Factors predictive of severe hypoglycemia in type 1 diabetes: analysis from the Juvenile Diabetes Research Foundation continuous glucose monitoring randomized control trial dataset.

Diabetes care (Impact Factor: 7.74). 03/2011; 34(3):586-90. DOI: 10.2337/dc10-1111
Source: PubMed

ABSTRACT Identify factors predictive of severe hypoglycemia (SH) and assess the clinical utility of continuous glucose monitoring (CGM) to warn of impending SH.
In a multicenter randomized clinical trial, 436 children and adults with type 1 diabetes were randomized to a treatment group that used CGM (N = 224), or a control group that used standard home blood glucose monitoring (N = 212) and completed 12 months of follow-up. After 6 months, the original control group initiated CGM while the treatment group continued use of CGM for 6 months. Baseline risk factors for SH were evaluated over 12 months of follow-up using proportional hazards regression. CGM-derived indices of hypoglycemia were used to predict episodes of SH over a 24-h time horizon.
The SH rate was 17.9 per 100 person-years, and a higher rate was associated with the occurrence of SH in the prior 6 months and female sex. SH frequency increased eightfold when 30% of CGM values were ≤ 70 mg/dL on the prior day (4.5 vs. 0.5%; P < 0.001), but the positive predictive value (PPV) was low (<5%). Results were similar for hypoglycemic area under the curve and the low blood glucose index calculated by CGM.
SH in the 6 months prior to the study was the strongest predictor of SH during the study. CGM-measured hypoglycemia over a 24-h span is highly associated with SH the following day (P < 0.001), but the PPV is low.

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    ABSTRACT: Background and aims Evaluation of incidence and correlates of severe hypoglycemia (SH) and diabetes ketoacidosis (DKA) in children and adolescents with T1DM. Methods and Results Retrospective study conducted in 29 diabetes centers from November 2011 to April 2012. The incidence of SH and DKA episodes and their correlates were assessed through a questionnaire administered to parents of patients aged 0-18 years. Incidence rates and incident rate ratios (IRRs) were estimated through multivariate Poisson regression analysis and multilevel analysis. Overall, 2025 patients were included (age 12.4±3.8 years; 53% males; diabetes duration 5.6±3.5 years; HbA1c 7.9±1.1%). The incidence of SH and DKA were of 7.7 and 2.4 events/100 py, respectively. The risk of SH was higher in females (IRR=1.44; 95%CI 1.04-1.99), in patients using rapid acting analogues as compared to regular insulin (IRR=1.48; 95%CI 0.97-2.26) and lower for patients using long acting analogues as compared to NPH insulin (IRR=0.40; 95%CI 0.19-0.85). No correlations were found between SH and HbA1c levels. The risk of DKA was higher in patients using rapid acting analogues (IRR=4.25; 95%CI 1.01-17.86) and increased with insulin units needed (IRR=7.66; 95%CI 2.83-20.74) and HbA1c levels (IRR=1.63; 95%CI 1.36-1.95). Mother’s age was inversely associated with the risk of both SH (IRR=0.95; 95%CI 0.92-0.98) and DKA (IRR=0.94; 95%CI 0.88-0.99). When accounting for center effect, the risk of SH associated with the use of rapid acting insulin analogues was attenuated (IRR=1.48; 95%CI 0.97-2.26); 33% and 16% of the residual variance in SH and DKA risk was explained by center effect. Conclusion The risk of SH and DKA is mainly associated with treatment modalities and strongly depends on the practice of specialist centers.
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    ABSTRACT: Objectives Optimizing glycemic control in pediatric type 1 diabetes (T1D) is essential to minimizing long-term risk of complications. We used the T1D Exchange database from 58 US diabetes clinics to identify differences in diabetes management characteristics among children categorized as having excellent vs. poor glycemic control.Methods Among registry participants 6–17 yr old with diabetes duration ≥2 yr, those with excellent control [(A1c <7%)(53 mmol/mol) (N = 588)] were compared with those with poor control [(A1c ≥9% )(75 mmol/mol) (N = 2684)] using logistic regression.ResultsThe excellent and poor control groups differed substantially in diabetes management (p < 0.001 for all) with more of the excellent control group using insulin pumps, performing blood glucose monitoring ≥5×/d, missing fewer boluses, bolusing before meals rather than at the time of or after a meal, using meal-specific insulin:carbohydrate ratios, checking their blood glucose prior to giving meal time insulin, giving insulin for daytime snacks, giving more bolus insulin, and using a lower mean total daily insulin dose than those in poor control. After adjusting for demographic and socioeconomic factors, diabetes management characteristics were still strongly associated with good vs. poor control. Notably, frequency of severe hypoglycemia was similar between the groups while DKA was more common in the poorly controlled group.Conclusions Children with excellent glycemic control tend to exhibit markedly different diabetes self-management techniques than those with poor control. This knowledge may further inform diabetes care providers and patients about specific characteristics and behaviors that can be augmented to potentially improve glycemic control.
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    ABSTRACT: Abstract Background: Nocturnal hypoglycemia was a common and serious problem among patients with type 2 diabetes (T2DM), especially in the elderly. This study investigated whether fasting glucose was an indicator of nocturnal hypoglycemia in elderly male patients with T2DM. Methods: A total of 291 elderly male type 2 diabetic patients who received continuous glucose monitoring (CGM) between January 2007 and January 2011 were enrolled in the study. The association of fasting glucose and nocturnal hypoglycemia based on CGM data was analyzed, comparing with bedtime glucose. Results: Based on CGM data, patients with nocturnal hypoglycemia had significantly lower fasting glucose (5.88 ± 1.29 versus 6.92 ± 1.32 mmol/L) and bedtime glucose (7.33 ± 1.70 versus 8.01 ± 1.95 mmol/L) than patients without nocturnal hypoglycemia (both p < 0.01). Compared with the highest quartile, the lowest quartile of fasting glucose had a significantly increased risk of nocturnal hypoglycemia after the multiple adjustments (pfor trend < 0.001). However, this association did not appear in bedtime glucose. When the prediction of nocturnal hypoglycemia either by fasting glucose or bedtime glucose using the area under receiver operating characteristic (ROC) curve, fasting glucose but not bedtime glucose, was an indicator of nocturnal hypoglycemia, with an area under the ROC curve (AUC) of 0.714 (95% CI: 0.653 ∼ 0.774, p < 0.001). On the ROC curve, the Youden index was maximal when fasting glucose was 6.1 mmol/L. Conclusions: Fasting glucose may be a convenient and clinically useful indicator of nocturnal hypoglycemia in elderly male patients with T2DM. Risk of nocturnal hypoglycemia significantly increased when fasting glucose was less than 6.1 mmol/L.
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