Establishment of a prediction model for hospital stay extension in patients with oral and maxillofacial space infection based on preoperative data

doi:10.19438/j.cjoms.2025.01.009

Abstract

Abstract: PURPOSE: To analyze the risk factors for prolonged hospitalization in patients with oral and maxillofacial space infection(OMSI) and establish a risk prediction model to provide reference for clinical intervention and management. METHODS: A retrospective analysis was conducted on 265 patients with OMSI admitted to the Affiliated Hospital of Xuzhou Medical University from July 2019 to July 2023. Using the 75th percentile of hospitalization time as the cutoff point, the patients were divided into prolonged hospitalization group and normal group. The differences in preoperative clinical data between the two groups were compared. The factors influencing the prolonged hospitalization time were analyzed through Lasso regression and multivariate logistic regression. Based on this, a novel risk assessment model for prolonged hospitalization in OMSI was established. The model was comprehensively evaluated using receiver operating characteristic(ROC) curve, Hosmer-Lemeshow calibration curve, and clinical decision curve. SPSS 26.0 software package and R Lanuguage software 4.2.2 were used for statistical analysis of the data. RESULTS: The variables with nonzero regression coefficients selected by Lasso regression were included in the multivariate logistic regression analysis. The results showed that underlying diseases (OR=2.43, 95%CI: 1.25-4.70), number of spaces (OR=1.67, 95%CI: 1.30-2.14), fibrinogen(OR=1.31, 95%CI: 1.08-1.60), and IL-6(OR=1.01, 95%CI: 1.00-1.01) were independent risk factors for prolonged hospitalization in OMSI patients(P<0.05). Using these independent risk factors, a predictive model was constructed with an AUC of 0.834(95%CI: 0.780-0.888). Hosmer-Lemeshow calibration curve test indicated good fit of the prediction model(P=0.4555), and decision curve analysis showed that the model had high clinical utility. CONCLUSIONS: The risk assessment model for prolonged hospitalization in patients with oral and maxillofacial space infection constructed in this study has good predictive performance, which helps to identify high-risk patients for long-term hospitalization at an early stage and take timely and effective intervention measures to alleviate the burden on patients and medical institutions.

Key words: Oral and maxillofacial space infection, Length of hospital stay, Risk prediction model, Preoperative data

CLC Number:

R782.3

ZHU Yan-yan, DING Jia-hui, LYU Zhong-jing, ZHENG Ji-wei, JIANG Ning-ning, ZHANG Yin-yin, SUN Yu-hua. Establishment of a prediction model for hospital stay extension in patients with oral and maxillofacial space infection based on preoperative data[J]. China Journal of Oral and Maxillofacial Surgery, 2025, 23(1): 48-54.

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