Clinical trial NCT06881797: Unrecognised Comorbidity Detection in Hospitalised Patients
Official title: Unrecognised Comorbidity Detection in Hospitalised Patients (CODETECT)
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Diabetes
Atrial Fibrillation (AF)
Cardiac Disease
Keywords: Statistical modeling
Machine learning
digital platform
Lead sponsor: University of Oxford
Sponsor class: OTHER
First posted: 2025-03-18
Enrollment: 4500000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
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.

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.

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.

Eligibility criteria: 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

Primary outcomes:
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.
timeFrame: 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:
measure: Generation of a list of risk factors for at least 2 chronic health problems that would be available at hospital discharge
timeFrame: Up to five years post-hospital discharge
measure: The implementation of externally validated prediction models into a novel digital platform to identify undiagnosed chronic health problems (comorbidities) in hospitalised patients
timeFrame: Up to five years post-hospital discharge
measure: The generation of an intuitive usable digital platform ready for clinical use
timeFrame: Up to five years post-hospital discharge
Other outcomes:
Locations:
city: Oxford
country: United Kingdom
facility: Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford
state: Oxfordshire
Study officials:
affiliation: University of Oxford
name: Peter Watkinson
role: PRINCIPAL_INVESTIGATOR
References: