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Pure tone hearing profiles in children with otitis media with effusion

Abstract Introduction: Otitis media with effusion (OME) is a common middle ear disease in children. The associated conductive hearing loss is a major concern for hearing health professionals. The aim of the present study was to describe the configuration of pure tone audiograms of children with OME and to design a statistical stratification algorithm to facilitate hearing loss profiling in children with OME. Methods: School age children with OME were recruited. Bone and air conduction thresholds were obtained using standard procedures. Hierarchical cluster analysis was employed to determine audiometric profile groups. The Mandarin Hearing in Noise Test was used to measure sentence perception in children for cluster analysis validity assessment. Results: Ninety-seven children (164 ears) aged between 72 months and 153 months were examined. Air conduction thresholds averaged for 500 Hz, 1000 Hz and 2000 Hz were in the range of 8.3–53.3 dB HL with a mean of 26.8 dB HL. Bone conduction thresholds were found to be influenced by middle ear pathology with a maximal elevation at 2000 Hz of 25 dB HL. Four audiometric profiles were identified. Cluster 1 contained 54 ears (32.9%) with normal or near normal hearing, Clusters 2 contained 37 ears (22.6%) with mild hearing loss, Cluster 3 included 48 ears (29.3%) and Cluster 4 included 25 ears (15.2%) with moderate hearing loss. Stability and validity of the four-cluster profiling procedure was examined and established with satisfactory results. Conclusions: OME in children is associated with pure tone hearing thresholds ranging from normal to moderate hearing loss. The hierarchical clustering algorithm proved useful as a novel means of profiling hearing loss in children with OME and may assist in identifying affected children at greater risk of auditory disadvantage. Implications for rehabilitation A hierarchical cluster analysis method can be used to determine audiometric profiles in children with OME. This algorithm assists to identify children at greater risk of auditory disadvantage. Cluster groups with more elevated pure tone thresholds may be targeted for priority in clinical surveillance and medical/surgical intervention.