Impact of sensor-augmented pump therapy with predictive low-glucose management on hypoglycemia and glycemic control in patients with type 1 diabetes mellitus: 1-year follow-up.

AIMS: To describe real-life experience with sensor-augmented pump therapy with predictive low-glucose management (SAPT-PLGM), in terms of hypoglycemia and glycemic control after one year of follow-up in T1D patients with hypoglycemia as the main indication of therapy. METHODS: Retrospective cohort study under real life conditions. Baseline and one-year follow-up variables of glycemic control, hypoglycemia and glycemic variability were compared. RESULTS: Fifty patients were included, 31 on prior treatment with SAPT with low-glucose suspend (LGS) feature and 19 on multiple dose insulin injections (MDI). Mean HbA1c decreased in the MDI group (8.24%-7.08%; p = 0.0001). HbA1c change was not significant in the SAPT-LGS group. Area under the curve (AUC) below 70 mg/dl improved in both SAPT-LGS and MDI groups while AUC, %time and events below 54 mg/dl decreased in SAPT-LGS group. Glycemic variability improved in the MDI group. Less patients presented severe hypoglycemia with SAPT-PLGM in both groups, however the change was non-significant. CONCLUSIONS: Under real life conditions, SAPT-PLGM reduced metrics of hypoglycemia in patients previously treaded with MDI and SAPT-LGS without deteriorating glycemic control in SAPT-LGS patients, while improving it in patients treated with MDI.

Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia.

Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in type 2 diabetes patients, the evidence on the use of one particular measure of GV in type 1 diabetes (T1DM) patients as a predictor of hypoglycemia is limited. Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events <54 and <70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. Receiver operating characteristic (ROC) curve analysis for each of the glycemic indexes was performed to define the best index and its optimal cutoff threshold to discriminate patients with events of hypoglycemia. Results: Seventy-three patients were included. A total of 128 events <54 mg/dL were recorded in 34 patients, and 350 events <70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (adjusted relative risk [aRR] 1.44, 95% confidence interval [CI]: 1.10-1.88, P = 0.008). CV, HbA1c (glycated hemoglobin), and mean glucose were associated with events <70 mg/dL. ROC curve analysis showed that, among GV metrics, CV had the best performance to discriminate patients with events <54 mg/dL (area under the curve [AUC] 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31%, respectively. Among glycemic risk (GR) indexes, low blood glucose index (LBGI) showed the best performance. Conclusions: This analysis shows that CV is the best GV index, and LBGI the best GR index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.