Unruptured intracranial aneurysm growth trajectory: occurrence and rate of enlargement in 520 longitudinally followed cases.

OBJECTIVEAs 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.METHODSFrom 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.RESULTSThe 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).CONCLUSIONSAnalyzing 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.