An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage - Hu, Ping; Xu, Yang; Liu, Yangfan; Li, Yuntao; Ye, Liguo; Zhang, Si; Zhu, Xinyi; Qi, Yangzhi; Zhang, Huikai; Sun, Qian; Wang, Yixuan; Deng, Gang; et al. (Frontiers in Neurology, 2021)
Background: Aneurysmal subarachnoid hemorrhage (aSAH) offend leads to severe disability and functional dependence. However, there is no reliable method to predict the clinical prognosis after aSAH. The aim of this study is to develop a web-based dynamic nomogram application to precisely evaluate the risk of poor outcomes in patients with aSAH. Methods: The clinical data of patients were analyzed retrospectively in two medical centers. One center with 126 patients was performed to develop model. The least absolute shrinkage and selection operator (Lasso) analysis was used to select optimal variables. Based on the selected variables, multivariable logistic regression was applied to identify the independent prognostic factors and then construct a nomogram model. C-index, and Hosmer-Lemeshow P value and Brier Score were used to reflect discrimination and calibration capacities of the model. Receiver operating characteristic (ROC) curve and calibration curve (1000 bootstrap resamples) were performed for internal validation, while another center with 85 patients was used to externally validate the model. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical usefulness of the nomogram. Results: Unfavorable prognosis was observed in 46 (37%) patients of the training cohort and 24 (29%) patients of the external validation cohort. The independent prognostic factors of the nomogram, including NLR (P=0.005), WFNS grade (P=0.002), and DCI (P=0.0003), were identified by Lasso and multivariable logistic regression. A dynamic nomogram https://hu-ping.shinyapps.io/DynNomapp/ was then developed. The nomogram model demonstrated excellent discrimination with a bias corrected C-index of 0.85 and calibration capacities (Hosmer-Lemeshow P value, 0.412; Brier Score, 0.12) in training cohort. When the model applied to external validation cohort, it yielded a C-index of 0.84 and Brier Score of 0.13. Both DCA and CIC showed a superior overall net benefit at the total range of threshold probabilities. Conclusion: This study identified that NLR on admission, the WFNS grade, and DCI can independently predict unfavorable prognosis in patients with aSAH. On this basis, a web-based dynamic nomogram application was developed to calculate the precise probability of poor outcome. This will benefit to personalized treatment and patient management, and help neurosurgeons make better clinical decisions.