Prediction models for clinical outcome after cochlear implantation: a systematic review

Velde, Hedwig M.; Rademaker, Maaike M.; Damen, Johanna A A; Smit, A.L.; Stegeman, Inge

doi:10.1016/j.jclinepi.2021.04.005

Cited by 14 publications

(12 citation statements)

References 31 publications

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“…Accurate prediction of the clinical outcome after CI will help project realistic expectations of the benefit for each patient with SNHL, prepare additional rehabilitation for patients with under-performance, and even improve the implantation criteria and procedures ( Velde et al, 2021 ). The CI outcome is assessed by the categories of auditory performance (CAP) score, which can be predicted by the preoperative auditory brainstem response (ABR) and the area ratio of the vestibulocochlear nerve to the facial nerve ( Han et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation

Song

Jin

et al. 2022

Front. Comput. Neurosci.

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Identification of congenital sensorineural hearing loss (SNHL) and early intervention, especially by cochlear implantation (CI), are crucial for restoring hearing in patients. However, high accuracy diagnostics of SNHL and prognostic prediction of CI are lacking to date. To diagnose SNHL and predict the outcome of CI, we propose a method combining functional connections (FCs) measured by functional magnetic resonance imaging (fMRI) and machine learning. A total of 68 children with SNHL and 34 healthy controls (HC) of matched age and gender were recruited to construct classification models for SNHL and HC. A total of 52 children with SNHL that underwent CI were selected to establish a predictive model of the outcome measured by the category of auditory performance (CAP), and their resting-state fMRI images were acquired. After the dimensional reduction of FCs by kernel principal component analysis, three machine learning methods including the support vector machine, logistic regression, and k-nearest neighbor and their voting were used as the classifiers. A multiple logistic regression method was performed to predict the CAP of CI. The classification model of voting achieves an area under the curve of 0.84, which is higher than that of three single classifiers. The multiple logistic regression model predicts CAP after CI in SNHL with an average accuracy of 82.7%. These models may improve the identification of SNHL through fMRI images and prognosis prediction of CI in SNHL.

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Section: Introductionmentioning

confidence: 99%

Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation

Song

Jin

et al. 2022

Front. Comput. Neurosci.

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“…Over the past 5 years, machine learning has been increasingly used to automate intelligent processes and improve the efficiency of medical processes. For example, cochlear implants can be enhanced by adopting machine learning techniques, which have been applied to create predictive models ( Crowson et al, 2020 ; Velde et al, 2021 ) (see Velde et al, 2021 for a recent review). In addition, machine learning algorithms have been used to predict cochlear implantation (CI) outcomes.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of the Outcomes of Cochlear Implantation in Patients With Cochlear Nerve Deficiency and Normal Cochlea: A 2-Year Follow-Up of 70 Children

Xie

Wei

et al. 2022

Front. Neurosci.

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Cochlear nerve deficiency (CND) is often associated with variable outcomes of cochlear implantation (CI). We assessed previous investigations aiming to identify the main factors that determine CI outcomes, which would enable us to develop predictive models. Seventy patients with CND and normal cochlea who underwent CI surgery were retrospectively examined. First, using a data-driven approach, we collected demographic information, radiographic measurements, audiological findings, and audition and speech assessments. Next, CI outcomes were evaluated based on the scores obtained after 2 years of CI from the Categories of Auditory Performance index, Speech Intelligibility Rating, Infant/Toddler Meaningful Auditory Integration Scale or Meaningful Auditory Integration Scale, and Meaningful Use of Speech Scale. Then, we measured and averaged the audiological and radiographic characteristics of the patients to form feature vectors, adopting a multivariate feature selection method, called stability selection, to select the features that were consistent within a certain range of model parameters. Stability selection analysis identified two out of six characteristics, namely the vestibulocochlear nerve (VCN) area and the number of nerve bundles, which played an important role in predicting the hearing and speech rehabilitation results of CND patients. Finally, we used a parameter-optimized support vector machine (SVM) as a classifier to study the postoperative hearing and speech rehabilitation of the patients. For hearing rehabilitation, the accuracy rate was 71% for both the SVM classification and the area under the curve (AUC), whereas for speech rehabilitation, the accuracy rate for SVM classification and AUC was 93% and 94%, respectively. Our results identified that a greater number of nerve bundles and a larger VCN area were associated with better CI outcomes. The number of nerve bundles and VCN area can predict CI outcomes in patients with CND. These findings can help surgeons in selecting the side for CI and provide reasonable expectations for the outcomes of CI surgery.

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“…Given the great degree of observed variability in individual outcomes after CI, there has been robust literature focused on identifying variables and creating models to predict the success of CI, identify patients that would benefit from the procedure, and assist clinical decision making (11). Primary factors studied have included age at implantation, duration and etiology of hearing loss, and preoperative audiometric thresholds and speech recognition testing scores (12)(13)(14)(15). In the pediatric population, multiple studies have also evaluated predictors of second-side CI speech performance outcomes, with age at implantation, interval between first-and second-side implantation, and first implant speech performance being commonly identified predictive factors (10,(16)(17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

Predictive Ability of First-Side Cochlear Implant Performance in Adult Sequential Bilateral Cochlear Implantation

et al. 2022

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Objective: Predictors of second-side cochlear implant performance have not been well studied. We sought to assess whether speech recognition scores from first-side cochlear implant (CI1) could predict second-side cochlear implant (CI2) scores in sequential bilaterally implanted adults. Study Design: Retrospective review using a prospectively collected database. Setting: Academic tertiary care hospital. Patients: Fifty-seven adults with postimplantation speech recognition testing performed at least 12 months after CI2. Intervention: Sequential bilateral CI. Main Outcome Measures: CI2 performance at ≥12 months as measured using consonant-nucleus-consonant (CNC) words and AzBio sentences in quiet and +10 dB signal-to-noise ratio (S/N). Results: CI1 performance scores at ≥12 months were independently associated with CI2 performance scores at ≥12 months for CNC words (β = 0.371 [0.136-0.606], p = 0.003), AzBio sentences in quiet (β = 0.614 [0.429-0.80], p < 0.0001), and AzBio +10 dB S/N (β = 0.712 [0.459-0.964], p < 0.0001). CI1 scores on AzBio in quiet at 0 to 6 months were also independently associated with CI2 AzBio in quiet scores at ≥12 months (β = 0.389 [0.004-0.774], p = 0.048). Hearing loss etiology and duration, age at implantation, interval between CI1 and CI2, duration of hearing aid use, and preimplantation speech recognition testing scores were not consistently associated with CI2 scores at ≥12 months. Conclusions: CI1 performance is an independent predictor of second-side performance as measured ≥12 months postimplantation. This may be a clinically useful metric when considering adult sequential bilateral implantation.

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Prediction models for clinical outcome after cochlear implantation: a systematic review

Cited by 14 publications

References 31 publications

Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation

Functional Brain Connections Identify Sensorineural Hearing Loss and Predict the Outcome of Cochlear Implantation

Machine Learning-Based Prediction of the Outcomes of Cochlear Implantation in Patients With Cochlear Nerve Deficiency and Normal Cochlea: A 2-Year Follow-Up of 70 Children

Predictive Ability of First-Side Cochlear Implant Performance in Adult Sequential Bilateral Cochlear Implantation

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