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Published on: February 26, 2013
Prediction of new onset atrial fibrillation after cardiac revascularization surgery
Mikhael F El-Chami1, Patrik D Kilgo, K Miriam Elfstrom
1Emory University, Atlanta, Georgia, USA. melcham@emory.edu
Insights
A new risk index can predict new-onset atrial fibrillation (AF) after coronary artery bypass grafting (CABG). This tool helps identify patients who may benefit from AF prevention strategies.
Area of Science:
- Cardiology
- Medical Informatics
Background:
- Postoperative atrial fibrillation (AF) after coronary artery bypass grafting (CABG) increases morbidity and mortality.
- Identifying high-risk patients is crucial for implementing preventive strategies.
Purpose of the Study:
- To develop a simple risk index for predicting new-onset AF in patients undergoing CABG.
- To validate the predictive accuracy of the developed risk index.
Main Methods:
- A risk index was derived from a cohort of 18,517 patients using multivariate logistic regression.
- The index was validated in a separate cohort of 1,378 patients.
- Key predictors included age, height, weight, and peripheral vascular disease.
Main Results:
- The incidence of AF was 18.83% in the derivation cohort and 19.52% in the validation cohort.
- Advanced age was the strongest predictor of AF (OR 1.059/year).
- The AF risk index demonstrated adequate discriminatory power (C-index 0.68).
Conclusions:
- A straightforward, preoperative clinical risk index effectively predicts new-onset AF post-CABG.
- This tool aids in identifying at-risk individuals for targeted AF prevention.
Abstract:
The aim of this study was to create a simple risk index to predict new-onset atrial fibrillation (AF) after coronary artery bypass grafting in patients with histories of AF. AF after coronary artery bypass grafting (referred to here as AF) is associated with increased morbidity and mortality. Identifying patients at high risk for developing AF may help identify a group of patients who might benefit from strategies to prevent postoperative AF. A cohort of 18,517 patients enrolled from January 1, 1996, to December 31, 2009, was used to derive a risk index for AF prediction. A multivariate logistic regression model determined the independent predictive impact of clinical and demographic characteristics on the occurrence of AF. A subset of these variables was used to construct a risk index to predict AF. This risk index was validated in a sequential cohort of 1,378 consecutive patients who underwent coronary artery bypass grafting from January 1, 2010, to June 30, 2011. AF occurred in 3,486 patients in the calibration cohort (18.83%) and in 269 patients in the validation cohort (19.52%). After considering patients' demographics, co-morbid conditions, and severity of illness, advanced age appeared as the most powerful predictor of AF (odds ratio 1.059/year, 95% confidence interval 1.055 to 1.063). Age, height, weight, and the presence of peripheral vascular disease contributed most to the prediction model. An AF risk index including these variables had adequate discriminatory power, with a concordance index of 0.68. In conclusion, using a large cohort of patients, a simple risk index relying only on preoperative clinical variables was developed, which will help predict AF. This risk index can be used clinically to identify patients at high risk for the development of AF.
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