scholarly journals Health Care Costs, Hospital Admissions, and Glycemic Control Using a Standalone, Real-Time, Continuous Glucose Monitoring System in Commercially Insured Patients With Type 1 Diabetes

2018 ◽  
Vol 12 (4) ◽  
pp. 800-807 ◽  
Author(s):  
Max Gill ◽  
Cyrus Zhu ◽  
Mona Shah ◽  
Harmeet Chhabra
Keyword(s):  
Health Care ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Real Time ◽  
Health Care Costs ◽  
Continuous Glucose Monitoring ◽  
Hospital Admissions ◽  
Glucose Monitoring ◽  
Care Costs

Background: Studies comparing standalone real-time continuous glucose monitoring (rtCGM) to self-monitoring of blood glucose (SMBG) in patients with type 1 diabetes mellitus (T1DM) have found that rtCGM is associated with lower glycated hemoglobin (A1C) levels, yet does not increase the risk of severe hypoglycemia. However, little is known about the relationship between rtCGM and health care costs and utilization. The objective of this study was to compare health care spending, hospital admissions, and A1C levels of patients using rtCGM to that of patients not using rtCGM. Methods: This retrospective, cross-sectional analysis used a large repository of health plan administrative data to compare average health care costs (excluding durable medical equipment), hospital admissions, and A1C levels of those using rtCGM (N = 1027) versus not using rtCGM (N = 32 583). To control for potentially confounding variables, a propensity score method was used to match patients using rtCGM to those not using rtCGM, based on characteristics such as age, gender, and comorbidities. Results: Patients using rtCGM spent an average of approximately $4200 less in total health care costs, when compared to patients not using rtCGM ( P < .05). They also experienced fewer hospital admissions ( P < .05) and lower A1C ( P < .05) during the postindex year. Conclusions: Use of rtCGM by patients with T1DM is associated with lower health care costs, fewer hospital admissions, and better glycemic control.

scholarly journals Improved Glycemic Control in Poorly Controlled Patients with Type 1 Diabetes Using Real-Time Continuous Glucose Monitoring

Diabetes Care ◽  
2006 ◽  
Vol 29 (12) ◽  
pp. 2730-2732 ◽  
Author(s):  
D. Deiss ◽  
J. Bolinder ◽  
J.-P. Riveline ◽  
T. Battelino ◽  
E. Bosi ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring

The impact of real-time continuous glucose monitoring on metabolic control in children with type 1 diabetes

2020 ◽  
Author(s):  
Ruxandra Calapod Ioana ◽  
Irina Bojoga ◽  
Duta Simona Gabriela ◽  
Ana-Maria Stancu ◽  
Amalia Arhire ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Metabolic Control ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
The Impact

scholarly journals Heterogeneity of health care costs among medicare advantage patients with type 1 diabetes

2014 ◽  
Vol 17 (3) ◽  
pp. A246
Author(s):  
G.S. Clore ◽  
S.L. Slabaugh ◽  
B.H. Curtis ◽  
H. Fu ◽  
D.P. Schuster
Keyword(s):  
Health Care ◽  
Type 1 Diabetes ◽  
Health Care Costs ◽  
Medicare Advantage ◽  
Care Costs

scholarly journals Functional data analysis and prediction tools for continuous glucose-monitoring studies

2020 ◽  
pp. 1-10
Author(s):  
Emrah Gecili ◽  
Rui Huang ◽  
Jane C. Khoury ◽  
Eileen King ◽  
Mekibib Altaye ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Functional Data ◽  
Diabetes Management ◽  
Glucose Monitoring ◽  
Patient Specific ◽  
Prediction Errors ◽  
Medical Monitoring

Abstract Introduction: To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes. Methods: A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions. Results: The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions. Conclusions: By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual’s glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.

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