Unrecognised Comorbidity Detection in Hospitalised Patients

NCT06881797 | Active Not Recruiting

• 1 other identifier: PID 17881
• Study Type: observational
• Target: 4,500,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Diabetes; Atrial Fibrillation (AF); Cardiac Disease

Keywords

Statistical modeling; Machine learning; digital platform

Outcome Measures

Primary Outcomes (1)

• 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

  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)

• Generation of a list of risk factors for at least 2 chronic health problems that would be available at hospital discharge

  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

  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

Study Arms (2)

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• Adults aged 18 years or above.
• Admitted to a participating NHS hospital
• Registered with a primary care practice

You may not qualify if:

• Has "opted-out" of having their data used for research purposes using the national data opt-out service

Sponsors & Collaborators

• University of Oxford: lead

Study Sites (1)

Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford

Oxford, Oxfordshire, OX3 9DU, United Kingdom

Location

MeSH Terms

Conditions

Diabetes Mellitus; Atrial Fibrillation; Heart Diseases

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Arrhythmias, Cardiac; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Peter Watkinson

  University of Oxford

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 12 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 17, 2025
• First Posted: March 18, 2025
• Study Start: July 1, 2024
• Primary Completion (Estimated): November 30, 2027
• Study Completion (Estimated): November 30, 2027
• Last Updated: March 18, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)