Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

Background: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework.

Hypertension control after intracerebral hemorrhage among varying small vessel disease etiologies.

Introduction: Intracerebral hemorrhage (ICH) is attributable to cerebral small vessel disease (cSVD), which includes cerebral amyloid angiopathy (CAA) and hypertensive-cSVD (HTN-cSVD). HTN-cSVD includes patients with strictly deep ICH/microbleeds and mixed location ICH/microbleeds, the latter representing a more severe form of HTN-cSVD. We test the hypothesis that more severe forms of HTN-cSVD are related to worse hypertension control in long-term follow-up after ICH.