Magee Mitchell J, Herbert Morley A, Dewey Todd M, Edgerton James R, Ryan William H, Prince Syma, Mack Michael J
Medical City Dallas Hospital, Dallas, Texas 75230, USA.
Ann Thorac Surg. 2007 May;83(5):1707-12; discussion 1712. doi: 10.1016/j.athoracsur.2006.12.032.
Atrial fibrillation is a costly complication occurring in 15% to 40% of patients after coronary artery bypass grafting (CABG). Aggressive prophylactic treatment should be directed toward and limited to selected high-risk patients. Utilizing perioperative risk factors, we sought to develop an algorithm to predict the relative risk of developing postoperative atrial fibrillation in patients undergoing CABG.
Data were extracted from our Society of Thoracic Surgeons Database on 19,620 patients undergoing CABG between January 1995 and July 2006. We used perioperative risk factors to develop a logistic regression equation predictive for the development of postoperative atrial fibrillation. A total of 19,083 patients had complete data and were used to construct the final model. The model was used to compare the predicted probability of atrial fibrillation with the known outcome in the patients divided into deciles by probability. Bootstrap procedures were used to determine the confidence limits of the beta coefficients.
A regression model was developed with 14 significant indicators. Those showing the greatest predictive influence included the patient age, the need for prolonged ventilation (24 hours or more), the use of cardiopulmonary bypass, and preoperative arrhythmias. The model showed acceptable concordance between observed and predicted (72.3%), a receiver operating characteristic curve area of 0.72, and Hosmer-Lemeshow probability of 0.19. When applied to the patient population, the calculated risk in those who did not develop AF was 0.179 +/- 0.116 and for those with AF, 0.284 +/- 0.153 (p < 0.001).
A validated predictive risk algorithm for developing postoperative atrial fibrillation can reliably stratify patients undergoing CABG into high-risk and low-risk groups. This may be used preoperatively to appropriately target high-risk patients for aggressive prophylactic treatment.
心房颤动是冠状动脉旁路移植术（CABG）后15%至40%的患者会出现的一种代价高昂的并发症。积极的预防性治疗应针对并限于选定的高危患者。我们利用围手术期风险因素，试图开发一种算法来预测接受CABG的患者发生术后心房颤动的相对风险。
从我们的胸外科医师协会数据库中提取了1995年1月至2006年7月期间接受CABG的19620例患者的数据。我们使用围手术期风险因素建立了一个预测术后心房颤动发生的逻辑回归方程。共有19083例患者有完整数据，并用于构建最终模型。该模型用于比较按概率分为十分位数的患者中心房颤动的预测概率与已知结果。采用自助法确定β系数的置信限。
开发了一个包含14个显著指标的回归模型。显示出最大预测影响的因素包括患者年龄、需要长时间通气（24小时或更长时间）、使用体外循环以及术前心律失常。该模型在观察值和预测值之间显示出可接受的一致性（72.3%），受试者工作特征曲线面积为0.72，Hosmer-Lemeshow概率为0.19。应用于患者群体时，未发生房颤者的计算风险为0.179±0.116，发生房颤者为0.284±0.153（p<0.001）。
一种经过验证的预测术后心房颤动发生的风险算法可以可靠地将接受CABG的患者分为高危和低危组。这可在术前用于适当确定高危患者以进行积极的预防性治疗。
