Earlier detection of light-chain (AL) amyloidosis
Official title: Early diagnosis of light chain amyloidosis
Funded at Medical College of Wisconsin · NIH-11289334 · Data updated 2026-11-12
This project will use Medicare health records and machine learning to spot early warning patterns so doctors can consider AL amyloidosis sooner for people at risk.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Medical College of Wisconsin NIH-funded |
| Lab location | 1 site (Milwaukee, United States) |
| Project start | February 1, 2023 |
| Project end | January 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $273,000 |
| NIH project number | 5R01HL166339-04 |
| Project ID | NIH-11289334 on NIH RePORTER |
Who could benefit from this research
✓Could help
The findings would most directly apply to U.S. Medicare-age adults, especially people with monoclonal gammopathy (MGUS), unexplained heart or kidney problems, or those from populations like Black Americans who may be underdiagnosed.
✕Less likely to help
People who are younger than Medicare age, who lack the precursor diagnosis patterns used by the algorithm, or who have non–light-chain forms of amyloidosis may not benefit from this approach.
What this research studies
From my point of view as a patient, researchers will look at large sets of Medicare medical records to find patterns of earlier, non-specific diagnoses that often come before AL amyloidosis. They will use Bayesian machine learning to build an alert system that flags patients whose history matches those warning patterns. The team aims to help clinicians notice and test for AL amyloidosis earlier, especially in groups that may be underdiagnosed. The work pays special attention to differences by race and common precursor conditions like MGUS, heart problems, and kidney disease.
Why it matters
Potential benefit: If successful, this could help clinicians diagnose AL amyloidosis earlier and lower early deaths and complications from heart involvement.
How similar studies have gone: Other electronic health record and algorithm-based screening efforts have helped detect forms of cardiac amyloidosis, but applying Bayesian machine learning to national Medicare data specifically for early AL detection is relatively new.
Where this research is happening
-
Medical College of Wisconsin
Milwaukee, United States Active
Researchers
- Principal investigator: D'souza, Anita — Medical College of Wisconsin
- Study coordinator: D'souza, Anita
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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.