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Record W4307805300 · doi:10.1210/jendso/bvac150.593

LBSUN214 Accuracy Of A Continuous Glucose Monitor In The Intensive Care Unit

2022· article· en· W4307805300 on OpenAlexaboutno aff
Sewon Bann, JESS C. HERCUS, J Loyal, PAUL ATKINS, M Sekhon, D Thompson

Bibliographic record

VenueJournal of the Endocrine Society · 2022
Typearticle
Languageen
FieldMedicine
TopicHyperglycemia and glycemic control in critically ill and hospitalized patients
Canadian institutionsnot available
Fundersnot available
KeywordsGlycemicMedicineContinuous glucose monitoringHypoglycemiaEmergency medicineIntensive care unitDiabetes mellitusGold standard (test)Blood Glucose Self-MonitoringPoint of careIntensive care medicineInternal medicineNursingEndocrinology

Abstract

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Abstract Studies have shown that hyper/hypoglycemia and glycemic variation are associated with adverse outcomes in critically ill patients. Currently, frequent blood point-of-care (POC) glucose measurements from an arterial or capillary sample is the only technology available to minimize glycemic excursions in the ICU. Continuous glucose monitoring (CGM) is becoming the standard of care for outpatient diabetes care and has shown improved glycemic control in the non-ICU inpatient setting. The Dexcom G6 sensor (G6) is the first CGM device approved by Health Canada for outpatient diabetes management without the need for calibration, but it has not yet been approved for inpatient use. We collected data from 23 adults who were on an insulin infusion in a medical-surgical ICU in Vancouver, British Columbia to evaluate the accuracy of uncalibrated CGM in the ICU. A blinded G6 was attached to the patient's arm and collected glucose measurements every five minutes without calibration. Nursing staff continued POC arterial glucose measurements using the AccuChek Inform II machine per standard of care. Excluding four outliers (with mean absolute relative difference (MARD) ≥ 25%), the overall MARD was 13.24% (SE 0.43) over 649 matched CGM and arterial glucose values. A Clarke Error Grid demonstrated 99.1% of CGM measurements within zones A and B. The MARD using three-point rolling averages of CGM measurements in five-minute intervals was 13.49% (SE 0.68). There were 573 matched pairs between glucose ranges of 3.9-13.9 mmol/L with two pairs <3.9 and 74 pairs >13.9 mmol/L. Eleven patients had renal replacement therapy and twelve had vasopressor use. There was no significant difference in MARD with renal replacement or across glycemic ranges ≥3.9. The MARD for patients with vasopressors was lower than for patients without (13% vs 13.55%, p<0. 01), a finding of doubtful clinical relevance. There is no expert agreement yet about the acceptable accuracy for CGM use in hospital. Our overall MARD meets the 2013 Critical Care expert consensus recommendations for MARD <14%. The FDA guidance on standard of accuracy for conventional POC glucometers require 98% of values within 15% for BG ≥ 75mg/dL. Our data showed 65.18% of values were within 15%. Previous studies using CGM in the ICU, even those using G6, all used calibration. The majority were non-blinded, and none met FDA criteria for inpatient use. Our study is the first uncalibrated and blinded study that was able to demonstrate acceptable accuracy in a large sample size. Since CGM accuracy may be affected by various factors, our comprehensive data can potentially identify specific interferences and quantify a calibration or correction criteria to improve CGM accuracy in critically ill patients. Overall, our results show that CGM shows strong potential to be an accurate, resource-efficient, and intuitive alternative to POC glucose monitoring to meet glycemic targets in the ICU. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.307
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2022
Admission routes1
Has abstractyes

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