IMPORTANCE Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients' quality of life and outcomes. OBJECTIVES To compare machine learning approaches with traditional logistic regression in predicting key outcomes in patients with HF and evaluate the added value of augmenting claimsbased predictive models with electronic medical record (EMR)-derived information. DESIGN, SETTING, AND PARTICIPANTS A prognostic study with a 1-year follow-up period was conducted including 9502 Medicare-enrolled patients with HF from 2 health care provider networks in Boston, Massachusetts ("providers" includes physicians, clinicians, other health care professionals,

Rivaroxaban and dabigatran demonstrated better persistence than VKA at Day 360. Furthermore, rivaroxaban was associated with better persistence and adherence than dabigatran. Further studies are needed to identify factors responsible for this difference and evaluate the impact on outcomes.

In older European men, increased PP is an important independent determinant of coronary risk, even among those initially considered normotensive.

Rivaroxaban and apixaban were associated with less ICH than warfarin and both are likely associated with reductions in the combined endpoint. Further investigation to validate the numerically higher rate of ischemic stroke with apixaban versus warfarin is required.