Distinguishing Between Presbycusis and Noise-Induced Hearing Loss With a Joint-Otoacoustic Emission Profile.

OBJECTIVES: The aim of this study was to examine whether a Joint-Otoacoustic Emission (OAE) Profile, a combined analysis of both distortion- and reflection-type OAEs in the same ear, can distinguish between hearing loss due to noise exposure versus presbycusis. Reflection- and distortion-type emissions arise via distinct cochlear generation mechanisms and have shown different sensitivity to hearing loss. By measuring both OAEs together in each ear, we hope to access and exploit two distinct intracochlear generation processes to improve the differential diagnosis of hearing loss. DESIGN: A total of 122 individuals with mild-to-moderate hearing loss served as subjects. Seventy-five of these had hearing loss primarily due to aging, and 47 had hearing loss due to noise exposure. Rapidly swept tones (calibrated in forward pressure level) were presented to evoke distortion-product OAEs (DPOAEs) and stimulus-frequency OAEs (SFOAEs) in an interleaved fashion. Both fixed-level OAE metrics and input/output-function parameters were analyzed. The analysis was two-pronged: (1) Descriptive statistics and analyses of variance were applied to test for group differences between noise-induced hearing loss (NIHL) and presbycusis, and (2) Random Forest machine-learning was applied with OAE predictors to distinguish between etiologies. RESULTS: Group differences indicate that DPOAE level and loss at 40 dB FPL are similar between the two etiologies, while SFOAE level and loss are selectively sensitive to noise-induced hearing loss. When OAE metrics (and two non-OAE variables) were utilized by the Random Forest machine-learning algorithm, the model classified etiology with up to 95% accuracy. The most accurate predictor sets included DPOAE metrics at the two highest frequencies (8 and 12 kHz), age, and/or audiogram shape; SFOAE metrics at 8 kHz were also included in effective predictor sets but were not required to achieve peak performance. CONCLUSIONS: In any given ear, DPOAEs and SFOAEs show distinct effects of and sensitivities to hearing loss. Exploiting these fundamental differences may enhance the differential diagnosis of hearing impairment and allow us to distinguish between common etiologies of hearing loss. In this study, the DPOAE at high frequencies was the most effective predictor of etiology. SFOAEs showed a selective sensitivity to NIHL, though their contribution to distinguishing etiology was limited. It is likely that findings were impacted by the relatively small cohort. Application of a Joint-OAE Profile for the differential diagnosis of hearing loss warrants further development with larger groups and varied etiologies. Identifying the dominant etiology in impaired ears could guide targeted intervention and assist in the selection of candidates for genetic and/or pharmaceutical therapies.