Discussion
In this retrospective cohort study, we constructed a predictive model based on nomogram to assess risk of pulmonary infection in patients with TBI in ICU during ICU admission. Research findings revealed that in this population, race, mechanical ventilation, antibiotics, CHF, kidney disease, GCS, temperature, breath rate and INR were key predictive factors for pulmonary infection. Therefore, this study provided clinicians with an effective tool to identify individuals at high risk of pulmonary infection among patients with TBI in ICU. Furthermore, validation of the model demonstrated good performance, solidifying its reliability in practical clinical applications.
In patients with TBI, pulmonary complications and associated respiratory distress are considered one of the most common and life-threatening extracranial effects.20 21 About one-third of moderate and severe patients with TBI develop acute lung injury, manifested as bilateral shadows on pulmonary imaging and respiratory failure within 7 days after onset.22–24 Neuronal and cellular processes, including as high mobility group box 1 release, cytokine release and lymphatic system involvement, are strongly linked to lung injury induced by TBI. These mechanisms may decrease systemic and pulmonary immunity and raise the risk of infection.8 25 In immunology, the brain is thought to be nominally independent, but in reality, it interacts with other organs.25 For instance, when microglia and astrocytes cause inflammation in the brain, neutrophils become activated and adhere to the blood-brain barrier, disrupting it. TBI also causes a surge in neutrophils and inflammatory cytokines, such as tumour necrosis factor α, interleukin (IL)-1 and IL-6, to accumulate in the pulmonary air spaces.26–28
This study demonstrated that mechanical ventilation constituted a significant contributing factor to pulmonary infection in patients with TBI in ICU. Severe brain injury may trigger inflammation and affect tolerance of patients to mechanical stress generated by subsequent mechanical ventilation.29 In mechanical ventilation, the operator provides respiratory support by adjusting tidal volume, positive end-expiratory pressure, respiratory rate and inspiratory airway pressure. However, inappropriate application of these parameters may lead to lung tissue damage, associated with unfavourable patient prognosis.30 High VT ventilation may induce alveolar overexpansion, inflammatory mediator spillage and ventilator-associated pneumonia.31 The increase in this risk is partly due to tracheal intubation or tracheotomy, which allows bacteria from the oral cavity and upper respiratory tract to enter the lower respiratory tract, increasing the risk of pulmonary infection.32 Furthermore, pulmonary infections in patients are associated with antibiotic use. Non-absorbable antibiotics have been linked to intestinal dysbiosis, which might decrease broad immune cell responses and have a negative clinical impact on Pseudomonas aeruginosa lung infections.33 In a Streptococcus pneumoniae infection model, antibiotic-induced intestinal dysbiosis caused continuous impairment of macrophage function and was associated with adverse outcomes.34
In terms of comorbidities, heart failure can lower immunological function, which weakens the body’s defences against infections, particularly those caused by lung bacteria.35 Reduced cardiac pumping capacity in CHF causes blood stasis in the veins, which increases lung moisture and increases the risk of bacterial infection by congesting the pulmonary veins and capillaries.36 Proinflammatory cytokine levels in the serum are also higher in individuals with AKI, suggesting a strong relationship between the two conditions,37–40 which may directly damage pulmonary endothelial cells, leading to non-cardiogenic pulmonary oedema and lung injury.41–43 In adult patients undergoing mechanical ventilation in the ICU, low GCS score (GCS score <8) is an independent predictor factor for mixed bacterial ventilator-associated pneumonia.44 GCS is also involved in our predictive nomogram.
In this nomogram, temperature, breath rate and INR were also served as predictors to assess a patient’s risk of developing pulmonary infection. Fever and other hyperthermic states cause a rise in core temperature, which is a potent biological response modifier with profound but unpredictable consequences, especially in critically ill patients.45 In lipopolysaccharide and hyperoxia-induced acute lung injury models, febrile hyperthermia exposure is linked to significant increase in neutrophil infiltration, thus facilitating occurrence of pneumonia.46 47 In clinical practice, breath rate is frequently used as a screening tool for lower respiratory tract infections. The guideline defines tachypnoea as a breath rate greater than 20 breaths/min (beats/min) and recommends further evaluation.48 A study suggests that it may be feasible to distinguish between those who test positive for COVID-19 and those who have symptoms but test negative for the virus based on breath rate variability.49 INR may be associated with the haemostatic balance (antithrombotic–profibrinolytic) within the alveoli. In acute lung injury and fibrotic lung diseases, this balance significantly tilts towards procoagulant and inhibition of fibrinolysis, leading to the accumulation of fibrin in the extravascular alveoli and the formation of a hyaline membrane, which is a characteristic feature of acute lung injury/acute respiratory distress syndrome (ALI/ARDS).50
This study established and validated a predictive model for lung infection risk in ICU patients with TBI, which holds significant clinical implications. First, survival curve results demonstrated a detrimental impact of lung infection on the survival of patients with TBI, emphasising the importance of early identification and management of lung infections. Second, the study identified independent risk factors contributing to lung infection, such as race, respiratory rate, temperature, mechanical ventilation, antibiotic use and CHF, providing valuable therapeutic insights for clinicians. By identifying relevant risk factors, healthcare providers can implement preventive measures and treatment strategies to reduce the incidence of lung infections and improve patient outcomes. Additionally, the predictive nomogram serves as a practical tool for risk stratification and decision-making in clinical practice. Healthcare providers can use the nomogram to calculate individualised risk scores for patients with TBI, aiding in early identification and focused intervention for high-risk patients. We must admit, nonetheless, that this study has certain shortcomings. Since this is a retrospective cohort study, the research findings may be impacted by the use of exclusion processing for variables in vital signs and biochemical indicators when the missing value percentage exceeds 20% of the total sample size. Furthermore, in order to confirm the robustness and efficacy of nomogram, future research based on our own data would require external validation, as we have only carried out internal validation using this database. Finally, certain important factors, such as C reactive protein and cytokine level data, were left out of the analysis because of the restricted variety of variables in the public database.
In summary, the predictive nomogram developed in this study fills a gap in clinical practice in the field, providing clinicians with an effective tool for predicting the risk of secondary pulmonary infection in patients with TBI in the ICU. Early identification of high-risk patients and targeted interventions can improve patient outcomes and alleviate the burden of pulmonary infection in susceptible populations. However, further research and validation are needed to confirm the utility and applicability of this nomogram.