BLOod Test Trend for cancEr Detection (BLOTTED): An Observational and Prediction Model Development Study Using English Primary Care Electronic Health Records Data
This research assesses combinations of blood tests and their changes over time to enhance cancer diagnosis efficiency, potentially leading to earlier detection, more successful treatments, and improved survival rates while reducing unnecessary testing.
Recent studies confirm that blood tests have an important role in identifying patients for cancer investigations. However, smarter use of these tests is needed to reduce delays in referring patients to specialist diagnostics and services.
We want to see if combining different blood tests and tracking changes over time can help identify patients who might have cancer. This could be more effective than looking at symptoms or single blood test results alone, and we aim to create tools that doctors can use to spot patients who need further cancer tests, hopefully leading to earlier cancer diagnosis when treatment has a greater chance of success, potentially saving lives. We are working with a group of patients with experience of cancer to make sure our research helps everyone.
Ultimately, we aim to make cancer detection more efficient, reducing unnecessary tests and increasing the chances of successful treatment and survival.
Project lead / contact: Sufen Zhu — Nuffield Department of Primary Care Health Sciences, University of Oxford
ARC theme: Digital Transformation of Health and Care
Who we're working with
- Big Data Institute - Home — Oxford Big Data Institute
- Centre for Statistics in Medicine - NDORMS Home — Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (ox.ac.uk)