Better prediction tools for cancer outcomes and postoperative opioid needs
New Statistical Methods for Modelling Cancer Outcomes
This project creates improved statistical tools to help doctors predict opioid needs after surgery and to predict outcomes for adults with lung cancer.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Michigan at Ann Arbor NIH-funded |
| Lab location | 1 site (Ann Arbor, United States) |
| Project ID | NIH-11321191 on NIH RePORTER |
What this research studies
From a patient's point of view, researchers are using large real-world hospital databases to build new prediction methods that estimate how much opioid medicine adult surgery patients may need and how muscle-related measurements relate to lung cancer survival. They will combine data from thousands of surgical patients across many hospitals and from lung cancer cohorts to train these models and measure how uncertain the predictions are. The goal is to make predictions more reliable so clinicians can personalize prescriptions and care plans, including clinical, nutritional, and physical interventions. The work focuses on adult patients and uses existing clinical and laboratory data rather than experimental treatments.
Who could benefit from this research
Good fit: Ideal candidates are adults who are opioid-naïve undergoing surgery at participating hospitals and adults diagnosed with lung cancer who are included in the study cohorts.
Not a fit: Children, people under 21, and adults who are not treated at participating hospitals or who are outside the lung cancer or surgical cohorts are unlikely to benefit directly from this work.
Why it matters
Potential benefit: If successful, this could lead to safer, more personalized opioid prescribing after surgery and better-tailored care plans that may improve lung cancer outcomes.
How similar studies have performed: Related prediction efforts for opioid prescribing and for muscle metrics in cancer have shown promise, but combining large clinical databases with advanced uncertainty-focused statistical methods is relatively new.
Where this research is happening
Ann Arbor, United States
- University of Michigan at Ann Arbor — Ann Arbor, United States (Active)
Researchers
- Principal investigator: Li, Yi — University of Michigan at Ann Arbor
- Study coordinator: Li, Yi
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.