title: Home blood glucose prediction: clinical feasibility and validation in islet cell transplantation candidates.
abstract: OBJECTIVE
  Diabetic subjects do home monitoring to substantiate their success (or failure) in meeting blood glucose targets set by their providers. To succeed, patients require decision support, which, until now, has not included knowledge of future blood glucose levels or of hypoglycaemia. To remedy this, we devised a glucose prediction engine. This study validates its predictions.
  
  
  METHODS
  The prediction engine is a computer program that accesses a central database in which daily records of self-monitored blood glucose data and life-style parameters are stored. New data are captured by an interactive voice response server on-line 24 h a day, 7 days a week. Study subjects included 24 patients with debilitating hypoglycaemia (unawareness), which qualified them for islet cell transplantation. Comparison of each prediction with the actually observed data was done using a Clarke Error Grid (CEG). Patients and providers were blinded as to the predictions.
  
  
  RESULTS
  Prior to transplantation, a total of 31,878 blood glucose levels were reported by the study subjects. Some 31,353 blood glucose predictions were made by the engine on a total of 8,733 days-used. Of these, 79.4% were in the clinically acceptable Zones of the CEG. Of 728 observed episodes of hypoglycaemia, 384 were predicted. After transplantation, a total of 45,529 glucose measurements were reported on a total of 12,906 days-used. Some 42,316 glucose predictions were made, of which 97.5% were in the acceptable CEG Zones A and B. Successful transplantation eliminated hypoglycaemia, improved glycaemic control, lowered HbA(1)c and freed 10 of 24 patients from daily insulin therapy.
  
  
  CONCLUSIONS
  It is clinically feasible to generate valid predictions of future blood glucose levels. Prediction accuracy is related to glycaemic stability. Risk of hypoglycaemia can be predicted. Such knowledge may be useful in self-management.
authors.affiliation: The Bioengineering Department, University of California San Diego, La Jolla, CA, USA. albisser@NIDM.org
authors.forename: A M
authors.initials: AM
authors.lastname: Albisser
authors.forename: D
authors.initials: D
authors.lastname: Baidal
authors.forename: R
authors.initials: R
authors.lastname: Alejandro
authors.forename: C
authors.initials: C
authors.lastname: Ricordi
mesh_terms: D015190:Blood Glucose Self-Monitoring / Q000379:methods* / Q000592:standards; D016208:Databases, Factual; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy / Q000601:surgery*; D005240:Feasibility Studies; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D007328:Insulin / Q000627:therapeutic use; D016381:Islets of Langerhans Transplantation / Q000502:physiology*; D018579:Patient Selection; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012984:Software
chemical_list: D007004:Hypoglycemic Agents; D007328:Insulin