A nomogram for predicting the risk of acute respiratory distress syndrome in patients with severe acute brain injury

OBJECTIVE: The aim of this study is to investigate risk factors for acute respiratory distress syndrome (ARDS) in severe acute brain injury (SABI) patients and construct a nomogram-based predictive model. METHODS: A retrospective analysis was conducted on 200 SABI patients admitted to Lishui Hospital between January 2021 and April 2025, who were randomly allocated into training group (n = 140) and validation group (n = 60). ARDS risk factors were identified and incorporated into a predictive model. Model performance was evaluated via Receiver operating characteristic curve (ROC curve), calibration plots, and decision curve analysis (DCA). RESULTS: Multivariate logistic regression revealed three independent predictors of ARDS in SABI patients: Sepsis, PaO₂/FiO₂, Pulmonary infection, (all P < 0.05). The area under the ROC curve (AUC) was 0.778 for the training set and 0.754 for the validation set. Calibration curves demonstrated good predicted-observation agreement, while DCA confirmed the clinical utility of the nomogram. CONCLUSION: This study developed and validated a nomogram prediction model incorporating three variables: Sepsis, PaO₂/FiO₂ and Pulmonary infection. The model demonstrated good discriminative ability and calibration in predicting the risk of ARDS in patients with SABI, thereby facilitating early risk stratification and supporting clinical decision-making.