A novel nomogram model for clinical outcomes of severe subarachnoid hemorrhage patients

Abstract

Background: Systemic responses, especially inflammatory responses, after aneurysmal subarachnoid hemorrhage (SAH) are closely related to clinical outcomes. Our study aimed to explore the correlation between the systemic responses in the acute stage and the mid-term outcomes of severe SAH patients (Hunt-Hess grade III-V). Materials and methods: Severe SAH patients admitted to Jinling Hospital from January 2015 to December 2019 were retrospectively analyzed in the study. The univariate and multivariate logistic regression analyses were used to explore the risk factors of 6-month clinical outcomes in severe SAH patients. A predictive model was established based on those risk factors and was visualized by a nomogram. Then, the predictive nomogram model was validated in another severe SAH patient cohort from January 2020 to January 2022. Results: A total of 194 patients were enrolled in this study. 123 (63.4%, 123 of 194) patients achieved good clinical outcomes at the 6-month follow-up. Univariate and multivariate logistic regression analysis revealed that age, Hunt-Hess grade, neutrophil-to-lymphocyte ratio (NLR), and complications not related to operations were independent risk factors for unfavorable outcomes at 6-month follow-up. The areas under the curve (AUC) analysis showed that the predictive model based on the above four variables was significantly better than the Hunt-Hess grade (0.812 vs. 0.685, P = 0.013). In the validation cohort with 44 severe SAH patients from three different clinical centers, the AUC of the prognostic nomogram model was 0.893. Conclusion: The predictive nomogram model could be a reliable predictive tool for the outcome of severe SAH patients. Systemic inflammatory responses after SAH and complications not related to operations, especially hydrocephalus, delayed cerebral ischemia, and pneumonia, might be the important risk factors that lead to poor outcomes in severe SAH patients.