Analysis of risk factors of secondary intracranial infection in patients with severe traumatic brain injury and construction of a Nomogram prediction model

Abstract

Objective To screen the risk factors of intracranial infection in patients with severe traumatic brain injury (sTBI) and establish a Nomogram model based on these risk factors. Methods A total of 130 patients with sTBI admitted to The Second Affiliated Hospital of Shandong First Medical University from January 2021 to June 2022 were enrolled. They were divided into a group with intracranial infection (n = 27) and a group without intracranial infection (n = 103) according to whether complicated with intracranial infection. To analyze the risk factors of intracranial infection in patients with sTBI by univariate and multivariate stepwise Logistic regression, and construct a Nomogram model based on the risk factors to draw receiver operating characteristic (ROC) curve and calibration curve of this model and perform Hosmer⁃Lemeshow goodness of fit test. Results The proportion of diabetes (χ2 = 5.356, P = 0.021), open traumatic brain injury (χ2 = 4.248, P = 0.039), cerebrospinal fluid (CBF) leakage (adjusted χ2 = 4.731, P = 0.030), surgical treatment (χ2 = 8.284, P = 0.004), severe infection (adjusted χ2 = 6.479, P = 0.011), tracheal intubation (χ2 = 6.487, P = 0.011) and tracheotomy (χ2 = 4.072, P = 0.044) in intracranial infection group were higher than those in non ⁃ intracranial infection group. Logistic regression analysis showed diabetes (OR = 2.748, 95%CI: 1.417-8.654; P = 0.047), CBF leakage (OR = 4.483, 95%CI: 1.852-8.341; P = 0.031), surgical treatment (OR = 1.941, 95%CI: 1.483-8.842; P = 0.031) and severe infection (OR = 1.614, 95%CI: 1.113-5.682; P = 0.041) were risk factors for sTBI complicated with intracranial infection. The area under the curve (AUC) of ROC curve was 0.758 (95%CI: 0.641-0.875, P = 0.001), and the optimal cut⁃off value for predicting sTBI complicated with intracranial infection was 175. The calibration curve showed good consistency between the predicted probability and the actual probability, while the Hosmer⁃Lemeshow goodness of fit test showed no statistically significant difference (χ2 = 4.613, P = 4.412), indicating the Nomogram model has good differentiation, calibration and stability. Conclusions Diabetes, CBF leakage, surgical treatment and severe infection can increase the risk of sTBI complicated with intracranial infection. The Nomogram model can better predict the risk of sTBI complicated with intracranial infection.

Keywords

• brain injuries, traumatic
• infections
• risk factors
• logistic model
• nomograms