Influencing factors and prediction model construction for early clinical prognosis of patients with low⁃grade aneurysmal subarachnoid hemorrhage

Abstract

Objective To screen the risk factors for short⁃term poor clinical prognosis in patients with low⁃grade aneurysmal subarachnoid hemorrhage (aSAH), and to construct the Nomogram model. Methods A total of 293 aSAH patients with World Federation of Neurosurgical Societies (WNFS) grade Ⅰ-Ⅱ treated by aneurysm clipping or aneurysm embolization in The People's Hospital of Kaizhou District of Chongqing from January 2016 to December 2021 were enrolled. The modified Rankin Scale (mRS) was used to evaluate the clinical prognosis at 3 months after surgery. Univariate and multivariate stepwise Logistic regression analysis was used to screen the risk factors for short⁃term poor clinical prognosis in patients with low⁃grade aSAH, and Nomogram model was constructed. The receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated to evaluate the prediction efficiency of the model. The Bootstrap internal verification method, Hosmer⁃Lemeshow goodness of fit test and calibration curve were used to verify the model. Results Total 293 patients were divided into good prognosis group (0-2 score, n=238) and poor prognosis group (3-6 score, n=55) according to mRS score. The proportion of poor prognosis group was age ≥ 65 years old (χ2=18.516, P=0.000) and WFNS grade Ⅱ (χ2=9.491, P=0.002), the incidences of postoperative delayed cerebral ischemia (χ2=28.355, P=0.000), shunt dependent hydrocephalus (χ2=33.497, P=0.000) and intracranial hemorrhage (χ2=17.744, P=0.000) were higher than those of good prognosis group. Logistic regression analysis showed age ≥ 65 years old (OR=1.241, 95%CI: 1.021-1.772; P=0.000), postoperative delayed cerebral ischemia (OR=9.462, 95%CI: 1.302-23.823; P=0.010) and shunt dependent hydrocephalus (OR=6.092, 95%CI: 2.730-16.201; P=0.000) were the risk factors for short⁃term poor clinical prognosis in patients with low⁃grade aSAH. The Nomogram model was constructed based on the above 3 risk factors. ROC curve showed the AUC was 0.833 (95%CI: 0.772-0.897, P=0.000), when Youden index was 0.520, the corresponding sensitivity was 79.28%, specificity was 72.69%, and the cut⁃off value was 128 points. The prediction efficiency of the model was better than that of the above 3 factors alone (P=0.000, for all). Furthermore, the Bootstrap internal verification method, Hosmer⁃Lemeshow goodness of fit test and calibration curve confirmed the good discrimination (CI=0.908), stability (χ2=1.078, P=0.693) and calibration. Conclusions Age ≥ 65 years old, postoperative delayed cerebral ischemia and shunt dependent hydrocephalus are the risk factors for short⁃term poor clinical prognosis in patients with low⁃grade aSAH. The Nomogram model constructed based on these 3 factors has good predictive value for short⁃term clinical prognosis in low⁃grade aSAH.

Keywords

• intracranial aneurysm
• subarachnoid hemorrhage
• prognosis
• risk factors
• logistic models
• nomograms
• roc curve