# DATA-DRIVEN MODELS TO PREDICT DELAYED CEREBRAL ISCHEMIA AFTER SUBARACHNOID HEMORRHAGE

> **NIH NIH R21** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2021 · $251,625

## Abstract

Intracranial Aneurysm (IA) are characterized by a localized dilation and thinning of the
blood vessel, and although they only affect 6% of the population, bleeding from them accounts
for about 25% of cerebrovascular deaths. Rupture of intracranial aneurysms (IAs) causes one of
the most lethal types of hemorrhagic stroke, subarachnoid hemorrhage-SAH. Despite
improvements in SAH management, mortality and morbidity rates remain high, largely due to
delayed ischemic complications. Although symptomatic in up to 40%, because of its severe
consequences and because we cannot identify who will develop spasm, all patients are subject
to extensive monitoring protocols, entailing enormous resources and additional risk for
monitoring and treatment.
 This proposal seeks to develop predictive analytics, integrating quantitative angiography,
non-invasive imaging, and clinical data, to improve outcomes for patients suffering
subarachnoid hemorrhage by providing real time patient-specific guidance. Our central
hypothesis is that angiographic parametric imaging (API) hemodynamic biomarkers correlate
with vasospasm and impaired cerebral autoregulation, both of which are associated with poor
outcomes in delayed cerebral ischemia (DCI). API provides a set of maps of image-biomarkers
that may be combined with patient-specific clinical information to robustly predict poor outcomes
due to DCI. The proposal’s objective is to develop, standardize, and validate a diagnostic
pipeline that uses image-based biomarkers and patient characteristics to predict patient-specific
risk of developing DCI, as well as functional and cognitive outcomes.
 Our application is significant since there is currently no reliable way to predict DCI early
in a patient’s course, and reliable predictions could help to guide therapy and resource
allocation. To achieve this, we propose two aims. In the first aim, we will expand on prior work
using a machine learning framework to predict which patients are at lowest risk of developing
DCI. In aim two we will develop tools to extend predictions to functional and cognitive
outcomes. If successful, this will be one of the first machine learning applications to produce an
integrated prediction tool that allows clinicians to modify treatment plans in real time to reduce
patient risk and resource utilization.

## Key facts

- **NIH application ID:** 10288178
- **Project number:** 1R21NS123478-01
- **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO
- **Principal Investigator:** Jason Davies
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $251,625
- **Award type:** 1
- **Project period:** 2021-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10288178

## Citation

> US National Institutes of Health, RePORTER application 10288178, DATA-DRIVEN MODELS TO PREDICT DELAYED CEREBRAL ISCHEMIA AFTER SUBARACHNOID HEMORRHAGE (1R21NS123478-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10288178. Licensed CC0.

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