Chien Aichi, Callender Rashida A, Yokota Hajime, Salamon Noriko, Colby Geoffrey P, Wang Anthony C, Szeder Viktor, Jahan Reza, Tateshima Satoshi, Villablanca Juan, Duckwiler Gary, Vinuela Fernando, Ye Yuanqing, Hildebrandt Michelle A T
Departments of1Radiology and.
2Department of Epidemiology, MD Anderson Cancer Center, University of Texas, Houston, Texas.
J Neurosurg. 2019 Mar 1;132(4):1077-1087. doi: 10.3171/2018.11.JNS181814. Print 2020 Apr 1.
As imaging technology has improved, more unruptured intracranial aneurysms (UIAs) are detected incidentally. However, there is limited information regarding how UIAs change over time to provide stratified, patient-specific UIA follow-up management. The authors sought to enrich understanding of the natural history of UIAs and identify basic UIA growth trajectories, that is, the speed at which various UIAs increase in size.
From January 2005 to December 2015, 382 patients diagnosed with UIAs (n = 520) were followed up at UCLA Medical Center through serial imaging. UIA characteristics and patient-specific variables were studied to identify risk factors associated with aneurysm growth and create a predicted aneurysm trajectory (PAT) model to differentiate aneurysm growth behavior.
The PAT model indicated that smoking and hypothyroidism had a large effect on the growth rate of large UIAs (≥ 7 mm), while UIAs < 7 mm were less influenced by smoking and hypothyroidism. Analysis of risk factors related to growth showed that initial size and multiplicity were significant factors related to aneurysm growth and were consistent across different definitions of growth. A 1.09-fold increase in risk of growth was found for every 1-mm increase in initial size (95% CI 1.04-1.15; p = 0.001). Aneurysms in patients with multiple aneurysms were 2.43-fold more likely to grow than those in patients with single aneurysms (95% CI 1.36-4.35; p = 0.003). The growth rate (speed) for large UIAs (≥ 7 mm; 0.085 mm/month) was significantly faster than that for UIAs < 3 mm (0.030 mm/month) and for males than for females (0.089 and 0.045 mm/month, respectively; p = 0.048).
Analyzing longitudinal UIA data as continuous data points can be useful to study the risk of growth and predict the aneurysm growth trajectory. Individual patient characteristics (demographics, behavior, medical history) may have a significant effect on the speed of UIA growth, and predictive models such as PAT may help optimize follow-up frequency for UIA management.
随着成像技术的进步，越来越多的未破裂颅内动脉瘤（UIA）被偶然发现。然而，关于UIA随时间如何变化以提供分层的、针对患者的UIA随访管理的信息有限。作者试图加深对UIA自然病史的理解，并确定UIA的基本生长轨迹，即各种UIA大小增加的速度。
2005年1月至2015年12月，382例诊断为UIA（n = 520）的患者在加州大学洛杉矶分校医学中心通过系列成像进行随访。研究UIA特征和患者特异性变量，以确定与动脉瘤生长相关的危险因素，并创建一个预测动脉瘤轨迹（PAT）模型来区分动脉瘤的生长行为。
PAT模型表明，吸烟和甲状腺功能减退对大型UIA（≥7 mm）的生长速率有很大影响，而<7 mm的UIA受吸烟和甲状腺功能减退的影响较小。对与生长相关的危险因素分析表明，初始大小和多发性是与动脉瘤生长相关的重要因素，并且在不同的生长定义中是一致的。初始大小每增加1 mm，生长风险增加1.09倍（95% CI 1.04 - 1.15；p = 0.001）。患有多个动脉瘤的患者的动脉瘤生长可能性是患有单个动脉瘤患者的2.43倍（95% CI 1.36 - 4.35；p = 0.003）。大型UIA（≥7 mm；0.085 mm/月）的生长速率明显快于<3 mm的UIA（0.030 mm/月）速率，男性的生长速率快于女性（分别为0.089和0.045 mm/月；p = 0.048）。
将纵向UIA数据作为连续数据点进行分析，对于研究生长风险和预测动脉瘤生长轨迹可能是有用的。个体患者特征（人口统计学、行为、病史）可能对UIA生长速度有显著影响，而PAT等预测模型可能有助于优化UIA管理的随访频率。
