Supplementary Material for: Using Clinical Audiologic Measures to Determine Cochlear Implant Candidacy

Introduction: Only a small percentage (6–10%) of patients who are candidates receive cochlear implants (CIs). One potential reason contributing to low usage rates may be confusion regarding which patients to refer for CI evaluation. The extent to which information provided by standard clinical audiologic assessments is sufficient for selecting appropriate CI evaluation referrals is uncertain. The objective of this study is to evaluate the capacity of standard clinical audiologic measures to differentiate CI candidates from noncandidates. Method: The study design is a retrospective review of a prospectively maintained CI database from a university-based tertiary medical center of 518 patients undergoing CI evaluations from 2012 to 2020. Each ear of each patient was treated as an independent value. Receiver operating characteristic (ROCs) curves were constructed using aided AzBio sentence recognition scores in quiet and aided AzBio +10 dB signal-to-noise ratio scores <60% as binary classifiers for CI candidacy. For each ROC, we examined the capacity of multiple pure-tone thresholds, pure-tone average (PTA), and CNC word recognition scores (WRSs) measured under earphones to determine CI candidacy. Area under the curve ROC (AUC-ROC) values were calculated to demonstrate the capacity of each model to differentiate CI candidates from noncandidates. Results: Variables with the greatest capacity to accurately differentiate CI candidates from noncandidates using aided AzBio in quiet scores were earphone CNC WRS, earphone pure-tone threshold at 1,000 Hz, and earphone PTA (AUC-ROC values = 0.86–0.88). Using aided AzBio +10 scores as the measure for candidacy, only CNC word recognition had a fair capacity to identify candidates (AUC-ROC value = 0.73). Based on the ROCs, a 1,000 Hz pure-tone threshold >50 dB HL, PTA >57 dB HL, and a monosyllabic WRS <60% can each serve as individual indicators for referral for CI evaluations. Conclusion: The current study provides initial indicators for referral and a first step at developing evidence-based criteria for CI evaluation referral using standard audiologic assessments.引言：符合人工耳蜗（cochlear implants, CIs）候选患者中，仅有6%~10%实际接受了人工耳蜗植入手术。导致植入率偏低的潜在原因之一，可能是临床医师对哪些患者应当转诊接受人工耳蜗评估存在认知混淆。目前尚无定论，即标准临床听力学评估所提供的信息是否足以筛选出适宜转诊接受人工耳蜗评估的患者。本研究旨在评估标准临床听力学检测方法区分人工耳蜗候选患者与非候选患者的效能。 方法：本研究采用回顾性分析方法，对某大学附属三级医疗中心2012年至2020年间518名接受人工耳蜗评估的患者的前瞻性维护人工耳蜗数据库进行分析。将每名患者的每侧耳朵均视为独立分析样本。以安静环境下助听后AzBio语句识别得分，以及信噪比+10dB条件下助听后AzBio得分低于60%作为人工耳蜗候选资格的二分类判别标准，构建受试者工作特征（Receiver Operating Characteristic, ROC）曲线。针对每条ROC曲线，我们均分析了耳机下测得的多项纯音听阈、纯音听阈均值（pure-tone average, PTA）以及CNC单词识别得分（word recognition scores, WRSs）在判断人工耳蜗候选资格时的效能。计算ROC曲线下面积（Area Under the Curve ROC, AUC-ROC）值，以体现各模型区分人工耳蜗候选患者与非候选患者的效能。 结果：以安静环境下助听后AzBio得分为判别标准时，区分效能最高的变量为耳机测得的CNC单词识别得分、1000Hz纯音听阈以及纯音听阈均值，其ROC曲线下面积为0.86~0.88。若以信噪比+10dB条件下助听后AzBio得分为候选资格判别标准时，仅CNC单词识别得分具备中等的候选患者识别效能，其ROC曲线下面积为0.73。基于上述ROC曲线，1000Hz纯音听阈＞50dB听力级（dB HL）、纯音听阈均值＞57dB听力级，以及单音节单词识别得分＜60%，均可作为转诊接受人工耳蜗评估的独立预警指标。 结论：本研究提出了初始的转诊评估指标，同时为基于标准听力学评估建立人工耳蜗评估转诊的循证标准迈出了第一步。