Predictive Modeling Using Six-Month Performance Assessments to Forecast Long-Term Cognitive and Verbal Development in...

Objective This study aims to develop predictive models for speech outcomes at 6, 12, and 24 months post-cochlear implantation in pre-lingual deaf children. Using longitudinal Category of Auditory Performance (CAP), Speech Intelligibility Rating (SIR), and Parents' Evaluation of Aural/Oral Performance of Children (PEACH) scores, it seeks to forecast cognitive and verbal development. The study addresses the gap in correlating auditory performance with cognitive milestones by integrating longitudinal auditory data with cognitive and verbal benchmarks to identify predictive relationships. Method In this retrospective study, auditory performance data from hospital records of 157 post-cochlear implant children were analyzed using mixed-effects models, repeated measures ANOVA, and Tukey's HSD (honestly significant difference) post-hoc tests. The predictive value of outcomes at 6, 12, and 24 months was evaluated, focusing on temporal improvements and the interplay of demographic and procedural variables. Results The children had a mean implantation age of 3.7 years and a median switch-on time of 29 days; 58% were male. Their auditory and speech performance demonstrated significant improvement over time, with CAP scores increasing from 1.56 at 6 months to 4.55 at 24 months, SIR scores improving from 1.03 to 2.04, and PEACH scores rising from 17.91 to 38.14 (p < 0.0001 for all). Predictive modeling revealed that early improvements at 6 and 12 months were strong indicators of speech and cognitive outcomes at 24 months. The findings highlight significant predictive relationships, demonstrating that early auditory performance assessments correlate with later cognitive and verbal competencies. Conclusion This study demonstrates that early auditory outcomes at 6 and 12 months can reliably predict long-term developmental trajectories following cochlear implantation. It establishes a framework for integrating predictive analytics into pediatric audiology, enhancing speech and cognitive outcomes for pre-lingual deaf children.