Early warning model of postoperative infection of internal fixation device in maxillofacial fracture based on the synthetic minority over-sampling technique algorithm.
10.7518/hxkq.2025.2025040
- Author:
Jinfeng JIANG
1
;
Haiyan WANG
1
;
Yanfeng SHI
1
;
Ke XU
1
Author Information
1. Dept. of Oral and Maxillofacial Surgery, Affiliated Hospital of Nantong University, Nantong 226000, China.
- Publication Type:Journal Article
- Keywords:
early warning model;
internal fixation device infection;
maxillofacial fracture;
postoperative;
synthetic minority over-sampling technique
- MeSH:
Humans;
Algorithms;
Retrospective Studies;
Male;
Female;
Fracture Fixation, Internal/instrumentation*;
Risk Factors;
Middle Aged;
Adult;
Logistic Models;
Surgical Wound Infection/epidemiology*;
Aged;
Internal Fixators/adverse effects*;
Maxillofacial Injuries/surgery*;
Adolescent
- From:
West China Journal of Stomatology
2025;43(6):837-844
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:This study investigates independent risk factors for postoperative internal fixation device infection in patients with maxillofacial fractures and proposes an early warning model based on the synthetic minority over-sampling technique (SMOTE) algorithm.
METHODS:A total of 1 104 patients who underwent surgical treatment for maxillofacial fractures at Oral and Maxillofacial Surgery Department, Affiliated Hospital of Nantong University from January 2021 to December 2024 were retrospectively analyzed. The patients were divided into two groups based on the presence of postoperative internal fixation device infection: the infection group (27 cases) and non-infection group (1 077 cases). Clinical data from both groups were collected and subjected to statistical analysis. Univariate and binary Logistic regression analysis were used to identify risk factors for postoperative internal fixation device infection in maxillofacial fractures. Subsequently, a Logistic regression model was established, and the dataset was improved based on the SMOTE algorithm to construct an early warning model with the improved dataset. The prediction performance of the models was compared and validated.
RESULTS:Among the 1 104 patients who underwent surgical treatment for maxillofacial fractures, 27 cases of postoperative internal fixation device infections were identified, corresponding to an infection rate of 2.45% (27/1 104). Age, diabetes history, fracture severity, and oral hygiene status were all identified as risk factors for postoperative internal fixation device infections in maxillofacial fractures (all P<0.05). The prediction model based on the original data (P1). The prediction model based on the SMOTE algorithm (P2). Receiver operating characteristic (ROC) curve analysis shows that the area under curve (AUC) for the P2 model was 0.882, the P1 model was 0.861, indicating the superior predictive performance of the P2 model. The DeLong test results show that the difference in AUC between the two models was statistically significant (P<0.05).
CONCLUSIONS:Age, diabetes history, postoperative fracture severity, and oral hygiene status are all risk factors for infections associated with internal fixation devices after maxillofacial fracture surgery. The proposed early warning model demonstrated good predictive performance. Medical professionals can utilize this model to effectively intervene and anticipate infections related to internal fixation devices after maxillofacial fracture surgery.
