Development and validation of a predictive model for the prognosis in aneurysmal subarachnoid hemorrhage.

Background: This study was to conduct a predictive model for the prognosis of aneurysmal subarachnoid hemorrhage (aSAH) and validate the clinical data.

Methods: A total of 235 aSAH patients were enrolled in this study, dividing into the favorable or poor prognosis groups based on Modified Rankin Scale (mRS) at 3 months postoperatively. Multivariate analysis was assessed using binary Logistic regression and Fisher discriminant analysis. The receiver operating characteristic (ROC) curve was used to determine the cut-off value.

Results: Our findings showed that the high Glasgow Coma Scale (GCS) score 24-hour after surgery reduced the risk of poor prognosis, and the surgical clipping and elevated neutrophil-lymphocyte ratio (NLR) increased the risk of poor prognosis. The discriminant function was V = 0.881 × GCS score - 0.523 × NLR - 0.422 × therapeutic approach, and V = -0.689 served as a cut-off value. When V ≥ -0.689, the good prognosis was considered among these patients with aSAH. The correctness for predicting the prognostic outcomes by self-validation was 85.11%.

Conclusion: This predictive model established by a discriminant analysis is a useful tool for predicting the prognostic outcomes of aSAH patients, which may help clinicians identify patients at high risk for poor prognosis and optimize treatment after surgery.