AUTHOR=Mei Qing , Shen Hui , Liu Jian 

TITLE=A nomogram for the prediction of short-term mortality in patients with aneurysmal subarachnoid hemorrhage requiring mechanical ventilation: a post-hoc analysis

JOURNAL=Frontiers in Neurology

VOLUME=Volume 14 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1280047

DOI=10.3389/fneur.2023.1280047

ISSN=1664-2295

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 shortterm 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 post-hoc 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： Admission GCS, SAPS II, rebleeding, early brain injury(EBI), and external ventricular drain (EVD) were significantly associated 30-day mortality in patients with aSAH requiring mechanical ventilation. The model A incorporated four clinical factors available in the early stage of the aSAH: GCS, SAPS II, rebleeding, EBI. Then, the prediction model B with the 5 predictors was developed and presented in a nomogram. The predictive nomogram 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 developed two models and 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.