Using Thresholds in Noise to Identify Hidden Hearing Loss in Humans

Objectives: Recent animal studies suggest that noise-induced synaptopathy may underlie a phenomenon that has been labeled hidden hearing loss (HHL). Noise exposure preferentially damages low spontaneous-rate auditory nerve fibers, which are involved in the processing of moderate- to high-level sounds and are more resistant to masking by background noise. Therefore, the effect of synaptopathy may be more evident in suprathreshold measures of auditory function, especially in the presence of background noise. The purpose of this study was to develop a statistical model for estimating HHL in humans using thresholds in noise as the outcome variable and measures that reflect the integrity of sites along the auditory pathway as explanatory variables. Our working hypothesis is that HHL is evident in the portion of the variance observed in thresholds in noise that is not dependent on thresholds in quiet, because this residual variance retains statistical dependence on other measures of suprathreshold function. Design: Study participants included 13 adults with normal hearing (≤15 dB HL) and 20 adults with normal hearing at 1 kHz and sensorineural hearing loss at 4 kHz (>15 dB HL). Thresholds in noise were measured, and the residual of the correlation between thresholds in noise and thresholds in quiet, which we refer to as thresholds-in-noise residual, was used as the outcome measure for the model. Explanatory measures were as follows: (1) auditory brainstem response (ABR) waves I and V amplitudes; (2) electrocochleographic action potential and summating potential amplitudes; (3) distortion product otoacoustic emissions level; and (4) categorical loudness scaling. All measurements were made at two frequencies (1 and 4 kHz). ABR and electrocochleographic measurements were made at 80 and 100 dB peak equivalent sound pressure level, while wider ranges of levels were tested during distortion product otoacoustic emission and categorical loudness scaling measurements. A model relating the thresholds-in-noise residual and the explanatory measures was created using multiple linear regression analysis. Results: Predictions of thresholds-in-noise residual using the model accounted for 61% (p

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