Zhang Xiaote, Xie Qiaoyi, Wu Ganggang
Department of Otolaryngology Head and Neck Surgery, Ningbo Yinzhou No.2 Hospital, Ningbo, Zhejiang, China.
Department of Pediatrics, The Affiliated People's Hospital of Ningbo University, Ningbo, Zhejiang, China.
Front Pediatr. 2025 Jun 19;13:1614495. doi: 10.3389/fped.2025.1614495. eCollection 2025.
Otitis media with effusion (OME) affects a significant proportion of children with adenoid hypertrophy (AH) and can lead to developmental sequelae when chronic. Current non-invasive screening modalities rely predominantly on acoustic immittance measurements, which demonstrate variable diagnostic performance. Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.
This study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.
A retrospective analysis was conducted on 847 pediatric patients with AH. Five ML algorithms were developed to identify OME using demographic, clinical, laboratory, and acoustic immittance parameters. The dataset underwent 7:3 stratified partitioning for training and testing cohorts. Within the training cohort, models were initially optimized through randomized grid search with 5-fold cross-validation, followed by comprehensive training with optimized parameters. Model performance was evaluated in the testing cohort using discrimination, calibration, clinical utility metrics, and confusion matrix-derived statistics. The optimal ML model was subsequently analyzed through SHapley Additive exPlanations (SHAP) methodology for interpretability, with sequential ablation testing performed to identify critical predictive variables.
Among 847 children with AH, 262 (30.9%) were diagnosed with OME. The Random Forest (RF) model demonstrated superior performance with the highest discrimination (area under the receiver operating characteristic curve = 0.919), balanced calibration (Brier score = 0.102), and optimal clinical utility across decision thresholds of 0.4-0.6. Confusion matrix analysis further confirmed RF as the optimal model, achieving 0.875 accuracy and robust inter-rater agreement (Cohen's kappa coefficient = 0.696) in the testing cohort. SHAP analysis identified the adenoid-to-nasopharyngeal ratio as the predominant diagnostic indicator, followed by tympanometric type and history of recurrent respiratory infections.
An RF-based diagnostic model effectively identifies OME in AH children by integrating anatomical, functional, and inflammatory parameters, providing a clinically applicable tool for enhanced diagnostic accuracy and evidence-based management decisions.
分泌性中耳炎（OME）在腺样体肥大（AH）儿童中占相当比例，若为慢性则可导致发育后遗症。目前的非侵入性筛查方法主要依赖声导抗测量，但其诊断性能各异。鉴于迫切需要改进诊断方法并广泛描述AH儿童OME的危险因素，开发诊断模型是提高临床实践中识别准确性的有效策略。
本研究旨在通过比较多种算法方法，将临床指标与声学测量结果整合到一个广泛适用的诊断工具中，开发并验证一种基于机器学习（ML）的最佳AH儿童OME预测模型。
对847例AH儿科患者进行回顾性分析。开发了五种ML算法，使用人口统计学、临床、实验室和声导抗参数来识别OME。数据集按7:3进行分层划分，用于训练和测试队列。在训练队列中，模型首先通过随机网格搜索和5折交叉验证进行优化，然后使用优化参数进行全面训练。在测试队列中，使用鉴别力、校准、临床效用指标和基于混淆矩阵的统计数据评估模型性能。随后通过SHapley加性解释（SHAP）方法对最佳ML模型进行分析以实现可解释性，并进行顺序消融测试以识别关键预测变量。
在847例AH儿童中，262例（30.9%）被诊断为OME。随机森林（RF）模型表现出卓越性能，具有最高的鉴别力（受试者操作特征曲线下面积=0.919）、平衡校准（布里尔评分=0.102），且在0.4 - 0.6的决策阈值范围内具有最佳临床效用。混淆矩阵分析进一步证实RF为最佳模型，在测试队列中准确率达到0.875，且评分者间一致性良好（科恩kappa系数=0.696）。SHAP分析确定腺样体与鼻咽部比值为主要诊断指标，其次是鼓室图类型和反复呼吸道感染史。
基于RF的诊断模型通过整合解剖学、功能和炎症参数，有效识别AH儿童中的OME，为提高诊断准确性和基于证据的管理决策提供了一种临床适用工具。
