Predicting and preventing infections after pediatric surgeries
Official title: PreoP-SSI: Prediction and prevention of pediatric surgical site infections
Funded at University of California, San Francisco · NIH-11049009 · Data updated 2026-11-12
This study is looking to help keep kids safe from infections after surgery by using smart technology to find out what makes some children more at risk, so doctors can give them personalized care and better recommendations during their operations.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | May 1, 2024 |
| Project end | April 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $46,623 |
| NIH project number | 5F31HD114398-02 |
| Project ID | NIH-11049009 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are children aged 0 to 21 years who are scheduled for surgical procedures.
✕Less likely to help
Patients who are not undergoing surgery or who are outside the pediatric age range may not benefit from this research.
What this research studies
This research aims to reduce the risk of surgical site infections (SSIs) in children by using advanced machine learning techniques to analyze electronic health record data. By identifying specific risk factors associated with SSIs, the study seeks to develop personalized prevention strategies that can be integrated into clinical practice. The project will create a clinical decision support tool that provides real-time recommendations to healthcare providers, enhancing patient care and safety during pediatric surgeries.
Why it matters
Potential benefit: If successful, this research could significantly lower the incidence of surgical site infections in children, leading to better health outcomes and reduced healthcare costs.
How similar studies have gone: Previous research has shown promise in using machine learning for predicting surgical outcomes, indicating that this approach could be effective in preventing SSIs in pediatric patients.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Chan, Carrie — University of California, San Francisco
- Study coordinator: Chan, Carrie
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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.