# Using AI on Routine Clinical and Imaging Data from Acute Stroke Encounter to Predict Post-Stroke Vascular Contributions to Cognitive Impairment (AI - RESPECT)

> **NIH NIH RF1** · EMORY UNIVERSITY · 2024 · $2,340,263

## Abstract

Project Summary
 Poststroke cognitive impairment (PSCI) was found to be common in various research studies. PSCI is
ideally recognized through cognitive screening and test but they are not standard clinical practices and hence
stroke recovery and prevention of recurrent strokes may be undermined by concurrent but poorly recognized
cognitive issues, e.g., patient compliance to follow blood pressure control medication may be poorer among
those with PSCI. Therefore, a significant unmet need for optimizing poststroke care is to recognize patients at
high risk of PSCI to tailor for them an appropriate stroke recovery and recurrent stroke prevention strategy.
With many of the plausible determinants of PSCI being available in electronic health record (EHR) systems,
machine learning (ML) methods to process routine clinical data to predict risk of PSCI is highly feasible. We
propose to combine a large retrospective dataset from EHR and a smaller prospective dataset with more
accurate ascertainment of PSCI based on purposefully administered cognitive tests, serving as gold-standard.
The necessity of prospective cognitive tests to accurately ascertain PSCI further allows us to explore biological
and physiological variables related to pathologies of Alzheimer disease and related dementia (ADRD). We will
pursue three specific aims: 1) Learn to predict PSCI using routine neuro images and EHR data from large
clinical cohorts; 2) Use prospective data to adapt and validate models learned from existing clinical cohorts; 3)
Phenotype PSCI with cognitive tests, physiological, and biological metrics one-year poststroke. Prediction of
PSCI could aid optimizing stroke recovery and recurrent stroke prevention strategies. Our proposed novel
physiological and biological metrics have the potential to further improve PSCI prediction and characterize
PSCI granularly with the consideration of cerebrovascular and neurodegenerative underpinnings.

## Key facts

- **NIH application ID:** 10985212
- **Project number:** 1RF1NS139325-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Xiao Hu
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,340,263
- **Award type:** 1
- **Project period:** 2024-09-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10985212

## Citation

> US National Institutes of Health, RePORTER application 10985212, Using AI on Routine Clinical and Imaging Data from Acute Stroke Encounter to Predict Post-Stroke Vascular Contributions to Cognitive Impairment (AI - RESPECT) (1RF1NS139325-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10985212. Licensed CC0.

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