A mathematical model to predict "low-lying" posterior communicating artery aneurysms in neurosurgical practice

"Low-lying" posterior communicating artery (PCoA) aneurysms require great attention in surgical clipping due to their distinct anatomical characteristics. In this study, we propose an easy method to immediately recognize "low-lying" PCoA aneurysms in neurosurgical practice. A total of 89 cases with "low-lying" PCoA aneurysms were retrospectively analyzed. All patients underwent preoperative digital subtraction angiography (DSA) examinations and microsurgical clipping. Cases were classified into the "low-lying" and regular groups based on intraoperative findings. The distance- and angle-relevant parameters that reflected the relative location of the aneurysms and tortuosity of the internal carotid artery were measured using 3D-DSA images. The data were sequentially integrated into a mathematical analysis to obtain the prediction model. Finally, we proposed a novel mathematical formula to preoperatively predict the existence of "low-lying" PCoA aneurysms with great accuracy. Neurosurgeons might benefit from this model, which enables them to directly identify "low-lying" PCoA aneurysms and make appropriate surgical decisions accordingly.