A Five-Year Update on the Profile of Adults Undergoing Cochlear Implant Evaluation and Surgery—Are We Doing Better?

Patro, Ankita; Lindquist, Nathan R.; Tawfik, Kareem O.; O’Malley, Matthew; Bennett, Marc; Haynes, David S.; Gifford, René H.; Holder, Jourdan T.; Perkins, Elizabeth

doi:10.1097/mao.0000000000003670

Cited by 9 publications

(18 citation statements)

References 52 publications

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“…In addition to audiometry, our referral algorithm includes age and insurance status as input variables to further tailor diagnostic criteria. Age has previously been shown to influence CI referral and ultimately qualification, with younger patients qualifying for implantation at higher rates than their older counterparts (10,41). Recent literature has also highlighted that delayed diagnosis and treatment can negatively impact postoperative outcomes (9,12,42,43).…”

Section: Discussionmentioning

confidence: 99%

“…As previously described (10,34), candidacy at our center was determined by individual ear performance on AzBio testing in +5 dB signal-to-noise ratio. Per clinic protocol, patients who score lower than 30% on AzBio in quiet were not tested in noise.…”

Section: Candidacy Criteriamentioning

confidence: 99%

“…Sentence scores up to 40 and 60% reflect Medicare-eligible and traditional criteria, respectively. Expanded criteria included hybrid or electric acoustic stimulation (EAS) (PTA ≤60 dB HL through 500 Hz, CNC ≤60% in the ear to be implanted and ≤80% in the contralateral ear), SSD (PTA ≥70 dB HL in the ear to be implanted and ≤30 dB HL in the contralateral ear), and AHL (PTA ≥70 dB HL in the ear to be implanted and >30 dB HL in the contralateral ear, with AzBio ≤60% in the ear to be implanted and >40% in the contralateral ear) (9,10).…”

Section: Candidacy Criteriamentioning

confidence: 99%

“…For individuals with moderate-to-profound hearing loss who no longer derive meaningful benefit from their hearing aids, cochlear implants (CIs) have become the standard of care, demonstrating improvements in speech perception, quality of life, and cognitive abilities (5)(6)(7)(8). Their widespread success has led to further expansion in candidacy criteria in the last decade to include patients with residual low-frequency hearing, single-sided deafness (SSD), and asymmetric hearing loss (AHL) (9)(10)(11)(12)(13)(14). However, less than 10% of eligible adults are receiving CIs under traditional criteria, where the better hearing ear has severe-to-profound hearing loss (15,16).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, hearing professionals perform standard audiometric tests to determine when CI candidacy evaluation (CICE) referral is appropriate. However, despite broadening of candidacy criteria, most patients continue to present significantly below the upper range of current indications (i.e., 60% on open-set sentence testing) and with at least severe hearing loss (10). Lack of clarity regarding referral criteria, both among providers and patients, is known to play a large factor in underutilization of this technology (15,(18)(19)(20)(21).…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Patro

Perkins

Ortega³

et al. 2023

Otol Neurotol

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Objective To develop a machine learning–based referral guideline for patients undergoing cochlear implant candidacy evaluation (CICE) and to compare with the widely used 60/60 guideline. Study Design Retrospective cohort. Setting Tertiary referral center. Patients 772 adults undergoing CICE from 2015 to 2020. Interventions Variables included demographics, unaided thresholds, and word recognition score. A random forest classification model was trained on patients undergoing CICE, and bootstrap cross-validation was used to assess the modeling approach's performance. Main Outcome Measures The machine learning–based referral tool was evaluated against the 60/60 guideline based on ability to identify CI candidates under traditional and expanded criteria. Results Of 587 patients with complete data, 563 (96%) met candidacy at our center, and the 60/60 guideline identified 512 (87%) patients. In the random forest model, word recognition score; thresholds at 3000, 2000, and 125; and age at CICE had the largest impact on candidacy (mean decrease in Gini coefficient, 2.83, 1.60, 1.20, 1.17, and 1.16, respectively). The 60/60 guideline had a sensitivity of 0.91, a specificity of 0.42, and an accuracy of 0.89 (95% confidence interval, 0.86–0.91). The random forest model obtained higher sensitivity (0.96), specificity (1.00), and accuracy (0.96; 95% confidence interval, 0.95–0.98). Across 1,000 bootstrapped iterations, the model yielded a median sensitivity of 0.92 (interquartile range [IQR], 0.85–0.98), specificity of 1.00 (IQR, 0.88–1.00), accuracy of 0.93 (IQR, 0.85–0.97), and area under the curve of 0.96 (IQR, 0.93–0.98). Conclusions A novel machine learning–based screening model is highly sensitive, specific, and accurate in predicting CI candidacy. Bootstrapping confirmed that this approach is potentially generalizable with consistent results.

