Scorpecci Alessandro, D'Elia Alessandra, Malerba Paolo, Cantore Italo, Consolino Patrizia, Trabalzini Franco, Paludetti Gaetano, Quaranta Nicola
ENT Department, A. Gemelli University Hospital, Catholic University of the Sacred Heart, Rome, Italy.
ENT Department, Section of Otologic and Neurotologic Surgery, University of Bari, Bari, Italy.
Eur Arch Otorhinolaryngol. 2016 Dec;273(12):4167-4173. doi: 10.1007/s00405-016-4115-1. Epub 2016 May 30.
In uncooperative patients, electrical compound action potential (ECAP) thresholds are reliable in predicting T-levels, but are not in determining the C-level profile. The present study aims to assess if the C-level profile can be predicted by a new objective procedure (C-NRT) which uses the amplitude growth function (AGF) and is based on the assumption that equal ECAP amplitudes elicit equal loudness percepts. This is a correlational study conducted in five tertiary care referral hospitals with 21 post-lingually deaf adult cochlear implant users. Two maps were created: a behavioral, bitonal balanced (BB) map and an objective map, in which T-levels were the same as in the BB map, and C-levels were obtained with C-NRT. C-NRT consisted of performing the AGF of nine electrodes, and of setting the current level eliciting a 100 μV ECAP amplitude as C-level in the map. AutoNRT was also measured. Main outcome measures were correlation between behavioral C-profile level, objective C-profile level, behavioral T-profile level and objective T-profile (AutoNRT) level; disyllabic word recognition scores in quiet and in noise conditions (SNR = + 10 and 0) with both maps. A strong correlation was found between behavioral and C-NRT-derived C-levels (mean per electrode correlation: R = 0.862, p < 0.001). C-NRT could predict behavioral C-levels with a greater accuracy than AutoNRT. Word recognition was significantly better with BB maps only in the quiet condition (p = 0.002). C-NRT is more accurate than AutoNRT in predicting the C-level profile in adult cochlear implant users. This finding encourages future application in uncooperative patients, especially in very young children.
在不配合的患者中,电复合动作电位(ECAP)阈值在预测T级方面是可靠的,但在确定C级分布时则不然。本研究旨在评估一种新的客观程序(C-NRT)是否能够预测C级分布,该程序使用振幅增长函数(AGF),并基于这样的假设:相等的ECAP振幅会引发相等的响度感知。这是一项在五家三级医疗转诊医院对21名语后聋成年人工耳蜗使用者进行的相关性研究。创建了两张图谱:一张行为性的双音平衡(BB)图谱和一张客观图谱,其中T级与BB图谱中的相同,C级通过C-NRT获得。C-NRT包括对九个电极进行AGF测量,并将引发100μV ECAP振幅的电流水平设定为图谱中的C级。还测量了自动神经反应阈值(AutoNRT)。主要结局指标包括行为C级分布水平、客观C级分布水平、行为T级分布水平和客观T级(AutoNRT)水平之间的相关性;两张图谱在安静和噪声条件下(信噪比= +10和0)的双音节词识别分数。发现行为性C级与C-NRT得出的C级之间存在强相关性(每个电极的平均相关性:R = 0.862,p < 0.001)。C-NRT预测行为性C级的准确性高于AutoNRT。仅在安静条件下,BB图谱的单词识别效果明显更好(p = 0.)。在预测成年人工耳蜗使用者的C级分布方面,C-NRT比AutoNRT更准确。这一发现鼓励了其在不配合患者,尤其是非常年幼的儿童中的未来应用。