Predictive factors associated with three years of response to HbA1c goals with exenatide QW or insulin glargine: Post-hoc analysis of the DURATION-3 study.

This post-hoc analysis of the DURATION-3 study aimed to identify factors associated with sustained glycaemic response with exenatide once weekly (QW) or insulin glargine (IG) among patients with type 2 diabetes. Response was defined as achieving treatment target of HbA1c <7.0% (<53 mmol/mol) at Week 26; sustained responders maintained the treatment target for ≥80% of remaining visits, including one during the final 6 months. Of 467 patients, 287 (61.5%) completed 156 weeks of treatment. At Week 26, 175 patients (61.0%) (exenatide QW, n = 95; IG, n = 80) achieved an HbA1c response. At Week 156, 84 of 175 responders (48.0%) had sustained response, with more sustained responders with exenatide QW (22.7% vs 13.9% with IG; P < 0.03). Logistic regression identified three predictors of sustained response: (a) exenatide QW vs IG treatment (odds ratio, 2.584 [95% confidence interval, 1.288-5.187]; P = 0.0075), (b) lower HbA1c at Week 26 (0.139 [0.053-0.366]; P < 0.0001), and (c) lower fasting serum glucose at Week 26 (0.693 [0.541-0.888]; P = 0.0037). A regression model was used to estimate the likelihood of sustained response with either treatment. This analysis provides a helpful tool for predicting sustained response with exenatide QW or IG.

Significant insulin dose errors may occur if blood glucose results are obtained from miscoded meters.

OBJECTIVE: The objective of this study was to determine inaccuracies of miscoded blood glucose (BG) meters and potential errors in insulin dose based on values from these meters. RESEARCH DESIGN: Fasting diabetic subjects at three clinical centers participated in a 2-hour meal tolerance test. At various times subjects' blood was tested on five BG meters and on a Yellow Springs Instruments laboratory glucose analyzer. Some meters were purposely miscoded. Using the BG values from these meters, along with three insulin dose algorithms, Monte Carlo simulations were conducted to generate ideal and simulated-meter glucose values and subsequent probability of insulin dose errors based on normal and empirical distribution assumptions. RESULTS: Maximal median percentage biases of miscoded meters were +29% and -37%, while maximal median percentage biases of correctly coded meters were only +0.64% and -10.45% (p = 0.000, chi(2) test, df = 1). Using the low-dose algorithm and the normal distribution assumption, the combined data showed that the probability of insulin error of +/-1U, +/-2, +/-3, +/-4, and +/-5U for miscoded meters could be as high as 49.6, 50.0, 22.3, 1.4, and 0.04%, respectively. This is compared to manually, correctly coded meters where the probability of error of +/-1, +/-2, and +/-3U could be as high as 44.6, 7.1, and 0.49%, respectively. There was no instance of a +/-4 or +/-5U insulin dose error with a manually, correctly coded meter. For autocoded meters, the probability of +/-1 and +/-2U could be as high as 35.4 and 1.4%, respectively. For autocoded meters there were no calculated insulin dose errors above +/-2U. The probability of insulin misdosing with either manually, correctly coded or autocoded meters was significantly lower than that with miscoded meters. Results using empirical distributions showed similar trends of insulin dose errors. CONCLUSIONS: Blood glucose meter coding errors may result in significant insulin dosing errors. To avoid error, patients should be instructed to code their meters correctly or be advised to use an autocoded meter that showed superior performance over manually, correctly coded meters in this study.