Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT05837364 |
| Other study ID # |
318197 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
November 2, 2020 |
| Est. completion date |
October 31, 2023 |
Study information
| Verified date |
May 2024 |
| Source |
University of Leeds |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical 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. 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.
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.
