儿童腺样体切除术修正的预测：一项10年回顾性研究

文献信息

中文摘要

目的： 本研究旨在识别儿童腺样体切除术修正的危险因素，并开发及验证一个个体化风险预测的临床模型，以支持临床决策。
方法： 通过巢式病例对照设计，从苏州大学附属儿童医院接受腺样体切除术的16,457名儿童源队列中选取222名患者。数据集按7:3比例随机分为训练集和验证集。通过单因素和多因素logistic回归分析确定独立预测因子，并构建了七个预测模型进行比较。最终模型的性能基于其区分度（受试者工作特征曲线下面积，AUC）、校准度（校准图）和临床实用性（决策曲线分析，DCA）进行评估。
结果： 多因素分析确定了六个腺样体切除术修正的独立危险因素：腺样体大小、手术器械类型、初次手术时同时行扁桃体部分切除术、过敏性鼻炎、分泌性中耳炎和围手术期抗生素使用。在比较的七个模型中，logistic回归模型显示出最佳的区分能力，验证集中的AUC为0.921。开发了列线图和可免费访问的网页应用程序，以便于床旁风险计算。
结论： 本研究提供了首个经过验证的儿童腺样体切除术修正的临床预测模型。该模型表现出优异的性能，可以帮助临床医生准确识别需要修正手术的高风险儿童。

英文摘要

OBJECTIVE: This study aimed to identify risk factors for revision adenoidectomy in children and to develop and validate a clinical model for individualized risk prediction to support clinical decision-making.
METHODS: A total of 222 patients were selected via a nested case-control design from a source cohort of 16,457 children who underwent adenoidectomy at Children's Hospital of Soochow University. The dataset was randomly split into a training set and a validation set at a 7:3 ratio. Independent predictors were identified through univariate and multivariate logistic regression analyses, and seven predictive models were constructed for comparison. The final model's performance was evaluated based on its discrimination (area under the receiver operating characteristic curve, AUC), calibration (calibration plot), and clinical utility (decision curve analysis, DCA).
RESULTS: Multivariate analysis identified six independent risk factors for revision adenoidectomy: adenoid size, surgical instrument type, concurrent tonsillotomy during the initial surgery, allergic rhinitis, otitis media with effusion, and perioperative antibiotic use. Among seven compared models, the logistic regression model showed the best discriminative ability with an AUC of 0.921 in the validation set. A nomogram and a freely accessible web application were developed to facilitate bedside risk calculation.
CONCLUSION: This study provides the first validated clinical prediction model for revision adenoidectomy in children. The model demonstrates excellent performance and can help clinicians accurately identify children at high risk of needing a revision surgery.