Evidence map›Paper›PMID 29032732›Full record

ArticleJournal of diabetes science and technology2017

A Personalized Week-to-Week Updating Algorithm to Improve Continuous Glucose Monitoring Performance.

Stamatina Zavitsanou, Joon Bok Lee, Jordan E Pinsker, Mei Mei Church, Francis J Doyle, Eyal Dassau

Registry-linked trialOpen access · bronzeAbstract readComparative StudyEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02290600 (Feasibility Study Using Run-to-Run Control to Optimize Continuous Glucose Sensor Bias), which is not on this map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02290600 completednot on this map

Feasibility Study Using Run-to-Run Control to Optimize Continuous Glucose Sensor Bias

TypeobservationalSponsorSansum Diabetes Research InstituteRan2015 to 2016Enrolled23ConditionsDiabetesArmsContinuous Glucose Monitor
3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Stamatina Zavitsanou1 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Joon Bok Lee1 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Jordan E Pinsker2 William Sansum Diabetes Center, Santa Barbara, CA, USA.
Mei Mei Church2 William Sansum Diabetes Center, Santa Barbara, CA, USA.
Francis J Doyle1 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Eyal Dassau1 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Harvard University · USSansum Diabetes Research Institute · US

Funding

Pediatric Artificial Pancreas System for Enhanced Diabetes Management in Young Children with Type 1 DiabetesDP3DK104057 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI DASSAU, EYAL, DOYLE, FRANCIS J · 2014 to 2014
$1.8M
NIDDK NIH HHS DP3 DK104057
6 · The paper itself

Abstract

backgroundContinuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends.

methodsIn this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week.

resultsThe proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance.

conclusionsAlthough not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.

Indexed as

AlgorithmsSignal Processing, Computer-AssistedAdultBiomarkersBlood GlucoseBlood Glucose Self-MonitoringCalibrationDiabetes Mellitus, Type 1Equipment DesignFemaleHumansHypoglycemic AgentsLeast-Squares AnalysisLinear ModelsMaleMiddle AgedBiomarkersBlood GlucoseHypoglycemic Agentscontinuous glucose monitoring (CGM)glucose sensorstype 1 diabetes mellitusweekly updating

Identifiers

PMID29032732
PMCPMC5951058
OpenAlexW2767034358

What OpenQuestion holds

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Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.