Predicting whether a child will die within one hour after withdrawing breathing support
Official title: Death One Hour After Terminal Extubation (DONATE) 2.0 Study
Funded at Children's Hospital of Los Angeles · NIH-11289307 · Data updated 2026-11-12
This project uses a computer model to help doctors predict if a child will pass away within one hour after breathing support is removed, to guide organ donation decisions.
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 | 2026 |
| Award (this year) | $222,505 |
| NIH project number | 5R21HD117339-02 |
| Project ID | NIH-11289307 on NIH RePORTER |
Who could benefit from this research
✓Could help
Children who are potential donation-after-circulatory-death (DCD) candidates undergoing planned withdrawal of life-sustaining ventilation at participating hospitals and their families.
✕Less likely to help
Patients who are not being considered for organ donation, whose clinical situation is unrelated to DCD, or who die on a different timeline are unlikely to benefit.
What this research studies
If my child might be considered for donation after circulatory death, timing is critical because organs must be recovered within about an hour after life support is stopped. Researchers trained a machine-learning model called DONATE using medical records from multiple hospitals to predict whether a child will die within that hour. They turned the model into a clinical decision support tool designed to help doctors and families decide who is a likely donor and to plan care and organ recovery. This project will test and refine that tool in real clinical settings and study how it affects families and hospital workflows.
Why it matters
Potential benefit: Could help recover more usable organs, reduce unsuccessful donation attempts, and ease decision-making and emotional burden for families.
How similar studies have gone: The team has already trained and tested the DONATE model on multi-site data and prototyped a clinical tool, but machine-learning prediction for DCD is relatively new and has limited real-world deployment.
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.