Association of hypoglycemic symptoms with patients' rating of their health-related quality of life state: a cross sectional study

<p>Abstract</p> <p>Background</p> <p>To evaluate the association between patient-reported hypoglycemic symptoms with ratings of their health-related quality of life state and patient-reported adverse events in patients with type 2 diabetes mellitus (T2DM).</p> <p>Methods</p> <p>This observational, multicenter, cross sectional study was based on a sample of patients with T2DM from seven European countries who added sulfonylurea or thiazolidinedione to metformin monotherapy between January 2001 and January 2006. Included patients were required to have at least one hemoglobin A_1c(HbA_1c) measurement in the 12 months before enrollment and to not be receiving insulin. Demographic and clinical data from medical records were collected using case report forms. Questionnaires measured patient-reported hypoglycemic symptoms, health-related quality of life (EuroQol visual analogue scale, EQ-5D VAS), and treatment-related adverse events.</p> <p>Results</p> <p>A total of 1,709 patients were included in the study. Mean patient age was 63 years, 45% were female, mean HbA_1cwas 7.06%, and 28% were at HbA_1cgoal (HbA_1c< 6.5%). Hypoglycemic symptoms during the 12 months before enrollment were reported by 38% of patients; among whom 68% reported their most severe symptoms were mild, 27% moderate, and 5% severe. Adjusted linear regression analyses revealed that patients reporting hypoglycemic symptoms had significantly lower EQ-5D VAS scores indicating worse patient-reported quality of life (mean difference -4.33, p < 0.0001). Relative to those not reporting symptoms, the adjusted decrement to quality of life increased with greater hypoglycemic symptom severity (mild: -2.68, <it>p </it>= 0.0039; moderate: -6.42, <it>p </it>< 0.0001; severe: -16.09, <it>p </it>< 0.0001). Patients with hypoglycemia reported significantly higher rates of shakiness, sweating, excessive fatigue, drowsiness, inability to concentrate, dizziness, hunger, asthenia, and headache (<it>p </it>< 0.0001 for each comparison).</p> <p>Conclusions</p> <p>Hypoglycemic symptoms and symptom severity have an adverse effect on patients' rating of their health related quality of life state. Hypoglycemic symptoms are correlated with treatment-related adverse effects. Minimizing the risk and severity of hypoglycemia may improve patients' quality of life and clinical outcomes. Results are subject to limitations associated with observational studies including the potential biases due to unobserved patient heterogeneity and the use of a convenience sample of patients.</p

Calculating individualized glycaemic targets using an algorithm based on expert worldwide diabetologists: Implications in real‐ life clinical practice

Background: The aim of this study was to assess the clinical implications of calculating an individualized HbA1c target using a recently published algorithm in a real‐life clinical setting. Methods: General practitioners (GPs) from the Spanish Society of Family Medicine Diabetes Expert Group were invited to participate in the study. Each GP selected a random sample of patients with diabetes from his or her practice and submitted their demographic and clinical data for analysis. Individualized glycaemic targets were calculated according to the algorithm. Predictors of good glycaemic control were studied. The rate of patients attaining their individualized glycaemic target or the uniform target of HbA1c < 7.0% was calculated. Results: Forty GPs included 408 patients in the study. Of the 8 parameters included in the algorithm, “comorbidities,” “risk of hypoglycaemia from treatment,” and “diabetes duration” had the greatest impact on determining the individualized glycaemic target. Number of glucose‐ lowering agents and adherence were independently associated with glycaemic control. Overall, 60.5% of patients had good glycaemic control per individualized target, and 56.1% were well controlled per the uniform target of HbA1c < 7.0% (P = .20). However, 12.8% (23 of 246) of the patients with HbA1c ≥ 7.0% were adequately controlled per individualized target, and 2.6% (6 of 162) of the patients with HbA1c < 7.0% were uncontrolled since their individualized target was lower. Conclusions: In a real‐life clinical setting, applying individualized targets did not change the overall rate of patients with good glycaemic control yet led to reclassification of 7.1% (29 of 408) of the patients. More studies are needed to validate these results in different population

Trends in HbA1c thresholds for initiation of hypoglycemic agents:Impact of changed recommendations for older and frail patients

