# An informatics framework for SUDEP Risk Marker Identification and Risk Assessment

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $309,577

## Abstract

PROJECT SUMMARY
Sudden Unexpected Death in Epilepsy (SUDEP) is the leading mode of epilepsy related death. Recent
estimates indicate that SUDEP is responsible for approximately 7,000 deaths each year in the United States
and Europe, and is the second most common cause of the number of adult life years lost after stroke. To
accelerate SUDEP research, the National Institute of Neurological Disorders and Stroke (NINDS) at the NIH-
funded Center for SUDEP Research (CSR), a network of 14 institutions collaborating in a broad spectrum of
basic science and clinical approaches to study possible biological mechanisms underlying this potentially
preventable mortality and develop predictive biomarkers for interventions. Identification and communication of
alterable SUDEP risk factors to affected patients is an important strategy to lower SUDEP incidence.
However, systematic individualized assessment of SUDEP risk is currently unavailable due to a number of
challenges. Often the required information is embedded in data residing in disparate, unlinked datasets and
systems; there is a lack of a specific controlled vocabulary for precise extraction of SUDEP risk factor
information with semantic uniformity; and the corresponding computational algorithms and tools needed for
important risk marker extraction from clinical text and electrophysiological signals are yet to be fully developed.
We propose to overcome these challenges by developing SURME, a SUDEP Risk Marker Extraction system
for automated extraction of known and putative SUDEP risk markers from the multimodal CSR data repository
(called MEDCIS) which contains over 1,600 patients enrolled from Epilepsy Monitoring Units in 7 medical
centers. In Aim 1 we will develop a dedicated controlled vocabulary building on our own Epilepsy and Seizure
Ontology and existing SUDEP risk guidelines and reported risk factors. We will develop an extraction pipeline,
leveraging our earlier epilepsy phenotype extraction tools, for detecting risk markers from clinical text. In Aim 2
we will develop a scalable approach for detecting two significant putative physiological biomarkers from
electrophysiological signals: postictal generalized EEG suppression; and root mean square differences of
successive R-R intervals. In Aim 3 we will perform pilot implementation of SURME on MEDCIS for automated
risk assessment using “SUDEP-7 Inventory” and “SUDEP and Seizure Safety Checklist”, as well as
assessment of putative SUDEP risk factors using CSR cohort. We expect SURME and its future versions to
become an invaluable SUDEP risk assessment tool as a part of standard epilepsy care. The long-term goal of
this study is to create evidence-based SUDEP risk assessment tools to improve epilepsy care, with
individualized risk scores and recommendations for managing modifiable risks, ultimately leading to reduced
SUDEP mortality and improved epilepsy patient care.

## Key facts

- **NIH application ID:** 10832620
- **Project number:** 5R01NS116287-05
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Licong Cui
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $309,577
- **Award type:** 5
- **Project period:** 2020-05-15 → 2026-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10832620

## Citation

> US National Institutes of Health, RePORTER application 10832620, An informatics framework for SUDEP Risk Marker Identification and Risk Assessment (5R01NS116287-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10832620. Licensed CC0.

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