Innovative prognostication: a novel nomogram for post-interventional aneurysmal subarachnoid hemorrhage patients

Spontaneous aneurysmal subarachnoid hemorrhage (aSAH) is a common acute cerebrovascular disease characterized by severe illness, high mortality, and potential cognitive and motor impairments. We carried out a retrospective study at Fujian Provincial Hospital to establish and validate a model for forecasting functional outcomes at 6 months in aSAH patients who underwent interventional embolization.

386 aSAH patients who underwent interventional embolization between May 2012 and April 2022 were included in the study. We established a logistic regression model based on independent risk factors associated with 6-month adverse outcomes (modified Rankin Scale Score ≥ 3, mRS). We evaluated the model's performance based on its discrimination, calibration, clinical applicability, and generalization ability. Finally, the study-derived prediction model was also compared with other aSAH prognostic scales and the model's itself constituent variables to assess their respective predictive efficacy.

The predictors considered in our study were age, the World Federation of Neurosurgical Societies (WFNS) grade of IV-V, mFisher score of 3-4, secondary cerebral infarction, and first leukocyte counts on admission. Our model demonstrated excellent discrimination in both the modeling and validation cohorts, with an area under the curve of 0.914 (p < 0.001, 95%CI = 0.873-0.956) and 0.947 (p < 0.001, 95%CI = 0.907-0.987), respectively. Additionally, the model also exhibited good calibration (Hosmer-Lemeshow goodness-of-fit test: X² = 9.176, p = 0.328). The clinical decision curve analysis and clinical impact curve showed favorable clinical applicability. In comparison to other prediction models and variables, our model displayed superior predictive performance.

The new prediction nomogram has the capability to forecast the unfavorable outcomes at 6 months after intervention in patients with aSAH.