Asymmetric hearing loss: definition, validation, and prevalence.
OBJECTIVE
An algorithm for identifying asymmetric hearing loss (AHL) can be constructed that performs as well or better than expert judges.


BACKGROUND
AMCLASS is a method for classifying audiograms based on configuration, severity, site of lesion, and interaural asymmetry. The development and clinician validation for all but asymmetry were reported separately. In this report, an algorithm for identifying AHL is described. Using the clinician-validated algorithm, the prevalence of AHL in a database from an academic health center audiology clinic was analyzed.


METHODS
: Five expert clinicians classified 199 audiograms as symmetric or asymmetric. Interjudge agreement was analyzed for each pair of judges and between each judge and the consensus of the panel. An algorithm was constructed based on the set of rules that maximized agreement between AMCLASS and judges. Using the clinician-validated algorithm, the prevalence of AHL was analyzed for groups based on quantity of bone conduction testing, hearing loss configuration, severity, and site of lesion.


RESULTS
There was substantial disagreement among judges that was similar to interjudge comparisons for other medical tests. Average agreement between AMCLASS and the judges was higher than agreement between the best judge and the consensus of the judges. Approximately 50% of all patients and 55% of patients with sensorineural hearing loss were classified as AHL by the clinician-validated algorithm.


CONCLUSIONS
The algorithm met the goal of equaling or exceeding the performance of expert judges. The prevalence of AHL was higher than expected and suggests that the algorithm is not useful for screening for acoustic neuroma or other conditions. Perhaps, a criterion based on the magnitude of the asymmetry would better serve that purpose. The symmetry category provided by AMCLASS provides a determination of clinically significant AHL that agrees with the consensus of expert judges.
Margolis
Robert H
RH
University of Minnesota, Minneapolis, Minnesota 55455, USA. margo001@umn.edu
Saly
George L
GL
D000465:Algorithms; D001299:Audiometry; D001844:Bone Conduction; D016208:Databases, Factual; D004428:Ear Neoplasms / Q000175:diagnosis; D034381:Hearing Loss / Q000175:diagnosis* / Q000453:epidemiology*; D006801:Humans; D015588:Observer Variation; D015203:Reproducibility of Results; D009626:Terminology as Topic