Literature DB >> 15364327

Clinical prediction rule for atrial fibrillation after coronary artery bypass grafting.

David Amar1, Weiji Shi, Charles W Hogue, Hao Zhang, Rod S Passman, Betsy Thomas, Peter B Bach, Ralph Damiano, Howard T Thaler.   

Abstract

OBJECTIVES: This study was designed to devise and validate a practical prediction rule for atrial fibrillation/atrial flutter (AF) after coronary artery bypass grafting (CABG) using easily available clinical and standard electrocardiographic (ECG) criteria.
BACKGROUND: Reported prediction rules for postoperative AF have suffered from inconsistent results and controversy surrounding the added predictive value of a prolonged P-wave duration.
METHODS: In 1,851 consecutive patients undergoing CABG with cardiopulmonary bypass, preoperative clinical characteristics and standard 12-lead ECG data were examined. Patients were continuously monitored for the occurrence of sustained postoperative AF while hospitalized. Multiple logistic regression was used to determine significant predictors of AF and to develop a prediction rule that was evaluated through jackknifing.
RESULTS: Atrial fibrillation occurred in 508 of 1,553 patients (33%). Multivariate analysis showed that greater age (odds ratio [OR] 1.1 per year [95% confidence intervals (CI) 1.0 to 1.1], p < 0.0001), prior history of AF (OR 3.7 [95% CI 2.3 to 6.0], p < 0.0001), P-wave duration >110 ms (OR 1.3 [95% CI 1.1 to 1.7], p = 0.02), and postoperative low cardiac output (OR 3.0 [95% CI 1.7 to 5.2], p = 0.0001) were independently associated with AF risk. Using the prediction rule we defined three risk categories for AF: <60 points, 61 of 446 (14%); 60 to 79 points, 330 of 908 (36%); and >or=80 points, 117 of 199 (59%). The area under the receiver-operator characteristic curve for the model was 0.69.
CONCLUSIONS: These data show that post-CABG AF can be predicted with moderate accuracy using easily available patient characteristics and may prove useful in prognostic and risk stratification of patients after CABG. The presence of intraatrial conduction delay on ECG contributed least to the prediction model.

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Year:  2004        PMID: 15364327     DOI: 10.1016/j.jacc.2004.05.078

Source DB:  PubMed          Journal:  J Am Coll Cardiol        ISSN: 0735-1097            Impact factor:   24.094


  49 in total

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2.  Prediction of postoperative atrial fibrillation in a large coronary artery bypass grafting cohort.

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3.  Association of P-wave duration, dispersion, and terminal force in relation to P-wave axis among outpatients.

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4.  Genetic and clinical risk prediction model for postoperative atrial fibrillation.

Authors:  Simon C Body; Dawood Darbar; Matthew J Kolek; J Daniel Muehlschlegel; William S Bush; Babar Parvez; Katherine T Murray; C Michael Stein; M Benjamin Shoemaker; Marcia A Blair; Kaylen C Kor; Dan M Roden; Brian S Donahue; Amanda A Fox; Stanton K Shernan; Charles D Collard
Journal:  Circ Arrhythm Electrophysiol       Date:  2015-01-07

5.  Predictors of atrial fibrillation occurrence after coronary artery bypass graft surgery.

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Journal:  Gen Thorac Cardiovasc Surg       Date:  2011-04-12

6.  Incidence of atrial fibrillation after off-pump versus on-pump coronary artery bypass grafting: A meta-analysis of randomized clinical trials and propensity score matching trials.

Authors:  Chuang-Yan Wu; Si-Hua Wang; Yu-Qiang Shang; Jia-Hong Xia
Journal:  J Huazhong Univ Sci Technolog Med Sci       Date:  2017-12-21

7.  Genome-wide association study of new-onset atrial fibrillation after coronary artery bypass grafting surgery.

Authors:  Miklos D Kertai; Yi-Ju Li; Yunqi Ji; Wenjing Qi; Frederick W Lombard; Svati H Shah; William E Kraus; Mark Stafford-Smith; Mark F Newman; Carmelo A Milano; Nathan Waldron; Mihai V Podgoreanu; Joseph P Mathew
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Review 8.  Prophylaxis and management of postoperative atrial fibrillation.

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9.  Hemodynamic parameters predict the risk of atrial fibrillation after cardiac surgery in adults.

Authors:  Rongxin Lu; Nan Ma; Zhaolei Jiang; Ju Mei
Journal:  Clin Cardiol       Date:  2017-08-29       Impact factor: 2.882

10.  Potential factors that affect electrocardiographic progression of interatrial block.

Authors:  Vignendra Ariyarajah; Mark Kranis; Sirin Apiyasawat; David H Spodick
Journal:  Ann Noninvasive Electrocardiol       Date:  2007-01       Impact factor: 1.468

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