A Nomogram Model for Prediction of Tracheostomy in Patients With Traumatic Cervical Spinal Cord Injury

Abstract

Objective: To develop a nomogram for the prediction of tracheostomy in patients with traumatic cervical spinal cord injury (TCSCI). Methods: A total of 689 TCSCI patients were included in our study. First, the variable se-lection was performed using between-group comparisons and LASSO regression analysis. Second, a multivariate logistic regression analysis (MLRA) with a step-by-step method was performed. A nomogram model was developed based on the MLRA. Finally, the model was validated on the training set and validation set. Results: The nomogram prediction model incorporated 5 predictors, including smoking history, dislocation, thoracic injury, American Spinal Injury Association (ASIA) grade, and neurological level of injury (NLI). The area under curve in the training group and in the validation group were 0. 883 and 0. 909, respectively. The Hosmer-Lemeshow test result was p = 0. 153. From the decision curve analysis curve, the model performed well and was feasible to make beneficial clinical decisions. Conclusion: The nomogram combining dislocation, thoracic injury, ASIA grade A, NLI, and smoking history was validated as a reliable model for the prediction of tracheostomy.