Development and validation of a predictive model for pulmonary infection risk in patients with traumatic brain injury in the ICU: a retrospective cohort study based on MIMIC-IV

Abstract

Objective To develop a nomogram for predicting occurrence of secondary pulmonary infection in patients with critically traumatic brain injury (TBI) during their stay in the intensive care unit, to further optimise personalised treatment for patients and support the development of effective, evidence-based prevention and intervention strategies. Data source This study used patient data from the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database. Design A population-based retrospective cohort study. Methods In this retrospective cohort study, 1780 patients with TBI were included and randomly divided into a training set (n=1246) and a development set (n=534). The impact of pulmonary infection on survival was analysed using Kaplan-Meier curves. A univariate logistic regression model was built in training set to identify potential factors for pulmonary infection, and independent risk factors were determined in a multivariate logistic regression model to build nomogram model. Nomogram performance was assessed with receiver operating characteristic (ROC) curves, calibration curves and Hosmer-Lemeshow test, and predictive value was assessed by decision curve analysis (DCA). Result This study included a total of 1780 patients with TBI, of which 186 patients (approximately 10%) developed secondary lung infections, and 21 patients died during hospitalisation. Among the 1594 patients who did not develop lung infections, only 85 patients died (accounting for 5.3%). The survival curves indicated a significant survival disadvantage for patients with TBI with pulmonary infection at 7 and 14 days after intensive care unit admission (p<0.001). Both univariate and multivariate logistic regression analyses showed that factors such as race other than white or black, respiratory rate, temperature, mechanical ventilation, antibiotics and congestive heart failure were independent risk factors for pulmonary infection in patients with TBI (OR>1, p<0.05). Based on these factors, along with Glasgow Coma Scale and international normalised ratio variables, a training set model was constructed to predict the risk of pulmonary infection in patients with TBI, with an area under the ROC curve of 0.800 in the training set and 0.768 in the validation set. The calibration curve demonstrated the model’s good calibration and consistency with actual observations, while DCA indicated the practical utility of the predictive model in clinical practice. Conclusion This study established a predictive model for pulmonary infections in patients with TBI, which may help clinical doctors identify high-risk patients early and prevent occurrence of pulmonary infections. Data sharing not applicable as no datasets generated and/or analysed for this study.