Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes
Authors' conclusion
Our scoring model captures the complexity of CGM data and can identify both the most alarming dimension of glycemia and the individuals in most urgent need of assistance. This could become a valuable tool for population management at diabetes clinics to enable healthcare providers to stratify care to the patients in greatest need of clinical attention.
Study context
- Population
- Individuals with T1D with continuous glucose monitoring (CGM) data 2017–2020 in electronic medical records82,114 days
- Participants
- 613 individuals
- Exposure
- Scoring model vs clinical expert board (CEB)
- Outcome
- Identification of time periods with 'true' target fulfillment (score ≥80 cut-off)
Methodological detailsmodel development
- Model development
Main finding
As reported in the abstract
Accuracy of score ≥80 cut-off for 'true' target fulfillment
93.4%
Specificity of score ≥80 cut-off for 'true' target fulfillment
97.1%
Accuracy of scoring model vs CEB for identifying the most alarming CGM curves (overall)
86.5%
Observational data: associations, not effects.
Further findings
Accuracy vs clinical expert board (CEB) in identifying most alarming CGM curves
- Overall accuracy vs CEB
- 86.5%
Original publication
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