DiabetesCODETECTNCT06881797
Unrecognised Comorbidity Detection in Hospitalised Patients
An observational study of Diabetes and Atrial Fibrillation (AF), run by University of Oxford at 1 site in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Diabetes, Atrial Fibrillation (AF), Cardiac Disease
- How many people
- 4,500,000 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Jul 2024to 30 Nov 2027
- How long will I take part?
- 12 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The analysis population will be based on all hospital admissions in both the retrospective and prospective cohorts. Any subgroup analysis will be prespecified in the statistical analysis plan with justification.
Who may be able to join
- Adults aged 18 years or above.
- Admitted to a participating NHS hospital
- Registered with a primary care practice
Who cannot join
- Has "opted-out" of having their data used for research purposes using the national data opt-out service
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford
Oxford, Oxfordshire, OX3 9DU
About this study
Official title: Unrecognised Comorbidity Detection in Hospitalised Patients (CODETECT)
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.
Read more
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.
Read the 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.
Atrial Fibrillation (AF) Atrial fibrillation (AF) is a common cardiac arrythmia, affecting 2.5 million people in England alone. Of these, 30% are undiagnosed. AF increases the risk of stroke five-fold, leading to decreased mobility and vascular dementia. There is currently no UK screening programme.
AF is a common complication of critical illness, associated with prolonged intensive care treatment and higher mortality. Lifestyle factors, such as obesity, smoking and high alcohol consumption also increase AF risk. People with AF are often prescribed anticoagulation to reduce stroke risk. However, the benefits of anticoagulation must be carefully balanced with the risk of bleeding, emphasising the need for more accurate prognostic models.
Study Activities
The investigators will reach our objectives by completing the following study activities:
- Use expert panels to agree existing diagnostic definitions for at least 2 long-term health conditions that can be defined from electronic health records.
- Identify risk factors for long-term health conditions through a literature review, expert panel, and machine learning methods (using retrospective data).
- Develop and validate diagnostic models (using retrospective data) to identify patients with previously undiagnosed long-term health conditions.
- Develop a real-time digital platform in at least one hospital to collect data to prospectively validate the diagnostic models.
- Conditions
- Diabetes, Atrial Fibrillation (AF), Cardiac Disease
- Keywords
- Statistical modeling, Machine learning, digital platform
- Medical subject headings
- Diabetes Mellitus, Atrial Fibrillation, Heart Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
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
Measured: 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
Secondary outcomes (3)
Association of risk factors that would be available at hospital discharge with at least 2 chronic health problems
Statistical measures of association including odds ratio with 95% confidence intervals.
Measured: Up to five years post-hospital discharge
The implementation of externally validated prediction models into a novel digital platform to identify undiagnosed chronic health problems (comorbidities) in hospitalised patients
Measured: Up to five years post-hospital discharge
The generation of an intuitive usable digital platform ready for clinical use
Measured: Up to five years post-hospital discharge
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Mar 2025
- Study start1 Jul 2024
- Primary completion30 Nov 2027 (estimated)
- Study completion30 Nov 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DiabetesStudies of the same condition, open and under way.
- More trials run by University of OxfordThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 4,500,000 participants (estimated)
- Target duration
- 12 Years
Sponsor and investigators
- Lead sponsor
- University of Oxford
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Peter Watkinson, Principal investigator, University of Oxford
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06881797
- Sponsor's protocol ID
- PID 17881
- Acronym
- CODETECT
- Status last verified
- Feb 2026
- First submitted
- 17 Feb 2025
- Last updated
- 19 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Unrecognised Comorbidity Detection in Hospitalised Patients (CODETECT)
- ClinicalTrials.gov ID
- NCT06881797
- Acronym / sponsor ID
- CODETECT / PID 17881
- Conditions
- Diabetes, Atrial Fibrillation (AF), Cardiac Disease
- Keywords
- Statistical modeling, Machine learning, digital platform
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 4,500,000 participants (estimated)
- Lead sponsor
- University of OxfordUniversity, hospital or other
- Study start
- 1 Jul 2024 (actual)
- Primary completion
- 30 Nov 2027 (estimated)
- Study completion
- 30 Nov 2027 (estimated)
- First posted
- 18 Mar 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Atrial Fibrillation (AF) Atrial fibrillation (AF) is a common cardiac arrythmia, affecting 2.5 million people in England alone. Of these, 30% are undiagnosed. AF increases the risk of stroke five-fold, leading to decreased mobility and vascular dementia. There is currently no UK screening programme.
AF is a common complication of critical illness, associated with prolonged intensive care treatment and higher mortality. Lifestyle factors, such as obesity, smoking and high alcohol consumption also increase AF risk. People with AF are often prescribed anticoagulation to reduce stroke risk. However, the benefits of anticoagulation must be carefully balanced with the risk of bleeding, emphasising the need for more accurate prognostic models.
Study Activities
The investigators will reach our objectives by completing the following study activities:
- Use expert panels to agree existing diagnostic definitions for at least 2 long-term health conditions that can be defined from electronic health records.
- Identify risk factors for long-term health conditions through a literature review, expert panel, and machine learning methods (using retrospective data).
- Develop and validate diagnostic models (using retrospective data) to identify patients with previously undiagnosed long-term health conditions.
- Develop a real-time digital platform in at least one hospital to collect data to prospectively validate the diagnostic models.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 12 Years
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The analysis population will be based on all hospital admissions in both the retrospective and prospective cohorts. Any subgroup analysis will be prespecified in the statistical analysis plan with justification.
Key inclusion criteria (3 listed)
- Adults aged 18 years or above.
- Admitted to a participating NHS hospital
- Registered with a primary care practice
Key exclusion criteria (1 listed)
- Has "opted-out" of having their data used for research purposes using the national data opt-out service
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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
3 secondary outcomes
Association of risk factors that would be available at hospital discharge with at least 2 chronic health problems
Time frame: Up to five years post-hospital discharge
The implementation of externally validated prediction models into a novel digital platform to identify undiagnosed chronic health problems (comorbidities) in hospitalised patients
Time frame: Up to five years post-hospital discharge
The generation of an intuitive usable digital platform ready for clinical use
Time frame: Up to five years post-hospital discharge
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 May 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Peter Watkinson principal investigator
University of Oxford
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetes and Atrial Fibrillation (AF), run by University of Oxford at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 19 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 May 2026.