Predicting treatment effects for patients with acute respiratory distress using clinical data
Individualized Prediction of Treatment Effects Using Data from Both Embedded Clinical Trials and Electronic Health Records
This study is looking to help people with acute respiratory distress syndrome (ARDS) by using real-world data to create personalized treatment plans that could lead to better care and outcomes for each patient.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project ID | NIH-10908391 on NIH RePORTER |
What this research studies
This research aims to improve treatment outcomes for patients suffering from acute respiratory distress syndrome (ARDS) by utilizing data from both clinical trials and electronic health records. By analyzing a vast amount of real-world data, the study seeks to create individualized predictions of treatment effects, which could lead to more effective and personalized care. The approach combines advanced data science techniques, particularly Bayesian methods, to address the limitations of traditional clinical trials, which often do not capture the complexity of patient responses. This innovative methodology aims to provide insights that can be rapidly implemented in clinical settings to enhance patient care.
Who could benefit from this research
Good fit: Ideal candidates for this research include adults diagnosed with acute respiratory distress syndrome or acute respiratory failure who are undergoing mechanical ventilation.
Not a fit: Patients with mild respiratory issues or those not requiring mechanical ventilation may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more personalized and effective treatment strategies for patients with acute respiratory distress, potentially reducing mortality rates.
How similar studies have performed: Previous research utilizing real-world data and Bayesian methods has shown promise in improving treatment predictions, indicating that this approach could be effective.
Where this research is happening
Pittsburgh, United States
- University of Pittsburgh at Pittsburgh — Pittsburgh, United States (Active)
Researchers
- Principal investigator: Cooper, Gregory F. — University of Pittsburgh at Pittsburgh
- Study coordinator: Cooper, Gregory F.
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 below.