Abstract
Objectives
Given the high incidence of hyperglycemia and hypoglycemia in hospital and the lack
of prediction tools for this problem, we developed a clinical tool to assist early
identification of individuals at risk for persistent adverse glycemia (AG) in hospital.
Methods
We analyzed a cohort of 594 consecutive adult inpatients with type 2 diabetes. We
identified clinical factors available early in the admission course that were associated
with persistent AG (defined as ≥2 days with capillary glucose <4 or >15 mmol/L during
admission). A prediction model for persistent AG was constructed using logistic regression
and internal validation was performed using a split-sample approach.
Results
Persistent AG occurred in 153 (26%) of inpatients, and was associated with admission
dysglycemia (odds ratio [OR], 3.65), glycated hemoglobin ≥8.1% (OR, 5.08), glucose-lowering
treatment regimen containing sulfonylurea (OR, 3.50) or insulin (OR, 4.22), glucocorticoid
medication treatment (OR, 2.27), Charlson Comorbidity Index score and the number of
observed days. An early-identification prediction tool, based on clinical factors
reliably available at admission (admission dysglycemia, glycated hemoglobin, glucose-lowering
regimen and glucocorticoid treatment), could accurately predict persistent AG (receiver-operating
characteristic area under curve = 0.806), and, at the optimal cutoff, the sensitivity,
specificity and positive predictive value were 84%, 66% and 53%, respectively.
Conclusions
A clinical prediction tool based on clinical risk factors available at admission to
hospital identified patients at increased risk for persistent AG and could assist
early targeted management by inpatient diabetes teams.
Résumé
Objectifs
Étant donné le grand nombre de cas d’hyperglycémie et d’hypoglycémie à l’hôpital et
l’absence d’outils de prédiction pour ces problèmes, nous avons conçu un outil clinique
pour déterminer de façon précoce les individus exposés au risque d’anomalie persistante
de la glycémie (AG pour anomalie de la glycémie) à l’hôpital.
Méthodes
Nous avons soumis à l’analyse une cohorte de 594 patients adultes hospitalisés consécutifs
qui étaient atteints de diabète de type 2. Nous avons déterminé les facteurs cliniques
disponibles au début de l’admission et associés à une AG persistante (soit ≥ 2 jours
en présence d’une glycémie capillaire < 4 ou > 15 mmol/L à l’admission). Nous avons
créé un modèle de prédiction de l’AG persistante à l’aide de la régression logistique
et nous avons effectué la validation interne à l’aide d’une approche à échantillon
fractionné.
Résultats
Une AG persistante survenue chez 153 (26 %) patients hospitalisés a été associée à
la dysglycémie à l’admission (ratio d’incidence approché [RIA], 3,65), à une hémoglobine
glyquée ≥ 8,1 % (RIA, 5,08), à un régime de traitement hypoglycémiant contenant une
sulfonylurée (RIA, 3,50) ou de l’insuline (RIA, 4,22), à un traitement médicamenteux
par glucocorticoïdes (RIA, 2,27), au score de comorbidité Charlson et au nombre de
jours observés. Un outil de prédiction pour déterminer de façon précoce les individus
exposés au risque d’AG persistante fondé sur des facteurs cliniques fiables à l’admission
(dysglycémie, hémoglobine glyquée, régime de traitement hypoglycémiant et traitement
par glucocorticoïdes) pouvait prédire de manière précise l’AG (surface située sous
la courbe ROC [de l’anglais, receiver-operating characteristic] = 0,806) et, au seuil optimal, la sensibilité, la spécificité et la valeur prédictive
positive étaient respectivement de 84 %, de 66 % et de 53 %.
Conclusions
Un outil de prédiction clinique fondé sur les facteurs de risque cliniques à l’admission
à l’hôpital a permis de déterminer les patients exposés à un risque accru d’AG persistante
et a contribué à la prise en charge ciblée précoce des patients hospitalisés par les
équipes spécialisées en soins du diabète.
Keywords
Mots clés
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Article info
Publication history
Published online: June 10, 2020
Accepted:
June 3,
2020
Received in revised form:
March 6,
2020
Received:
January 9,
2020
Identification
Copyright
© 2020 Canadian Diabetes Association.
