Atrial FibrillationFIND-AFNCT05837364
Predicting Risk of Atrial Fibrillation and Association With Other Diseases
An observational study of Atrial Fibrillation and Arrhythmias, Cardiac, run by University of Leeds at 1 site in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 30 years and older
- Conditions the study is for
- Atrial Fibrillation, Arrhythmias, Cardiac, Heart Diseases
- What is being tested?
- Development of an algorithm
- How many people
- 2,159,663 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 2 Nov 2020to 31 Oct 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
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 30 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- A least 1 year follow-up
Who cannot join
- Diagnosed AF before study entry
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
University of Leeds
Leeds, West Yorkshire, LS2 9NL
About this study
Official title: 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
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.
Read the detailed description
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 application of Random Forest will be investigated and multivariable logistic regression to predict incident AF within a 6 months prediction horizon, that is a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services dataset will be used for international external geographical validation. Both comprise a large representative population and include clinical outcomes across primary and secondary care. Analyses will include metrics of prediction performance and clinical utility. Only risk factors accessible in the community will be used and the model could thus enable passive screening for high-risk individuals in electronic health records that is updated with presentation of new data. The study aims to create a calculator from a parsimonious model. Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF will be calculated and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states.
To ascertain whether the prediction model is transportable to geographies outside of the UK, the model's performance will be externally validated in the Clalit Health Services database in Israel. The validation will include participants insured by Clalit with continuous membership for at least 1 year before 01/01/2019: 2,159,663 patients with 4,330 of them having a new incident of AF (Atrial fibrillation and/or atrial flutter) in the first half of 2019. The study population will comprise all available patients who have at least 1-year follow up. The outcome of interest is the first diagnosed AF after baseline and will be identified using Read codes and ICD-9/10 codes. Patients with less than one year of registration, who are under thirty years of age at point of study entry, or have a preceding diagnosis of atrial fibrillation, will be excluded.
- Conditions
- Atrial Fibrillation, Arrhythmias, Cardiac, Heart Diseases, Cardiovascular Diseases, Pathologic Processes
- Being tested
- Development of an algorithm
- Keywords
- Computation, Data Science
- Medical subject headings
- Atrial Fibrillation, Arrhythmias, Cardiac, Heart Diseases, Cardiovascular Diseases, Pathologic Processes
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Development of an algorithmOther
Development of an algorithm to predict the risk of new onset Atrial Fibrillation
What is being measured
The primary outcome is the main question the study is designed to answer.
1. To develop and validate a model for predicting the risk of new onset AF within the next 6 months
- 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.
Measured: 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
- 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.
Measured: Between 1st Jan 1998 and 31st December 2018
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 May 2023
- Study start2 Nov 2020
- Primary completion31 Oct 2023
- Study completion31 Oct 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Atrial FibrillationStudies of the same condition, open and under way.
- 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
- Retrospective
- Enrolment
- 2,159,663 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Leeds
- Sponsor type
- University, hospital or other
- Collaborators
- British Heart Foundation, Clalit Health Services, Ben-Gurion University of the Negev
- Responsible party
- Principal investigator, Dr Christopher Gale, University of Leeds
- Investigators
- Christopher P Gale, Principal investigator, University of Leeds
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05837364
- Sponsor's protocol ID
- 318197
- Acronym
- FIND-AF
- Status last verified
- May 2024
- First submitted
- 18 Apr 2023
- Last updated
- 8 May 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: No individual participant data will be shared.
Publications
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.
PubMed 38070890
Study at a glance
From the registry record, last updated by the sponsor on 8 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT05837364
- Acronym / sponsor ID
- FIND-AF / 318197
- Conditions
- Atrial Fibrillation, Arrhythmias, Cardiac, Heart Diseases, Cardiovascular Diseases, Pathologic Processes
- Keywords
- Computation, Data Science
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,159,663 participants (actual)
- Lead sponsor
- University of LeedsUniversity, hospital or other
- Collaborators
- British Heart Foundation, Clalit Health Services, Ben-Gurion University of the Negev
- Study start
- 2 Nov 2020 (actual)
- Primary completion
- 31 Oct 2023 (actual)
- Study completion
- 31 Oct 2023 (actual)
- First posted
- 1 May 2023 (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.
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.
Detailed description
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 application of Random Forest will be investigated and multivariable logistic regression to predict incident AF within a 6 months prediction horizon, that is a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services dataset will be used for international external geographical validation. Both comprise a large representative population and include clinical outcomes across primary and secondary care. Analyses will include metrics of prediction performance and clinical utility. Only risk factors accessible in the community will be used and the model could thus enable passive screening for high-risk individuals in electronic health records that is updated with presentation of new data. The study aims to create a calculator from a parsimonious model. Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF will be calculated and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states.
To ascertain whether the prediction model is transportable to geographies outside of the UK, the model's performance will be externally validated in the Clalit Health Services database in Israel. The validation will include participants insured by Clalit with continuous membership for at least 1 year before 01/01/2019: 2,159,663 patients with 4,330 of them having a new incident of AF (Atrial fibrillation and/or atrial flutter) in the first half of 2019. The study population will comprise all available patients who have at least 1-year follow up. The outcome of interest is the first diagnosed AF after baseline and will be identified using Read codes and ICD-9/10 codes. Patients with less than one year of registration, who are under thirty years of age at point of study entry, or have a preceding diagnosis of atrial fibrillation, will be excluded.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 30 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- A least 1 year follow-up
Key exclusion criteria (1 listed)
- Diagnosed AF before study entry
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Development of an algorithmDevelopment of an algorithm to predict the risk of new onset Atrial Fibrillation | Other | – |
Primary outcome
1. To develop and validate a model for predicting the risk of new onset AF within the next 6 months
Time frame: Between 1st Jan 1998 and 31st December 2018
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.
1. To quantify the association between risk of new-onset AF and the hazard of other cardio-renal-metabolic diseases and death
Time frame: Between 1st Jan 1998 and 31st December 2018
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 May 2024.
- Derived from this study: PubMed 38070890Nadarajah 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.
- Individual participant dataSharing planned: No. No individual participant data will be shared.
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Atrial Fibrillation (AF)
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.
Christopher P Gale principal investigator
University of Leeds
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 Atrial Fibrillation and Arrhythmias, Cardiac, run by University of Leeds 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 1 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: finished, as of the sponsor's last update on 8 May 2024. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
1. To develop and validate a model for predicting the risk of new onset AF within the next 6 months (time frame: Between 1st Jan 1998 and 31st December 2018). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05837364, which also lists 1 linked publication.
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 8 May 2024.