Dataset Information

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm-guided intervention to identify undiagnosed atrial fibrillation.

ABSTRACT:

Introduction

Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening. The objectives of the FIND-AF pilot study are to determine yields of AF during ECG monitoring across AF risk estimates and establish rates of recruitment and protocol adherence in a remote AF screening pathway.

Methods and analysis

The FIND-AF Pilot is an interventional, non-randomised, single-arm, open-label study that will recruit 1955 participants aged 30 years or older, without a history of AF and eligible for oral anticoagulation, identified as higher risk and lower risk by the FIND-AF risk score from their primary care EHRs, to a period of remote ECG monitoring with a Zenicor-ECG device. The primary outcome is AF diagnosis during ECG monitoring, and secondary outcomes include recruitment rates, withdrawal rates, adherence to ECG monitoring and prescription of oral anticoagulation to participants diagnosed with AF during ECG monitoring.

Ethics and dissemination

The study has ethical approval (the North West-Greater Manchester South Research Ethics Committee reference 23/NW/0180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder's open access policy.

Trial registration number

NCT05898165.

SUBMITTER: Nadarajah R

PROVIDER: S-EPMC10546147 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm-guided intervention to identify undiagnosed atrial fibrillation.

Nadarajah Ramesh R Wahab Ali A Reynolds Catherine C Raveendra Keerthenan K Askham Deborah D Dawson Richard R Keene John J Shanghavi Sagar S Lip Gregory Y H GYH Hogg David D Cowan Campbel C Wu Jianhua J Gale Chris P CP

Open heart 20230901 2

<h4>Introduction</h4>Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening. The objectives of the FIND-AF pilot study are to determine yields of AF during ECG ...[more]

PMID: 37777255

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Project description:ImportanceIn approximately 20% of atrial fibrillation (AF)-related ischemic strokes, stroke is the first clinical manifestation of AF. Strategies are needed to identify and therapeutically address previously undetected AF.ObjectiveTo quantify the incidence of AF in patients at high risk for but without previously known AF using an insertable cardiac monitor.Design, setting, and participantsThis prospective, single-arm, multicenter study was conducted from November 2012 to January 2017. Visits took place at 57 centers in the United States and Europe. Patients with a CHADS2 score of 3 or greater (or 2 with at least 1 additional risk factor) were enrolled. Approximately 90% had nonspecific symptoms potentially compatible with AF, such as fatigue, dyspnea, and/or palpitations.ExposuresPatients underwent monitoring with an insertable cardiac monitor for 18 to 30 months.Main outcomes and measuresThe primary end point was adjudicated AF lasting 6 or more minutes and was assessed at 18 months. Other analyses included detection rates at points from 30 days to 30 months and among CHADS2 score subgroups. Median time from insertion to detection and the percentage of patients subsequently prescribed oral anticoagulation therapy was also determined.ResultsA total of 446 patients were enrolled; 233 (52.2%) were male, and the mean (SD) age was 71.5 (9.9) years. A total of 385 patients (86.3%) received an insertable cardiac monitor, met the primary analysis cohort definition, and were observed for a mean (SD) period of 22.5 (7.7) months. The detection rate of AF lasting 6 or more minutes at 18 months was 29.3%. Detection rates at 30 days and 6, 12, 24, and 30 months were 6.2%, 20.4%, 27.1%, 33.6%, and 40.0%, respectively. At 18 months, AF incidence was similar among patients with CHADS2 scores of 2 (24.7%; 95% CI, 17.3-31.4), 3 (32.7%; 95% CI, 23.8-40.7), and 4 or greater (31.7%; 95% CI, 22.0-40.3) (P = .23). Median (interquartile) time from device insertion to first AF episode detection was 123 (41-330) days. Of patients meeting the primary end point, 13 (10.2%) had 1 or more episodes lasting 24 hours or longer, and oral anticoagulation therapy was prescribed for 72 patients (56.3%).Conclusions and relevanceThe incidence of previously undiagnosed AF may be substantial in patients with risk factors for AF and stroke. Atrial fibrillation would have gone undetected in most patients had monitoring been limited to 30 days. Further trials regarding the value of detecting subclinical AF and of prophylactic therapies are warranted.Trial registrationclinicaltrials.gov Identifier: NCT01727297.

Project description:BackgroundAtrial fibrillation (AF) is a major risk factor for ischemic stroke, and early AF diagnosis may reduce associated morbidity and mortality. A 10-variable predictive model (UNAFIED) was previously developed to estimate patients' 2-year AF risk. This study evaluated a clinical workflow incorporating UNAFIED for screening, education, and follow-up evaluation of patients visiting a cardiology clinic who may be at an elevated risk of developing AF within 2 years.MethodsPatients were included if they were aged ≥ 40 years with a scheduled in-person visit at the Eskenazi Health Cardiology Clinic between October 25, 2021, and August 10, 2022. Clinical decision support identified patients with an elevated AF risk. Initial screening with 1-lead electrocardiogram devices was offered, and routine clinical practice for diagnosis and management was followed. Physicians were surveyed on their use of the workflow, attitudes toward implementation, and perceived impact on patient care.ResultsA total of 2827 patients had a clinic visit during the study period, of whom 1395 were eligible to be screened because they were classified as "elevated risk" by the UNAFIED predictive model. AF or atrial flutter diagnosis was newly documented for 29 patients during the study period. Of the newly diagnosed patients, 13 began anticoagulant therapy to mitigate stroke risk. Physicians (n = 13) who used the workflow most clinic days were more likely to indicate that it was easy to use, was not time-consuming, and improved patient care compared with physicians who only used the workflow occasionally.ConclusionsTo our knowledge, this study is the first of its kind to demonstrate clinical application of an electronic health record-based AF predictive model. The newly documented diagnoses, however, did not solely result from implementation of UNAFIED. This non-invasive, inexpensive approach could be adopted by other sites wishing to proactively screen patients at elevated risk for AF. Other sites should verify the model's performance in their own settings and ensure compliance with evolving regulatory requirements where applicable.

Dataset Information

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm-guided intervention to identify undiagnosed atrial fibrillation.

Introduction

Methods and analysis

Ethics and dissemination

Trial registration number

Publications

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm-guided intervention to identify undiagnosed atrial fibrillation.

Similar Datasets

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