# PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP)

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $329,031

## Abstract

Project Summary:
The goals of the parent PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) study are to
discover novel biomarker signatures after cardiac arrest that predict treatment responsiveness and
long-term recovery. Cardiac arrest is a major public health problem with high morbidity and
mortality. Improving survival and functional recovery are critical public health objectives. We
hypothesize that not all patients are identical and that through innovative, multi-parametric data-
driven approaches we will be able to identify novel signatures to identify distinct patient subgroups.
The PRECICECAP analysis plan allows us to meet our study needs but is not intrinsically
generalizable or usable by others. An ever-growing number of NIH-funded projects amass and
analyze related datasets from hospitalized patients and develop their own custom solutions. Such
an ad hoc approach is costly, inefficient, and threatens research rigor and reproducibility. The
objective of this supplement to PRECICECAP is to develop a freely-available software platform that
allows artificial intelligence/machine learning (AI/ML) analysis of complex neurocritical care data
and to provide a curated dataset from PRECICECAP ready for AI/ML. The knowledge learned here
will be applied to help achieve NIH goals for modernizing the biomedical research data ecosystem
by developing a software product that can handle AI/ML on this type of complex neurocritical care
data. It will also allow the sharing of a cleaned, annotated comprehensive data set. Through a
thoughtful collaboration between clinician investigators, data scientists and industry, we will take
NIH-supported data from the PRECICECAP study and make it broadly available and easily usable.
The project will deliver an important software tool that can be used by others conducting similar
research, advancing the NIH’s mission to make complex data FAIR (Findable, Accessible,
Interoperable, and Reusable). We will develop a series of modular functions (for example, to
support data harmonization, annotation or visualization) that permit users to graphically construct
processing pipelines maximizing automation where appropriate and allowing facile interaction with
data when needed. We will develop a user-friendly dashboard interface to allow individual study
sites and coordinating hubs to understand complex data, inspect key meta-data features, and
identify potential errors. Modular design facilitates dynamic configuration of AI/ML architectures
within the same interface, allowing individual modules to combine synergistically to maximize
efficiency and reproducibility. The result will facilitate an open, wide collaboration between scientists
using similar data.

## Key facts

- **NIH application ID:** 10412861
- **Project number:** 3R01NS119825-01S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Jonathan Elmer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $329,031
- **Award type:** 3
- **Project period:** 2020-12-15 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412861

## Citation

> US National Institutes of Health, RePORTER application 10412861, PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) (3R01NS119825-01S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10412861. Licensed CC0.

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