From Hearing Patterns to Functional Outcomes: Quantifying Audiometric Profiles for Precision Hearing Care

Hearing aid (HA) outcomes vary substantially despite similar hearing loss severity. We tested whether audiometric configuration improves outcome prediction beyond traditional measures.

We conducted a retrospective analysis of 22,694 adults (aged 50-90 years) fitted with HAs across 780 French audiology centers (2018-2021). Eight audiometric profiles were defined a priori based on pathophysiological classifications. Machine learning models identified predictors of speech reception threshold improvement in quiet (SRT_Q, ≥6 dB) and noise (SRT_N, ≥2 dB).

Overall, 93% of participants showed demonstrated meaningful improvement in SRT_Q and/or SRT_N. Adding profile membership variable increased prediction accuracy by 6% for SRT_N and 5% for SRT_Q. Profile-stratified analysis revealed distinct optimization pathways: Profile A (moderate presbycusis) benefited from 4 kHz amplification and follow-up appointments; Profile C (ski slope) required premium technology and high compliance; Profile D (advanced presbycusis) responded exclusively to frequency-specific precision, resisting behavioral interventions. Mid-to-high frequencies (2-6 kHz) drove noise outcomes; low frequencies (250-1,000 Hz) and clinical support predicted quiet performance. Globally, the amplification of both low and high frequencies are beneficial for both SRT_Q and SRT_N, and high frequencies especially for SRT_N.

Audiometric profiling adds significant predictive value beyond traditional severity measures, identifying profile-specific intervention levers (amplification strategy, technology; choice, compliance, follow-up appointments) for personalized optimization. This configuration-aware approach supports precision audiology, where treatment is guided by an audiometric profile and expected outcomes.