Department of Medicine, Division of Cardiology, College of Medicine, University of Illinois at Chicago, 840 S. Wood St., 920S (MC 715), Chicago, IL, 60612, USA.
Department of Physiology and Biophysics and the Center for Cardiovascular Research, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
J Interv Card Electrophysiol. 2022 Oct;65(1):179-182. doi: 10.1007/s10840-022-01243-8. Epub 2022 May 17.
As AF-associated morbidity and mortality are increasing, there is an acute need for improved surveillance and prevention strategies to reduce the impact of AF and related strokes. Specific echocardiographic parameters that can best predict future onset of AF within 3 months are lacking.
Twenty patients with AF, as identified by presence of ICD-9 diagnosis code, were compared with a control group of twenty age- and sex-matched patients selected from the same clinic population but without a diagnosis of AF. Transthoracic echocardiograms (TTE) obtained within 90 days prior to first documented AF episode (study group) or obtained closest to first clinic visit (control) were selected for review.
Baseline characteristics, including age, BMI, presence of hypertension, hyperlipidemia, diabetes, and heart failure were comparable. Increased left atrial (LA) size (end systolic major axis in 2-chamber view: AF 4.62±0.03 vs control 3.79±0.21, P =0.03), increased mitral inflow (E/A ratio: AF 1.35±0.15 vs control 1.06±0.07, P =0.04), and reduced LA global longitudinal strain (AF -2.69±0.26 vs control - 3.59±0.31, P =0.04) were most closely associated with AF compared with the control group. Multivariate logistic regression was used to develop predictive models for AF onset. A combination of imaging and traditional clinical risk factors was the best AF prediction model with AUC of 0.94, which greatly exceeds the current best predictors published. From these parameters, we developed the SMASH2 scoring system for 90- day AF risk estimation.
Risk factors for AF and early features of atrial cardiomyopathy including male sex, hypertension, LA enlargement, reduced mitral inflow, and reduced LA strain are powerful predictors of AF onset within 90 days, and may be used to prognosticate future AF risk.
随着房颤相关发病率和死亡率的增加,迫切需要改进监测和预防策略,以降低房颤和相关中风的影响。目前缺乏能够在 3 个月内最佳预测房颤未来发生的特定超声心动图参数。
选择 20 例通过 ICD-9 诊断代码确定的房颤患者作为病例组,并与同一诊所人群中选择的 20 例年龄和性别匹配但无房颤诊断的患者作为对照组进行比较。选择在首次记录的房颤发作前 90 天内(研究组)或在首次就诊时(对照组)获得的经胸超声心动图(TTE)进行回顾。
基线特征,包括年龄、BMI、高血压、高血脂、糖尿病和心力衰竭的存在,在两组间无显著差异。左心房(LA)增大(LA 收缩末期长轴在 2 腔视图中的大小:房颤组 4.62±0.03 比对照组 3.79±0.21,P=0.03)、二尖瓣血流(E/A 比值:房颤组 1.35±0.15 比对照组 1.06±0.07,P=0.04)和 LA 整体纵向应变降低(房颤组-2.69±0.26 比对照组-3.59±0.31,P=0.04)与对照组相比,与房颤的相关性最密切。使用多变量逻辑回归建立房颤发作的预测模型。影像学和传统临床危险因素的组合是预测房颤的最佳模型,AUC 为 0.94,大大超过了目前发表的最佳预测因子。基于这些参数,我们开发了用于 90 天房颤风险评估的 SMASH2 评分系统。
房颤的危险因素以及心房心肌病的早期特征,包括男性、高血压、LA 增大、二尖瓣血流减少和 LA 应变减少,是 90 天内房颤发作的有力预测因子,可用于预测未来的房颤风险。