Clinical Predictive Models for Delayed Cerebral Infarction After Ruptured Intracranial Aneurysm Clipping for Patients: A Retrospective Study

Objective: A nomogram was developed in this work to predict the probability of delayed cerebral infarction (DCI) after ruptured intracranial aneurysms (RIA) clipping. Methods: Clinical data of patients with intracranial aneurysm were obtained from the neurosurgery department of the First Affiliated Hospital of Chongqing Medical University from January 2016 to December 2020. A total of 419 patients receiving surgery of ruptured intracranial aneurysm clipping were included and a total of 37 patients with DCI were set as the observation group. The control group consisted of 382 patients without DCI. Risk factors of DCI were screened by univariate and multivariate logistic regression analysis and included in the nomogram. Results: Univariate analysis showed that female (P = 0.009), small aneurysm (P = 0.031), intraoperative aneurysm rupture (P = 0.007) and cerebral vasospasm (P < 0.001) were risk factors for postoperative DCI while smoking history (P = 0.044) were protective factors for postoperative DCI. Multivariate Logistic regression analysis showed that small aneurysm (P = 0.002, OR = 3.332, 95%-7.104), intraoperative aneurysm rupture (P = 0.004, OR = 0.122, 95%-CI, 0.029-0.504)and cerebral vasospasm (P < 0.001, OR = 0.153, 95%-CI, 0.070-0.333) were independent risk factors of postoperative DCI. The calibration curve of the probability of occurrence showed that the nomogram was in good correspondence with the observed results with a C-index of 0.766 (95% CI, 0.684-0.848). Meanwhile, the Decision curve analysis (DCA) showed that the established predictive model had a good clinical net benefit. Conclusion: The well-established nomogram is expected to be an effective tool to predict the occurrence of DCI after intracranial ruptured aneurysm and can be used to assist clinicians to develop more effective treatment strategies and improve the prognosis of patients.