Journal article Open Access

An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia

Camerlingo Nunzio; Vettoretti Martina; Facchinetti Andrea; Sparacino Giovanni; Mader Julia K.; Choudhary Pratik; Del Favero Simone


JSON-LD (schema.org) Export

{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Diabetes is a chronic metabolic disease that causes blood glucose (BG) concentration to make dangerous excursions outside its physiological range. Measuring the fraction of time spent by BG outside this range, and, specifically, the time-below-range (TBR), is a clinically common way to quantify the effectiveness of therapies. TBR is estimated from data recorded by continuous glucose monitoring (CGM) sensors, but the duration of CGM recording guaranteeing a reliable indicator is under debate in the literature. Here we framed the problem as random variable estimation problem and studied the convergence of the estimator, deriving a formula that links the TBR estimation error variance with the CGM recording length. Validation is performed on CGM data of 148 subjects with type-1-diabetes. First, we show the ability of the formula to predict the uncertainty of the TBR estimate in a single patient, using patient-specific parameters; then, we prove its applicability on population data, without the need of parameters individualization. The approach can be straightforwardly extended to other similar metrics, such as time-in-range and time-above-range, widely adopted by clinicians. This strengthens its potential utility in diabetes research, e.g., in the design of those clinical trials where minimal CGM monitoring duration is crucial in cost-effectiveness terms.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "University of Padova", 
      "@id": "https://orcid.org/0000-0003-3222-2479", 
      "@type": "Person", 
      "name": "Camerlingo Nunzio"
    }, 
    {
      "affiliation": "University of Padova", 
      "@type": "Person", 
      "name": "Vettoretti Martina"
    }, 
    {
      "affiliation": "University of Padova", 
      "@type": "Person", 
      "name": "Facchinetti Andrea"
    }, 
    {
      "affiliation": "University of Padova", 
      "@id": "https://orcid.org/0000-0002-3248-1393", 
      "@type": "Person", 
      "name": "Sparacino Giovanni"
    }, 
    {
      "affiliation": "Medical University of Graz", 
      "@type": "Person", 
      "name": "Mader Julia K."
    }, 
    {
      "affiliation": "King's College London", 
      "@type": "Person", 
      "name": "Choudhary Pratik"
    }, 
    {
      "affiliation": "University of Padova", 
      "@id": "https://orcid.org/0000-0002-8214-2752", 
      "@type": "Person", 
      "name": "Del Favero Simone"
    }
  ], 
  "headline": "An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-10-23", 
  "url": "https://zenodo.org/record/4139769", 
  "version": "Final published version", 
  "keywords": [
    "time in range", 
    "hypoglycemia", 
    "estimation error", 
    "statistical description"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1038/s41598-020-75079-5", 
  "@id": "https://doi.org/10.1038/s41598-020-75079-5", 
  "@type": "ScholarlyArticle", 
  "name": "An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia"
}
61
52
views
downloads
Views 61
Downloads 52
Data volume 132.8 MB
Unique views 61
Unique downloads 52

Share

Cite as