Predicting which children will die shortly after terminal extubation to improve organ donation outcomes
Official title: Death One Hour After Terminal Extubation (DONATE) 2.0 Study
Funded at Children's Hospital of Los Angeles · NIH-11039253 · Data updated 2026-11-12
This study is working on a way to help doctors decide if children who are very sick and likely to pass away soon can be considered for organ donation, using smart technology to make the process easier and less stressful for families.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Children's Hospital of Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project start | April 1, 2025 |
| Project end | March 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $286,820 |
| NIH project number | 1R21HD117339-01 |
| Project ID | NIH-11039253 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are children who are terminally ill and may be considered for organ donation after circulatory death.
✕Less likely to help
Patients who are not terminally ill or those who do not meet the criteria for organ donation will not benefit from this research.
What this research studies
This research focuses on improving organ donation processes for children by predicting which patients are likely to die within one hour after terminal extubation. Using a machine learning model called DONATE, the team analyzes multi-site data to enhance clinical decision-making regarding organ donation after circulatory death (DCD). The goal is to develop a clinical decision support tool that helps clinicians assess the candidacy of children for DCD, ultimately aiming to increase the number of viable organ donations while minimizing emotional strain on families. The research seeks to ensure that only those patients who are likely to pass away within the critical timeframe are considered for organ donation.
Why it matters
Potential benefit: If successful, this research could significantly increase the number of organs available for transplantation in children, potentially saving many lives.
How similar studies have gone: Previous research has shown promise in using machine learning models for predicting patient outcomes in critical care settings, indicating potential success for this approach.
Where this research is happening
-
Children's Hospital of Los Angeles
Los Angeles, United States Active
Researchers
- Principal investigator: Winter, Meredith Chan — Children's Hospital of Los Angeles
- Study coordinator: Winter, Meredith Chan
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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.