title: Reduction of number and duration of hypoglycemic events by glucose prediction methods: a proof-of-concept in silico study.
abstract: BACKGROUND
  Hypoglycemia prevention is one of the major challenges in diabetes research. Recently, it has been suggested that continuous glucose monitoring (CGM)-based short-term glucose prediction algorithms could be exploited to generate alerts when hypoglycemia is forecasted, allowing the patient to take appropriate countermeasures to avoid/mitigate the event. However, quantifying the potential benefits of prediction in terms of reduction of number/duration of hypoglycemia requires an in silico assessment that is the object of the present article.
  
  
  METHODS
  Data for 50 virtual subjects were generated by using the University of Virginia/Padova type 1 diabetes simulator (54-h monitoring), made more credible by adding realistic measurement noise and perturbations of meals and insulin injections. CGM was assumed to be well calibrated. Occurrence and duration of hypoglycemic events were compared in three scenarios: (1) hypoglycemia was not recognized and not dealt with; (2) 15 g of carbohydrates was ingested when CGM crossed the hypoglycemia threshold; or (3) 15 g of carbohydrates was ingested when the 30-min ahead-of-time CGM prediction crossed the hypoglycemia threshold. The effectiveness of alerts was investigated also in the case of delayed/absent ingestion of carbohydrates.
  
  
  RESULTS
  In Scenario 1, each virtual subject spent 17.7% of the time in the hypoglycemic range, with a median of four events of 120 min in the 54-h period monitored. In Scenario 2, the time spent in hypoglycemia was reduced to 4.7% (four events of 40 min). In Scenario 3, the time spent in hypoglycemia was further reduced to 1.2% (one event of 15 min). Absent/delayed patient's responses to alerts slightly increase these percentages, but improvements remain significant.
  
  
  CONCLUSIONS
  This in silico proof-of-concept study demonstrates that using predicted rather than measured CGM allows a significant reduction of the number of hypoglycemic events and the time spent in hypoglycemic range both by 75%, stimulating further research and clinical investigation on the generation of preventive hypoglycemic alerts exploiting glucose prediction methods.
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Zecchin
authors.forename: Andrea
authors.initials: A
authors.lastname: Facchinetti
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D002138:Calibration; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy / Q000378:metabolism*; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000517:prevention & control*; D018670:Monitoring, Ambulatory / Q000379:methods*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results
chemical_list: D001786:Blood Glucose