Moving Beyond the 60/60 Rule: Data-Driven Multi-Parameter Predictive Analysis of CI Outcomes

Introduction: Current cochlear implant (CI) candidacy relies primarily on the binary “60/60 rule” (pre-operative pure tone average (PTA)>60 dB or pre-operative word recognition scores (WRS) < 60%), which provides limited guidance for personalized counselling. We developed multi-dimensional outcome matrices to identify more nuanced, data-driven predictors of CI success.

Methods: De-identified data were collected from a tertiary academic center and a third-party cochlear implant company dataset in May 2025. CI users with complete pre- and post-operative data were categorized into three matrices examining 12-month outcomes: “pre-WRS–PTA” (n=45), “pre-WRS–age” (n=221), and “duration of deafness (DoD)–pre-WRS” (n=52). Significant post-surgical improvement was defined as post-WRS > 59% for patients with pre-WRS < 30%, or >20% WRS gain for all other patients. Heat maps were created to visualize probability distributions for meeting the pre-defined rule for significant improvement across the parameter combinations.

Results: Pre-operative WRS surfaced as the single most dominant predictor of 12-month cochlear-implant benefit across all matrices. The “sweet spot” of 30–50% pre-WRS yielded 70-89% chance of improvement regardless of implantation age, pre-PTA, or deafness duration of up to 30 years. In the pre-WRS–PTA matrix, the highest probability of improvements clustered at PTA 40–80 dB and pre-WRS 30–50%. In the pre-WRS–age matrix, users < 60 y consistently outperformed older cohorts, although the age effects were neutralized when pre-WRS≥30%. Additionally, two profiles, age > 60 with WRS < 20% and DoD > 35 years with WRS < 10% were identified as having low probabilities of improvement (38.9% and 33.3%), highlighting the limits of neural plasticity at the extremes.

Conclusions: Pre-operative WRS is the most powerful predictor of CI benefit, with the 30-50% range representing a high probability zone for success. These probability matrices enable more sophisticated candidate counseling, enhance decision making through quantified risk assessment, and identify patients requiring additional rehabilitative support.