Development of a hypoglycaemia risk score to identify high-risk individuals with advanced type 2 diabetes in DEVOTE.

AIMS: The ability to differentiate patient populations with type 2 diabetes at high risk of severe hypoglycaemia could impact clinical decision making. The aim of this study was to develop a risk score, using patient characteristics, that could differentiate between populations with higher and lower 2-year risk of severe hypoglycaemia among individuals at increased risk of cardiovascular disease. MATERIALS AND METHODS: Two models were developed for the risk score based on data from the DEVOTE cardiovascular outcomes trials. The first, a data-driven machine-learning model, used stepwise regression with bidirectional elimination to identify risk factors for severe hypoglycaemia. The second, a risk score based on known clinical risk factors accessible in clinical practice identified from the data-driven model, included: insulin treatment regimen; diabetes duration; sex; age; and glycated haemoglobin, all at baseline. Both the data-driven model and simple risk score were evaluated for discrimination, calibration and generalizability using data from DEVOTE, and were validated against the external LEADER cardiovascular outcomes trial dataset. RESULTS: Both the data-driven model and the simple risk score discriminated between patients at higher and lower hypoglycaemia risk, and performed similarly well based on the time-dependent area under the curve index (0.63 and 0.66, respectively) over a 2-year time horizon. CONCLUSIONS: Both the data-driven model and the simple hypoglycaemia risk score were able to discriminate between patients at higher and lower risk of severe hypoglycaemia, the latter doing so using easily accessible clinical data. The implementation of such a tool (http://www.hyporiskscore.com/) may facilitate improved recognition of, and education about, severe hypoglycaemia risk, potentially improving patient care.

Risk of severe hypoglycaemia and its impact in type 2 diabetes in DEVOTE.

AIMS: To undertake a post-hoc analysis, utilizing a hypoglycaemia risk score based on DEVOTE trial data, to investigate if a high risk of severe hypoglycaemia was associated with an increased risk of cardiovascular events, and whether reduced rates of severe hypoglycaemia in patients identified as having the highest risk affected the risk of cardiovascular outcomes. MATERIALS AND METHODS: The DEVOTE population was divided into quartiles according to patients' individual hypoglycaemia risk scores. For each quartile, the observed incidence and rate of severe hypoglycaemia, major adverse cardiovascular event (MACE) and all-cause mortality were determined to investigate whether those with the highest risk of hypoglycaemia were also at the greatest risk of MACE and all-cause mortality. In addition, treatment differences within each risk quartile [insulin degludec (degludec) vs. insulin glargine 100 units/mL (glargine U100)] in terms of severe hypoglycaemia, MACE and all-cause mortality were investigated. RESULTS: Patients with the highest risk scores had the highest rates of severe hypoglycaemia, MACE and all-cause mortality. Treatment ratios between degludec and glargine U100 in the highest risk quartile were 95% confidence interval (CI) 0.56 (0.39; 0.80) (severe hypoglycaemia), 95% CI 0.76 (0.58; 0.99) (MACE) and 95% CI 0.77 (0.55; 1.07) (all-cause mortality). CONCLUSIONS: The risk score demonstrated that a high risk of severe hypoglycaemia was associated with a high incidence of MACE and all-cause mortality and that, in this high-risk group, those treated with degludec had a lower incidence of MACE. These observations support the hypothesis that hypoglycaemia is a risk factor for cardiovascular events.

Day-to-day fasting self-monitored blood glucose variability is associated with risk of hypoglycaemia in insulin-treated patients with type 1 and type 2 diabetes: A post hoc analysis of the SWITCH Trials.

AIMS: To investigate the association between day-to-day fasting self-monitored blood glucose (SMBG) variability and risk of hypoglycaemia in type 1 (T1D) and type 2 diabetes (T2D), and to compare day-to-day fasting SMBG variability between treatments with insulin degludec (degludec) and insulin glargine 100 units/mL (glargine U100). MATERIALS AND METHODS: Data were retrieved from two double-blind, randomized, treat-to-target, two-period (32 weeks each) crossover trials of degludec vs glargine U100 in T1D (SWITCH 1, n = 501) and T2D (SWITCH 2, n = 720). Available fasting SMBGs were used to determine the standard deviation (SD) of day-to-day fasting SMBG variability for each patient and the treatment combination. The association between day-to-day fasting SMBG variability and overall symptomatic, nocturnal symptomatic and severe hypoglycaemia was analysed for the pooled population using linear regression, with fasting SMBG variability included as a three-level factor defined by population tertiles. Finally, day-to-day fasting SMBG variability was compared between treatments. RESULTS: Linear regression showed that day-to-day fasting SMBG variability was significantly associated with overall symptomatic, nocturnal symptomatic and severe hypoglycaemia risk in T1D and T2D (P < 0.05). Day-to-day fasting SMBG variability was significantly associated (P < 0.01) with all categories of hypoglycaemia risk, with the exception of severe hypoglycaemia in T2D when analysed within tertiles. Degludec was associated with 4% lower day-to-day fasting SMBG variability than glargine U100 in T1D (P = 0.0082) and with 10% lower day-to-day fasting SMBG variability in T2D (P < 0.0001). CONCLUSIONS: Higher day-to-day fasting SMBG variability is associated with an increased risk of overall symptomatic, nocturnal symptomatic and severe hypoglycaemia. Degludec has significantly lower day-to-day fasting SMBG variability vs glargine U100.

