Ollermann Rieke, Böscke Robert, Neidhardt John, Radeloff Andreas
Human Genetics, Faculty of Medicine and Health Science, University of Oldenburg, 26129 Oldenburg, Germany.
Division of Otolaryngology, Head and Neck Surgery, University of Oldenburg, 26129 Oldenburg, Germany.
Audiol Res. 2025 Jun 12;15(3):69. doi: 10.3390/audiolres15030069.
: Rehabilitation success with a cochlear implant (CI) varies considerably and identifying predictive factors for the reliable prediction of speech understanding with CI remains a challenge. Hoppe and colleagues have recently described a predictive model, which was specifically based on Cochlear™ recipients with a four-frequency pure tone average (4FPTA) ≤ 80 dB HL. The aim of this retrospective study is to test the applicability to an independent patient cohort with extended inclusion criteria. : The Hoppe et al. model was applied to CI recipients with varying degrees of hearing loss. Model performance was analyzed for Cochlear™ recipients with 4FPTA ≤ 80 dB HL and for all recipients regardless of 4FPTA. Subgroup analyses were conducted by and CI manufacturer. : The model yielded comparable results in our patient cohort when the original inclusion criteria were met (n = 24). Extending the model to patients with profound hearing loss (4FPTA > 80 dB HL; n = 238) resulted in a weaker but significant correlation (r = 0.273; < 0.0001) between predicted and measured word recognition score at 65 dB with CI (()). Also, a higher percentage of data points deviated by more than 20 pp, either better or worse. When patients provided with CIs from different manufacturers were enrolled, the prediction error was also higher than in the original cohort. In Cochlear™ recipients with a maximum word recognition score () > 0% (n = 83), we found a moderate correlation between measured and predicted scores (r = 0.3274; = 0.0025). : In conclusion, as long as the same inclusion criteria are used, the Hoppe et al. (2021) prediction model results in similar prediction success in our cohort, and thus seems applicable independently of the cohort used. Nevertheless, it has limitations when applied to a broader and more diverse patient cohort. Our data suggest that the model would benefit from adaptations for broader clinical use, as the model lacks sufficient sensitivity in identifying poor performers.
人工耳蜗植入（CI）的康复成功率差异很大，确定能够可靠预测CI语音理解能力的预测因素仍然是一项挑战。霍佩及其同事最近描述了一种预测模型，该模型特别基于四频率纯音平均听阈（4FPTA）≤80 dB HL的科利耳公司（Cochlear™）的接受者。这项回顾性研究的目的是测试该模型对纳入标准更广泛的独立患者队列的适用性。：霍佩等人的模型应用于不同程度听力损失的CI接受者。分析了4FPTA≤80 dB HL的科利耳公司接受者以及所有接受者（无论4FPTA如何）的模型性能。按年龄和CI制造商进行了亚组分析。：当满足原始纳入标准时（n = 24），该模型在我们的患者队列中产生了可比的结果。将该模型扩展到重度听力损失患者（4FPTA> 80 dB HL；n = 238）时，CI在65 dB时预测的和实测的单词识别分数之间的相关性较弱但具有统计学意义（r = 0.273；P <0.0001）（图）。此外，更高比例的数据点偏差超过20个百分点，无论是更好还是更差。当纳入使用不同制造商CI的患者时，预测误差也高于原始队列。在最大单词识别分数（MWRS）> 0%的科利耳公司接受者中（n = 83），我们发现实测分数与预测分数之间存在中度相关性（r = 0.3274；P = 0.0025）。：总之，只要使用相同的纳入标准，霍佩等人（2021年）的预测模型在我们的队列中就能取得相似的预测成功率，因此似乎可以独立于所使用的队列进行应用。然而，当应用于更广泛、更多样化的患者队列时，它存在局限性。我们的数据表明，该模型需要进行调整以用于更广泛的临床应用，因为该模型在识别表现不佳者方面缺乏足够的敏感性。
