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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


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  <identifier identifierType="URL">https://zenodo.org/record/4139769</identifier>
  <creators>
    <creator>
      <creatorName>Camerlingo Nunzio</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3222-2479</nameIdentifier>
      <affiliation>University of Padova</affiliation>
    </creator>
    <creator>
      <creatorName>Vettoretti Martina</creatorName>
      <affiliation>University of Padova</affiliation>
    </creator>
    <creator>
      <creatorName>Facchinetti Andrea</creatorName>
      <affiliation>University of Padova</affiliation>
    </creator>
    <creator>
      <creatorName>Sparacino Giovanni</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3248-1393</nameIdentifier>
      <affiliation>University of Padova</affiliation>
    </creator>
    <creator>
      <creatorName>Mader Julia K.</creatorName>
      <affiliation>Medical University of Graz</affiliation>
    </creator>
    <creator>
      <creatorName>Choudhary Pratik</creatorName>
      <affiliation>King's College London</affiliation>
    </creator>
    <creator>
      <creatorName>Del Favero Simone</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8214-2752</nameIdentifier>
      <affiliation>University of Padova</affiliation>
    </creator>
  </creators>
  <titles>
    <title>An analytical approach to determine the optimal duration of continuous glucose monitoring data required to reliably estimate time in hypoglycemia</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>time in range</subject>
    <subject>hypoglycemia</subject>
    <subject>estimation error</subject>
    <subject>statistical description</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-10-23</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4139769</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1038/s41598-020-75079-5</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/unipd-hyporesolve</relatedIdentifier>
  </relatedIdentifiers>
  <version>Final published version</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
    <description descriptionType="Other">This study is part of the Hypo-RESOLVE project. The project has received funding from the Innovative Medicines Initiative 2 (https://www.imi.europa.eu/) Joint Undertaking (JU) under grant agreement No 777460. The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and T1D Exchange, JDRF, International Diabetes Federation (IDF), The Leona M. and Harry B. Helmsley Charitable Trust.</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/777460/">777460</awardNumber>
      <awardTitle>Hypoglycaemia - REdefining SOLutions for better liVEs</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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