Increased Derived Time in Range Is Associated with Reduced Risk of Major Adverse Cardiovascular Events, Severe Hypoglycemia, and Microvascular Events in Type 2 Diabetes: A Post Hoc Analysis of DEVOTE.

Time spent in glycemic target range (time in range [TIR]; plasma glucose 70-180 mg/dL [3.9-10.0 mmol/L]) as a surrogate endpoint for long-term diabetes-related outcomes requires validation. This post hoc analysis investigated the association between TIR, derived from 8-point glucose profiles (derived TIR [dTIR]) at 12 months, and time to cardiovascular or severe hypoglycemic episodes in people with type 2 diabetes in the DEVOTE trial. At 12 months, dTIR was significantly negatively associated with time to first major adverse cardiovascular event (P = 0.0087), severe hypoglycemic episode (P < 0.0001), or microvascular event (P = 0.024). A nonsignificant trend was seen toward association between 12-month hemoglobin A1c (HbA1c) and these outcomes, but this was no longer seen after addition of dTIR to the model. The results support targeting TIR >70% and suggest dTIR could be used in addition to, or in some instances in place of, HbA1c as a clinical biomarker. Trial registration details: ClinicalTrials.gov, NCT01959529.

Clinical outcome after hydrothermal ablation treatment of menorrhagia in patients with and without submucous myomas.

STUDY OBJECTIVE: To analyze the long-term efficacy of hydrothermal ablation (HTA) in women with a normal uterine cavity and submucous uterine myomas. DESIGN: Retrospective study (Canadian Task Force classification II-2). SETTING: Roskilde University Hospital surgical daycare unit. PATIENTS: One hundred sixty-six consecutive patients with abnormal uterine bleeding who underwent HTA from 2004 to 2008. All patients were asked to fill out a specific questionnaire that included questions related to postoperative bleeding patterns, complications, and satisfaction with the procedure. One hundred thirty-six patients (81.9%) completed the questionnaire, including 33 women (24.3%) with submucous myomas. INTERVENTION: The HTA procedure was performed in accordance with previous descriptions. MEASUREMENTS AND MAIN RESULTS: Mean (SD; 95% CI) follow-up was 33 (10.5; 25.5-31.8) months. Amenorrhea was achieved in 57 women with myomas (55.3%) and 17 women (51.5%) without myomas (p = .47). Postoperative light bleeding was reported in 26 women with myomas (25.2%) and 6 women (18.2%) without myomas. During follow-up, 13 women with myomas (12.6%) underwent hysterectomy, compared with 6 women without myomas (18.2%). Postoperatively, the mean (SD; 95% CI) number of days per month with bleeding was reduced in both groups, from 13.62 (7.90; 11.87-15.37) to 1.34 (2.32; 0.84-1.84) days in the group with myomas, and from 14.78 (8.81; 10.97-18.59), to 1.88 (4.38; 0.12-3.65) without myomas. Similarly, both groups reported improved quality of life: 74.8% and 72.7%, respectively (p = .59). Compared with patients with myomas 3 cm or smaller, patients with myomas larger than 3 cm demonstrated a significantly higher rate of severe bleeding postoperatively (p = .02). CONCLUSION: Our results demonstrate that HTA is associated with a high rate of amenorrhea in patients with or without submucous myomas. However, a significantly lower effect may be observed in patients with myomas larger than 3 cm.

Fast Assessment of Glycemic Control based on Continuous Glucose Monitoring Data.

Diabetes has become a major public health problem in the world. In this context, early assessment of glycemic control is essential in order to avoid life-threatening health complications. A panel of diabetes experts have recently proposed a list of recommendations when using Continuous Glucose Monitoring (CGM) for glycemic control assessment including a minimum of two weeks of CGM data. A recent study has further introduced a metric called Glucose Profile Indicator (GPI) for CGM based diabetes management including a subset of the recommended CGM metrics. In this pilot study, it was investigated if less than two weeks of CGM data would impact the performance of GPI compared to the proposed two weeks of CGM data. Furthermore, logistic regression (LR) was used to examine if an improvement could be achieved taking as input the CGM metrics used to quantify GPI. The population mean accuracy for accumulated day 1 to 13 varied between 72.8 ± 2.0% - 98.3 ± 0.4% with no clear sign of improvement using LR. Hence, this indicates a trade-off between the amount of available CGM data and the precision in which the GPI outcome using all 14 days can be achieved when considering features of the GPI alone. Future work is needed to investigate if this trade-off can be improved by the use of additional features of the CGM.