Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Department of General Surgery, Division of Vascular Surgery, Medical University of Vienna, Vienna, Austria.
Br J Surg. 2022 Feb 1;109(2):211-219. doi: 10.1093/bjs/znab407.
The most relevant determinant in scheduling monitoring intervals for abdominal aortic aneurysms (AAAs) is maximum diameter. The aim of the study was to develop a statistical model that takes into account specific characteristics of AAA growth distributions such as between-patient variability as well as within-patient variability across time, and allows probabilistic statements to be made regarding expected AAA growth.
CT angiography (CTA) data from patients monitored at 6-month intervals with maximum AAA diameters at baseline between 30 and 66 mm were used to develop the model. By extending the model of geometric Brownian motion with a log-normal random effect, a stochastic growth model was developed. An additional set of ultrasound-based growth data was used for external validation.
The study data included 363 CTAs from 87 patients, and the external validation set comprised 390 patients. Internal and external cross-validation showed that the stochastic growth model allowed accurate description of the distribution of aneurysm growth. Median relative growth within 1 year was 4.1 (5-95 per cent quantile 0.5-13.3) per cent. Model calculations further resulted in relative 1-year growth of 7.0 (1.0-16.4) per cent for patients with previously observed rapid 1-year growth of 10 per cent, and 2.6 (0.3-8.3) per cent for those with previously observed slow growth of 1 per cent. The probability of exceeding a threshold of 55 mm was calculated to be 1.78 per cent at most when adhering to the current RESCAN guidelines for rescreening intervals. An online calculator based on the fitted model was made available.
The stochastic growth model was found to provide a reliable tool for predicting AAA growth.
在安排腹主动脉瘤（AAA）监测间隔时间时，最重要的决定因素是最大直径。本研究的目的是开发一种统计模型，该模型考虑到 AAA 生长分布的特定特征，如患者间变异性以及随时间的患者内变异性，并允许对预期的 AAA 生长做出概率性陈述。
使用在基线时最大 AAA 直径在 30 至 66 毫米之间、以 6 个月为间隔进行监测的患者的 CT 血管造影（CTA）数据来开发该模型。通过扩展带有对数正态随机效应的几何布朗运动模型，开发了一种随机生长模型。还使用了一组额外的基于超声的生长数据进行外部验证。
研究数据包括 87 名患者的 363 次 CTA，外部验证集包括 390 名患者。内部和外部交叉验证表明，随机生长模型能够准确描述动脉瘤生长的分布。在 1 年内的中位数相对生长为 4.1％（5％至 95％分位数为 0.5％至 13.3％）。模型计算进一步导致先前观察到 1 年增长 10％的患者在 1 年内的相对增长为 7.0％（1.0％至 16.4％），而先前观察到增长 1％的患者的相对增长为 2.6％（0.3％至 8.3％）。当遵循当前的 RESCAN 重新筛查间隔指南时，计算出在大多数情况下超过 55 毫米阈值的概率为 1.78％。还提供了一个基于拟合模型的在线计算器。
发现随机生长模型为预测 AAA 生长提供了可靠的工具。
