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January 31, 2014IEEE Transactions on Biomedical Engineering

Improving Accuracy and Precision of Glucose Sensor Profiles: Retrospective Fitting by Constrained Deconvolution

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Population

24 datasets collected in a 20 h clinical trial where continuous glucose monitoring records and a median of…

Comparison

Algorithm performing constrained semiblind… vs Unprocessed continuous glucose monitoring data

Design

Other

Follow-up

20 hours

Authors

SFSimone Del FaveroUniversity of PaduaAFAndrea FacchinettiUniversity of PaduaGSGiovanni SparacinoUniversity of Padua

Discussion

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Implication

May enable reliable CGM use for trial endpoints and modeling; leaves open prospective validation before clinical adoption.

Structured PICO

P
Population
24 datasets collected in a 20 h clinical trial where continuous glucose monitoring (CGM) records and a median of 13 blood-glucose samples per day were available
I
Intervention
Algorithm performing constrained semiblind deconvolution to retrospectively reconstruct a continuous-time blood-glucose profile
C
Comparator
Unprocessed continuous glucose monitoring (CGM) data
O
Outcome
Mean absolute relative deviation (MARD)surrogate

The proposed algorithm significantly improves the accuracy and precision of continuous glucose monitoring profiles, making them suitable for clinical trial assessment and modeling.

Cite This Study

Favero et al. (2014) studied this question.

synapsesocial.com/papers/6a7023b18031ec7bb1dc2566https://doi.org/10.1109/tbme.2013.2293531
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