突发性感音神经性听力损失听力恢复个体化预测的机器学习模型的开发与内部验证
Development and Internal Validation of Machine-Learning Models for Individualized Prediction of Hearing Recovery in Sudden Sensorineural Hearing Loss.
文献信息
| PMID | 42925567 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Yingqiang Li |
| 作者单位 | Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. |
| 期刊 | Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology |
| SCI 分区 | Q2 |
| IF | 1.9 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 耳科 |
中文摘要
目的： 利用常规可获得的基线临床变量和标准听力检查，开发并内部验证一个用于预测突发性感音神经性听力损失（SSNHL）听力恢复的实用机器学习模型。
研究设计： 回顾性预后研究。
地点： 中国一家三级教学医院。
患者： 2017年1月1日至2025年8月31日期间接受评估、具有治疗前和随访听力检查的成人特发性SSNHL患者。最终队列包括1097例患者，共1205只受累耳。
干预： 常规临床管理，包括根据当代临床实践和医生判断给予全身和/或鼓室内糖皮质激素治疗。
主要结局指标： 根据2015年中国指南和Siegel标准对随访听力图上的听力恢复进行分类，并在临床相关阈值处进行二分类。主要模型选择基于3个中国指南定义的二分类结局的总体表现。
结果： 使用患者水平的5折交叉验证、折内插补、校准评估、决策曲线分析和SHAP解释比较了六种算法。基于树的模型总体优于逻辑回归模型。在排除治疗变量的主要模型中，LightGBM在中国指南定义的各结局中表现一致。ROC AUC分别为：完全恢复0.873、重要恢复及以上0.797、轻微恢复及以上0.753，相应准确率分别为89.07%、78.99%和69.87%。在Siegel标准下，LightGBM的ROC AUC分别为：完全恢复0.889、部分恢复及以上0.807、轻微改善及以上0.782。纳入治疗变量的敏感性模型表现相似。影响最大的预测因素为年龄、基线听阈、听力图构型和眩晕。
结论： 基于易于收集的基线临床和听力检查变量的LightGBM模型在预测SSNHL听力恢复方面显示出有用的内部表现，可能有助于早期、个体化的预后评估，尚待外部和前瞻性验证。
英文摘要
OBJECTIVE: To develop and internally validate a pragmatic machine-learning model for predicting hearing recovery in sudden sensorineural hearing loss (SSNHL) using routinely available baseline clinical variables and standard audiometry.
STUDY DESIGN: Retrospective prognostic study.
SETTING: Tertiary teaching hospital in China.
PATIENTS: Adults with idiopathic SSNHL evaluated between January 1, 2017, and August 31, 2025, who had pretreatment and follow-up audiometry. The final cohort included 1097 patients contributing 1205 affected ears.
INTERVENTIONS: Routine clinical management, including systemic and/or intratympanic corticosteroid therapy according to contemporary clinical practice and physician judgment.
MAIN OUTCOME MEASURES: Hearing recovery on follow-up audiogram classified by the 2015 Chinese guideline and Siegel criteria and dichotomized at clinically relevant thresholds. Primary model selection was based on overall performance across the 3 Chinese guideline-defined binary outcomes.
RESULTS: Six algorithms were compared using patient-level 5-fold cross-validation, within-fold imputation, calibration assessment, decision-curve analysis, and SHAP interpretation. Tree-based models outperformed logistic models overall. In the primary models excluding treatment variables, LightGBM showed consistent performance across Chinese guideline-defined outcomes. ROC AUCs were 0.873 for full recovery, 0.797 for important recovery and better, and 0.753 for minor recovery and better, with corresponding accuracies of 89.07%, 78.99%, and 69.87%. Under Siegel criteria, LightGBM achieved ROC AUCs of 0.889 for complete recovery, 0.807 for partial recovery and better, and 0.782 for slight improvement and better. Performance was similar in sensitivity models incorporating treatment variables. The most influential predictors were age, baseline hearing thresholds, audiogram configuration, and vertigo.
CONCLUSIONS: A LightGBM model based on easily collected baseline clinical and audiometric variables showed useful internal performance for predicting hearing recovery in SSNHL and may support early, individualized prognostic assessment, pending external and prospective validation.