Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative
Abstract Background Atrial 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 workf...
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BMC
2024-12-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02773-z |
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| author | Randall W. Grout Mohammad Ateya Baely DiRenzo Sara Hart Chase King Joshua Rajkumar Susan Sporrer Asad Torabi Todd A. Walroth Richard J. Kovacs |
| author_facet | Randall W. Grout Mohammad Ateya Baely DiRenzo Sara Hart Chase King Joshua Rajkumar Susan Sporrer Asad Torabi Todd A. Walroth Richard J. Kovacs |
| author_sort | Randall W. Grout |
| collection | DOAJ |
| description | Abstract Background Atrial 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. Methods Patients 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. Results A 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. Conclusions To 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. |
| format | Article |
| id | doaj-art-7f106c6aa23c48cf8ae6b2da887a89dd |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-7f106c6aa23c48cf8ae6b2da887a89dd2024-12-22T12:30:01ZengBMCBMC Medical Informatics and Decision Making1472-69472024-12-0124111010.1186/s12911-024-02773-zScreening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiativeRandall W. Grout0Mohammad Ateya1Baely DiRenzo2Sara Hart3Chase King4Joshua Rajkumar5Susan Sporrer6Asad Torabi7Todd A. Walroth8Richard J. Kovacs9Indiana University School of MedicinePfizer IncEskenazi HealthPfizer IncIndiana University School of MedicineFranciscan Physician Network – Indiana Heart PhysiciansPfizer IncIndiana University School of MedicineEskenazi HealthIndiana University School of MedicineAbstract Background Atrial 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. Methods Patients 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. Results A 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. Conclusions To 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.https://doi.org/10.1186/s12911-024-02773-zAtrial fibrillationElectronic health recordsPredictive modelsRisk assessment |
| spellingShingle | Randall W. Grout Mohammad Ateya Baely DiRenzo Sara Hart Chase King Joshua Rajkumar Susan Sporrer Asad Torabi Todd A. Walroth Richard J. Kovacs Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative BMC Medical Informatics and Decision Making Atrial fibrillation Electronic health records Predictive models Risk assessment |
| title | Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative |
| title_full | Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative |
| title_fullStr | Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative |
| title_full_unstemmed | Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative |
| title_short | Screening for undiagnosed atrial fibrillation using an electronic health record‒based clinical prediction model: clinical pilot implementation initiative |
| title_sort | screening for undiagnosed atrial fibrillation using an electronic health record based clinical prediction model clinical pilot implementation initiative |
| topic | Atrial fibrillation Electronic health records Predictive models Risk assessment |
| url | https://doi.org/10.1186/s12911-024-02773-z |
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