RESUMO
OBJECTIVES: An unexpectedly low word recognition (WR) score may be taken as evidence of increased risk for retrocochlear tumor. We sought to develop evidence for or against using a standardized WR (sWR) score in detecting retrocochlear tumors. The sWR is a z score expressing the difference between an observed WR score and a Speech Intelligibility Index-based predicted WR score. We retrospectively compared the sensitivity and specificity of pure-tone asymmetry-based logistic regression models that incorporated either the sWR or the raw WR scores in detecting tumor cases. Two pure-tone asymmetry calculations were used: the 4-frequency pure-tone asymmetry (AAO) calculation of the American Academy of Otolaryngology-Head and Neck Surgery and a 6-frequency pure-tone asymmetry (6-FPTA) calculation previously optimized to detect retrocochlear tumors. We hypothesized that a regression model incorporating the 6-FPTA calculation and the sWR would more accurately detect retrocochlear tumors. DESIGN: Retrospective data from all patients seen in the audiology clinic at Mayo Clinic in Florida in 2016 were reviewed. Cases with retrocochlear tumors were compared with a reference group with noise- or age-related hearing loss or idiopathic sensorineural hearing loss. Two pure-tone-based logistic regression models were created (6-FPTA and AAO). Into these base models, WR variables (WR, sWR, WR asymmetry [WRΔ], and sWR asymmetry [sWRΔ]) were added. Tumor detection performance for each regression model was compared twice: first, using all qualifying cases (61 tumor cases; 2332 reference group cases), and second, using a data set filtered to exclude hearing asymmetries greater than would be expected from noise-related or age-related hearing loss (25 tumor cases; 2208 reference group cases). The area under the curve and the DeLong test for significant receiver operating curve differences were used as outcome measures. RESULTS: The 6-FPTA model significantly outperformed the AAO model-with or without the addition of WR or WRΔ variables. Including sWR into the AAO base regression model significantly improved disease detection performance. Including sWR into the 6-FPTA model significantly improved disease detection performance when large hearing asymmetries were excluded. In the data set that included large pure-tone asymmetries, area under the curve values for the 6-FPTA + sWR and AAO + sWR models were not significantly better than the base 6-FPTA model. CONCLUSIONS: The results favor the superiority of the sWR computational method in identifying reduced WR scores in retrocochlear cases. The utility would be greatest where undetected tumor cases are embedded in a population heavily representing age- or noise-related hearing loss. The results also demonstrate the superiority of the 6-FPTA model in identifying tumor cases. The 2 computational methods may be combined (ie, the 6-FPTA + sWR model) into an automated tool for detecting retrocochlear disease in audiology and community otolaryngology clinics. The 4-frequency AAO-based regression model was the weakest detection method considered. Including raw WR scores into the model did not improve performance, whereas including sWR into the model did improve tumor detection performance. This further supports the contribution of the sWR computational method for recognizing low WR scores in retrocochlear disease cases.
