title: A dynamic risk measure from continuous glucose monitoring data.
abstract: BACKGROUND
  The quantitative analysis of glucose time-series can greatly help the management of diabetes. In particular, a static nonlinear transformation, which symmetrizes the distribution of glucose levels by bringing them in the so-called risk space, was proposed previously for both self-monitoring blood glucose and continuous glucose monitoring (CGM) and extensively used in the literature. The continuous nature of CGM data allows us to further refine the risk space concept in order to account for glucose dynamics.
  
  
  METHODS
  A new dynamic risk (DR) is proposed to explicitly consider the rate of change of glucose as a threat factor for the patient (e.g., risk levels in hypoglycemia and hyperglycemia are amplified in the presence of a decreasing and increasing glucose trend, respectively). The practical calculation of DR is made possible by the use of a regularized deconvolution algorithm that is able to deal with noise in CGM data and with the ill-conditioning of the time-derivative calculation, even in online applications.
  
  
  RESULTS
  Results on simulated and real data show that DR can be effectively computed and fruitfully used in real time (e.g., to generate early warnings of hypo-/hyperglycemic threshold crossings). Further applications of DR in the quantification of the efficiency of glucose control are also suggested.
  
  
  CONCLUSIONS
  Exploiting the information on glucose trends empowers the strength of risk measures in interpreting CGM time-series.
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Stefania
authors.initials: S
authors.lastname: Guerra
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.forename: Andrea
authors.initials: A
authors.lastname: Facchinetti
authors.forename: Michele
authors.initials: M
authors.lastname: Schiavon
authors.forename: Chiara Dalla
authors.initials: CD
authors.lastname: Man
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000097:blood*; D005189:False Positive Reactions; D006801:Humans; D006943:Hyperglycemia / Q000097:blood*; D007003:Hypoglycemia / Q000097:blood*; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose