构建和验证基于机器学习的慢性中耳炎患者术后并发症预测模型
Construction and validation of a machine learning-based prediction model for postoperative complications in patients with chronic otitis media.
摘要(中文译文)
开发并验证了一种利用临床多组学指标评估慢性中耳炎患者术后并发症风险的机器学习预测模型。该模型旨在辅助围手术期管理和个体化干预。患者按7：3的比例随机分配至训练集（n=237）和验证集（n=101）。共收集21项临床多组学指标，包括人口学、临床、实验室和影像学数据。在训练集中，采用单因素分析、最小绝对收缩和选择算子回归及多因素logistic回归筛选核心预测变量。基于这些变量构建了四种机器学习模型——随机森林、logistic回归、K近邻和梯度提升机。采用曲线下面积、校准曲线和决策曲线分析评估模型性能。通过SHapley加法解释进行可解释性分析。多因素分析确定糖尿病史、既往耳部手术史、耳漏、中耳黏膜状态、胆脂瘤存在、咽鼓管功能评分及术前C反应蛋白水平为独立危险因素。在构建的模型中，随机森林模型表现出最优的综合性能，训练集中曲线下面积为0.885，验证集中为0.853。该模型还显示出良好的校准度。决策曲线分析表明，在较宽的阈值概率范围内具有显著的临床净获益。SHAP分析证实，既往耳部手术史和胆脂瘤存在是最具影响力的预测因素。开发并验证了一种基于机器学习的慢性中耳炎术后并发症预测模型。随机森林模型表现最优，能够有效预测并发症风险，具有良好的性能和相当大的临床转化潜力。它可作为术前风险评估和针对性术后监测的有前景的工具。
Abstract
A machine learning prediction model was developed and validated using Clinical Multi-omics (CMO) indicators to assess the risk of postoperative complications in patients with Chronic Otitis Media (COM). This model is intended to aid in perioperative management and individualized intervention. Patients were randomly allocated into a training set ( = 237) and a validation set ( = 101) in a 7:3 ratio. A total of 21 CMO indicators, including demographic, clinical, laboratory, and imaging data, were collected. In the training set, univariate analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and multivariate logistic regression were used to identify core predictive variables. Four machine learning models-Random Forest, Logistic Regression, K-Nearest Neighbors, and Gradient Boosting Machine-were constructed using these variables. Model performance was assessed using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Interpretability was analyzed with SHapley Additive exPlanations (SHAP). Multivariate analysis identified a history of diabetes, previous ear surgery, otorrhea, middle ear mucosal status, cholesteatoma presence, Eustachian tube function score, and preoperative C-reactive protein level as independent risk factors. Among the constructed models, the Random Forest model demonstrated superior overall performance, and the model was 0.885 in the training set and 0.853 in the validation set. The model also showed good calibration. DCA indicated significant clinical net benefit across a wide threshold probability range. SHAP analysis confirmed that a history of previous ear surgery and cholesteatoma presence were the most influential predictors. A machine learning-based prediction model for complications after COM surgery was developed and validated. The Random Forest model performed optimally, effectively predicting complication risk with favorable performance and considerable potential for clinical translation. It can serve as a promising tool for preoperative risk assessment and targeted postoperative monitoring.
如何引用
AMA
Jinlei Wang, Fuxun Zhang, Lijing Liu, Ye Ju, Chenguang Zhan. Construction and validation of a machine learning-based prediction model for postoperative complications in patients with chronic otitis media.. Frontiers in medicine. 2026; doi:10.3389/fmed.2026.1805224.
APA
Jinlei Wang, Fuxun Zhang, Lijing Liu, Ye Ju, Chenguang Zhan (2026). Construction and validation of a machine learning-based prediction model for postoperative complications in patients with chronic otitis media.. Frontiers in medicine. https://doi.org/10.3389/fmed.2026.1805224
GB/T 7714
Jinlei Wang, Fuxun Zhang, Lijing Liu, Ye Ju, Chenguang Zhan. Construction and validation of a machine learning-based prediction model for postoperative complications in patients with chronic otitis media.[J]. Frontiers in medicine, 2026 doi:10.3389/fmed.2026.1805224.
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