title: STAR development and protocol comparison.
abstract: Accurate glycemic control (AGC) is difficult due to excessive hypoglycemia risk. Stochastic TARgeted (STAR) glycemic control forecasts changes in insulin sensitivity to calculate a range of glycemic outcomes for an insulin intervention, creating a risk framework to improve safety and performance. An improved, simplified STAR framework was developed to reduce light hypoglycemia and clinical effort, while improving nutrition rates and performance. Blood glucose (BG) levels are targeted to 80-145 mg/dL, using insulin and nutrition control for 1-3 h interventions. Insulin changes are limited to +3U/h and nutrition to ±30% of goal rate (minimum 30%). All targets and rate change limits are clinically specified and generalizable. Clinically validated virtual trials were run on using clinical data from 371 patients (39841 h) from the Specialized Relative Insulin and Nutrition Tables (SPRINT) cohort. Cohort and per-patient results are compared to clinical SPRINT data, and virtual trials of three published protocols. Performance was measured as time within glycemic bands, and safety by patients with severe (BG < 40 mg/dL) and mild (%BG < 72 mg/dL) hypoglycemia. Pilot trial results from the first ten patients (1486 h) are included to support the in-silico findings. In both virtual and clinical trials, mild hypoglycemia was below 2% versus 4% for SPRINT. Severe hypoglycemia was reduced from 14 (SPRINT) to 6 (STAR), and 0 in the pilot trial. AGC was tighter than both SPRINT clinical data and in-silico comparison protocols, with 91% BG within the specified target (80-145 mg/dL) in virtual trials and 89.4% in pilot trials. Clinical effort (measurements) was reduced from 16.2/day to 11.8/day (13.5/day in pilot trials). This STAR framework provides safe AGC with significant reductions in hypoglycemia and clinical effort due to stochastic forecasting of patient variation-a unique risk-based approach. Initial pilot trials validate the in-silico design methods and resulting protocol, all of which can be generalized to suit any given clinical environment.
authors.affiliation: Department of Mechanical Engineering, University of Canterbury, Christchurch 8140, New Zealand. liam.fisk@pg.canterbury.ac.nz
authors.forename: Liam M
authors.initials: LM
authors.lastname: Fisk
authors.forename: Aaron J
authors.initials: AJ
authors.lastname: Le Compte
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Shaw
authors.forename: Sophie
authors.initials: S
authors.lastname: Penning
authors.forename: Thomas
authors.initials: T
authors.lastname: Desaive
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis / Q000187:drug effects / Q000378:metabolism*; D002986:Clinical Trials as Topic; D015331:Cohort Studies; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000517:prevention & control*; D007004:Hypoglycemic Agents / Q000008:administration & dosage*; D007328:Insulin / Q000008:administration & dosage*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin