Abstract
OBJECTIVE: Aneurysmal subarachnoid hemorrhage (aSAH) is a common and potentially fatal cerebrovascular disease. Poor-grade aSAH (Hunt-Hess grades IV and V) accounts for 20-30% of patients with aSAH, with most patients having a poor prognosis. This study aimed to develop a stable nomogram model for predicting adverse outcomes at 6 months in patients with aSAH, and thus, aid in improving the prognosis. METHOD: The clinical data and imaging findings of 150 patients with poor-grade aSAH treated with microsurgical clipping of intracranial aneurysms on admission from December 2015 to October 2021 were retrospectively analyzed. Least absolute shrinkage and selection operator (LASSO), logistic regression analyses, and a nomogram were used to develop the prognostic models. Receiver operating characteristic (ROC) curves and Hosmer-Lemeshow tests were used to assess discrimination and calibration. The bootstrap method (1,000 repetitions) was used for internal validation. Decision curve analysis (DCA) was performed to evaluate the clinical validity of the nomogram model. RESULT: LASSO regression analysis showed that age, Hunt-Hess grade, Glasgow Coma Scale (GCS), aneurysm size, and refractory hyperpyrexia were potential predictors for poor-grade aSAH. Logistic regression analyses revealed that age (OR: 1.107, 95% CI: 1.056-1.116, P < 0.001), Hunt-Hess grade (OR: 8.832, 95% CI: 2.312-33.736, P = 0.001), aneurysm size (OR: 6.871, 95% CI: 1.907-24.754, P = 0.003) and refractory fever (OR: 3.610, 95% CI: 1.301-10.018, P < 0.001) were independent predictors of poor outcome. The area under the ROC curve (AUC) was 0.909. The calibration curve and Hosmer-Lemeshow tests showed that the nomogram had good calibration ability. Furthermore, the DCA curve showed better clinical utilization of the nomogram. CONCLUSION: This study provides a reliable and valuable nomogram that can accurately predict the risk of poor prognosis in patients with poor-grade aSAH after microsurgical clipping. This tool is easy to use and can help physicians make appropriate clinical decisions to significantly improve patient prognosis.