Predicting Patient-level New Onset Atrial Fibrillation

NCT04657900 | Completed

• 1 other identifier: 120029
• Study Type: observational
• Target: 140,000
• Locations: 1 country
• Sites: 1

Brief Summary

Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial health-care expenditure as a result of stroke, sudden death, heart failure and unplanned hospitalisation. There is a compelling argument for the early diagnosis of AF, before the first complication occurs, but population-based screening is not recommended. Strategies to identify individuals at higher risk of new onset AF are required. previous risk scores have been limited by data and methodology. The investigators will use routinely collected hospital-linked primary care data and focus on the use of artificial intelligence methods to develop and validate a model for the prediction of incident AF. Specifically, the investigators will investigate how population-based data may be used for precision medicine using a deep neural networks learning model. Using clinical factors readily accessible in primary care, the investigators will provide a method for the identification of individuals in the community who are at risk of AF, as well as when incident AF will occur in those at risk, thus accelerating research assessing technologies for the improvement of risk prediction, and the targeting of high-risk individuals for preventive measures and screening.

Conditions

Atrial Fibrillation

Outcome Measures

Primary Outcomes (2)

• To develop and validate a deep learning hierarchical model for predicting the risk, and where appropriate period, of new onset AF

  Predictive factors will be identified using Read codes (diagnoses), measurements and Prod codes (medications) in CPRD; ICD10 codes and statistical classification (OPCS) codes in Hospital Episode Statistics (HES); and ICD 10 codes (ICD9 codes for the period before 2001) in Office of National Statistics (ONS) data. All variables will be considered as potential predictors, and may include: 1. sociodemographic variables: age, sex, ethnicity, index of multiple deprivation; 2. all (repeated) hospitalised disease conditions during follow-up 3. clinical assessments, such as ECG, heart rate, height, weight, 4. medications prescribed, 5. lifestyle factors (e.g. smoking status, alcohol consumption); 6. all biomarkers collected during follow-up The temporal information of all clinical assessments, hospitalised events, medications will be included.

  Between 1st Jan 1998 and 31st December 2018

• To identify and quantify the magnitude of predictors of new onset AF

  The proposed deep learning model can extract informative risk factors from EHR data. Specifically, a risk factor selection strategy proposed in Huang et al will be adapted to identify informative risk factors. The model will provide weights of the identified risk factors to help understand the significance of risk factors at different risk levels. The impact of the number of risk factors on the performance of AF risk prediction will be assessed through the curves of both area under curve (AUC) and prediction accuracy plotted against the number of risk factors. Some predictors, such as BMI, blood pressure, frequency of General Practitioner (GP) visits, strength of prescribed medication, may change over time. The incremental prognostic values of including these variable trajectories will be explored and the impact on predictive accuracy will be assessed.

  Between 1st Jan 1998 and 31st December 2018

Study Arms (1)

All eligible patients

Observational cohort using anonymized patient-level primary care data linked to secondary administrative data; CPRD-GOLD and CPRD-AURUM.

Other: Observational

Interventions

Observational OTHER

Observational - no intervention given

All eligible patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will comprise all available patients in CPRD-GOLD who were eligible for data linkage and had at least 1-year follow-up in the period between 1st Jan 1998 and 31st December 2018. The outcome of interest is the first diagnosed AF after baseline (1 January 2009), and will be identified using Read codes (for the CPRD patient profile) and ICD-10 codes (for HES events). Patients with less than one year of registration in CPRD, those who are under eighteen years of age at the date of the first registration in CPRD, those who were diagnosed with AF before 1st Jan 1998, and those who were not eligible for data linkage will be excluded.

You may qualify if:

• Diagnosed AF after 1 January 2009 (Identified using Read codes (for the CPRD patient profile) and ICD-10 codes (for HES events)
• In Clinical Practice Research Datalink -Global initiative for chronic Obstructive Lung Disease (CPRD-GOLD) and eligible for data linkage.
• Have at least 1-year follow-up in the period between 1st Jan 1998 and 31st December 2018.

You may not qualify if:

• Under 18 at date of the first registration in CPRD
• Diagnosed with AF before 1st Jan 1998
• In CPRD-GOLD and not eligible for data linkage
• Has less than one year follow up in CPRD

Sponsors & Collaborators

• University of Leeds: lead
• British Heart Foundation: collaborator

Study Sites (1)

University of Leeds

Leeds, West Yorkshire, LS2 9JT, United Kingdom

Location

Related Publications (26)

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MeSH Terms

Conditions

Atrial Fibrillation

Interventions

Watchful Waiting

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Outcome Assessment, Health Care; Outcome and Process Assessment, Health Care; Quality of Health Care; Health Services Administration

Study Officials

• Christopher P Gale, PhD

  University of Leeds

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Cardiovascular Medicine

Study Record Dates

• First Submitted: December 1, 2020
• First Posted: December 8, 2020
• Study Start: November 2, 2020
• Primary Completion: October 31, 2023
• Study Completion: October 31, 2023
• Last Updated: May 8, 2024

Record last verified: 2024-05

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)