Unrecognised Comorbidity Detection in Hospitalised Patients (CODETECT)
Trial Snapshot
- Phase
- Not Applicable
- Status
- Active, not recruiting
- Sponsor
- University of Oxford
- Enrollment
- 4,500,000
- Locations
- 1
- Primary Endpoint
- Use data to design and use a real-time, digital platform to prospectively validate prediction models to identify hospitalised patients with potentially undiagnosed chronic health problems for at least 2 chronic health problems.
Study Overview
Brief Summary
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.
Detailed Description
This is a multi-centre observational cohort study of adult patients admitted to acute hospitals. Data will be collected from hospital systems sourcing data from both hospital and primary care electronic health record systems. The study will then use retrospective data to develop and validate tools to identify patients with undiagnosed long-term conditions.
These diagnostic tools will be implemented into a real-time digital platform and further validated on prospectively collected data. Once developed and validated, the digital platform could be used to identify patients who likely have undiagnosed long-term conditions and should undergo further investigation and preventative intervention.
The investigators will initially focus on two long-term conditions (diabetes and atrial fibrillation) and aim to expand this to others within the study period.
Why Diabetes and Atrial Fibrillation? Diabetes Diabetes is a major contributor to multimorbidity. More than 4.3 million people in the UK are living with this condition, with a further one million thought to be undiagnosed. Diabetes increases cardiovascular risk and can lead to chronic kidney disease and debilitating neuropathy. Current diabetes screening occurs through the NHS Health Checks and when people seek healthcare for unrelated symptoms. Early intervention can reduce the risk of long-term complications, including myocardial infarctions and death. However, diagnosing diabetes can be challenging when people are asymptomatic yet already have complications from their diabetes.
There are a range of well-established risk factors including non-white ethnicity, obesity, hypertension, family history, socioeconomic deprivation and increasing age. Recent systematic reviews of existing diabetes screening tools highlight poor or limited external validation, methodological weaknesses, and heterogenous definitions of diabetes that limit comparison between tools.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Other
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •Adults aged 18 years or above.
- •Admitted to a participating NHS hospital
- •Registered with a primary care practice
Exclusion Criteria
- •Has "opted-out" of having their data used for research purposes using the national data opt-out service
Arms & Interventions
Retrospective cohort
Retrospective Cohort: Around 3,600,000 hospital admissions from 3 sites over 12 years*
*Study will begin as single site, aiming for 3 participating Trusts Retrospective sub-study data collection period: 1st December 2015 to 31st August 2027 (retrospective cohort 1st December 2015 to 30th June 2024, with rolling follow-up to include data to 31st August 2027.
Prospective Cohort
Prospective Cohort: Around 900,000 hospital admissions from 3 sites over 3 years*
*Study will begin as single site, aiming for 3 participating Trusts Prospective sub-study data collection period: 1st July 2024 to 31st August 2027
Outcomes
Primary Outcomes
Use data to design and use a real-time, digital platform to prospectively validate prediction models to identify hospitalised patients with potentially undiagnosed chronic health problems for at least 2 chronic health problems.
Time Frame: Primary timepoint Within five years of hospital discharge. Secondary timepoints • Within three years of hospital discharge • Within two years of hospital discharge • Within one year of hospital discharge • Within six months of hospital discharge
Measure #1 Discrimination (c-statistic) and calibration (intercept and slope) of model predicting diagnosis of a new chronic health problem Measure #2 Positive and negative predictive values, sensitivity, and specificity
Secondary Outcomes
- The implementation of externally validated prediction models into a novel digital platform to identify undiagnosed chronic health problems (comorbidities) in hospitalised patients(Up to five years post-hospital discharge)
- The generation of an intuitive usable digital platform ready for clinical use(Up to five years post-hospital discharge)
- Association of risk factors that would be available at hospital discharge with at least 2 chronic health problems(Up to five years post-hospital discharge)