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.

Factors associated with hypoglycemia in cardiovascular surgery.

INTRODUCTION: An inadequate glycemic control in cardiovascular postoperative is a predictor of mortality, postoperative infections and a prolonged hospital stay. This study evaluates the incidence of hypoglycemia in the first 72 h of postoperative and its association with different factors, such as tissue perfusion parameters, and the administration of medicines that induct hypoglycemia during surgery, in patients that undergo cardiovascular surgery in a 4th level hospital in Bogota, Colombia. METHODS AND MATERIALS: A nested cases and controls study was developed, taking as cases the patients that presented hypoglycemia episodes (<70 mg/dl) and the controls were the patients without this outcome. A systematic record of the glycemic control in the first 72 h of postoperative was registered. An univariate and multivariate analysis was conducted to determine the factor associated with hypoglycemia. RESULTS: 327 patients were evaluated (35% with coronary bypass and 27% with valvular replacement). 7.65% of the patients presented at least one episode of hypoglycemia <70 mg/dL (25 episodes). The multivariate analysis showed an association with chronic kidney failure in dialysis (OR6,21; IC:0,97-43.27,p:0.05), administration of preoperative steroids (OR4,41; IC:1,27-15,20,p:0.02), intraoperative insulin (OR2,61; IC:1.09-6,22, p:0.03), and postoperative hydrocortisone (OR 7,15; IC 1,46-34,9 p:0.01). CONCLUSIONS: Hypoglycemia is frequent in patients that undergo a cardiovascular surgery. The associated factors were chronic kidney failure in dialysis, administration of preoperative steroids, intraoperative insulin and hydrocortisone during postoperative. Other factors such as tissue perfusion, administration of vasoactive or beta blockers substances, showed no association with hypoglycemia.

Different Indexes of Glycemic Variability as Identifiers of Patients with Risk of Hypoglycemia in Type 2 Diabetes Mellitus.

INTRODUCTION: Recent publications frequently introduce new indexes to measure glycemic variability (GV), quality of glycemic control, or glycemic risk; however, there is a lack of evidence supporting the use of one particular parameter, especially in clinical practice. METHODS: A cohort of type 2 diabetes mellitus (T2DM) patients in ambulatory care were followed using continuous glucose monitoring sensors (CGM). Mean glucose (MG), standard deviation, coefficient of variation (CV), interquartile range, CONGA1, 2, and 4, MAGE, M value, J index, high blood glucose index, and low blood glucose index (LBGI) were estimated. Hypoglycemia incidence (<54 mg/dl) was calculated. Area under the curve (AUC) was determined for different indexes as identifiers of patients with risk of hypoglycemia (IRH). Optimal cutoff thresholds were determined from analysis of the receiver operating characteristic curves. RESULTS: CGM data for 657 days from 140 T2DM patients (4.69 average days per patient) were analyzed. Hypoglycemia was present in 50 patients with 144 hypoglycemic events in total (incidence rate of 0.22 events per patient/day). In the multivariate analysis, both CV (OR 1.20, 95% CI 1.12-1.28, P < .001) and LBGI (OR 4.83, 95% CI 2.41-9.71, P < .001) were shown to have a statistically significant association with hypoglycemia. The highest AUC were for CV (0.84; 95% CI 0.77-0.91) and LBGI (0.95; 95% CI 0.92-0.98). The optimal cutoff threshold for CV as IRH was 34%, and 3.4 for LBGI. CONCLUSION: This analysis shows that CV can be recommended as the preferred parameter of GV to be used in clinical practice for T2DM patients. LBGI is the preferred IRH between glycemic risk indexes.