Data

scholarly journals The individualized statistical analysis of the continuous glucose monitoring data

2020 ◽  
Vol 48 (7) ◽  
pp. 459-468
Author(s):  
A. V. Dreval' ◽  
T. P. Shestakova ◽  
A. A. Manukyan ◽  
O. G. Brezhneva
Keyword(s):  
Type 1 Diabetes ◽  
Statistical Analysis ◽  
Pregnant Women ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Absolute Difference ◽  
Pump Therapy ◽  
Advantages And Disadvantages ◽  
Antidiabetic Treatment

Background: Continuous glucose monitoring (CGM) has shown its benefits in pregnant women with diabetes. Flash glucose monitoring (FGM), as one of the CGM types, has not been well assessed in this patient group. The interpretation of a  big volume of information on glycaemia obtained with various CGM devices is possible with statistical analysis according to the algorithms proposed by manufacturers. While these algorithms cannot be comprehensive, evaluation of alternative approaches to the CGM data statistical analysis and comparison of the results obtained with different devices seem reasonable. No unified algorithm for modification of antidiabetic treatment according to the CGM results has been yet developed. This study was performed in a  pregnant patient with type  1 diabetes mellitus (T1DM) to demonstrate the methods to individualized analysis of the data from various devices (CGM, FGM, glucometer) that could be used in routine clinical practice.Aim: To evaluate the individual advantages and disadvantages of the simultaneous use of FGM, CGM and SMBG in a pregnant woman with type 1 diabetes.Materials and methods: This was an observational case study with a retrospective assessment of the patient's data obtained with FGM, CGM and a glucometer in a 31-year female patient with T1DM of 6-year duration and 9 weeks of gestation, who had been on pump insulin therapy for one year and had an HbA1c level of 5.4%. During the study the patient continued her pump therapy and performed blood glucose self-monitoring (BGSM) and simultaneously used FGM and CGM. The following FGM data were compared with CGM and glucometer results: measurement numbers, time in range, mean daily glucose, mean absolute difference (MAD), and mean absolute relative difference (MARD).Results: The FGM-derived mean daily glucose was lower than that measured with the glucometer: 5.1±1.9  mmol/L vs 6.4±2.2  mmol/L (p<0.001). The number of measurements with FGM was 32.0±12.9  times daily and with a  glucometer 15.1±5.5  times daily (p<0.001). MAD values were minimal in the hypoglycemic range (0.5±0.3  mmol/L) and maximal in the hyperglycemic range (1.6±1.2 mmol/L, р<0.001). The MARD values were significantly smaller in the hyperglycemic than in the normoglycemic (16.6±12.6% vs 21.3±14.0%, р=0.035). The highest MAD and MARD were observed on the Day 1 of the sensor installation. The comparison of FGM and the glucometer readings with the Clarke consensus error grid showed that 82% of the FGM readings were in zone A or B. The FGM accuracy was higher from Day 2 to Day 9 (72.5% of the FGM readings in zone A). MAD between FGM and CGM readings was not different from that between FGM and the glucometer: 1.3±1.0  mmol/L and 1.2±0.9  mmol/L, respectively (p=0.09). MARD for the FGM and CGM comparison was higher than that for FGM and glucometer comparison: 24.4±23.0% and 18.8±13.5%, respectively (р<0.001). The Pearson's correlation coefficient FGM and CGM seemed lower than that between FGM and the glucometer (0.837 and 0.889, respectively). FGM has identified more hypoglycemic events compared to CGM: time below range was 29.4% and 8.8%, respectively, p<0.001).Conclusion: The FGM readings highly correlate with the glucometer. The FGM difference with the glucometer was lower in the hypo- and hyperglycemic ranges. FGM shows higher values for time below range than CGM. It is necessary to continue the study of the clinical acceptability of FGM in pregnant women and determination of its optimal regimen for the treatment of this patient category, as well as to develop an algorithm for treatment modification based on the results of FGM.

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