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在大型人工耳蜗植入受者临床数据集里,阻抗、编程和言语识别之间的关系。

The Relationship Between Impedance, Programming and Word Recognition in a Large Clinical Dataset of Cochlear Implant Recipients.

机构信息

Department of Otolaryngology - Head and Neck Surgery, 1811Harvard Medical School, Boston, MA, USA.

Eaton-Peabody Laboratories, Boston, MA, USA.

出版信息

Trends Hear. 2022 Jan-Dec;26:23312165211060983. doi: 10.1177/23312165211060983.

Abstract

Cochlear implant programming typically involves measuring electrode impedance, selecting a speech processing strategy and fitting the dynamic range of electrical stimulation. This study retrospectively analyzed a clinical dataset of adult cochlear implant recipients to understand how these variables relate to speech recognition. Data from 425 implanted post-lingually deafened ears with Advanced Bionics devices were analyzed. A linear mixed-effects model was used to infer how impedance, programming and patient factors were associated with monosyllabic word recognition scores measured in quiet. Additional analyses were conducted on subsets of data to examine the role of speech processing strategy on scores, and the time taken for the scores of unilaterally implanted patients to plateau. Variation in basal impedance was negatively associated with word score, suggesting importance in evaluating the profile of impedance. While there were small, negative bivariate correlations between programming level metrics and word scores, these relationships were not clearly supported by the model that accounted for other factors. Age at implantation was negatively associated with word score, and duration of implant experience was positively associated with word score, which could help to inform candidature and guide expectations. Electrode array type was also associated with word score. Word scores measured with traditional continuous interleaved sampling and current steering speech processing strategies were similar. The word scores of unilaterally implanted patients largely plateaued within 6-months of activation. However, there was individual variation which was not related to initially measured impedance and programming levels.

摘要

人工耳蜗编程通常包括测量电极阻抗、选择言语处理策略以及调整电刺激的动态范围。本研究回顾性分析了一组成年人工耳蜗植入患者的临床数据集,以了解这些变量与言语识别的关系。对使用先进仿生设备的 425 个后天失聪植入耳的数据集进行了分析。采用线性混合效应模型推断阻抗、编程和患者因素如何与安静环境下单音节词识别分数相关。还对数据子集进行了额外分析,以检查言语处理策略对分数的作用,以及单侧植入患者的分数达到稳定所需的时间。基底阻抗的变化与单词得分呈负相关,这表明评估阻抗分布的重要性。虽然编程水平指标与单词得分之间存在小的负二变量相关性,但这些关系并未得到考虑其他因素的模型的明确支持。植入时的年龄与单词得分呈负相关,植入经验的持续时间与单词得分呈正相关,这有助于为候选者提供信息并指导预期。电极阵列类型也与单词得分相关。使用传统的连续交错采样和电流导向言语处理策略测量的单词得分相似。单侧植入患者的单词得分在激活后 6 个月内基本达到稳定。然而,存在个体差异,这与最初测量的阻抗和编程水平无关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db00/8761885/858f22cbb868/10.1177_23312165211060983-fig1.jpg

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