1. When is surgical intervention needed in oral and maxillofacial space infection patients? A retrospective case control study in 46 patients
- Author
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Yimin Liu, Hanyi Zhu, Xin Bao, Yingyi Qin, Zhiyuan He, Lingyan Zheng, and Huan Shi
- Subjects
Neutrophil to lymphocyte ratio ,Oral and maxillofacial space infection ,Surgical intervention ,Prognostic factor ,Dentistry ,RK1-715 - Abstract
Abstract Objective Patients with mild oral and maxillofacial space infection (OMSI) usually need only antimicrobial therapy. However, surgical intervention is eventually needed after using antibiotics for a period. The objective of this study was to explore the risk factors for drug therapy failure in OMSI. Subjects and methods A retrospective case‒control study was designed. From August 2020 to September 2022, patients at Shanghai Jiao Tong University Affiliated Ninth People’s Hospital who were diagnosed with OMSI were retrospectively reviewed. The outcome variable was surgical intervention after the use of antibiotics. We collected common biological factors, including demographic characteristics, routine blood test results, C-reactive protein (CRP) levels and composite indicators, such as neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR). The χ2 test and binary logistic regression were used to examine the association between biological factors and the outcome variable. Results Forty-six patients were included in this study. Further surgical intervention was needed in 20 patients (43.5%). The NLR showed a significant association with further surgical drainage (p = 0.01). A binary logistic regression equation was found by using stepwise regression based on the Akaike information criterion (R2 = 0.443), which was associated with sex (odds ratio [OR], 0.216; p = 0.092), NLR (OR, 1.258; p = 0.045), red blood cell (RBC) count (OR, 4.372; p = 0.103) and monocyte (MONO) count (OR, 9.528, p = 0.023). Receiver operating characteristic analysis produced an area under the curve for NLR of 0.725 (p = 0.01) and for the binary logistic regression model of 0.8365 (p
- Published
- 2024
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