Aims: Less strict glycated hemoglobin (HbA1c) thresholds have been recommended in older and/or frail type 2 diabetes (T2D) patients than in younger and less frail patients for initiating hypoglycemic agents since 2011. We aimed to assess trends in HbA1c thresholds at initiation of a first hypoglycemic agent(s) in T2D patients and the influence of age and frailty on these trends. Materials and methods: The groningen initiative to analyze type 2 diabetes treatment (GIANTT) database was used, which includes primary care T2D patients from the north of the Netherlands. Patients initiating a first non-insulin hypoglycemic agent(s) between 2008 and 2014 with an HbA1c measurement within 120 days before initiation were included. The influence of calendar year, age, or frailty and the interaction between calendar year and age or frailty were assessed using multilevel regression analyses adjusted for confounders. Results: We included 4588 patients. The mean HbA1c threshold at treatment initiation was 7.4% up to 2010, decreasing to 7.1% in 2011 and increasing to 7.4% in 2014. This quadratic change over the years was significant (P 0.05). Conclusions: HbA1c thresholds at initiation of a first hypoglycemic agent(s) changed significantly over time, showing a decrease after 2010 and an increase after 2012. The HbA1c threshold at initiation was not influenced by age or frailty, which is in contrast with recommendations for more personalized treatment

Cost-Effectiveness Analysis of Insulin Detemir Compared to Neutral Protamine Hagedorn (NPH) in Patients with Type 1 and Type 2 Diabetes Mellitus in Spain

Introduction: An Excel® (Microsoft Corporation) model was adapted to estimate the short-term (1-year) cost effectiveness of insulin detemir (IDet) versus neutral protamine Hagedorn (NPH) insulin in patients initiating insulin treatment with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) in Spain. Methods: Clinical benefits included the non-severe hypoglycemia rate for T1DM and T2DM, and weight change for T2DM. Three scenarios were included with different hypoglycemia rates estimated on the basis of clinical trials and observational studies. Costs, estimated from perspective of the Spanish Public Healthcare System (Euros 2014), included insulin treatment and non-severe hypoglycemia management costs. Non-severe hypoglycemia, defined as a self-managed event, implied the use of extra glucose testing strips and a general practitioner visit during the week following the event for 25% of patients. An average disutility value was associated to non-severe hypoglycemia events and, for T2DM, to one body mass index unit gain to calculate quality-adjusted life years (QALYs). Results: For the three scenarios a range of 0.025–0.076 QALYs for T1DM and 0.014–0.051 QALYs for T2DM were gained for IDet versus NPH due to non-severe hypoglycemia and weight gain avoidance, in return of an incremental cost of €145–192 for T1DM and €128–206 for T2DM. This resulted in the IDet versus NPH incremental cost-effectiveness ratio (ICER) ranging between €1910/QALY and €7682/QALY for T1DM and €2522/QALY and €15,009/QALY for T2DM. Conclusion: IDet was a cost-effective alternative to NPH insulin in the first year of treatment of patients with T1DM and patients with T2DM in Spain, with ICERs under the threshold value commonly accepted in Spain (€30,000/QALY)

Towards the realization of a basic professional pro le model forScience, Technology and Mathematics (STEM) teachers

[EN] In this work it is both discussed and provided a framework of speci c competences that may serve asa guide for setting up an ongoing process in the professional development of Science, Technology andMathematics (STEM) teachers. The fundamentals of the TPACK model serve to base the theoreticalbackground of this scheme, to which we have incorporated an additional feature which means to considerthe linguistic (L) dimension, thus transforming it into the TPACLK model. The di erent detailed profes-sional STEM capacities have been classi ed into six main sections. The two rst points discussed establishthe STEM disciplinary and didactic capacities; the third section corresponds to the role of language in theSTEM classroom; the fourth category is focused on the motivational elements of the teaching and learningprocess; the fth corresponds to the self-perception of teachers and the last section summarises how tointegrate e ectively the information and communication technologies into the educational STEM activity.This professional development is framed within innovative and research educational activities[ES] En este trabajo se fundamenta y se establece un marco de competencias específicas que pueden servir como guía de un proceso continuo de desarrollo profesional del docente de Ciencias, Tecnología y Matemáticas(CTEM). La base teórica de este escenario corresponde al modelo TPACK, al que se ha añadido la dimensión lingüística (L), transformándolo en el modelo TPACLK. 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Flash Glucose-Sensing Technology as a Replacement for Blood Glucose Monitoring for the Management of Insulin-Treated Type 2 Diabetes: a Multicenter, Open-Label Randomized Controlled Trial

