Pourafkari Leili, Ghaffari Samad, Bancroft George R, Tajlil Arezou, Nader Nader D
Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Department of Anesthesiology, University at Buffalo, Buffalo, NY, USA.
Asian Cardiovasc Thorac Ann. 2015 Jan;23(1):17-23. doi: 10.1177/0218492314530134. Epub 2014 Apr 2.
Atrial fibrillation is a complication of mitral valve stenosis that causes several adverse neurologic outcomes. Our objective was to establish a mathematical model to predict the risk of atrial fibrillation in patients with mitral stenosis.
Of 819 patients with mitral stenosis who were screened, 603 were enrolled in the study and grouped according to whether they were in sinus rhythm or atrial fibrillation. Demographic, echocardiographic, and hemodynamic data were recorded. Logistic regression models were constructed to identify the relative risks for each contributing factor and calculate the probability of developing atrial fibrillation. Receiver operating characteristic curves were plotted.
Two hundred (33%) patients had atrial fibrillation; this group was older, in a higher functional class, more likely to have suffered previous thromboembolic events, and had significantly larger left atrial diameters, lower ejection fractions, and lower left atrial appendage emptying flow velocity. The factors independently associated with atrial fibrillation were left atrial strain (odds ratio = 7.53 [4.47-12.69], p < 0.001), right atrial pressure (odds ratio = 1.09 [1.02-1.17], p = 0.01), age (odds ratio = 1.14 [1.05-1.25], p = 0.002), and ejection fraction (odds ratio = 0.92 [0.87-0.97], p = 0.003). The area under the curve for the combined receiver operating characteristic for this model was 0.90 ± 0.12.
Age, right atrial pressure, ejection fraction, and left atrial strain can be used to construct a mathematical model to predict the development of atrial fibrillation in rheumatic mitral stenosis.
心房颤动是二尖瓣狭窄的一种并发症，会导致多种不良神经学后果。我们的目标是建立一个数学模型来预测二尖瓣狭窄患者发生心房颤动的风险。
在819例接受筛查的二尖瓣狭窄患者中，603例纳入研究，并根据其处于窦性心律还是心房颤动进行分组。记录人口统计学、超声心动图和血流动力学数据。构建逻辑回归模型以确定每个影响因素的相对风险，并计算发生心房颤动的概率。绘制受试者工作特征曲线。
200例（33%）患者发生心房颤动；该组患者年龄更大、心功能分级更高、更易发生既往血栓栓塞事件，且左心房直径显著更大、射血分数更低、左心耳排空血流速度更低。与心房颤动独立相关的因素为左心房应变（比值比=7.53[4.47 - 12.69]，p<0.001）、右心房压力（比值比=1.09[1.02 - 1.17]，p = 0.01）、年龄（比值比=1.14[1.05 - 1.25]，p = 0.002）和射血分数（比值比=0.92[0.87 - 0.97]，p = 0.003）。该模型联合受试者工作特征曲线下面积为0.90±0.12。
年龄、右心房压力、射血分数和左心房应变可用于构建一个数学模型，以预测风湿性二尖瓣狭窄患者心房颤动的发生。