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Section: Discussionmentioning

confidence: 99%

Section: Candidacy Criteriamentioning

confidence: 99%

Section: Candidacy Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Patro

Perkins

Ortega³

et al. 2023

Otol Neurotol

Self Cite

View full text Add to dashboard Cite

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Estimating the United States Patient Population Size Meeting Audiologic Candidacy for Cochlear Implantation

Yu,

Shen,

Bowditch

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveThis study aimed to estimate the size of the United States candidacy pool meeting expanded Center for Medicare Services criteria for cochlear implantation.Study DesignRetrospective cross‐sectional.SettingTertiary care center.MethodsPreimplantation audiometric data from 486 patients seen at a single academic medical center were collected retrospectively and used to generate a predictive model of AzBio score based on audiometric pure tone thresholds. This model was then used to estimate nationally representative cochlear implantation (CI)‐candidacy using pure tone averages included in the National Health and Nutrition Examination Survey. Qualitative and quantitative analyses were performed.ResultsWe find that the estimated prevalence of CI candidacy in individuals 65 years of age or older is expected to more than double with a change in the CI candidacy criteria from ≤40% to ≤60% (from 1.42%, 95% confidence interval [1.33, 1.63] to 3.73% [2.71, 6.56]) on speech testing. We also found the greatest absolute increase in candidacy in the 80+ age group, increasing from 4.14% [3.72, 5.1] of the population meeting the ≤40% criteria to 12.12% [9.19, 18.35] meeting the ≤60% criteria.ConclusionThe United States population size meeting expanded CMS audiologic criteria for cochlear implantation is estimated to be 2.5 million adults and 2.1 million age 65 or older. Changing the CI candidacy criteria from ≤40% to ≤60% on CI testing has the greatest effect on the eligible patient population in the >65‐year‐old age group. The determination of utilization rates in newly eligible patients will require further study.

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Machine Learning to Predict Adult Cochlear Implant Candidacy

Patro,

Freeman,

Haynes

2024

Curr Otorhinolaryngol Rep

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Purpose of Review The purpose of this review is to summarize candidacy criteria and commonly used referral guidelines for adult cochlear implant (CI) patients. This review describes how machine learning can be used to predict CI candidacy and the potential impact of an automated referral guideline. Recent Findings Less than 2% of eligible adults are receiving CIs under traditional and expanded candidacy criteria. Lack of understanding of referral criteria, both among providers and patients, significantly contributes to the underutilization of CIs. Recently, a novel machine learning-based CI referral algorithm has been developed that shows high sensitivity, specificity, and accuracy in predicting CI candidacy among adults. Summary An automated, machine learning-based referral guideline can mitigate the lack of clarity regarding when to refer a patient and help bridge the large gap in CI care delivery that currently exists. Future research needs to externally validate such an algorithm and evaluate its uptake in routine clinical settings.

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A Five-Year Update on the Profile of Adults Undergoing Cochlear Implant Evaluation and Surgery—Are We Doing Better?

Cited by 9 publications

References 52 publications

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Machine Learning Approach for Screening Cochlear Implant Candidates: Comparing With the 60/60 Guideline

Estimating the United States Patient Population Size Meeting Audiologic Candidacy for Cochlear Implantation

Machine Learning to Predict Adult Cochlear Implant Candidacy

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