AGATA

# Risk metrics

# lbgi

function lbgi = lbgi(data)

Function that computes the low blood glucose index (LBGI) of the glucose concentration (ignores nan values).

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • lbgi: double
    The low blood glucose index (LBGI) of the glucose concentration (unitless).

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Kovatchev et al., "Symmetrization of the blood glucose measurement scale and its applications", Diabetes Care, 1997, vol. 20, pp. 1655-1658. DOI: 10.2337/diacare.20.11.1655.

# hbgi

function hbgi = hbgi(data)

Function that computes the high blood glucose index (HBGI) of the glucose concentration (ignores nan values).

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • hbgi: double
    The low blood glucose index (HBGI) of the glucose concentration (unitless).

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Kovatchev et al., "Symmetrization of the blood glucose measurement scale and its applications", Diabetes Care, 1997, vol. 20, pp. 1655-1658. DOI: 10.2337/diacare.20.11.1655.

# bgri

function bgri = bgri(data)

Function that computes the blood glucose risk index (BRGI) of the glucose concentration (ignores nan values).

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • bgri: double
    The blood glucose risk index (BGRI) of the glucose concentration (unitless).

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Kovatchev et al., "Symmetrization of the blood glucose measurement scale and its applications", Diabetes Care, 1997, vol. 20, pp. 1655-1658. DOI: 10.2337/diacare.20.11.1655.

# dynamicRisk

function dynamicRisk = dynamicRisk(data,varargin)

Function that computes the dynamic risk based on current glucose concentration and its rate-of-change. An AmplificationFunction is used to eventually amplify the effect of the rate-of-change. The function treats nan values as missing values.

# Inputs

  • data: timetable (required)
    A timetable with column Time and glucose containing the glucose data to analyze (in mg/dl);
  • AmplificationFunction: vector of char (optional, default: 'tanh', {'tanh','exp'})
    Function to use for amplyfing the rate-of-change impact on the dynamic risk calculation;
  • MaximumAmplification: double (optional, default: 2.5)
    Intensity of amplification;
  • AmplificationRapidity: double (optional, default: 2)
    Rapidity of the amplification;
  • MaximumDamping: double (optional, default: 0.6)
    Damping of amplification.

# Output

  • dynamicRisk: vector of double
    The dynamic risk timeseries. It has the same length as data.

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose;
  • AmplificationFunction must be exp or tanh;
  • MaximumAmplification must be a scalar > 1;
  • AmplificationRapidity must be a scalar > 0;
  • MaximumDamping must be a scalar > 0.

# Reference

  • S. Guerra et al., "A Dynamic Risk Measure from Continuous Glucose Monitoring Data", Diabetes Technology & Therapeutics, 2011, vol. 13, pp. 843-852. DOI: 10.1089/dia.2011.0006

# adrr

function adrr = adrr(data)

Function that computes the average daily risk range (ADRR) of the glucose concentration (ignores nan values).

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • adrr: double
    The average daily risk range (ADRR) of the glucose concentration (unitless).

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Kovatchev et al., "Evaluation of a new measure of blood glucose variability in diabetes", Diabetes Care, 2006, vol. 29, pp. 2433-2438. DOI: 10.2337/dc06-1085.

# gri

function gri = gri(data)

Function that computes the Glycemic Risk Index (GRI) proposed Klonoff et al. (ignoring nan values). GRI seems to be rounded by the authors however it is not explicitly stated. Therefore, GRI is not rounded here.

# Input

  • data: timetable (required)
    A timetable with columns Time and glucose containing the glucose data to analyze (mg/dl).

# Output

  • adrr: double
    The average daily risk range (ADRR) of the glucose concentration (unitless).

# Preconditions

  • data must be a timetable having an homogeneous time grid;
  • data must contain a column named Time and another named glucose.

# Reference

  • Klonoff et al., "A Glycemia Risk Index (GRI) of hypoglycemia and hyperglycemia for continuous glucose monitoring validated by clinician ratings", Journal of Diabetes Science and Technology, 2022, pp. 1-17. DOI: 10.1177/19322968221085273.

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