Impact of Diabetic Status on Outcomes After Revascularization With Drug-Eluting Stents in Relation to Coronary Artery Disease Complexity: Patient-Level Pooled Analysis of 6081 Patients.

BACKGROUND Diabetes mellitus and angiographic coronary artery disease complexity are intertwined and unfavorably affect prognosis after percutaneous coronary interventions, but their relative impact on long-term outcomes after percutaneous coronary intervention with drug-eluting stents remains controversial. This study determined drug-eluting stents outcomes in relation to diabetic status and coronary artery disease complexity as assessed by the Synergy Between PCI With Taxus and Cardiac Surgery (SYNTAX) score. METHODS AND RESULTS In a patient-level pooled analysis from 4 all-comers trials, 6081 patients were stratified according to diabetic status and according to the median SYNTAX score ≤11 or >11. The primary end point was major adverse cardiac events, a composite of cardiac death, myocardial infarction, and clinically indicated target lesion revascularization within 2 years. Diabetes mellitus was present in 1310 patients (22%), and new-generation drug-eluting stents were used in 4554 patients (75%). Major adverse cardiac events occurred in 173 diabetics (14.5%) and 436 nondiabetic patients (9.9%; P<0.001). In adjusted Cox regression analyses, SYNTAX score and diabetes mellitus were both associated with the primary end point (P<0.001 and P=0.028, respectively; P for interaction, 0.07). In multivariable analyses, diabetic versus nondiabetic patients had higher risks of major adverse cardiac events (hazard ratio, 1.25; 95% confidence interval, 1.03-1.53; P=0.026) and target lesion revascularization (hazard ratio, 1.54; 95% confidence interval, 1.18-2.01; P=0.002) but similar risks of cardiac death (hazard ratio, 1.41; 95% confidence interval, 0.96-2.07; P=0.08) and myocardial infarction (hazard ratio, 0.89; 95% confidence interval, 0.64-1.22; P=0.45), without significant interaction with SYNTAX score ≤11 or >11 for any of the end points. CONCLUSIONS In this population treated with predominantly new-generation drug-eluting stents, diabetic patients were at increased risk for repeat target-lesion revascularization consistently across the spectrum of disease complexity. The SYNTAX score was an independent predictor of 2-year outcomes but did not modify the respective effect of diabetes mellitus. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00297661, NCT00389220, NCT00617084, and NCT01443104.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.