Clinical trial NCT06086041: Predictive Study on Hearing Rehabilitation After Cochlear Implant
Official title: Predictive Factors of Hearing Rehabilitation After Cochlear Implant Surgery in Deaf Patients
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Tractography
Cochlear Implant
Magnetic Resonance Imaging
Hearing Rehabilitation
Hearing Impairment
Keywords: predictive factors
magnetic resonance imaging
cochlear implant
hearing rehabilitation
hearing impairment
tractography
Lead sponsor: University Hospital, Grenoble
Sponsor class: OTHER
First posted: 2023-10-17
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Severely to profoundly deaf patients consulting in Grenoble University Hospital with cochlear implantation indication.

Brief summary: The aim of this study is to display the predictive factors of hearing rehabilitation after cochlear implant surgery in severely to profoundly deaf adults.

Detailed description: Cochlear implants are indicated in France in cases of severe to profound bilateral sensorineural hearing loss with an audiometric threshold of less than or equal to 50% speech discrimination in silence in the Fournier list (or equivalent) at 60 dB, in the free field, with well-fitted hearing aids. Cochlear implant represents a major advance in the management of severe to profoundly deaf patients and has also shown a benefit in the prevention of neurodegenerative diseases.

An average of 1800 cochlear implants are placed in France per year, 58% of which are placed in patients over 18 years of age.

The results of cochlear implants are in favour of a benefit in speech comprehension compared to hearing aids in cases of severe to profound deafness. However, there is a strong disparity in hearing performance after cochlear implantation from one patient to another, whether in silence or in noise.

Several factors influencing the results of the implant have been identified. Some of them are linked to the patient: etiology of the deafness, duration of auditory deprivation, age at implantation, residual hearing, pre- or post-lingual status of the deafness, some others are related to implant surgery (insertion of the electrode in the tympanic ramp, complete insertion, presence of a translocation, depth of electrode insertion).

Finally, there are factors related to the quality of the settings of the implant and to the brain plasticity of the patients.The 4 main factors seem to be the duration of the deafness, the age of onset of the deafness, its etiology and the duration of the patient's experience with the implant. It is assumed that the performance of cochlear implantation is strongly related to the individual's auditory processing abilities and the integrity of the central nervous system from the auditory nerve to the cortex.

At present, it is very difficult to predict the outcome of cochlear implants in deaf patients with a cochlear implant indication prior to implantation. The results remain variable from one patient to another and, to date, both the etiology and the state of the central auditory pathways are not taken into account in the indication for cochlear implantation. Animal studies have demonstrated anterograde degenerative neural damage in cochlear deafness (presbycusis, endolymphatic hydrops) and such damage is likely to explain the functional variability observed in humans in the case of neural stimulation with cochlear implants. Multiple integration of clinical data to propose a predictive model can now be done using both supervised (Deep Learning) and unsupervised (Manifold Learning) Machine Learning techniques, including for predicting auditory recovery. It is now possible to extend machine learning models to include quantitative data from diffusion MRI with the goal of providing an objective functional parameter from the central auditory pathways, then combined with clinical parameters and genetic to obtain a predictive model of hearing recovery after cochlear implantation. This study will allow us, through the study of brain tractography, to specify the role of the central auditory pathways in the results of cochlear implantation, a role that has not been determined to date, and to evaluate their correlation with clinical and genetic in order to create a predictive model of good auditory rehabilitation in artificial intelligence. The objective is to better select patients who can benefit from a cochlear implant in order to implant them in an optimal timing and to improve indications for cochlear implant.

Eligibility criteria: Inclusion Criteria:

Patient severely to profoundly deafed according French National Authority for Health (HAS) recommendations followed in Grenoble University Hospital.

Exclusion Criteria:

* cochlear malformation making impossible the cochlear implantation
* IRM contraindications
* Patient opposed to the use of their data in the context of the research

Primary outcomes:
measure: Identification of the predictive factors of hearing rehabilitation after cochlear implantation in severely to profoundly deaf adults.
timeFrame: 1 year post-surgery
Secondary outcomes:
measure: Evaluate the correlation between several predictive factors like clinical parameters, genetics ... with the MRI biomarker.
timeFrame: 3 months, 6 months and 1 year post-surgery.
Other outcomes:
Locations:
city: Grenoble
country: France
facility: Grenoble University hospital
Study officials:
affiliation: University Hospital, Grenoble
name: raphaële QUATRE
role: PRINCIPAL_INVESTIGATOR
References:
citation: Blamey P, Artieres F, Baskent D, Bergeron F, Beynon A, Burke E, Dillier N, Dowell R, Fraysse B, Gallego S, Govaerts PJ, Green K, Huber AM, Kleine-Punte A, Maat B, Marx M, Mawman D, Mosnier I, O'Connor AF, O'Leary S, Rousset A, Schauwers K, Skarzynski H, Skarzynski PH, Sterkers O, Terranti A, Truy E, Van de Heyning P, Venail F, Vincent C, Lazard DS. Factors affecting auditory performance of postlinguistically deaf adults using cochlear implants: an update with 2251 patients. Audiol Neurootol. 2013;18(1):36-47. doi: 10.1159/000343189. Epub 2012 Oct 19.
citation: Attye A, Renard F, Baciu M, Roger E, Lamalle L, Dehail P, Cassoudesalle H, Calamante F. TractLearn: A geodesic learning framework for quantitative analysis of brain bundles. Neuroimage. 2021 Jun;233:117927. doi: 10.1016/j.neuroimage.2021.117927. Epub 2021 Mar 6.
citation: Blamey PJ, Pyman BC, Gordon M, Clark GM, Brown AM, Dowell RC, Hollow RD. Factors predicting postoperative sentence scores in postlinguistically deaf adult cochlear implant patients. Ann Otol Rhinol Laryngol. 1992 Apr;101(4):342-8. doi: 10.1177/000348949210100410.