Pure-Tone Hearing Asymmetry: A Logistic Approach Modeling Age, Sex, and Noise Exposure History

Abstract

Background: Asymmetric hearing loss (AHL) can be an early sign of vestibular schwannoma (VS). However, recognizing VS-induced AHL is challenging. There is no universally accepted definition of a “medically significant pure-tone hearing asymmetry,” in part because AHL is a common feature of medically benign forms of hearing loss (e.g., age- or firearm-related hearing loss). In most cases, the determination that an observed AHL does not come from a benign cause involves subjective clinical judgment. Purpose: Our purpose was threefold: (1) to quantify hearing asymmetry distributions in a large group of patients with medically benign forms of hearing loss, stratifying for age, sex, and noise exposure history; (2) to assess how previously proposed hearing asymmetry calculations segregate tumor from nontumor cases; and (3) to present the results of a logistic regression method for defining hearing asymmetry that incorporates age, sex, and noise information. Research Design: Retrospective chart review. Study Sample: Five thousand six hundred and sixty-one patients with idiopathic, age- or noise exposure-related hearing loss and 85 untreated VS patients. Data Collection and Analysis: Audiometric, patient history, and clinical impression data were collected from 22,785 consecutive patient visits to the audiology section at Mayo Clinic in Florida from 2006 to 2009 to screen for eligibility. Those eligible were then stratified by VS presence, age, sex, and self-reported noise exposure history. Pure-tone asymmetry distributions were analyzed. Audiometric data from VS diagnoses were used to create four additional audiograms per patient to model the hypothetical development of AHL prior to the actual hearing test. The ability of 11 previously defined hearing asymmetry calculations to distinguish between VS and non-VS cases was described. A logistic regression model was developed that integrated age, sex, and noise exposure history with pure-tone asymmetry data. Regression model performance was then compared to existing asymmetry calculation methods. Results: The 11 existing pure-tone asymmetry calculations varied in tumor detection performance. Age, sex, and noise exposure history helped to predict benign forms of hearing asymmetry. The logistic regression model outperformed existing asymmetry calculations and better accounted for normal age-, sex-, and noise exposure-related asymmetry variability. Conclusions: Our logistic regression asymmetry method improves the clinician's ability to estimate risk of VS, in part by integrating categorical patient history and numeric test data. This form of modeling can enhance clinical decision making in audiology and otology.