A predictive model for the recurrence of intracranial aneurysms following coil embolization - He, Tao; Chen, Kun; Chen, Ru-Dong (Frontiers in Neurology, 2023)
Objective: To identify risk factors for intracranial aneurysms (IAs) recurrence and establish a predictive model to aid evaluation. Methods: 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). Delong test was performed to compare the AUCs. Results: Diameter 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). Cutoff value of predictive model and D max was 9.75 points and 6.65 mm, respectively. Conclusion: The 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.