Prediction of Individual Speech Outcomes with Cochlear Implants: Limitations and Opportunities for Clinical Translation

Objectives: Speech outcomes with cochlear implants (CIs) vary widely across patients and remain difficult to predict from pre-operative data. This study addressed three questions with clinical relevance: (RQ1) Can AI models predict individual CI speech outcomes, and if not, why? (RQ2) Can pre-operative data be used to identify patients at risk of poor outcomes? (RQ3) Do post-operative speech scores early after implantation contain prognostic information for later speech outcomes? Design: A retrospective analysis was performed using data from a single centre contributing to the Swiss National Cochlear Implant Database (N = 771 patients; 473 adults after exclusion of single-sided deafness). For RQ1, pre-operative data and seven prediction methods, from standard linear regression to recent machine-learning models, were used to predict post-operative word recognition scores. Prediction models were assessed on held-out data and on data from patients implanted later than the cohort used for model training. For RQ2, the best-performing model under RQ1 was used to predict patients with a poor outcome after implantation (word-recognition score below 40%), and pre-operative variables were grouped by clinical type to identify sources of information for risk prediction. For RQ3, the prognostic value of early post-operative speech measurement was assessed by adding six-month post-operative word scores to the pre-operative data and a clustering analysis was performed to identify distinct trajectories post implantation. Results: Cross-validated R2 for individual CI speech score prediction was small across all algorithms (best model with full feature set: R2 = 0.22) and conformal prediction intervals spanned +/- 41 percentage points, covering nearly the whole range of possible scores. Speech score distributions of patient groups defined by audiometric, aided-field and pre-operative speech variables converged after implantation (mean distance between group distributions decreased from 21.5 to 6.0 percentage points), while distributions of groups defined by developmental and communicative variables diverged. Pre-operative speech scores were much better predicted from pre-operative variables (R2 = 0.48 based on audiometric data only). For risk prediction, the best-performing model identified poor outcome with AUC = 0.71. Self-rated articulation was the dominant pre-operative predictor. Audiometric, aided-field, and pre-operative speech data on which other prediction models have relied classified poor outcome at chance (AUC = 0.53), whereas developmental and communicative variables performed better (AUC = 0.65). Adding the six-month post-operative word score to the pre-operative data substantially raised R2 for long-term outcome from 0.03 to 0.48. Four trajectory types (Early Responders, Late Responders, Non-Responders, and Decliners) emerged from the clustering analysis but were not predictable from pre-operative data. Conclusions: The limited prediction performance for CI speech outcomes appears to be a property of the pre-operative data rather than of the prediction methods applied to them. However, pre-operative data can partially identify patients at risk of poor outcome. Importantly, speech data obtained within six months after implantation significantly improves outcome prediction at later time points. Longitudinal assessments post implantation therefore have potential for clinical intervention to improve long-term CI outcome. These findings support a two-stage framework for clinical translation: data-based risk counselling before surgery and post-operative monitoring for selective intervention after surgery.