Prediction model of central nervous system infections in patients with severe traumatic brain injury after craniotomy.

BACKGROUND: At present, central nervous system (CNS) infection in patients with traumatic brain injury is usually diagnosed according to the clinical manifestations and results of cerebrospinal fluid (CSF) bacterial culture. However, there are difficulties in obtaining specimens in the early stage.

AIM: To develop and evaluate a nomogram to predict CNS infections in patients with severe traumatic brain injury (sTBI) after craniotomy.

METHODS: This retrospective study was conducted in consecutive adult patients with sTBI who were admitted to the neurointensive care unit (NCU) between January 2014 and September 2020. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were applied to construct the nomogram, and k-fold cross-validation (k = 10) to validate it.

FINDINGS: A total of 471 patients with sTBI who underwent surgical treatment were included, of whom 75 patients (15.7%) were diagnosed with CNS infections. The serum level of albumin, cerebrospinal fluid (CSF) otorrhoea at admission, CSF leakage, CSF sampling, and postoperative re-bleeding were associated with CNS infections and incorporated into the nomogram. Our model yielded satisfactory prediction performance with an area under the curve value of 0.962 in the training set and 0.942 in the internal validation. The calibration curve exhibited satisfactory concordance between the predicted and actual outcomes. The model had good clinical use since the DCA covered a large threshold probability.

CONCLUSION: Individualized nomograms for CNS infections in sTBI patients could help physicians screen for high-risk patients to perform early interventions, reducing the incidence of CNS infections.