Machine Learning Technique Reveals Prognostic Factors of Vibrant Soundbridge for Conductive or Mixed Hearing Loss Patients
Machine Learning Technique Reveals Prognostic Factors of Vibrant Soundbridge for Conductive or Mixed Hearing Loss Patients
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DOI：
10.1097/mao.0000000000003271
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发表时间：
2021-10
影响因子：
2.1
通讯作者：
Hajime Koyama;Anjin Mori;Daisuke Nagatomi;T. Fujita;Kazuya Saito;Y. Osaki;T. Yamasoba;K. Doi
中科院分区：
文献类型：
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作者：
Hajime Koyama;Anjin Mori;Daisuke Nagatomi;T. Fujita;Kazuya Saito;Y. Osaki;T. Yamasoba;K. Doi
目的：动态声桥（VSB）被开发用于治疗听力损失，但临床结果各不相同，预测治疗成功的预后因素仍然未知。我们通过分析预测模型和临界值来分析VSB对传导性或混合性听力损失的临床结果、预后因素。研究设计：回顾性图表回顾。环境：三级保健医院。患者：2017年1月至2019年12月我院行VSB手术患者30例。干预措施：术前和术后3个月进行听力学检查；患者在术后3个月完成问卷调查。主要结果测量：我们使用多元回归和随机森林算法进行预测。计算平均绝对误差和确定系数来估计预测精度。计算多元回归模型中的系数值和随机森林模型中特征的重要性，以澄清预后因素。绘制了受试者工作特性曲线。结果：术后所有听力学指标均有改善。随机森林模型（平均绝对误差：0.06）比多元回归模型（平均绝对误差：0.12）记录了更高的准确性。助听器患者沉默环境下言语辨别得分是影响听力的最大因素（系数值为0.51，特征值为0.71）。候选临界值为36%（敏感性89%，特异性75%）。结论：VSB是治疗传导性或混合性听力损失的有效方法。机器学习显示出更精确的预测，而助听器患者在沉默环境下的言语辨别分数是预测临床结果的最重要因素。
Objectives: Vibrant Soundbridge (VSB) was developed for treatment of hearing loss, but clinical outcomes vary and prognostic factors predicting the success of the treatment remain unknown. We examined clinical outcomes of VSB for conductive or mixed hearing loss, prognostic factors by analyzing prediction models, and cut-off values to predict the outcomes. Study design: Retrospective chart review. Setting: Tertiary care hospital. Patients: Thirty patients who underwent VSB surgery from January 2017 to December 2019 at our hospital. Intervention: Audiological tests were performed prior to and 3 months after surgery; patients completed questionnaires 3 months after surgery. Main outcome measures: We used a multiregression and the random forest algorithm for predictions. Mean absolute errors and coefficient of determinations were calculated to estimate prediction accuracies. Coefficient values in the multiregression model and the importance of features in the random forest model were calculated to clarify prognostic factors. Receiver operation characteristic curves were plotted. Results: All audiological outcomes improved after surgery. The random forest model (mean absolute error: 0.06) recorded more accuracy than the multiregression model (mean absolute error: 0.12). Speech discrimination score in a silent context in patients with hearing aids was the most influential factor (coefficient value: 0.51, featured value: 0.71). The candidate cut-off value was 36% (sensitivity: 89%, specificity: 75%). Conclusions: VSB is an effective treatment for conductive or mixed hearing loss. Machine learning demonstrated more precise predictions, and speech discrimination scores in a silent context in patients with hearing aids were the most important factor in predicting clinical outcomes.