Using digital tools to improve early detection of kidney disease
Official title: Digital Tools for Precision Nephrology
Funded at Columbia University Health Sciences · NIH-10984822 · Data updated 2026-11-12
This study is working on new ways to spot early signs of a kidney condition called Collagen Type IV-Associated Nephropathy by analyzing health records, so that doctors can provide better and more personalized care to patients with chronic kidney disease.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Columbia University Health Sciences NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $166,643 |
| NIH project number | 1K08DK138280-01A1 |
| Project ID | NIH-10984822 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals at risk for or diagnosed with Collagen Type IV-Associated Nephropathy or other genetic subtypes of chronic kidney disease.
✕Less likely to help
Patients with kidney diseases unrelated to Collagen Type IV or those who do not have access to electronic health records may not benefit from this research.
What this research studies
This research focuses on developing advanced methods to identify early signs of Collagen Type IV-Associated Nephropathy (COL4A-AN) through the analysis of electronic health records (EHR). By utilizing Natural Language Processing (NLP) techniques, the project aims to convert unstructured clinical data into structured information, which can help in recognizing different subtypes of chronic kidney disease (CKD). The goal is to create precise prediction models that can lead to timely and personalized interventions for patients, ultimately delaying the progression of kidney disease. This innovative approach seeks to standardize data across health systems to enhance the accuracy of diagnoses and treatment plans.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate diagnoses of kidney disease, allowing for timely interventions that may slow disease progression.
How similar studies have gone: Other research has shown promise in using electronic health records and NLP for disease detection, indicating that this approach has potential for success.
Where this research is happening
-
Columbia University Health Sciences
New York, United States Active
Researchers
- Principal investigator: Nestor, Jordan Gabriela — Columbia University Health Sciences
- Study coordinator: Nestor, Jordan Gabriela
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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.