Evidence map›Paper›PMID 30689675›Full record

ArticlePloS one2019

Prospective evaluation of a dynamic insulin infusion algorithm for non critically-ill diabetic patients: A before-after study.

Nathanaëlle Montanier, Lise Bernard, Céline Lambert, Bruno Pereira, Françoise Desbiez, Daniel Terral, Armand Abergel, Jérôme Bohatier, Eugenio Rosset, Jeannot Schmidt and 6 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 33% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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

16 authors at 3 institutions in 1 country.

Nathanaëlle MontanierCHU Clermont-Ferrand, Service d'endocrinologie, diabétologie et maladies métaboliques, Clermont-Ferrand, France.
Lise BernardPôle Pharmacie, CHU Clermont-Ferrand, Clermont-Ferrand, France.
Céline LambertDélégation à la Recherche Clinique et à l'Innovation (DRCI), CHU Clermont-Ferrand, Clermont-Ferrand, France.
Bruno PereiraDélégation à la Recherche Clinique et à l'Innovation (DRCI), CHU Clermont-Ferrand, Clermont-Ferrand, France.
Françoise DesbiezCHU Clermont-Ferrand, Service d'endocrinologie, diabétologie et maladies métaboliques, Clermont-Ferrand, France.
Daniel TerralCHU Clermont-Ferrand, Service de Pédiatrie Générale, Clermont-Ferrand, France.ORCID 0000-0003-3706-6786
Armand AbergelCHU Clermont-Ferrand, Service de Médecine Digestive, Clermont-Ferrand, France.
Jérôme BohatierCHU Clermont-Ferrand, Service de Court séjour Gériatrique, Riom, France.
Eugenio RossetCHU Clermont-Ferrand, Service de Chirurgie vasculaire, Clermont-Ferrand, France.
Jeannot SchmidtCHU Clermont-Ferrand, Pôle Urgences, Clermont-Ferrand, France.
Valérie SautouPôle Pharmacie, CHU Clermont-Ferrand, Clermont-Ferrand, France.
Samy HadjadjCHU Poitiers, Service de Médecine interne, endocrinologie et maladies métaboliques, Poitiers, France.
Marie Batisse-LignierCHU Clermont-Ferrand, Service d'endocrinologie, diabétologie et maladies métaboliques, Clermont-Ferrand, France.
Igor TauveronCHU Clermont-Ferrand, Service d'endocrinologie, diabétologie et maladies métaboliques, Clermont-Ferrand, France.
Salwan MaqdasyCHU Clermont-Ferrand, Service d'endocrinologie, diabétologie et maladies métaboliques, Clermont-Ferrand, France.ORCID 0000-0001-5164-9879
Béatrice RocheCHU Clermont-Ferrand, Service d'endocrinologie, diabétologie et maladies métaboliques, Clermont-Ferrand, France.
Centre Hospitalier Universitaire de Clermont-Ferrand · FRCentre National de la Recherche Scientifique · FRCentre Hospitalier Universitaire de Poitiers · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionInsulin infusion is recommended during management of diabetic patients in critical care units to rapidly achieve glycaemic stability and reduce the mortality. The application of an easy-to-use standardized protocol, compatible with the workload is preferred. Glycaemic target must quickly be reached, therefore static algorithms should be replaced by dynamic ones. The dynamic algorithm seems closer to the physiological situation and appreciates insulin sensitivity. However, the protocol must meet both safety and efficiency requirements. Indeed, apprehension from hypoglycaemia is the main deadlock with the dynamic algorithms, thus their application remains limited. In contrary to the critical care units, to date, no prospective study evaluated a dynamic algorithm of insulin infusion in non-critically ill patients.

aimThis study primarily aimed to evaluate the efficacy of a dynamic algorithm of intravenous insulin therapy in non-critically-ill patients, and addressed its safety and feasibility in different departments of our university hospital.

methodsA "before-after" study was conducted in five hospital departments (endocrinology and four "non-expert" units) comparing a dynamic algorithm (during the "after" period-P2) to the static protocol (the "before" period-P1). Static protocol is based on determining insulin infusion according to an instant blood glycaemia (BG) level at a given time. In the dynamic algorithm, insulin infusion rate is determined according to the rate of change of the BG (the previous and actual BG under a specific insulin infusion rate). Additionally, two distinct glycaemic targets were defined according to the patients' profile: 100-180 mg/dl (5.5-10 mmol/l) for vigorous patients and 140-220 mg/dl (7.8-12.2 mmol/l) for frail ones. Different BG measurements for each patient were collected and recorded in a specific database (e-CRF) in order to analyse the rates of hypo- and hyperglycaemia. A satisfaction survey was also performed. A study approval was obtained from the institutional revision board before starting the study.

resultsOver 8 months, 72 and 66 patients during P1 and P2 were respectively included. The dynamic algorithm was more efficient, with reduced time to control hyperglycaemia (P1 vs P2:8.3 vs 5.3 hours; HR: 2.02 [1.27; 3.21]; p<0.01), increased the number of in-target BG measurements (P1 vs P2: 37.0% vs 41.8%; p<0.05), and reduced the glycaemic variability related to each patient (P1 vs P2, %CV: 40.9 vs 38.2;p<0.05, Index Correlation Class:0.30 vs 0.14; p<0.05). In patients after the first event of hypoglycemia after having started the infusion, new events were lower (P1 vs P2: 19.4 vs 11.4; p<0.001) thanks to an earlier reaction to hypoglycaemia (8.3% during P1 vs 44.3% during P2; p = 0.004). With the dynamic algorithm, the percentage of recurrence of mild hypoglycaemia was significantly lower in frail patients (20.5% vs 10.2%; p<0.001), and in patients managed in the non-expert units (18 vs 7.1%, p<0.001). The %CV was significantly improved in frail patients (36.9%). Mean BG measurements for each patient/day were 5.5±1.1 during P1 and 6.0±1.6 during P2 (p = 0.6). The threat from hypoglycaemia and the difficulty in using dynamic algorithm are barriers for nurses' adherence.

conclusionsThis dynamic algorithm for non-critically-ill patients is more efficient and safe than the static protocol, and adapted for frail patients and non-expert units.

Indexed as

AlgorithmsAgedControlled Before-After StudiesCritical CareCritical IllnessDiabetes MellitusFemaleHumansHyperglycemiaHypoglycemiaHypoglycemic AgentsInfusions, IntravenousInsulinInsulin ResistanceMaleMiddle AgedHypoglycemic AgentsInsulin

Identifiers

PMID30689675
PMCPMC6349328
OpenAlexW2914786966

What OpenQuestion holds

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Registered trials

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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.