title: How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study.
abstract: In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study.
  
  
  
  We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis.
  
  
  
  For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin.
  
  
  
  In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Zecchin
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Facchinetti
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy cobelli@dei.unipd.it.
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use*; D007328:Insulin / Q000627:therapeutic use*; D007332:Insulin Infusion Systems; D062407:Meals
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin
keywords: continuous glucose monitoring; neural network; nonlinear modeling; sensitivity analysis; signal processing