Department of Audiology, Townsville Hospital and Health Service, Townsville, Australia.
School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia.
Ear Hear. 2018 Nov/Dec;39(6):1116-1135. doi: 10.1097/AUD.0000000000000565.
Wideband acoustic immittance (WAI) is an emerging test of middle-ear function with potential applications for neonates in screening and diagnostic settings. Previous large-scale diagnostic accuracy studies have assessed the performance of WAI against evoked otoacoustic emissions, but further research is needed using a more stringent reference standard. Research into suitable quantitative techniques to analyze the large volume of data produced by WAI is still in its infancy. Prediction models are an attractive method for analysis of multivariate data because they provide individualized probabilities that a subject has the condition. A clinically useful prediction model must accurately discriminate between normal and abnormal cases and be well calibrated (i.e., give accurate predictions). The present study aimed to develop a diagnostic prediction model for detecting conductive conditions in neonates using WAI. A stringent reference standard was created by combining results of high-frequency tympanometry and distortion product otoacoustic emissions.
High-frequency tympanometry and distortion product otoacoustic emissions were performed on both ears of 629 healthy neonates to assess outer- and middle-ear function. Wideband absorbance and complex admittance (magnitude and phase) were measured at frequencies ranging from 226 to 8000 Hz in each neonate at ambient pressure using a click stimulus. Results from one ear of each neonate were used to develop the prediction model. WAI results were used as logistic regression predictors to model the probability that an ear had outer/middle-ear dysfunction. WAI variables were modeled both linearly and nonlinearly, to test whether allowing nonlinearity improved model fit and thus calibration. The best-fitting model was validated using the opposite ears and with bootstrap resampling.
The best-fitting model used absorbance at 1000 and 2000 Hz, admittance magnitude at 1000 and 2000 Hz, and admittance phase at 1000 and 4000 Hz modeled as nonlinear variables. The model accurately discriminated between normal and abnormal ears, with an area under the receiver-operating characteristic curve (AUC) of 0.88. It effectively generalized to the opposite ears (AUC = 0.90) and with bootstrap resampling (AUC = 0.85). The model was well calibrated, with predicted probabilities aligning closely to observed results.
The developed prediction model accurately discriminated between normal and dysfunctional ears and was well calibrated. The model has potential applications in screening or diagnostic contexts. In a screening context, probabilities could be used to set a referral threshold that is intuitive, easy to apply, and sensitive to the costs associated with true- and false-positive referrals. In a clinical setting, using predicted probabilities in conjunction with graphical displays of WAI could be used for individualized diagnoses. Future research investigating the use of the model in diagnostic or screening settings is warranted.
宽频声导抗（WAI）是一种新兴的中耳功能测试方法，在筛查和诊断环境中，它可能适用于新生儿。之前的大规模诊断准确性研究已经评估了 WAI 与诱发耳声发射的性能，但需要使用更严格的参考标准进一步研究。WAI 产生的大量数据的合适定量技术的研究仍处于起步阶段。预测模型是分析多变量数据的一种有吸引力的方法，因为它们提供了个体患有该疾病的概率。一个临床有用的预测模型必须能够准确地区分正常和异常情况，并具有良好的校准（即，做出准确的预测）。本研究旨在使用 WAI 为新生儿建立一种用于检测传导性疾病的诊断预测模型。通过将高频鼓室图和畸变产物耳声发射的结果相结合，创建了一个严格的参考标准。
对 629 例健康新生儿的双耳进行高频鼓室图和畸变产物耳声发射检查，以评估外耳和中耳功能。使用点击刺激，在每个新生儿的环境压力下，在 226 至 8000Hz 的频率范围内测量宽带吸光度和复导纳（幅度和相位）。每个新生儿的一只耳朵的结果用于开发预测模型。WAI 结果被用作逻辑回归预测因子，以模拟耳朵存在外/中耳功能障碍的概率。WAI 变量被线性和非线性地建模，以测试允许非线性是否可以改善模型拟合度，从而提高校准度。使用对侧耳朵和引导抽样重新采样来验证最佳拟合模型。
最佳拟合模型使用 1000Hz 和 2000Hz 的吸光度、1000Hz 和 2000Hz 的导纳幅度以及 1000Hz 和 4000Hz 的导纳相位作为非线性变量。该模型能够准确地区分正常和异常耳朵，其受试者工作特征曲线下面积（AUC）为 0.88。它在对侧耳朵上（AUC=0.90）和引导抽样重采样（AUC=0.85）中有效推广。该模型校准良好，预测概率与观察结果密切匹配。
所开发的预测模型能够准确地区分正常和功能障碍的耳朵，并且校准良好。该模型在筛查或诊断环境中具有应用潜力。在筛查环境中，可以使用概率来设置直观、易于应用且对与真阳性和假阳性转诊相关的成本敏感的转诊阈值。在临床环境中，结合 WAI 的图形显示使用预测概率可以进行个体化诊断。需要进一步研究该模型在诊断或筛查环境中的应用。
