Aetiology of permanent childhood hearing loss at a population level

Development of Custom Sound® Pro software utilising big data and its clinical evaluation

OBJECTIVES

To inform and optimise a cochlear implant (CI) fitting software design through an analysis of big data to define array-specific comfort (C) level profiles, frequently-used MAP parameters, and the minimum number of Neural Response Telemetry thresholds (tNRT) needed to create an accurate profile. To evaluate the software's ease of use and completion time for AutoNRT®s.

DESIGN

MAPs analysis. Clinical study evaluating software use in creating MAPs, addressing sound-quality issues and setting patient goals.

STUDY SAMPLE

MAPs (N = 39,885); CI recipients (N = 47) and clinicians (N = 19).

RESULTS

Distinct C-level profiles were observed for lateral-wall, contour, and slim-modiolar electrode arrays. Default settings were used for most MAP parameters (13/16) except for Pulse Width, Rate, and Maxima. Nine tNRT measurements were required for an accurate C-level profile. Measurement-time of nine tNRTs via the new algorithm was comparable to five tNRTs using the previous algorithm. Nearly all (99%) clinical tasks were completed by clinicians with the first use of the software. Most CI recipients (79.5%) rated goal-setting as valuable.

CONCLUSION

Custom Sound Pro fitting software developed based on big data analysis incorporates a guided fitting workflow and expected fitting ranges. It helps to improve clinical efficiency, is easy to use and supports patient-centred care.

Diversity of Child and Family Characteristics of Children with Hearing Loss in Family-Centered Early Intervention in The Netherlands

Background: Family-centered early intervention (FCEI) for children with hearing loss (HL) supports caregivers to promote their children’s language development. To provide FCEI services that are relevant and accessible to meet diverse needs, insight into the characteristics of children with HL is important. In the current study, various characteristics of children with HL and intervention-related factors are examined in relation to spoken language outcomes. Methods: Child and family characteristics, language outcomes and data on intervention were extracted from FCEI records for 83 children. Family involvement ratings were obtained from EI providers. Relations between characteristics, intervention, family involvement and language outcomes were analyzed and predictors for children’s language outcomes were investigated. Results: The characteristics of children with HL in FCEI are very diverse. Family involvement and the occurrence of additional disabilities were predictive for children’s receptive and expressive language abilities; the start of FCEI was not. Maternal education was predictive for expressive language outcomes only. Conclusions: The current study showed the diversity in characteristics of children with HL and their families in the degree of HL, etiology, cultural background, home language, family involvement and additional disabilities. We conclude that ’one size does not fit all’, and FCEI programs should acknowledge the unique strengths and challenges of every individual family.