Preliminary Guidelines for Replacing Word-Recognition in Quiet With Speech in Noise Assessment in the Routine Audiologic Test Battery

OBJECTIVES: For decades, monosyllabic word-recognition in quiet (WRQ) has been the default test of speech recognition in routine audiologic assessment. The continued use of WRQ scores is noteworthy in part because difficulties understanding speech in noise (SIN) is perhaps the most common complaint of individuals with hearing loss. The easiest way to integrate SIN measures into routine clinical practice would be for SIN to replace WRQ assessment as the primary test of speech perception. To facilitate this goal, we predicted classifications of WRQ scores from the QuickSIN signal to noise ratio (SNR) loss and hearing thresholds. DESIGN: We examined data from 5808 patients who underwent audiometric assessment at the Stanford Ear Institute. All individuals completed pure-tone audiometry, and speech assessment consisting of monaural WRQ, and monaural QuickSIN. We then performed multiple-logistic regression to determine whether classification of WRQ scores could be predicted from pure-tone thresholds and QuickSIN SNR losses. RESULTS: Many patients displayed significant challenges on the QuickSIN despite having excellent WRQ scores. Performance on both measures decreased with hearing loss. However, decrements in performance were observed with less hearing loss for the QuickSIN than for WRQ. Most important, we demonstrate that classification of good or excellent word-recognition scores in quiet can be predicted with high accuracy by the high-frequency pure-tone average and the QuickSIN SNR loss. CONCLUSIONS: Taken together, these data suggest that SIN measures provide more information than WRQ. More important, the predictive power of our model suggests that SIN can replace WRQ in most instances, by providing guidelines as to when performance in quiet is likely to be excellent and does not need to be measured. Making this subtle, but profound shift to clinical practice would enable routine audiometric testing to be more sensitive to patient concerns, and may benefit both clinicians and researchers.