Predicting post-coronary bypass surgery atrial arrhythmias from the preoperative electrocardiogram.
Publication/Presentation Date
11-1-2001
Abstract
BACKGROUND: Atrial fibrillation (AF) after coronary artery bypass grafting (CABG) is a common occurrence and adds to the morbidity and cost associated with the procedure. Various therapies have been demonstrated to reduce the risk of post-CABG AF, but their use should be targeted to high-risk patients. The purpose of this study was to develop a prediction rule for post-CABG AF on the basis of patient age and the preoperative electrocardiogram (ECG).
METHODS: The charts of 152 consecutive patients undergoing isolated CABG at one institution were reviewed and the preoperative ECG was analyzed with use of commercially available software. Logistic regression was performed and age-adjusted predictors of the primary end point, any post-CABG AF, were derived. The discriminatory values of the various models were compared by receiver-operating characteristic curves.
RESULTS: Sixty-four patients (42.1%) had AF. Multivariable predictors were dichotomized on the basis of variable distribution, and a high-risk patient population was identified by age >65 years, PR interval >/=180 milliseconds (age-adjusted odds ratio [OR] 2.12, P =.05), and a P-wave duration in lead V1 >/=110 milliseconds (age-adjusted OR 2.30, P =.02).
CONCLUSIONS: This study demonstrates that post-CABG AF can be predicted preoperatively from patient age and evidence of intra-atrial conduction delay on ECG. Such information can be used to guide prophylactic therapy.
Volume
142
Issue
5
First Page
806
Last Page
810
ISSN
1097-6744
Published In/Presented At
Passman, R., Beshai, J., Pavri, B., & Kimmel, S. (2001). Predicting post-coronary bypass surgery atrial arrhythmias from the preoperative electrocardiogram. American heart journal, 142(5), 806–810. https://doi.org/10.1067/mhj.2001.118736
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
11685166
Department(s)
Administration and Leadership
Document Type
Article