# Real-Time Prediction of Delayed Cerebral Ischemia after Subarachnoid Hemorrhage

> **NIH NIH K23** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $200,880

## Abstract

Of the 30,000 Americans who develop SAH each year, 20-40% have a subsequent event of delayed cerebral
ischemia (DCI), which triples the risk of poor functional outcome, producing impairment at a relatively young age
compared with other stroke subtypes. Because DCI following SAH is commonly detected only after it is
irreversible, an accurate tool is needed to identify impending DCI so that therapies can begin before DCI is well
established and so that new clinical therapies for DCI prevention have the best opportunity for success. Ideally,
monitoring tools should afford real-time assessment of neuronal function impacted by the diverse vascular and
metabolic mechanisms leading to DCI.
 The proposed scientific program aims to examine the accuracy of real-time clinical assessments and
continuous EEG for predicting impending DCI by evaluating for early, pre-symptomatic deterioration in brain
activity. The principal investigator has three specific research aims: (1) to determine whether incorporating real-
time clinical data extending through the postoperative period improves prediction of DCI compared to admission
data alone; (2) to determine whether real-time clinical neurophysiologist reporting of pre-specified EEG features
improves DCI prediction; and 3) to examine the influence of blood pressure on EEG measures of neuronal
dysfunction before and after DCI. The proposed studies build on pilot data collected by the applicant by using
real-time clinical data beyond admission, performing blinded assessments for DCI based on NIH Common Data
Elements, and extending the study of blood pressure and EEG alpha power to a large cohort with synchronized
multimodality monitoring. Under the mentorship of primary mentor Dr. Jonathan Rosand, and co-mentors Drs.
Sydney Cash, Hang Lee, and Nazem Atassi, the PI proposes a career development plan leveraging the
resources of a world-class environment at Massachusetts General Hospital and Harvard Medical School to
develop proficiency in biostatistics, multi-state survival analysis, and signal processing, carry out the project and
transition to an independent clinician-scientist studying SAH-related morbidity and its prevention.
 The overall goal of this project is to develop strategies for detecting early signs of impending DCI to enable
preventative therapies to be delivered 1) in a timely manner and 2) to the appropriate patients. This will provide
a framework for efficient, successful future clinical trials of DCI prevention. Bringing together cutting-edge clinical
neurophysiology, analytic resources at MGH, and a team of mentors with relevant expertise, this project will
define the population of SAH patients at greatest risk for DCI, while drawing understanding about the impact of
blood pressure on brain function at various stages leading to DCI. The proposed patient-oriented research
project, in concert with mentorship and a structured curriculum in methods including biostatistics, multi-state
survival analysis, s...

## Key facts

- **NIH application ID:** 10375445
- **Project number:** 5K23NS105950-05
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Eric S. Rosenthal
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $200,880
- **Award type:** 5
- **Project period:** 2018-04-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10375445

## Citation

> US National Institutes of Health, RePORTER application 10375445, Real-Time Prediction of Delayed Cerebral Ischemia after Subarachnoid Hemorrhage (5K23NS105950-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10375445. Licensed CC0.

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