Unrecognised Comorbidity Detection in Hospitalised Patients

Over two million people in the UK are unaware that they're living with a long-term (chronic) health condition, such as diabetes or a heart problem. These chronic conditions can lead to serious complications such as heart attacks, strokes, and kidney problems. By diagnosing these conditions earlier, effective treatments can be started sooner which will reduce the risk of harm. However, diagnosis relies on people having symptoms and contacting their doctor or attending NHS Health Checks. There are over 16 million admissions to English hospitals each year. Hospitals collect a lot of information during a hospital stay including patients' age, blood test results and blood pressure measurements. Research has shown that this information can be helpful in spotting people with chronic conditions. This study aims to design and test a digital platform to find the patients in hospital who are most likely to have a chronic disease or develop one in the near future. To do this, the investigators will: * Use information from earlier research studies and experts to pinpoint which patient information (for example, certain blood tests) would be most useful to spot people with chronic conditions. * Extract relevant information from historical patient records, looking at who has these risk factors and which patients developed chronic conditions. The investigators will use information from hospital and general practitioner records. * Build tools to combine this information to predict which people have, or will develop, chronic conditions. * Implement these tools into a "real-time" digital platform that could be used to find which people should undergo further testing for a chronic condition. * Test the platform usability with clinical stake holders.

NCT Number	NCT06881797
Lead Sponsor	University of Oxford
Conditions	Diabetes, Atrial Fibrillation (AF), Cardiac Disease
Enrollment	4,500,000 participants
Start Date	2024-07-01
Primary Completion	2027-11-30 (estimated)
Study Completion	2027-11-30 (estimated)
Updated on ClinicalTrials.gov	2026-05-19