BACKGROUND: Rational prediction of the probability of decannulation in tracheotomy patients is of great importance to clinicians and patients' families. This study aimed to develop a prediction model for decannulation in tracheotomized patients with neurological injury using routine clinical data and blood tests. METHODS: We developed a prediction model based on 186 tracheotomized patients, and data were collected from January 2018 to March 2021. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimize feature selection for the decannulation risk model. The performance of the prediction model was evaluated in terms of discrimination, calibration, and clinical utility using measures such as C-index, calibration plot, and decision curve analysis (DCA). Internal validation was performed through bootstrapping validation. RESULTS: A total of 66.13% (123/186) of patients were decannulated. Predictors included in the prediction nomogram were age, gender, subtype of neurological injury, Glasgow Coma Scale (GCS) score, swallowing function, duration of tracheotomy, procalcitonin (PCT) level, white blood cell (WBC) count, and serum albumin (ALB) level. The predictive model showed good discrimination, with a C-index of 0.755 (95% confidence interval: 0.68-0.83). Internal validation also confirmed a satisfactory C-index of 0.690. The DCA indicated that the nomogram added substantial value in predicting decannulation risk for patients with threshold probabilities falling between >21% and <98% compared to the existing scheme. CONCLUSIONS: This predictive model serves as a valuable instrument for clinicians to quantitatively assess the probability of decannulation in patients with neurological injury, aiding in informed decision-making and patient management.