A Nomogram for Prediction of 30-Day Mortality in Patients with Aneurysmal Subarachnoid Hemorrhage Requiring Mechanical Ventilation

Abstract Background：Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating stroke subtype with high morbidity and mortality. Although several studies have developed a prediction model in aSAH to predict individual outcomes, few have addressed short-term mortality in patients requiring mechanical ventilation. The study aimed to construct a user-friendly nomogram to provide a simple, precise, and personalized prediction of 30-day mortality in patients with aSAH requiring mechanical ventilation.Methods：We conducted a secondary analysis based on a retrospective study in a French university hospital intensive care unit (ICU). All patients with aSAH requiring mechanical ventilation from January 2010 to December 2015 were included. Demographic and clinical variables were collected to develop a nomogram for predicting 30-day mortality. The least absolute shrinkage and selection operator (LASSO) regression method was performed to identify predictors, and multivariate logistic regression was used to establish a nomogram. The discriminative ability, calibration, and clinical practicability of the nomogram to predict short-term mortality were tested using the area under the curve (AUC), calibration plot, and decision curve analysis (DCA). Results：A nomogram was developed to predict the 30-day mortality with the following five variables: admission GCS, SAPS II, rebleeding, early brain injury(EBI), and external ventricular drain (EVD) (P < 0.05). The predictive model yielded an AUC of 0.795 [95% CI, 0.731–0.858]), and in the internal validation with bootstrapping, the AUC was 0.780. The predictive model was well-calibrated, and decision curve analysis further confirmed the clinical usefulness of the nomogram.Conclusions：We have constructed a nomogram that included five clinical characteristics to predict 30-day mortality in patients with aSAH requiring mechanical ventilation, which may aid clinical decision-making.