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Risk factors and nomogram predictive model of surgical site infection in closed pilon fractures

OBJECTIVES: In this study, we try to investigate the risk factors of postoperative surgical site infection (SSI) in closed pilon fractures and establish a nomogram prediction model. METHODS: From January 2012 to June 2021, 516 closed pilon fracture patients were included in this study. Of these, 387...

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Detalles Bibliográficos
Autores principales: Ke, Chenrong, Dong, Xiaoyu, Xiang, Guangheng, Zhu, Juanjuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408134/
https://www.ncbi.nlm.nih.gov/pubmed/37553679
http://dx.doi.org/10.1186/s13018-023-04058-z
Descripción
Sumario:OBJECTIVES: In this study, we try to investigate the risk factors of postoperative surgical site infection (SSI) in closed pilon fractures and establish a nomogram prediction model. METHODS: From January 2012 to June 2021, 516 closed pilon fracture patients were included in this study. Of these, 387 patients were randomly assigned to the training group and 129 patients were assigned to the validation group (3:1). By univariate and multivariate Cox analysis, we identified independent risk factors for postoperative SSI after Pilon fracture. We established a nomogram model and used receiver operating characteristic (ROC) and calibration chart to evaluate its discriminant and calibration. RESULTS: SSI occurred in 71 patients in the training group and 23 patients in the validation group. Ultimately, age, preoperative blood sugar, operative time, Tscherne classification and fracture classification were identified as independent risk factors for SSI. The AUC values for SSI of the training and validation group were 0.898 and 0.880, and the P value of the Hosmer–Lemeshow test was 0.125. We established a nomogram prediction model based on age, preoperative blood sugar, operative time, Tscherne classification and fracture classification. CONCLUSION: Our nomogram model had good discrimination and calibration power, so it could be used to predict SSI risk in patients with pilon fracture.