Derivation and validation model for hospital hypoglycemia

Ena, Javier; Gaviria, A. Zapatero; Romero-Sánchez, M.; Carretero-Gómez, Juana; Carrasco-Sánchez, Francisco Javier; Heras, José Vicente Segura; Porto-Perez, Ana Belkis; Vázquez-Rodríguez, Patricia; González-Becerra, C.; Gómez‐Huelgas, Ricardo

doi:10.1016/j.ejim.2017.08.024

Cited by 14 publications

(18 citation statements)

References 25 publications

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“…Similar to our study, Ena et al derived a predictive model including four indicators: glomerular filtration rate, insulin dose, length of hospital stay, and episodes of hypoglycemia during the previous 3 months [ 23 ]. Shah et al developed a hypoglycemic predictive scoring tool for inpatients with diabetes and included five variables: age, emergency department visit in the previous 6 months, insulin use, use of oral agents that do not induce hypoglycemia, and severe chronic kidney disease [ 25 ].…”

Section: Discussionmentioning

confidence: 67%

“…Hypoglycemia interacts with the length of hospital stay, and hypoglycemia is related to the prolonged hospital stay [ 21 , 22 ]. At the same time, the length of hospital stay is also used as an indicator of the risk of hypoglycemia in T2DM patients [ 23 ]. The interpretation is that the more serious the patient's condition, the longer the hospital stay.…”

Section: Discussionmentioning

confidence: 99%

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Development and Validation of a Hypoglycemia Risk Model for Intensive Insulin Therapy in Patients with Type 2 Diabetes

Lin

et al. 2020

Journal of Diabetes Research

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Aims. To develop a simple hypoglycemic prediction model to evaluate the risk of hypoglycemia during hospitalization in patients with type 2 diabetes treated with intensive insulin therapy. Methods. We performed a cross-sectional chart review study utilizing the electronic database of the Third Affiliated Hospital of Sun Yat-sen University, and included 257 patients with type 2 diabetes undergoing intensive insulin therapy in the Department of Endocrinology and Metabolism. Logistic regression analysis was used to derive the clinical prediction rule with hypoglycemia (blood glucose≤3.9 mmol/L) as the main result, and internal verification was performed. Results. In the derivation cohort, the incidence of hypoglycemia was 51%. The final model selected included three variables: fasting insulin, fasting blood glucose, and total treatment time. The area under the curve (AUC) of this model was 0.666 (95% CI: 0.594–0.738, P<0.001). Conclusions. The model’s hypoglycemia prediction and the actual occurrence are in good agreement. The variable data was easy to obtain and the evaluation method was simple, which could provide a reference for the prevention and treatment of hypoglycemia and screen patients with a high risk of hypoglycemia.

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Section: Discussionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Hypoglycemia Risk Model for Intensive Insulin Therapy in Patients with Type 2 Diabetes

Lin

et al. 2020

Journal of Diabetes Research

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“…Not surprisingly, one variable that has been shown to be a strong determinant for hypoglycemia is a prior episode of hypoglycemia. 27 28 In our models, prior episodes of hypoglycemia were captured in the admission nadir BG variable, which was indeed a strong predictor of hypoglycemia. In the BG range of ≤88 mg/dL, each 10 mg/dL decrease in the admission nadir BG was associated with a 13% and a 14% increase in the odds of biochemical and clinically significant hypoglycemia, respectively ( table 2 , admission nadir 1 ).…”

