A nomogram for predicting the necessity of tracheostomy after severe acute brain injury in patients within the neurosurgery intensive care unit: A retrospective cohort study

Abstract

Objective

This retrospective study was aimed to develop a predictive model for assessing the necessity of tracheostomy (TT) in patients admitted to the neurosurgery intensive care unit (NSICU).

Method

We analyzed data from 1626 NSICU patients with severe acute brain injury (SABI) who were admitted to the Department of NSICU at the Affiliated People's Hospital of Jiangsu University between January 2021 and December 2022. Data of the patients were retrospectively obtained from the clinical research data platform. The patients were randomly divided into training (70%) and testing (30%) cohorts. The least absolute shrinkage and selection operator (LASSO) regression identified the optimal predictive features. A multivariate logistic regression model was then constructed and represented by a nomogram. The efficacy of the model was evaluated based on discrimination, calibration, and clinical utility.

Results

The model highlighted six predictive variables, including the duration of NSICU stay, neurosurgery, orotracheal intubation time, Glasgow Coma Scale (GCS) score, systolic pressure, and respiration rate. Receiver operating characteristic (ROC) analysis of the nomogram yielded area under the curve (AUC) values of 0.854 (95% confidence interval [CI]: 0.822–0.886) for the training cohort and 0.865 (95% CI: 0.817–0.913) for the testing cohort, suggesting commendable differential performance. The predictions closely aligned with actual observations in both cohorts. Decision curve analysis demonstrated that the numerical model offered a favorable net clinical benefit.

Conclusion

We developed a novel predictive model to identify risk factors for TT in SABI patients within the NSICU. This model holds the potential to assist clinicians in making timely surgical decisions concerning TT.