Literature DB >> 28882417

Derivation and validation model for hospital hypoglycemia.

Javier Ena1, Antonio Zapatero Gaviria2, Marta Romero-Sánchez2, Juana Carretero-Gómez3, Francisco Javier Carrasco-Sánchez4, José Vicente Segura-Heras5, Ana Belkis Porto-Perez6, Patricia Vázquez-Rodriguez6, Concepción González-Becerra7, Ricardo Gómez-Huelgas8.   

Abstract

BACKGROUND: An objective and simple prognostic model for hospitalized patients with hypoglycemia could be helpful in guiding initial intensity of treatment.
METHODS: We carried out a derivation rule for hypoglycemia using data from a nationwide retrospective cohort study of patients with diabetes or hyperglycemia carried out in 2014 (n=839 patients). The rule for hypoglycemia was validated using a second data set from a nationwide retrospective cohort study carried out in 2016 (n=561 patients). We derived our prediction rule using logistic regression with hypoglycemia (glucose less than 70mg/dL) as the primary outcome.
RESULTS: The incidence of hypoglycemia in the derivation cohort was 10.3%. Patient's characteristics independently associated with hypoglycemia included episodes of hypoglycemia during the previous three months (odds ratio [OR]: 6.29, 95% confidence interval [95%CI]: 3.37-11.79, p<0.001) estimated glomerular filtration rate lower than 30mL/min/1.73m2 (OR: 2.32, 95%CI: 1.23-4.35, p=0.009), daily insulin dose greater than 0.3units per Kg (OR: 1.74, 95%CI: 1.06-2.85, p=0.028), and days of hospitalization (OR: 1.03, 95%CI: 1.01-1.04, p=0.001). The model showed an area under the curve (AUC): 0.72 (95%CI: 0.66-0.78, p<0.001). The AUC in the validation cohort was: 0.71 (95%CI: 0.63-0.79, p<0.001).
CONCLUSIONS: The rule showed fair accuracy to predict hypoglycemia. Implementation of the rule into computer systems could be used in guiding initial insulin therapy.
Copyright © 2017 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Diabetes mellitus; Hospital; Hypoglycemia; Prediction rule

Mesh:

Substances:

Year:  2017        PMID: 28882417     DOI: 10.1016/j.ejim.2017.08.024

Source DB:  PubMed          Journal:  Eur J Intern Med        ISSN: 0953-6205            Impact factor:   4.487


  7 in total

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

Authors:  Xiling Hu; Weiran Xu; Shuo Lin; Cang Zhang; Cong Ling; Miaoxia Chen
Journal:  J Diabetes Res       Date:  2020-09-19       Impact factor: 4.011

Review 2.  Inpatient Management of T2DM and Hyperglycemia in Older Adults.

Authors:  Kristen DeCarlo; Amisha Wallia
Journal:  Curr Diab Rep       Date:  2019-09-14       Impact factor: 4.810

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

Authors:  Nestoras Nicolas Mathioudakis; Estelle Everett; Shuvodra Routh; Peter J Pronovost; Hsin-Chieh Yeh; Sherita Hill Golden; Suchi Saria
Journal:  BMJ Open Diabetes Res Care       Date:  2018-03-02

4.  Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients.

Authors:  Nestoras N Mathioudakis; Mohammed S Abusamaan; Ahmed F Shakarchi; Sam Sokolinsky; Shamil Fayzullin; John McGready; Mihail Zilbermint; Suchi Saria; Sherita Hill Golden
Journal:  JAMA Netw Open       Date:  2021-01-04

5.  Identifying patients at increased risk of hypoglycaemia in primary care: Development of a machine learning-based screening tool.

Authors:  Stijn Crutzen; Sunil Belur Nagaraj; Katja Taxis; Petra Denig
Journal:  Diabetes Metab Res Rev       Date:  2021-02-23       Impact factor: 4.876

Review 6.  Machine Learning Models for Inpatient Glucose Prediction.

Authors:  Andrew Zale; Nestoras Mathioudakis
Journal:  Curr Diab Rep       Date:  2022-06-27       Impact factor: 5.430

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

Authors:  Luis M Pérez-Belmonte; Juan J Gómez-Doblas; Mercedes Millán-Gómez; María D López-Carmona; Ricardo Guijarro-Merino; Fernando Carrasco-Chinchilla; Eduardo de Teresa-Galván; Manuel Jiménez-Navarro; M Rosa Bernal-López; Ricardo Gómez-Huelgas
Journal:  J Clin Med       Date:  2018-09-11       Impact factor: 4.241

  7 in total

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