Introduction Glycemic control in participants with insulin-treated diabetes remains challenging. We assessed safety and efficacy of new flash glucose-sensing technology to replace self-monitoring of blood glucose (SMBG). Methods This open-label randomized controlled study (ClinicalTrials.gov, NCT02082184) enrolled adults with type 2 diabetes on intensive insulin therapy from 26 European diabetes centers. Following 2 weeks of blinded sensor wear, 2:1 (intervention/control) randomization (centrally, using biased-coin minimization dependant on study center and insulin administration) was to control (SMBG) or intervention (glucose-sensing technology). Participants and investigators were not masked to group allocation. Primary outcome was difference in HbA1c at 6 months in the full analysis set. Prespecified secondary outcomes included time in hypoglycemia, effect of age, and patient satisfaction. Results Participants (n = 224) were randomized (149 intervention, 75 controls). At 6 months, there was no difference in the change in HbA1c between intervention and controls: −3.1 ± 0.75 mmol/mol, [−0.29 ± 0.07% (mean ± SE)] and −3.4 ± 1.04 mmol/mol (−0.31 ± 0.09%) respectively; p = 0.8222. A difference was detected in participants aged <65 years [−5.7 ± 0.96 mmol/mol (−0.53 ± 0.09%) and −2.2 ± 1.31 mmol/mol (−0.20 ± 0.12%), respectively; p = 0.0301]. Time in hypoglycemia <3.9 mmol/L (70 mg/dL) reduced by 0.47 ± 0.13 h/day [mean ± SE (p = 0.0006)], and <3.1 mmol/L (55 mg/dL) reduced by 0.22 ± 0.07 h/day (p = 0.0014) for intervention participants compared with controls; reductions of 43% and 53%, respectively. SMBG frequency, similar at baseline, decreased in intervention participants from 3.8 ± 1.4 tests/day (mean ± SD) to 0.3 ± 0.7, remaining unchanged in controls. Treatment satisfaction was higher in intervention compared with controls (DTSQ 13.1 ± 0.50 (mean ± SE) and 9.0 ± 0.72, respectively; p < 0.0001). No serious adverse events or severe hypoglycemic events were reported related to sensor data use. Forty-two serious events [16 (10.7%) intervention participants, 12 (16.0%) controls] were not device-related. Six intervention participants reported nine adverse events for sensor-wear reactions (two severe, six moderate, one mild). Conclusion Flash glucose-sensing technology use in type 2 diabetes with intensive insulin therapy results in no difference in HbA1c change and reduced hypoglycemia, thus offering a safe, effective replacement for SMBG

Preoperative dexamethasone reduces postoperative pain, nausea and vomiting following mastectomy for breast cancer

<p>Abstract</p> <p>Background</p> <p>Dexamethasone has been reported to reduce postoperative symptoms after different surgical procedures. We evaluated the efficacy of preoperative dexamethasone in ameliorating postoperative nausea and vomiting (PONV), and pain after mastectomy.</p> <p>Methods</p> <p>In this prospective, double-blind, placebo-controlled study, 70 patients scheduled for mastectomy with axillary lymph node dissection were analyzed after randomization to treatment with 8 mg intravenous dexamethasone (<it>n </it>= 35) or placebo (<it>n </it>= 35). All patients underwent standardized procedures for general anesthesia and surgery. Episodes of PONV and pain score were recorded on a visual analogue scale. Analgesic and antiemetic requirements were also recorded.</p> <p>Results</p> <p>Demographic and medical variables were similar between groups. The incidence of PONV was lower in the dexamethasone group at the early postoperative evaluation (28.6% <it>vs</it>. 60%; <it>p </it>= 0.02) and at 6 h (17.2% <it>vs</it>. 45.8%; <it>p </it>= 0.03). More patients in the placebo group required additional antiemetic medication (21 <it>vs</it>. 8; <it>p </it>= 0.01). Dexamethasone treatment significantly reduced postoperative pain just after surgery (VAS score, 4.54 ± 1.55 <it>vs</it>. 5.83 ± 2.00; <it>p </it>= 0.004), at 6 h (3.03 ± 1.20 <it>vs</it>. 4.17 ± 1.24; <it>p </it>< 0.0005) and at 12 h (2.09 ± 0.85 <it>vs</it>. 2.54 ± 0.98; <it>p </it>= 0.04). Analgesics were required in more patients of the control group (21 <it>vs</it>. 10; <it>p </it>= 0.008). There were no adverse events, morbidity or mortality.</p> <p>Conclusions</p> <p>Preoperative intravenous dexamethasone (8 mg) can significantly reduce the incidence of PONV and pain in patients undergoing mastectomy with axillary dissection for breast cancer.</p> <p>Trial registration number</p> <p>NCT01116713</p