Respiratory tract infection after oral and maxillofacial surgery under general anesthesia and related factors
DOI:
https://doi.org/10.3855/jidc.16810Keywords:
Oral and maxillofacial surgery, general anesthesia, pathogen, infection, influencing factorAbstract
Introduction: We aimed to explore the respiratory tract infection after oral and maxillofacial surgery under general anesthesia and related factors
Methodology: A total of 494 patients receiving oral and maxillofacial surgery under general anesthesia with tracheal intubation were assigned to a non-infection group (n=469) and an infection group (n=25). Another 494 healthy people undergoing physical examination in the same period were enrolled to establish a classification tree model. The distribution of pathogens, drug resistance of main pathogens, and related influencing factors of postoperative respiratory tract infection were analyzed. The influencing factors of respiratory tract infection were screened by logistic regression analysis. After construction of the classification and regression tree (CART) model based on the influencing factors, the accuracy was evaluated by plotting receiver operating characteristic (ROC) curve.
Results: Pseudomonas aeruginosa was highly resistant to cefazolin and more sensitive to cefoperazone, ciprofloxacin, norfloxacin and imipenem. Staphylococcus aureus was highly resistant to gentamicin and more sensitive to vancomycin. Age ≥ 60 years old, history of lung diseases, operation time ≥ 4 h, anesthesia ventilation time ≥ 120 min, and orotracheal intubation were independent influencing factors of respiratory tract infection (p< 0.05). The results of the gain chart, index map, and Risk value indicated a high predictive value of the CART model for the risk of postoperative respiratory tract infection. The area under the ROC curve was 0.869 [95% confidence interval: 0.795-0.947].
Conclusions: The CART model has a high predictive value and may reduce the risk of postoperative infection.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Feng Chen, Leilei Fang, Kunkun Feng, Jianbo Xu

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).