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Aim: We aimed to compare cardiovascular outcomes of patients with type 2 diabetes (T2D) who initiated GLP-1 receptor agonists (GLP-1RA) or basal insulin (BI) under routine care. Methods: We accessed the administrative claims database of the Veneto Region (Italy) to identify new users of GLP-1RA or BI in 2014–2018. Propensity score matching (PSM) was implemented to obtain two cohorts of patients with superimposable characteristics. The primary endpoint was the 3-point major adverse cardiovascular events (3P-MACE). Secondary endpoints included 3P-MACE components, hospitalization for heart failure, revascularizations, and adverse events. Results: From a background population of 5,242,201 citizens, 330,193 were identified as having diabetes. PSM produced two very well matched cohorts of 4063 patients each, who initiated GLP-1RA or BI after an average of 2.5 other diabetes drug classes. Patients were 63-year-old and only 15% had a baseline history of cardiovascular disease. During a median follow-up of 24 months in the intention-to-treat analysis, 3P-MACE occurred less frequently in the GLP-1RA cohort (HR versus BI 0.59; 95% CI 0.50–0.71; p < 0.001). All secondary cardiovascular endpoints were also significantly in favor of GLP-1RA. Results were confirmed in the as-treated approach and in several stratified analyses. According to the E-value, confounding by unmeasured variables were unlikely to entirely explain between-group differences in cardiovascular outcomes. Conclusions: Patients with T2D who initiated a GLP-1RA experienced far better cardiovascular outcomes than did matched patients who initiated a BI in the same healthcare system. These finding supports prioritization of GLP-1RA as the first injectable regimen for the management of T2D. [ABSTRACT FROM AUTHOR]

People with diabetes require lifelong access to healthcare services to delay the onset of complications. Their disease management processes generate great volumes of data across several domains, from clinical to administrative. Difficulties in accessing and processing these data hinder their secondary use in an institutional setting, even for highly desirable applications, such as the prediction of cardiovascular disease, the main driver of excess mortality in diabetes. Hence, in the present work, we propose a deep learning model for the prediction of major adverse cardiovascular events (MACE), developed and validated using the administrative claims of 214,676 diabetic patients of the Veneto region, in North East Italy. Specifically, we use a year of pharmacy and hospitalisation claims, together with basic patient's information, to predict the 4P-MACE composite endpoint, i.e., the first occurrence of death, heart failure, myocardial infarction, or stroke, with a variable prediction horizon of 1 to 5 years. Adapting to the time-to-event nature of this task, we cast our problem as a multi-outcome (4P-MACE and components), multi-label (1 to 5 years) classification task with a custom loss to account for the effect of censoring. Our model, purposefully specified to minimise data preparation costs, exhibits satisfactory performance in predicting 4P-MACE at all prediction horizons: AUROC from 0.812 (C.I.: 0.797 – 0.827) to 0.792 (C.I.: 0.781 – 0.802); C-index from 0.802 (C.I.: 0.788 – 0.816) to 0.770 (C.I.: 0.761 – 0.779). Components’ prediction performance is also adequate, ranging from death's 0.877 1-year AUROC to stroke's 0.689 5-year AUROC. [ABSTRACT FROM AUTHOR]

Aim: Concerns have been raised that dipeptidyl‐peptidase 4 inhibitors (DPP‐4i) may increase the risk of pneumonia. We analysed observational data and clinical trials to explore whether use of DPP‐4i modifies the risk of pneumonia. Methods: We identified patients with diabetes in the Veneto region administrative database and performed propensity score matching between new users of DPP‐4 inhibitors and new users of other oral glucose‐lowering medications (OGLMs). We compared the rate of hospitalization for pneumonia between matched cohorts using the Cox proportional hazard model. The same analysis was repeated using the database of a local diabetes outpatient clinic. We retrieved similar observational studies from the literature to perform a meta‐analysis. Results from trials reporting pneumonia rates among patients randomized to DPP‐4 inhibitors versus placebo/active comparators were also meta‐analysed. Results: In the regional database, after matching 6495 patients/group, new users of DPP‐4 inhibitors had a lower rate of hospitalization for pneumonia than new users of other OGLMs (HR 0.76; 95% CI 0.61‐0.95). In the outpatient database, after matching 867 patients/group, new users of DPP‐4 inhibitors showed a non‐significantly lower rate of hospitalization for pneumonia (HR 0.65; 95% CI 0.41‐1.04). The meta‐analysis of observational studies yielded an overall non‐significant lower risk of hospitalization for pneumonia among DPP‐4 inhibitor users (RR 0.81; 95% CI 0.65‐1.01). The meta‐analysis of randomized controlled trials showed no overall effect of DPP‐4 inhibitors on pneumonia risk (RR 1.06; 95% CI 0.93‐1.20). Conclusion: The use of DPP‐4 inhibitors can be considered as safe with regard to the risk of pneumonia. [ABSTRACT FROM AUTHOR]