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Development and Validation of 3‐Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models
BACKGROUND: Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF‐related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and compu...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238645/ https://www.ncbi.nlm.nih.gov/pubmed/35699164 http://dx.doi.org/10.1161/JAHA.121.024045 |
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author | Yum, Yunjin Shin, Seung Yong Yoo, Hakje Kim, Yong Hyun Kim, Eung Ju Lip, Gregory Y. H. Joo, Hyung Joon |
author_facet | Yum, Yunjin Shin, Seung Yong Yoo, Hakje Kim, Yong Hyun Kim, Eung Ju Lip, Gregory Y. H. Joo, Hyung Joon |
author_sort | Yum, Yunjin |
collection | PubMed |
description | BACKGROUND: Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF‐related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. METHODS AND RESULTS: A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3‐year new‐onset AF prediction (C‐statistic, 0.796 [95% CI, 0.785–0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C‐statistic, 0.777 [95% CI, 0.766–0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. CONCLUSIONS: Although the 3‐year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF. |
format | Online Article Text |
id | pubmed-9238645 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92386452022-06-30 Development and Validation of 3‐Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models Yum, Yunjin Shin, Seung Yong Yoo, Hakje Kim, Yong Hyun Kim, Eung Ju Lip, Gregory Y. H. Joo, Hyung Joon J Am Heart Assoc Original Research BACKGROUND: Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF‐related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. METHODS AND RESULTS: A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3‐year new‐onset AF prediction (C‐statistic, 0.796 [95% CI, 0.785–0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C‐statistic, 0.777 [95% CI, 0.766–0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. CONCLUSIONS: Although the 3‐year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF. John Wiley and Sons Inc. 2022-06-14 /pmc/articles/PMC9238645/ /pubmed/35699164 http://dx.doi.org/10.1161/JAHA.121.024045 Text en © 2022 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Yum, Yunjin Shin, Seung Yong Yoo, Hakje Kim, Yong Hyun Kim, Eung Ju Lip, Gregory Y. H. Joo, Hyung Joon Development and Validation of 3‐Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models |
title | Development and Validation of 3‐Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models |
title_full | Development and Validation of 3‐Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models |
title_fullStr | Development and Validation of 3‐Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models |
title_full_unstemmed | Development and Validation of 3‐Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models |
title_short | Development and Validation of 3‐Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models |
title_sort | development and validation of 3‐year atrial fibrillation prediction models using electronic health record with or without standardized electrocardiogram diagnosis and a performance comparison among models |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9238645/ https://www.ncbi.nlm.nih.gov/pubmed/35699164 http://dx.doi.org/10.1161/JAHA.121.024045 |
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