Accuracy of a Continuous Glucose Monitor During Pediatric Type 1 Diabetes Inpatient Admissions.
Erin C CobryLaura PyleLauren A WatermanGregory P ForlenzaLindsey TowersAngela J KaramiEmily JostCari BergetR Paul WadwaPublished in: Diabetes technology & therapeutics (2024)
Objective: Continuous glucose monitors (CGMs) used for type 1 diabetes management are associated with lower hemoglobin A1c. CGMs are not approved for inpatient use, when close glucose monitoring and intensive insulin management are essential for optimal health. Accuracy data from adult hospitalizations have been published, but pediatric data are limited. Design and Methods: This retrospective review of Dexcom G6 data from youth with type 1 diabetes during hospitalization assessed CGMs and matched (within 5 min) point-of-care (POC) and laboratory glucose values. Glucose values >400 and <40 mg/dL were excluded due to sensor reporting capabilities. Standard methods for CGM accuracy were used including mean absolute relative difference (MARD), Clarke Error Grids, and percentage of CGM values within 15%/20%/30% if glucose value is >100 mg/dL and 15/20/30 mg/dL if value is ≤100 mg/dL. Results: A total of 1120 POC and 288 laboratory-matched pairs were collected from 83 unique patients (median age 12.0 years, 68.7% non-Hispanic white, 54.2% male) during 100 admissions. For POC values, overall, MARD was 11.8%, that on the medical floor was 13.5%, and that in the intensive care unit was 7.9%. The MARD for all laboratory values was 6.5%. In total, 98% of matched pairs were within Clarke Error Grid A and B zones. Conclusions: Findings from our pediatric population were similar to accuracy reported in hospitalized adults, indicating the potential role for CGM use during pediatric hospitalizations. Additional research is needed to assess accuracy under various conditions, including medication use, as well as development of safe hospital protocols for successful CGM implementation for routine inpatient care.
Keyphrases
- type diabetes
- healthcare
- blood glucose
- mental health
- palliative care
- glycemic control
- electronic health record
- acute care
- end stage renal disease
- cardiovascular disease
- big data
- public health
- primary care
- ejection fraction
- insulin resistance
- prognostic factors
- young adults
- chronic kidney disease
- physical activity
- adverse drug
- childhood cancer
- peritoneal dialysis
- metabolic syndrome
- data analysis
- patient reported outcomes
- climate change
- machine learning
- risk assessment
- weight loss
- skeletal muscle
- artificial intelligence
- health insurance