Department of Statistics, Pukyong National University, Busan, Republic of Korea.
Department of Otorhinolaryngology-Head and Neck Surgery, Pusan National University College of Medicine, Pusan National University Hospital, Busan, Republic of Korea.
Am J Otolaryngol. 2021 Mar-Apr;42(2):102858. doi: 10.1016/j.amjoto.2020.102858. Epub 2021 Jan 4.
Idiopathic sudden sensorineural hearing loss (ISSHL) is an emergency otological disease, and its definite prognostic factors remain unclear. This study applied machine learning methods to develop a new ISSHL prognosis prediction model.
This retrospective study reviewed the medical data of 244 patients who underwent combined intratympanic and systemic steroid treatment for ISSHL at a tertiary referral center between January 2015 and October 2019. We used 35 variables to predict hearing recovery based on Siegel's criteria. In addition to performing an analysis based on the conventional logistic regression model, we developed prediction models with five machine learning methods: least absolute shrinkage and selection operator, decision tree, random forest (RF), support vector machine, and boosting. To compare the predictive ability of each model, the accuracy, precision, recall, F-score, and the area under the receiver operator characteristic curves (ROC-AUC) were calculated.
Former otological history, ear fullness, delay between symptom onset and treatment, delay between symptom onset and intratympanic steroid injection (ITSI), and initial hearing thresholds of the affected and unaffected ears differed significantly between the recovery and non-recovery groups. While the RF method (accuracy: 72.22%, ROC-AUC: 0.7445) achieved the highest predictive power, the other methods also featured relatively good predictive power. In the RF model, the following variables were identified to be important for hearing-recovery prediction: delay between symptom onset and ITSI or the initial treatment, initial hearing levels of the affected and non-affected ears, body mass index, and a previous history of hearing loss.
The machine learning models predictive of hearing recovery following treatment for ISSHL showed superior predictive power relative to the conventional logistic regression method, potentially allowing for better patient treatment outcomes.
特发性突发性聋（ISSHL）是一种紧急的耳科疾病，其明确的预后因素仍不清楚。本研究应用机器学习方法开发了一种新的 ISSHL 预后预测模型。
本回顾性研究分析了 2015 年 1 月至 2019 年 10 月在一家三级转诊中心接受鼓室内和全身皮质类固醇联合治疗的 244 例 ISSHL 患者的医疗数据。我们使用 35 个变量根据 Siegel 的标准预测听力恢复。除了基于常规逻辑回归模型进行分析外，我们还使用五种机器学习方法开发了预测模型：最小绝对收缩和选择算子、决策树、随机森林（RF）、支持向量机和提升。为了比较每个模型的预测能力，计算了准确性、精度、召回率、F-分数和接收器操作特征曲线（ROC-AUC）下的面积。
在恢复组和未恢复组之间，既往耳部病史、耳部饱满感、症状发作与治疗之间的延迟、症状发作与鼓室内皮质类固醇注射（ITSI）之间的延迟以及受累和未受累耳的初始听力阈值存在显著差异。RF 方法（准确性：72.22%，ROC-AUC：0.7445）的预测能力最高，而其他方法也具有较好的预测能力。在 RF 模型中，确定以下变量对听力恢复预测很重要：症状发作与 ITSI 或初始治疗之间的延迟、受累和未受累耳的初始听力水平、体重指数和既往听力损失史。
治疗 ISSHL 后预测听力恢复的机器学习模型相对于传统逻辑回归方法具有更高的预测能力，可能会带来更好的患者治疗效果。
