Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients.

Age acceleration (Age-A) is a useful tool that is able to predict a broad range of health outcomes. It is necessary to determine DNA methylation levels to estimate it, and it is known that Age-A is influenced by environmental, lifestyle, and vascular risk factors (VRF). The aim of this study is to estimate the contribution of these easily measurable factors to Age-A in patients with cerebrovascular disease (CVD), using different machine learning (ML) approximations, and try to find a more accessible model able to predict Age-A.

Long-term vascular events after subarachnoid hemorrhage.

Background: Spontaneous subarachnoid hemorrhage (SAH) long-term risk is not well known. Our aims are: describing long-term vascular event (VE) incidence rates in SAH survivors; describing VE: ischemic and/or hemorrhagic; identifying independent association of factors related to VE; and analyzing the usefulness of factors to increase predictive ability.

Methods: A prospective cohort study of consecutive patients admitted to Hospital del Mar with a diagnosis of SAH (n = 566) between January 2007 and January 2020 was carried out.