Section: Discussionmentioning

confidence: 87%

Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults

Mathioudakis

Everett

Routh

et al. 2018

BMJ Open Diab Res Care

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ObjectiveTo develop and validate a multivariable prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.Research design and methodsWe collected pharmacologic, demographic, laboratory, and diagnostic data from 128 657 inpatient days in which at least 1 unit of subcutaneous insulin was administered in the absence of intravenous insulin, total parenteral nutrition, or insulin pump use (index days). These data were used to develop multivariable prediction models for biochemical and clinically significant hypoglycemia (blood glucose (BG) of ≤70 mg/dL and <54 mg/dL, respectively) occurring within 24 hours of the index day. Split-sample internal validation was performed, with 70% and 30% of index days used for model development and validation, respectively.ResultsUsing predictors of age, weight, admitting service, insulin doses, mean BG, nadir BG, BG coefficient of variation (CVBG), diet status, type 1 diabetes, type 2 diabetes, acute kidney injury, chronic kidney disease (CKD), liver disease, and digestive disease, our model achieved a c-statistic of 0.77 (95% CI 0.75 to 0.78), positive likelihood ratio (+LR) of 3.5 (95% CI 3.4 to 3.6) and negative likelihood ratio (−LR) of 0.32 (95% CI 0.30 to 0.35) for prediction of biochemical hypoglycemia. Using predictors of sex, weight, insulin doses, mean BG, nadir BG, CVBG, diet status, type 1 diabetes, type 2 diabetes, CKD stage, and steroid use, our model achieved a c-statistic of 0.80 (95% CI 0.78 to 0.82), +LR of 3.8 (95% CI 3.7 to 4.0) and −LR of 0.2 (95% CI 0.2 to 0.3) for prediction of clinically significant hypoglycemia.ConclusionsHospitalized patients at risk of insulin-associated hypoglycemia can be identified using validated prediction models, which may support the development of real-time preventive interventions.

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“…Patients with moderate degrees of hyperglycaemia, patients without previous insulin therapy and elderly or frail patients at high risk of hypoglycaemia may be treated with a combination of DPP4i and basal insulin according to a recent therapeutic algorithm [ 23 ]. Treatment with intensive insulin regimens is part of our routine clinical practice for all hospitalized patients with T2D but an important proportion of these patients could be highly exposed to suffer hypoglycaemia [ 11 , 24 , 25 , 26 ], which would significantly impact in our clinical practice [ 27 , 28 , 29 ]. For this reason, we should develop protocols with differentiated treatment regimens according to glycaemic control before hospitalization.…”

Section: Discussionmentioning

confidence: 99%

Use of Linagliptin for the Management of Medicine Department Inpatients with Type 2 Diabetes in Real-World Clinical Practice (Lina-Real-World Study)

Pérez‐Belmonte

Gómez‐Doblas

Millán-Gómez

et al. 2018

JCM

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The use of noninsulin antihyperglycaemic drugs in the hospital setting has not yet been fully described. This observational study compared the efficacy and safety of the standard basal-bolus insulin regimen versus a dipeptidyl peptidase-4 inhibitor (linagliptin) plus basal insulin in medicine department inpatients in real-world clinical practice. We retrospectively enrolled non-critically ill patients with type 2 diabetes with mild to moderate hyperglycaemia and no injectable treatments at home who were treated with a hospital antihyperglycaemic regimen (basal-bolus insulin, or linagliptin-basal insulin) between January 2016 and December 2017. Propensity score was used to match patients in both treatment groups and a comparative analysis was conducted to test the significance of differences between groups. After matched-pair analysis, 227 patients were included per group. No differences were shown between basal-bolus versus linagliptin-basal regimens for the mean daily blood glucose concentration after admission (standardized difference = 0.011), number of blood glucose readings between 100–140 mg/dL (standardized difference = 0.017) and >200 mg/dL (standardized difference = 0.021), or treatment failures (standardized difference = 0.011). Patients on basal-bolus insulin received higher total insulin doses and a higher daily number of injections (standardized differences = 0.298 and 0.301, respectively). Basal and supplemental rapid-acting insulin doses were similar (standardized differences = 0.003 and 0.012, respectively). There were no differences in hospital stay length (standardized difference = 0.003), hypoglycaemic events (standardized difference = 0.018), or hospital complications (standardized difference = 0.010) between groups. This study shows that in real-world clinical practice, the linagliptin-basal insulin regimen was as effective and safe as the standard basal-bolus regimen in non-critical patients with type 2 diabetes with mild to moderate hyperglycaemia treated at home without injectable therapies.

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Derivation and validation model for hospital hypoglycemia

Cited by 14 publications

References 25 publications

Development and Validation of a Hypoglycemia Risk Model for Intensive Insulin Therapy in Patients with Type 2 Diabetes

Development and Validation of a Hypoglycemia Risk Model for Intensive Insulin Therapy in Patients with Type 2 Diabetes

Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults

Use of Linagliptin for the Management of Medicine Department Inpatients with Type 2 Diabetes in Real-World Clinical Practice (Lina-Real-World Study)

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