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Clinical Prediction Tool To Identify Adults With Type 2 Diabetes at Risk for Persistent Adverse Glycemia in Hospital

      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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