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.

Keywords: infection control; opportunist lung infections.

Figure 1. Flow chart of selection. ICU, intensive care unit; MIMIC-IV, Medical Information Mart for Intensive Care IV.

Figure 2. Kaplan-Meier curves of survival probability before and after the landmark time grouped by pulmonary infection. (A) Kaplan-Meier curves before the landmark time. (B–D) Kaplan-Meier curves after the landmark time set as 7, 14 and 21 days, respectively. The above and bottom log-rank p refer to values of Kaplan-Meier curves before and after the landmark time, respectively.

Figure 3. Nomogram for predicting probability of pulmonary infection in participants. Cyan box sizes indicate relative proportion differences among subgroups, while the grey density plot displays total points distribution. GCS, Glasgow Coma Scale; INR, international normalised ratio.

Figure 4. The receiver operating characteristic (ROC) curve of the nomogram for both training (A) and development (BB) sets, with consistent variable entries. AUC, area under the ROC curve.

Figure 5. Calibration curves for nomograms in the training set (A) and development set (B). The diagonal line represents perfect prediction by an ideal model. The red and green lines correspond to the initial cohort and bias corrected by bootstrapping (B=1000 repetitions), respectively.

Figure 6. Decision curve analysis (DCA) curves for nomogram in both training set (A) and development set (B). The horizontal line denotes the scenario where no participants develop pulmonary infection, and the grey oblique line represents those who develop pulmonary infection. The red solid line corresponds to the pulmonary infection risk nomogram. The horizontal line reflects the absence of sample intervention with a net benefit of 0, while the red solid line indicates universal intervention receipt.