Addressing disparities in out-of-hospital cardiac arrest using health needs and predictive analytics
Official title: Addressing Disparities in Out-of-Hospital Cardiac Arrest: Utilization of Health-Related Social Needs and Predictive Analytics to Improve Clinical Outcomes
Funded at Icahn School of Medicine at Mount Sinai · NIH-11411358 · Data updated 2026-11-12
A physician-scientist project uses data from OHCA patients and language-based AI tools to improve predictions of survival and identify social needs that influence outcomes.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Icahn School of Medicine at Mount Sinai NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $167,725 |
| NIH project number | 5K08HL169980-03 |
| Project ID | NIH-11411358 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients who have experienced out-of-hospital cardiac arrest and have data available in electronic health records.
✕Less likely to help
Patients whose data or conditions are not represented in the predictive models or who do not interact with OHCA care pathways may see limited direct benefit.
What this research studies
This project aims to help OHCA patients by combining clinical data with health-related social needs extracted from text using NLP and large language models. The goal is to improve prediction of 30-day survival and survival to hospital discharge, which could guide interventions. The work is grounded in mentorship and career development to establish the investigator as an independent physician-scientist and prepare for future funding. The approach focuses on integrating health needs into predictive models to close gaps in care and outcomes for OHCA patients.
Why it matters
Potential benefit: If successful, the project could lead to more accurate survival predictions and targeted interventions that reduce mortality after OHCA.
How similar studies have gone: Prior studies show promise using NLP and data science to enhance health predictions, but applying it specifically to OHCA survivors and health-needs integration is an evolving area.
Where this research is happening
-
Icahn School of Medicine at Mount Sinai
New York, United States Active
Researchers
- Principal investigator: Abbott, Ethan — Icahn School of Medicine at Mount Sinai
- Study coordinator: Abbott, Ethan
Related research
Other NIH-funded projects on the same topics.
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Multi-center study on how ventilation during CPR relates to cardiac arrest outcomes
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Specialized post-arrest care after cardiac arrest
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Improving predictions of brain recovery after cardiac arrest
This project compares outcomes after cardiac arrest in the U.S. and Brazil to learn which tests best predict who will regain brain function for people who survive cardiac arrest.
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Reducing biased predictions of brain recovery after cardiac arrest
This project looks at cardiac arrest survivors in the US and Brazil to find more accurate ways to predict brain recovery and avoid premature stopping of life support.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.