A predictive model for the recurrence of intracranial aneurysms following coil embolization

Abstract

ObjectiveThis study aimed to identify risk factors for intracranial aneurysms (IAs) recurrence and establish a predictive model to aid evaluation.MethodsA total of 302 patients with 312 IAs undergoing coil embolization between September 2017 and October 2022 were divided into two groups based on digital subtraction angiography follow-up. Clinical characteristics, operation-related factors, and morphologies were measured. Cox proportional hazard regression was used to identify the risk factors. Hazard ratios (HRs) were used to score points, and a predictive model was established. The test cohorts consisted of 51 IAs. Receiver operating characteristic curves were generated to determine the cutoff values and area under the curves (AUCs). A Delong test was performed to compare the AUCs.ResultsDiameter maximum (D max) (p < 0.001, HR = 1.221), Raymond-Roy occlusion classification (RROC) II or III (p = 0.004, HR = 2.852), and ruptured status (p < 0.001, HR = 7.782) were independent risk factors for the recurrence of IAs. A predictive model was established: D max + 2 * RROC (II or III; yes = 1, no = 0) + 6 * ruptured status (yes = 1; no = 0). The AUC of the predictive model (0.818) was significantly higher than those of D max (0.704), RROC (II or III) (0.645), and rupture status (0.683), respectively (Delong test, p < 0.05). The cutoff values of the predictive model and D max were 9.75 points and 6.65 mm, respectively.ConclusionThe D max, RROC (II or III), and ruptured status could independently predict the recurrence of IAs after coil embolization. Our model could aid in practical evaluations.