Following ECG patterns over time to spot heart risk in chronic kidney disease
Dynamic Longitudinal Functional Models with Applications to the CRIC Study
This project builds computer tools that watch routine ECGs from people with chronic kidney disease to spot who might be at higher risk for heart problems.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pennsylvania NIH-funded |
| Lab location | 1 site (Philadelphia, United States) |
| Project ID | NIH-11070343 on NIH RePORTER |
What this research studies
Researchers will use yearly twelve-lead ECG recordings collected from people in the Chronic Renal Insufficiency Cohort (CRIC) to look for changing heartbeat patterns over time. They will create new statistical and computational methods that extract features from raw ECG traces and model how those features evolve. The team will build fast, real-time risk prediction algorithms that aim to flag patients at high risk for events like heart failure, heart attack, stroke, atrial fibrillation, or cardiovascular death. Findings will be checked by applying the methods to an independent group of CKD patients to confirm the discoveries.
Who could benefit from this research
Good fit: Adults with chronic kidney disease who have repeated ECG recordings or who are enrolled in CKD cohorts like CRIC are the ideal candidates.
Not a fit: People without chronic kidney disease, those without serial ECG data, or patients whose heart issues do not show up on ECGs may not benefit directly.
Why it matters
Potential benefit: If successful, this could help detect rising heart risk earlier using routine ECGs so clinicians can act sooner to prevent complications.
How similar studies have performed: Machine learning on single ECGs has shown promise for heart-risk prediction, but using dynamic longitudinal ECG patterns in CKD with real-time algorithms is a newer and less-tested approach.
Where this research is happening
Philadelphia, United States
- University of Pennsylvania — Philadelphia, United States (Active)
Researchers
- Principal investigator: Guo, Wensheng — University of Pennsylvania
- Study coordinator: Guo, Wensheng
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.