Predicting which colorectal cancer patients in South Africa might stop attending oncology care
Official title: A clinical prediction rule for identifying South African colorectal cancer patients who will fail to engage in oncology care
Funded at University of Kwazulu-Natal · NIH-11159685 · Data updated 2026-11-12
This project uses patient surveys, medical data, and machine learning to find newly diagnosed colorectal cancer patients in South Africa who are likely to stop attending oncology care so they can get extra support.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Kwazulu-Natal NIH-funded |
| Lab location | 1 site (Durban, South Africa) |
| Project start | September 2, 2021 |
| Project end | August 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $43,925 |
| NIH project number | 5K43CA268978-05 |
| Project ID | NIH-11159685 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are newly diagnosed colorectal cancer patients seen in the public healthcare sector in KwaZulu-Natal, South Africa.
✕Less likely to help
People without a new colorectal cancer diagnosis, patients already consistently attending oncology care, or those treated only in private-sector settings are unlikely to be included or to benefit directly.
What this research studies
If you are newly diagnosed with colorectal cancer in KwaZulu-Natal, researchers will first review published studies and ask about barriers to care using a short survey of about 109 patients. They will then collect clinical and social information from roughly 398 newly diagnosed patients using an electronic data form at local hospitals. Machine learning methods will be applied to these data to create and internally test a simple clinical prediction rule that flags patients at high risk of failing to engage with oncology care. The aim is a practical tool clinicians can use to target outreach and support to those who need it most.
Why it matters
Potential benefit: If successful, clinicians could target support to patients at high risk of dropping out, helping them stay in care and complete treatment.
How similar studies have gone: Prediction rules and machine-learning tools have helped target care in other areas, but applying this approach specifically to colorectal cancer engagement in South Africa is relatively new.
Where this research is happening
-
University of Kwazulu-Natal
Durban, South Africa Active
Researchers
- Principal investigator: Moodley, Yoshan — University of Kwazulu-Natal
- Study coordinator: Moodley, Yoshan
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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.