Literature DB >> 15249354

A probabilistic model for predicting hypoglycemia in type 2 diabetes mellitus: The Diabetes Outcomes in Veterans Study (DOVES).

Glen H Murata1, Richard M Hoffman, Jayendra H Shah, Christopher S Wendel, William C Duckworth.   

Abstract

BACKGROUND: To develop and validate a method for estimating hypoglycemia risk in stable, insulin-treated subjects with type 2 diabetes mellitus.
METHODS: Subjects (n = 195) monitored their blood glucose levels 4 times daily for 8 weeks. An 8-week mean blood glucose value (GLUMEAN) with standard deviation (GLUSD) was derived for each patient. Subjects were then randomly allocated to a derivation or validation set. For the derivation set, we developed a logistic function based on GLUMEAN and GLUSD to describe the 8-week risk of hypoglycemia (blood glucose < or =60 mg/dL [3.3 mmol/L]). This function was used to assign a predicted probability of hypoglycemia to each subject in the validation set. Subjects were assigned to risk quartiles and followed up for up to 52 weeks.
RESULTS: We evaluated 195 subjects, 95% of whom were men and 69% of whom were non-Hispanic white. For 72 derivation subjects, GLUMEAN and GLUSD were highly influential determinants of hypoglycemia during intensified monitoring. The 123 validation subjects were followed up for 39.7 +/- 7.1 weeks (mean +/- SD). The occurrence of long-term hypoglycemia differed significantly across risk quartiles (19.4%, 36.7%, 61.3%, and 77.4%, respectively; P<.001). Receiver operating characteristic curve analysis showed that the area for the probability function (0.746 +/- 0.046) was significantly higher than the area for hemoglobin A1c (0.549 +/- 0.052) because their 95% confidence intervals did not overlap. The function also identified subjects who developed long-term hypoglycemia at a rate exceeding the median frequency.
CONCLUSIONS: Self-monitoring of blood glucose is superior to hemoglobin A1c measurement in predicting long-term hypoglycemia in persons with type 2 diabetes. The risk of hypoglycemia associated with treatment intensification may be offset by strategies that reduce glucose variability.

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Year:  2004        PMID: 15249354     DOI: 10.1001/archinte.164.13.1445

Source DB:  PubMed          Journal:  Arch Intern Med        ISSN: 0003-9926


  22 in total

Review 1.  Near normal HbA1c with stable glucose homeostasis: the ultimate target/aim of diabetes therapy.

Authors:  L Monnier; C Colette; S Dejager; D R Owens
Journal:  Rev Endocr Metab Disord       Date:  2016-03       Impact factor: 6.514

Review 2.  Structured SMBG in early management of T2DM: Contributions from the St Carlos study.

Authors:  Teresa Ruiz Gracia; Nuria García de la Torre Lobo; Alejandra Durán Rodríguez Hervada; Alfonso L Calle Pascual
Journal:  World J Diabetes       Date:  2014-08-15

3.  Multiple predictively equivalent risk models for handling missing data at time of prediction: With an application in severe hypoglycemia risk prediction for type 2 diabetes.

Authors:  Sisi Ma; Pamela J Schreiner; Elizabeth R Seaquist; Mehmet Ugurbil; Rachel Zmora; Lisa S Chow
Journal:  J Biomed Inform       Date:  2020-01-28       Impact factor: 6.317

4.  Computing the risk of postprandial hypo- and hyperglycemia in type 1 diabetes mellitus considering intrapatient variability and other sources of uncertainty.

Authors:  Maira García-Jaramillo; Remei Calm; Jorge Bondia; Cristina Tarín; Josep Vehí
Journal:  J Diabetes Sci Technol       Date:  2009-07-01

5.  Treatment intensification and blood glucose control among hospitalized diabetic patients.

Authors:  Michael E Matheny; Maria Shubina; Zebadiah M Kimmel; Merri L Pendergrass; Alexander Turchin
Journal:  J Gen Intern Med       Date:  2007-12-08       Impact factor: 5.128

6.  Practical strategies to normalize hyperglycemia without undue hypoglycemia in type 2 diabetes mellitus.

Authors:  Christopher T Kodl; Elizabeth R Seaquist
Journal:  Curr Diab Rep       Date:  2008-10       Impact factor: 4.810

7.  Hypoglycaemia Among Insulin-Treated Patients with Diabetes: Evaluation of the United Arab Emirates cohort of the International Operations-Hypoglycaemia Assessment Tool study.

Authors:  Salah Abusnana; Salem A Beshyah; Nawal Al-Mutawa; Rima Tahhan; Mahir Jallo; Ravi Arora; Hazem Aly; Sagar Singhal
Journal:  Sultan Qaboos Univ Med J       Date:  2019-03-28

8.  How can we easily measure glycemic variability in diabetes mellitus?

Authors:  Suk Chon
Journal:  Diabetes Metab J       Date:  2015-04       Impact factor: 5.376

9.  Fear of hypoglycaemia: defining a minimum clinically important difference in patients with type 2 diabetes.

Authors:  Tom Stargardt; Linda Gonder-Frederick; Karl J Krobot; Charles M Alexander
Journal:  Health Qual Life Outcomes       Date:  2009-10-22       Impact factor: 3.186

10.  Glucose variability: where it is important and how to measure it.

Authors:  J Hans DeVries
Journal:  Diabetes       Date:  2013-05       Impact factor: 9.461

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