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 x GCS score - 0.523 x NLR - 0.422 x 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.