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Recursive partitioning-based preoperative risk stratification for atrial fibrillation after coronary artery bypass
Artyom Sedrakyan1, Heping Zhang, Tom Treasure
1Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Insights
Tree-based analysis identified patient risk groups for atrial fibrillation (AF) after coronary artery bypass graft (CABG) surgery. This stratification helps guide decisions on prophylactic therapy for AF prevention.
Area of Science:
- Cardiology
- Medical Informatics
- Surgical Outcomes
Background:
- Atrial fibrillation (AF) is a common complication following coronary artery bypass graft (CABG) surgery.
- Predicting AF risk is crucial for optimizing prophylactic treatment strategies.
- Current risk stratification methods may not fully capture patient heterogeneity after CABG.
Purpose of the Study:
- To employ tree-based methods for stratifying patients undergoing CABG based on their risk of developing postoperative AF.
- To identify key predictors of AF that vary across different patient subgroups.
- To inform clinical decision-making regarding the necessity and intensity of AF prophylaxis.
Main Methods:
- A cohort of 1209 consecutive patients undergoing isolated CABG between 1998-1999 was analyzed.
- Patients with preoperative AF were excluded from the study.
- Tree-based analysis was utilized to stratify patients into distinct risk groups for AF.
- Relative risks (RRs) and 95% confidence intervals (CIs) were calculated for risk factors at each stratification level.
Main Results:
- Age emerged as the most significant predictor of postoperative AF.
- Risk factor importance differed between younger (< or =60 years) and older patients.
- In younger patients, coronary artery disease severity and hypertension were key predictors.
- In older patients, ejection fraction <40% was a notable predictor, while other factors were less significant.
- AF occurrence ranged from 10% in the lowest-risk group to 55% in the highest-risk group.
Conclusions:
- Age and indicators of heart disease severity are significant predictors of AF after CABG.
- Tree-based stratification effectively categorizes patients by AF risk.
- This method can assist clinicians in identifying patients most likely to benefit from aggressive AF prophylaxis, optimizing resource allocation and patient care.
Background:
Knowledge of the risk of atrial fibrillation (AF) for patients undergoing coronary artery bypass graft surgery (CABG) can guide decisions about prophylactic therapy. Accordingly, we sought to use tree-based methods to stratify patients into groups that will have similar risk of AF after CABG and informed decision making regarding aggressive prophylaxis of AF.
Methods:
We studied 1209 consecutive patients with isolated CABG performed in 1998-1999 at Yale-New Haven Hospital. Patients with preoperative AF were excluded. Tree-based analysis was carried out to stratify patients into similar groups regarding the risk of AF. Relative risks (RRs) and 95% CIs were calculated at each level of stratification.
Results:
Age was the most important variable. The importance of other risk factors seemed to be different for younger and older patients. Although in the younger age group (< or =60 years) severity of coronary artery disease (RR 2.19, 95% CI 1.12-3.34) followed by hypertension (RR 1.82, 95% CI 1.23-2.68) were important predictors, in the older age subgroups (61-69 and > or =70 years), nothing or only ejection fraction <40% (RR 1.31, 95% CI 1.08-1.59) was important. In the highest-risk group, AF occurrence was 55% and, in the lowest-risk group, it was 10%. In the low-risk groups, aggressive prophylaxis may not be justified in light of the smaller number of events that would be prevented, possible adverse events, and costs.
Conclusion:
Age and variables related to heart disease severity are predictors of AF. The tree-based method may be a useful tool for clinicians who seek to determine who is more or less likely to benefit from aggressive arrhythmia prophylaxis.
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