Predicting Risk of Atrial Fibrillation and Association With Other Diseases

NCT05837364 | Completed

• 1 other identifier: 318197
• Study Type: observational
• Target: 2,159,663
• Locations: 1 country
• Sites: 1

Brief Summary

Atrial fibrillation (AF) is a major public health issue: it is increasingly common, incurs substantial healthcare expenditure, and is associated with a range of adverse outcomes. There is rationale for the early diagnosis of AF, before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies. An accurate model that utilises existing routinely-collected data is needed to inform clinicians of patient-level risk of AF, inform national screening policy and highlight opportunities to improve patient outcomes from AF screening beyond that of only stroke prevention. The investigators will use routinely-collected hospital-linked primary care data to develop and validate a model for prediction of incident AF within a short prediction horizon, incorporating both a machine learning and traditional regression method. They will also investigate how atrial fibrillation risk is associated with other diseases and death. Using only clinical factors readily accessible in the community, the investigators will provide a method for the identification of individuals in the community who are at risk of AF, thus accelerating research assessing whether atrial fibrillation screening is clinically effective when targeted to high-risk individuals.

Conditions

Atrial Fibrillation; Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Pathologic Processes

Keywords

Computation; Data Science

Outcome Measures

Primary Outcomes (2)

• 1. To develop and validate a model for predicting the risk of new onset AF within the next 6 months

  a. Predictive factors will be identified using Read codes and ICD-9/10 codes (diagnoses) Variables considered as potential predictors may include sociodemographic variables (age, sex, ethnicity) and morbidities.

  Between 1st Jan 1998 and 31st December 2018

• 1. To quantify the association between risk of new-onset AF and the hazard of other cardio-renal-metabolic diseases and death

  a. All patients categorized as lower or higher predicted AF risk by the developed prediction model will be included. The initial presentation of a cardiovascular, renal, or metabolic disease or death will be considered because AF is associated with a high risk of adverse clinical outcomes. The occurrence of death by any cause will be quantified. Incident diagnoses will be defined as the first record of that condition in primary or secondary care records from any diagnostic position. Kaplan-Meier plots will be created for individuals identified as higher and lower predicted risk of AF and derive the cumulative incidence rate for each outcome at 1, 5 and 10 years considering the competing risk of death, as well as death at 5 and 10 years. For each specified outcome, the hazard ratio (HR) will be calculated between higher and lower predicted risk of AF using the Fine and Gray's model with adjustment for the competing risk of death.

  Between 1st Jan 1998 and 31st December 2018

Interventions

Development of an algorithm OTHER

Development of an algorithm to predict the risk of new onset Atrial Fibrillation

Eligibility Criteria

• Age: 30 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The derivation dataset will be the Clinical Practice Research Datalink-GOLD (CPRD-GOLD) dataset. The extracted dataset, including linked data, comprises all patients for the period between 2nd January 1998 and 30th November 2018 from the snapshot of CPRD-GOLD in October 2019.

You may qualify if:

• A least 1 year follow-up

You may not qualify if:

• Diagnosed AF before study entry

Sponsors & Collaborators

• University of Leeds: lead
• British Heart Foundation: collaborator
• Clalit Health Services: collaborator
• Ben-Gurion University of the Negev: collaborator

Study Sites (1)

University of Leeds

Leeds, West Yorkshire, LS2 9NL, United Kingdom

Location

Related Publications (1)

• Nadarajah R, Wu J, Arbel R, Haim M, Zahger D, Benita TR, Rokach L, Cowan JC, Gale CP. Risk of atrial fibrillation and association with other diseases: protocol of the derivation and international external validation of a prediction model using nationwide population-based electronic health records. BMJ Open. 2023 Dec 9;13(12):e075196. doi: 10.1136/bmjopen-2023-075196.

  PMID: 38070890 DERIVED

MeSH Terms

Conditions

Atrial Fibrillation; Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Pathologic Processes

Condition Hierarchy (Ancestors)

Pathological Conditions, Signs and Symptoms

Study Officials

• Christopher P Gale

  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: April 18, 2023
• First Posted: May 1, 2023
• 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)