Key result
Protocol outlines derivation and validation of an EHR-based prediction model for 6-month incident AF.
Why the study?
Previous AF screening research has low yields, and existing prediction models for incident AF have been limited by their data sources and methodologies.
Can a prediction model using random forest and multivariable logistic regression accurately predict incident atrial fibrillation within a 6-month horizon using nationwide electronic health records?
Observational
Yes
Can a prediction model using random forest and multivariable logistic regression accurately predict incident atrial fibrillation within a 6-month horizon using nationwide electronic health records?
Answers are grounded in this paper and cite their sources.
This protocol outlines the development and international validation of a machine learning and logistic regression model to predict incident atrial fibrillation within 6 months using large-scale electronic health records.
INTRODUCTION: Atrial fibrillation (AF) is a major public health issue and 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. METHODS AND ANALYSIS: We will investigate the application of random forest and multivariable logistic regression to predict incident AF within a 6-month 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 (CHS) dataset will be used for international external geographical validation. Analyses will include metrics of prediction performance and clinical utility. We will create Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF 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. ETHICS AND DISSEMINATION: Permission for CPRD-GOLD was obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. CHS Helsinki committee approval 21-0169 and data usage committee approval 901. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. TRIAL REGISTRATION NUMBER: A systematic review to guide the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT05837364).
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Nadarajah et al. (2023) conducted an observational in Atrial fibrillation. Prediction model (random forest and multivariable logistic regression) was evaluated on Incident AF within a 6-month prediction horizon. This study protocol outlines the derivation and international external validation of a prediction model for incident atrial fibrillation within a 6-month horizon using electronic health records.
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