ArticleBMC anesthesiology2025
Construction and evaluation of a nomogram prediction model for the risk of epistaxis following nasotracheal intubation: a single-center retrospective cohort study.
Article in BMC anesthesiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND AND
objectiveNasotracheal intubation (NTI) is a common procedure in oral and maxillofacial surgery; however, it is associated with a significant risk of post-procedural epistaxis. This study aims to identify risk factors for epistaxis following NTI and develop a nomogram-based prediction model to provide clinicians with a reliable risk assessment tool.
methodsThis single-center, retrospective study included 640 adult patients who underwent NTI at Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, between June 2020 and December 2021. Clinical data and imaging findings were collected, and the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression analysis, were used to identify predictors of epistaxis. A nomogram prediction model was constructed, and its performance was evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow test, and decision curve analysis.
resultsA total of 574 patients were included. Five epistaxis risk predictors were identified: age, history of radiotherapy/chemotherapy, nasal intubation duration, maximum nasal septum deviation on the coronal plane, and location of this deviation on the cross-section. The model's area under curve (AUC) was 0.957 in the training set and 0.900 in the validation set, indicating good discrimination and accuracy. The calibration curve was well-fitted, and decision curve analysis showed good clinical utility.
conclusionThe nomogram developed in this study effectively predicts the risk of epistaxis following NTI, offering clinicians an intuitive and practical decision-making tool. This model may help reduce adverse events and enhance surgical safety by guiding preoperative risk stratification and personalized management strategies.
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