# Sudden Cardiac Arrest (SCA): Prediction and Prevention

> **NIH NIH F30** · JOHNS HOPKINS UNIVERSITY · 2020 · $35,760

## Abstract

Project Summary: The recent advances in information technologies and biotechnologies is an opportunity to
substantially improve healthcare. To exploit the power of data to benefit patients, however, effective clinical
decision support tools and novel, individualized interventions must be designed, tested, and implemented.
Although there has been progress in the development of statistical/machine learning methods, numerous
challenges remain to tailor and translate them into useful clinical decision support tools.
Sudden cardiac arrest (SCA) accounts for 15-20% of all adult deaths and is the industrial world’s leading cause
of death. Clinical studies of SCA produce repeated measures on risk factors and multiple different kinds of
events over time. We refer to these data as
survival, longitudinal, and multivariate (SLAM) data.
In this project,
we will develop novel statistical learning methods for SLAM data and apply them to two distinct aspects of the
SCA problem. First, we propose to develop novel statistical learning algorithms that better predict an
individual’s multivariate longitudinal data with a focus on the risk of first and subsequent SCA. Second, we
propose to develop micro-randomization and
just-in-time adaptive intervention trial
designs to reduce
behavioral risk factors for SCA among persons at high risk.
The methods that we propose to develop will be applicable in many areas of medicine. However, they are
motivated by and applied to SCA in this project. Our team has expertise in statistics including causal inference,
longitudinal data and survival analyses, plus machine learning, epidemiology, cardiology, and behavioral
interventions through mobile health (mHealth). This proposed collaboration has the following specific aims:
Aim 1: Develop and test statistical learning tools for real-time risk prediction of survival, longitudinal,
and multivariate (SLAM) outcome data.
Aim 2: Estimate the risk of SCA and its dependence on dynamic modifiable and non-modifiable factors
in population-based and clinical cohorts.
Aim 3: Plan and conduct a feasibility-usability study of micro-randomization and just-in-time adaptive
intervention trial designs for behavioral change to reduce SCA risk.
Upon successful completion of these aims, we will have contributed to the progress of healthcare delivery
through the application of computational statistics to medicine. !
!

## Key facts

- **NIH application ID:** 9986884
- **Project number:** 5F30HL142131-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Shannon Wongvibulsin
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $35,760
- **Award type:** 5
- **Project period:** 2018-09-01 → 2021-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986884

## Citation

> US National Institutes of Health, RePORTER application 9986884, Sudden Cardiac Arrest (SCA): Prediction and Prevention (5F30HL142131-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9986884. Licensed CC0.

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