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Objectives To describe and evaluate outcomes in STEMI patients sustained on clopidogrel compared to those switched to ticagrelor following fibrinolysis. Background World‐wide, many STEMI patients cannot achieve timely PCI and therefore require fibrinolysis. Although comparable 30‐day and 1‐year safety was shown with clopidogrel or ticagrelor in the TREAT study, there is paucity of long‐term outcomes in pharmacoinvasive treated STEMI. Methods We conducted an observational cohort study evaluating consecutive pharmacoinvasive STEMI patients treated in a network, comparing those switched to ticagrelor to those sustained on clopidogrel. The primary efficacy composite was one‐year all‐cause death, recurrent myocardial infarction, and stroke with major bleeding and intracranial hemorrhage (ICH) as the safety outcomes. Multivariable Cox regression model was used to examine the association between P2Y12 inhibitor and outcomes with inverse probability weighting. Results Of 1426 pharmacoinvasive STEMI patients, 28% (n = 396) were converted to ticagrelor at a mean of 9.9 h after fibrinolysis with comparable GRACE Risk Scores (median; 158 vs 157, p0.352). The primary composite occurred in 3.5% of ticagrelor and 7.0% of clopidogrel treated patients (p0.014). Following adjustment, ticagrelor was associated with a 54% lower composite outcome (adjusted HR 0.46, 95% confidence interval 0.26–0.84). Major bleeding 6.3% vs 6.1% (NS) and ICH 0.0% vs 0.2% (NS) were similar. Conclusions In a prospective STEMI cohort, switching to ticagrelor compared with sustaining clopidogrel following fibrinolysis pharmacoinvasive reperfusion reduced recurrent ischemic events at 1‐year with no differences in major bleeding or ICH. Aligned with randomized data, these findings provide support to switch pharmaco‐invasively treated STEMI patients.

Aims Whether glycaemic control is associated with cardiovascular outcomes in patients with type 2 diabetes (T2D) is unclear. Consequently, we assessed the relationship between glycated haemoglobin (HbA1c ) and cardiovascular outcomes in a placebo-controlled randomized trial which demonstrated no cardiovascular effect of sitagliptin in patients with T2D and atherosclerotic vascular disease. Methods and results Secondary analysis of 14 656 TECOS participants with time to event analyses using multivariable Cox proportional hazard models. During a median 3.0 (interquartile range 2.3-3.8) year follow-up, 456 (3.1% of 14 656) patients had first hospitalization for heart failure (HF), 1084 (11.5%) died, 1406 (9.6%) died or were hospitalized for HF, and 1689 (11.5%) had a non-HF cardiovascular event (cardiovascular death, non-fatal stroke, non-fatal myocardial infarction, or hospitalization for unstable angina). Associations between baseline or time-varying HbA1c and cardiovascular outcomes were U-shaped, with the lowest risk when HbA1c was around 7%. Each one-unit increase in the time-varying HbA1c above 7% was associated with an adjusted hazard ratio (HR) of 1.21 [95% confidence interval (CI) 1.11-1.33] for first HF hospitalization, 1.11 (1.03-1.21) for all-cause death, 1.18 (1.09-1.26) for death or HF hospitalization, and 1.10 (1.02-1.17) for non-HF cardiovascular events. Each one-unit decrease in the time-varying HbA1c below 7% was associated with an adjusted HR of 1.35 (95% CI 1.12-1.64) for first HF hospitalization, 1.37 (1.16-1.61) for death, 1.42 (1.23-1.64) for death or HF hospitalization, and 1.22 (1.06-1.41) for non-HF cardiovascular events. Conclusion Glycated haemogobin exhibits a U-shaped association with cardiovascular outcomes in patients with T2D and atherosclerotic vascular disease, with nadir around 7%. Clinical trial registration ClinicalTrials.gov Identifier: NCT00790205.

Aim To examine fracture incidence among participants in the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS). Research design and methods We used data from 14 671 participants in the TECOS study who were randomized double-blind to sitagliptin (n = 7332) or placebo (n = 7339). Cumulative fracture incidence rates were calculated and their association with study treatment assignment was examined using multivariable Cox proportional hazards regression. Results The baseline mean (standard deviation) participant age was 65.5 (8.0) years, diabetes duration was 11.6 (8.1) years and glycated haemoglobin level was 7.2 (0.5)% [55.2 (5.5) mmol/mol], and 29.3% of participants were women and 32.1% were non-white. During 43 222 person-years’ follow-up, 375 (2.6%; 8.7 per 1000 person-years) had a fracture; 146 were major osteoporotic fractures (hip, n = 34; upper extremity, n = 81; and clinical spine, n = 31). Adjusted analyses showed fracture risk increased independently with older age (P < .001), female sex (P < .001), white race (P < .001), lower diastolic blood pressure (P < .001) and diabetic neuropathy (P = .003). Sitagliptin, compared with placebo, was not associated with a higher fracture risk [189 vs 186 incident fractures: unadjusted hazard ratio (HR) 1.01, 95% confidence interval (CI) 0.82 to 1.23, P = .944; adjusted HR 1.03, P = .745], major osteoporotic fractures (P = .673) or hip fractures (P = .761). Insulin therapy was associated with a higher fracture risk (HR 1.40, 95% CI 1.02-1.91; P = .035), and metformin with a lower risk (HR 0.76, 95% CI 0.59-0.98; P = .035). Conclusion Fractures were common among people with diabetes in the TECOS study, but were not related to sitagliptin therapy. Insulin and metformin treatment were associated with higher and lower fracture risks, respectively.

The purpose of this study is to describe how the attribute hierarchy method (AHM) can be used to evaluate differential group performance at the cognitive attribute level. The AHM is a psychometric method for classifying examinees’ test item responses into a set of attribute-mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated on each attribute for each examinee thereby providing specific information about the examinee’s attribute-mastery level. These probabilities can also be compared across groups. We describe a four-step procedure for estimating and interpreting group differences using the AHM. We also provide an example using student response data from a sample of algebra items on the SAT to illustrate our pattern recognition approach for studying group differences. Assessment engineering (AE) is emerging as a new research area in educational and psychological measurement (Luecht, 2006a, 2006b). AE is an innovative approach to measurement, where engineering-like principles are used to direct the design and the analysis of assessments as well as the scoring and the reporting of the results. With this approach, an assessment begins with specific, empirically derived cognitive models (e.g., Leighton & Gierl, 2007). Next, assessment task templates are created using established frameworks derived from the cognitive model to produce test items. Finally, psychometric methods are applied to the examinee response data, typically in a confirmatory mode, to produce scores that are interpretable (Luecht, Gierl, Tan, & Huff, 2006). AE differs from more traditional approaches to test design and analysis in three fundamental ways. First, cognitive models guide task design and item development, rather than content-based test specifications. While the categories in content blueprints can be included in the task templates, the assessment principles used to develop items are based on cognitive principles and, thus, provide more specific information for measuring problem-solving skills. Second, task templates are created to control and manipulate both the content and cognitive attributes of the items. Item writers are required to use the templates during development, thereby producing items that adhere to strict quality controls and that meet high psychometric standards. Third, psychometric models are employed in a confirmatory, versus exploratory, manner to assess the model-data fit relative to the intended underlying structure of the constructs or traits the test is designed to measure. The outcomes from these model-data fit analyses also provide developers with guidelines for